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Developments of Nondestructive Techniques for Evaluating Quality Attributes of Cheeses: A Review
Abstract
Background: Cheese is produced around the world in a wide range of flavours, varieties, textures, and shapes, which can be used as a final product in human diet, and as an important ingredient in various foods. With consumer's continuing demand for quality cheese as well as increasing challenge from production facing the industry, nondestructive techniques are increasingly used to evaluate cheese quality. Scope and approach: Considering the rapid development of novel nondestructive techniques, relevant literatures in the past 15 years (2004–2018) are reviewed in this paper. The main quality attributes of cheese and the importance of evaluating these attributes are discussed. The principles, developments and applications of computer vision (CV), computed tomography (CT), X-ray system, magnetic resonance imaging (MRI), fluorescence spectroscopy, near-infrared (NIR) spectroscopy, Fourier transform infrared (FTIR) spectroscopy, hyperspectral imaging (HSI), Raman imaging, ultrasonic and acoustic sensing and other nondestructive techniques are discussed in this review. Key findings and conclusions: Among all nondestructive techniques used for cheese quality evaluation, fluorescence spectroscopy is the most used method for classification, NIR spectroscopy is mainly used for predicting chemical components, FTIR spectroscopy shows the greatest scope of applications, while CV, X-ray and MRI are only used for monitoring eye growth. HSI and Raman imaging begins to attract research interest, and synchronous fluorescence spectroscopy gradually replaces front-face fluorescence spectroscopy, whereas CV is no longer applied to cheese evaluation and literatures about NIR spectroscopy also becomes less in recent years. It is hoped that this review should provide information on current research gaps and set some directions for future studies.
... Determining cheese quality involves evaluating its chemical components, internal structure, and sensory aspects triggered by specific properties and components [1]. The critical step in the cheese-making process is detecting cheese ripeness, which is a specialized task predominantly reliant on the keen observation and sensory evaluation of experts who assess cheese wheels visually, by scent, or by weight • Public dataset release. ...
... Within the realm of cheese production, different methodologies, including CV with digital or hyperspectral imaging, near-infrared (NIH) spectroscopy, Fourier-transformed infrared (FTIR) spectroscopy, and other analytical techniques have been developed for monitoring the cheese production process [1]. These methods serve not only for quality assessment but also for determining the geographical origin and detecting potential adulteration in cheese products. ...
... In any case, the quality of the images highly depends on the constancy of the direction and strength of the light source. To overcome this issue, more complex techniques such as X-ray, MRI, and CT can provide a more robust representation of the image, along with a 3D representation of the inside of the cheese wheel, enabling cheese eye analysis [1,[22][23][24]. Of course, these approaches require specific material that is normally used in a different context, such as the medical context. ...
... Сыр относится к числу древнейших и важнейших пищевых продуктов, получивших широкое признание во всем мире. Он производится из молока различных животных с разнообразными вкусом, консистенцией, внешним видом и формой [1,2,3]. Кусок сыра обладает способностью передавать атмосферу самобытности разных стран и потому этот продукт по праву считается маркером идентичности во многих культурах [4,5,6]. ...
... Особое внимание в последние годы привлечено к неразрушающим методам оценки качества сыра. Для контроля за формированием рисунка продукта используются рентгеновские системы, компьютерное зрение (CV), компьютерная томография (CT), магнитно-резонансная томография (MRI) [2]. Использование указанных методов в условиях производственных лабораторий сыродельных предприятий проблематично по причине высокой стоимости оборудования и высоких требований к обслуживающему персоналу. ...
... Компьютерное зрение (CV) предполагает обработку изображений поверхности сыра, полученных с помощью фотокамеры [2,10,51,52]. Систему компьютерного зрения составляют пять основных компонентов: источник света, камера, плата захвата изображения, компьютерное оборудование и программное обеспечение [2]. ...
The results of scientific research of the process of eyes formation in cheeses depending on the molding method and modes of pressing the cheese mass; the type of gassing microorganisms that make up the starter culture; rheological properties of curd; the presence in the cheese mass of “germs” — the centers of the formation of eyes — are considered. It is noted that the most studied in terms of the formation of the pattern are large cheeses of the Emmental or Swiss type with eyes up to 3 cm in diameter, which can be easily estimated by calculating their quantity and volume. For this, there are methods for visualizing eyes in the volume of cheese: X-ray, computed and magnetic resonance imaging, ultrasound and acoustic sounding. The least studied is the process of pattern formation in cheeses like Tilsiter and Russian, molded in bulk, with a large number of irregular, angular eyes. In connection with the observed tendency to the loss of the distinctive features of this type of cheese (rare, insufficiently pronounced eyes), great importance is attached to objective methods for assessing the pattern in these cheeses as one of the most important indicators of the product quality. Since computed tomography, X-ray and magnetic resonance imaging are methods that require expensive equipment, the need for a simpler procedure, suitable for production laboratories of cheesemaking enterprises, is justified for use in routine examinations. Based on the assumption that cheeses with a frequent pattern of angular, irregular shape can be considered as porous bodies, an assumption was made about the advisability of developing a method for measuring the porosity coefficient, which could supplement the organoleptic assessment of the pattern with an objective indicator that would allow the rejection of cheeses on this basis.
... The water activity of the Gouda cheese itself has been reported as an average of 0.972 [2]. When contaminated with pathogenic bacteria or toxigenic fungi, a rapid growth fosters deterioration of the texture and mycotoxin production [3,4]. ...
... Enhanced moisture absorption is most likely attributed to (i) CH: protein interactions and thus lowering the cheese's protective lipid layer [18], (ii) CE being a starch (good free water binder) incorporator into chitosan-based films [24], and (iii) TA, via its cross-linking mechanism, being able to strengthen the film matrix to be resilient to moisture absorption [59]. Controlling moisture during cheese processing has a technical connotation for final cheese quality [3], and these results show the benefits of CE in regard to moisture absorption, performing absorption to a lower extent and preserving cheese moisture levels time-wise. ...
Active chitosan-based films, blended with fibrous chestnut (Castanea sativa Mill.) tannin-rich extract were used to pack Gouda cheese that has been contaminated with spoilage microflora Pseudomonas fluorescens, Escherichia coli, and fungi Penicillium commune. A comprehensive experimental plan including active chitosan-based films with (i) chestnut extract (CE), (ii) tannic acid (TA), and (iii) without additives was applied to evaluate the film′s effect on induced microbiological spoilage reduction and chemical indices of commercial Gouda cheese during 37 days while stored at 4 °C and 25 °C, respectively. The cheese underwent microbiology analysis and chemical assessments of ultra-high-performance liquid chromatography (UHPLC) (cyclopiazonic acid), pH, and moisture content. The biopackaging used for packing cheese was characterized by mechanical properties before food packaging and analyzed with the same chemical analysis. The cheese microbiology showed that the bacterial counts were most efficiently decreased by the film without additives. However, active films with CE and TA were more effective as they did not break down around the cheese and showed protective properties against mycotoxin, moisture loss, and pH changes. Films themselves, when next to high-fat content food, changed their pH to less acidic, acted as absorbers, and degraded without plant-derived additives.
... Although historically HSI has been applied to remote sensing [3], in recent years this technology has become a trending topic in different research fields such as food quality analysis [4,5], military and security applications [6] or agriculture [7,8], among many others [9]. HSI is also an emerging imaging modality in the medical field. ...
... First, the high dimensionality of spectral data together with a limited dataset can lead to the curse of dimensionality. This phenomenon offers more detailed information about the captured scene, but it also contains redundant information and increases the computational time required to process the data [4]. Second, the high variability shown in the spectral data due to different lighting conditions, instrumentation noise, or other phenomena, makes the classification based only on the spectral information a challenge. ...
Hyperspectral imaging (HSI) is a technology able to measure information about the spectral reflectance or transmission of light from the surface. The spectral data, usually within the ultraviolet and infrared regions of the electromagnetic spectrum, provide information about the interaction between light and different materials within the image. This fact enables the identification of different materials based on such spectral information. In recent years, this technology is being actively explored for clinical applications. One of the most relevant challenges in medical HSI is the information extraction, where image processing methods are used to extract useful information for disease detection and diagnosis. In this chapter, we provide an overview of the information extraction techniques for HSI. First, we introduce the background of HSI, and the main motivations of its usage for medical applications. Second, we present information extraction techniques based on both light propagation models within tissue and machine learning approaches. Then, we survey the usage of such information extraction techniques in HSI biomedical research applications. Finally, we discuss the main advantages and disadvantages of the most commonly used image processing approaches and the current challenges in HSI information extraction techniques in clinical applications.
... Cheese is a fundamental ingredient in numerous culinary recipes and is often enjoyed on its own. Consequently, evaluating its quality becomes essential for consumers and the industry [1]. ...
The production of cheese, a beloved culinary delight worldwide, faces challenges in maintaining consistent product quality and operational efficiency. One crucial stage in this process is determining the precise cutting time during curd formation, which significantly impacts the quality of the cheese. Misjudging this timing can lead to the production of inferior products, harming a company's reputation and revenue. Conventional methods often fall short of accurately assessing variations in coagulation conditions due to the inherent potential for human error. To address this issue, we propose an anomaly detection-based approach. In this approach, we treat the class representing curd formation as the anomaly to be identified. Our proposed solution involves utilizing a one-class, fully convolutional data description network, which we compared against several state-of-the-art methods to detect deviations from the standard coagulation patterns. Encouragingly, our results show F1 scores of up to 0.92, indicating the effectiveness of our approach.
... NIR has a great degree of spectrum stability, which has made it quite popular at the industry level (42). However, literature related to NIR spectroscopy for cheese quality evaluation has become less in recent years (43) because applications for evaluating other attributes are inadequate, and innovative chemometric methods are not extensively used in research. ...
The aims of this proof of principle study were to compare two different chemometric approaches using a Bayesian method, Partial Least Square (PLS) and PLS-discriminant analysis (DA), for the prediction of the chemical composition and texture properties of the Grana Padano (GP) and Parmigiano Reggiano (PR) PDO cheeses by using NIR and Raman spectra and quantify their ability to distinguish between the two PDO and among their ripening periods. For each dairy chain consortium, 9 cheese samples from 3 dairy industries were collected for a total of 18 cheese samples. Three seasoning times were chosen for each dairy industry: 12, 20, and 36 months for GP and 12, 24, and 36 months for PR. A portable NIR instrument (spectral range: 950–1,650 nm) was used on 3 selected spots on the paste of each cheese sample, for a total of 54 spectra collected. An Alpha300 R confocal Raman microscope was used to collect 10 individual spectra for each cheese sample in each spot for a total of 540 Raman spectra collected. After the detection of eventual outliers, the spectra were also concatenated together (NIR + Raman). All the cheese samples were assessed in terms of chemical composition and texture properties following the official reference methods. A Bayesian approach and PLS-DA were applied to the NIR, Raman, and fused spectra to predict the PDO type and seasoning time. The PLS-DA reached the best performances, with 100% correctly identified PDO type using Raman only. The fusion of the data improved the results in 60% of the cases with the Bayesian and of 40% with the PLS-DA approach. A Bayesian approach and a PLS procedure were applied to the NIR, Raman, and fused spectra to predict the chemical composition of the cheese samples and their texture properties. In this case, the best performance in validation was reached with the Bayesian method on Raman spectra for fat (R2VAL = 0.74). The fusion of the data was not always helpful in improving the prediction accuracy. Given the limitations associated with our sample set, future studies will expand the sample size and incorporate diverse PDO cheeses.
... These studies are motivated by trends known as "green thinking" that induct preferences to artisanal cheese due to its health benefits. The continuous development of portable and inexpensive devices for measuring the quality indicators of cheese will motivate a lot of studies based on some technologies that are typical for medicine [33]. For example, the development of low-cost smartphonebased colorimetric devices is not only a dream [34]. ...
A review of current trends in CVSs (computer vision systems) regarding hardware and software components, in the context of cheese quality evaluation, is presented in this paper, and some directions for future development in the field are discussed. The application of methods of computer vision for evaluating the quality indicators of different types of cheese is explored in detail regarding systems for data gathering and algorithms for their processing. We discuss opportunities for the usage of advancements in AI (artificial intelligence) for a fast and effective quality control of food production. Modern computer-based concepts are viewed in the context of food quality control such as CAFE (computer-aided food engineering) and digital twins. Methods for further enhancement of the quality of human life are highlighted in the context of sustainability related to contemporary computer-based technologies.
... These differences in color may also be associated with the curing time of the cheese samples, as color is a good indicator of their maturity. The most cured cheeses seem to be those with PDO, as their colors are more yellow-brown as previously described [57]. ...
The external appearance of some of the Protected Designation of Origin (PDO) cured cheeses is similar to other cheese samples made in Spain: 1 kg and 2.5–3 kg formats, cylindrical, and with or without a pleita mark on the surface. In this work, commercial cured ewe’s milk cheese samples with a similar external appearance were analyzed, including five PDO and five non-PDO samples. The parameters analyzed were color, texture, pH, humidity, water activity, and the volatile profile. Additionally, a descriptive and consumer-sensory analysis of the cheese samples was carried out. Statistical analysis of the results showed that luminosity, color coordinates a* and b*, percentage of deformation, humidity, water activity, and acid contents were significantly higher in non-PDO cheese samples. The breaking force, maximum force, and the content of esters were significantly higher in those cheese samples with PDO. In addition, PDO cheese samples showed higher scores for all attributes evaluated by consumers, except for color. These results suggest that PDO cheeses are placed on the market with a higher degree of ripening than non-PDO ones and that consequently they are more positively valued by consumers.
... Food processors and regulatory agencies would prefer a technique that can be used to quickly screen complex food matrices like cheese. Vibrational spectroscopies, namely Fourier-transform infrared (FTIR) and Raman spectroscopies, are sensitive to large amounts of molecular information related to sample composition and have been successfully applied in many areas of food science, including characterizing alcoholic spirits [5], wine [6], beef [7,8], fish [9], and cheese [10,11]. ...
Cheese is a nutritious dairy product and a valuable commodity. Internationally, cheddar cheese is produced and consumed in large quantities, and it is the main cheese variety that is exported from Australia. Despite its importance, the analytical methods to that are used to determine cheese quality rely on traditional approaches that require time, are invasive, and which involve potentially hazardous chemicals. In contrast, spectroscopic techniques can rapidly provide molecular information and are non-destructive, fast, and chemical-free methods. Combined with partner recognition methods (chemometrics), they can identify small changes in the composition or condition of cheeses. In this work, we combined FTIR and Raman spectroscopies with principal component analysis (PCA) to investigate the effects of aging in commercial cheddar cheeses. Changes in the amide I and II bands were the main spectral characteristics responsible for classifying commercial cheddar cheeses based on the ripening time and manufacturer using FTIR, and bands from lipids, including β’-polymorph of fat crystals, were more clearly determined through changes in the Raman spectra.
... Therefore, selecting the optimal hardness moment for the cut is necessary, which induces the drainage process [6]. The cut should be performed when the curd is sufficiently cohesive but has not reached excessive hardening [7]. The changes in the optical properties of milk during coagulation have allowed, primarily using optical fibers, the development of a series of instruments based on determinations of reflection, absorbance, dispersion, and refraction of light [8]. ...
This study aims to provide the dairy industry with a direct control model focused on milk coagulation by using multifiber probes to determine parameters in the curding process, such as cutting time, at a lower cost. The main objective of the research is to confirm that a multifiber NIR light scattering probe can be used to predict the elastic modulus of curd during milk coagulation in cheese production. Two randomized complete block designs were used with a 3 × 3 factorial arrangement of three protein levels (3%, 3.5% and 4%) and three wavelengths (870 nm, 880 nm and 890 nm). Using a multifiber probe at a wavelength of 880 nm allowed obtaining a better optical response of the sensor during enzymatic milk coagulation than the 870 nm. It showed greater sensitivity to variations in the protein content of the milk and lower variation in the response. The multifiber probe at a wavelength of 880 nm generated a NIR light backscatter profile like those obtained with other systems. The results showed that the prediction model parameters had a variation as a function of the protein content, which opens the possibility of improving the prediction model’s performance substantially. Furthermore, the initial voltage obtained with the probe responded linearly to the different protein levels in milk. This fact would make it possible, at least theoretically, to estimate protein concentration with the same inline probe for G’ determination, facilitating the incorporation of a corrective protein factor in the prediction models using a single instrument.
... Cheese was traditionally produced as a small-scale farm product, but is currently mainly manufactured on a large industrial scale, either as a standalone food item or as a food ingredient (Everett & Auty, 2008), and the evaluation of quality features of cheese is critical for consumers and the industry (Lei & Sun, 2019). Numerous desirable features of cheese are now widely acknowledged as being primarily determined by its structure. ...
... Commonly, the quality of cheese products, fresh or matured, is influenced by the origin of the milk, the chemical and microbiological properties, the type, the concentration of started cultures added, and the time and temperature during the ripening stage, affecting the chemical processes during its production and maturation (Jiménez-Fernández et al., 2021;Lei & Sun, 2019;Everett & Auty, 2017;Guerra-Martínez et al., 2012). Nowadays, globalization and industrial advancements have led to reducing or losing some dissimilarities between products from specific regions, leading to producers being distinguished based solely on the quality of milk and the selection of dairy cultures and rennet (Ostarić et al., 2022;Nieto-Arribas, et al., 2009). ...
... The ability to apply combined analytical spectroscopic and imaging methods to analyse the microstructure of food systems has been a relatively recent development, largely because of the increasing capability and availability of suitable imaging technologies. Developments in instrumentation and spectral and spatial resolution, along with an increased signal-to-noise ratio in these systems, have allowed detailed chemical and structural information in food systems to be gathered at a microscale (Lei and Sun 2019). The development of Raman imaging using confocal microscopy systems has allowed research into the chemical and corresponding microstructure of various components in a food system without some of the many artefacts introduced with other methods (Smith et al. 2017). ...
Infrared spectroscopy and Raman spectroscopy, when applied to food samples, have the unique ability to interrogate their chemical structure. In this research, Raman and mid‐infrared (MIR) spectroscopy in conjunction with selected chemometrics approaches were used to investigate the effects of ageing on the protein structure in Cheddar cheese. The results illustrate the potential of Raman confocal imaging and MIR spectroscopy for an understanding of chemical changes in complex dairy matrices. Raman and mid‐infrared spectroscopy along with chemometrics show correlations to the effects of maturity in Cheddar cheese.
... position, width, and area) and the latter enhances resolution by narrowing the component bands ( Figure 2). Both methods have been widely employed to investigate biological systems, mainly for determining the secondary structure of proteins [6][7][8][9][10][11]. However, because of the well-established shortcomings of these tools (e.g. ...
Infrared spectroscopy is a useful tool to assess the physical and chemical properties of different compounds. However, its use in Food Science and Technology is mainly descriptive and/or combined with chemometric analysis. The aim of this review was to underline available approaches for the analysis physical and chemical properties associated to food products and processing.
In this context, this review covers recent applications of infrared spectroscopy in Food Science and Technology, focusing on physical-chemical properties of food products and ingredients, including macromolecules, and alterations arising from processing and storage.
Overall, the work underlines the powerfulness of infrared spectroscopy, a currently underexploited technique in the field, whose use should be stimulated for rapid and accurate determinations both in academic and industrial contexts.
... Similarly, CT has been extensively used to study eye formation in Swiss-type cheeses (Huc et al., 2014;Guggisberg et al., 2015;O'Sullivan et al., 2016) and pores caused by freezing in cheese (Conte et al., 2017). The use of CT is facilitated by the high contrast between air and soft materials, but its use in cheese has been mainly limited to the previously mentioned applications (Lei and Sun, 2019). Being a nondestructive technique that allows obtaining quantitative 3-dimensional structural information, its application for studying the microstructure of Cheddar cheese and, in particular, the presence of crystals in this type of product, is evaluated in this work. ...
This study explored the use of X-ray computerized microtomography (micro-CT) and confocal Raman microscopy to provide complementary information to well-established techniques, such as confocal laser scanning microscopy (CLSM), for the microstructural characterization of cheese. To evaluate the potential of these techniques, 5 commercial Cheddar cheese samples, 3 with different ripening times and 2 with different fat contents, were analyzed. Confocal laser scanning microscopy was particularly useful to describe differences in fat and protein distribution, especially between the 2 samples with different fat contents. The quantitative data obtained through image analysis correlated well with the nutritional information provided in the product labels. Conversely, micro-CT was more advantageous for studying the size and spatial distribution of microcrystals present within the cheese matrix. Two types of microcrystals were identified that differed in size, shape, and X-ray attenuation. The smallest, with a diameter of approximately 10 to 20 μm, were more abundant in the samples and presented a more uniform roundish shape and higher X-ray attenuation. Larger and more heterogeneous crystals with diameters reaching 50 μm were also observed in scarcer numbers and showed lower X-ray attenuation. Confocal Raman microscopy was useful not only for identifying the distribution of all these components but also allowed comparing the presence of micronutrients such as carotenoids in the cheeses and provided compositional information on the crystals detected. Small and large crystals were identified as calcium phosphate and calcium lactate, respectively. Overall, using micro-CT, confocal Raman microscopy, and CLSM in combination generated novel and complementary information for the microstructural and nutritional characterization of Cheddar cheese. These techniques can be used to provide valuable knowledge when studying the effect of milk composition, processing, and maturation on the cheese quality attributes.
... Compared with the machine learning model based on RGB images, the combination of the HSI technique and machine learning can obtain more diversified food features, which can be used to quantitatively and qualitatively analyze the physical and chemical properties of food by identifying the spectral features of target molecules and the spatial features based on sensory qualities and can obtain better detection results. Therefore, machine learning and HSI techniques show strong potential for quality and safety testing in food engineering and are now widely used for physical properties, chemical composition, microbial content, authenticity, and traceability testing of food products [17][18][19][20][21]. ...
Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.
... Besides spectroscopic and imaging techniques, a wide range of analytical methods have been developed in recent years. These include acoustic and ultrasound sensing (Caladcad et al., 2020;Lei and Sun, 2019), machine vision system and computer vision (El-Mesery et al., 2019;Kakani et al., 2020;Saberioon et al., 2017), bioelectrical impedance analysis (Fan et al., 2021;Huh et al., 2021), wireless chemical sensors and biosensors, such as radio-frequency identification (RFID) (Karuppuswami et al., 2020;Kassal et al., 2018), electronic nose and electronic tongue (Di Rosa et al., 2017), just to mention a few. However, most of these techniques are still under development and require more research to meet industrial needs. ...
Food quality has recently received considerable attention from governments, researchers, and consumers due to the increasing demand for healthier and more nutritious food products. Traditionally, food quality is determined using a range of destructive and time-consuming approaches with modest analytical performance, underscoring the urgent need to develop novel analytical techniques. The Fourth Industrial Revolution (called Industry 4.0) is progressing exponentially, driven by the advent of a range of digital technologies and other innovative technological advances. “Food Quality 4.0” is a new concept referring to the use of Industry 4.0 technologies in food analysis to achieve rapid, reliable, and objective assessment of food quality. In this review, we will first discuss the fundamentals and principles of Food Industry 4.0 technologies and their connections with the Food Quality 4.0 concept. Then, the most common techniques used to determine food quality will briefly be reviewed before highlighting the advancements made in analytical techniques to assess food quality in the era of Industry 4.0.
Food Quality 4.0 is characterized by growing digitalization and automation of food analysis using the most advanced technologies in the food industry. Key aspects of Food Quality 4.0, including, among others, non-destructive fingerprinting techniques, omics technologies and bioinformatics tools, Artificial Intelligence and Big Data, have great potential to revolutionize food quality. Although most of these technologies are still under development, it is anticipated that future research will overcome current limitations for large-scale applications.
... There have been a few other recent reviews, including Panikuttira et al. (2018) and Pu et al. (2020), on the use of nondestructive techniques to evaluate process analytical technology in cheesemaking plants. De Marchi et al. (2018) presented an overview of infrared applications for assessing several characteristics of different dairy products, which included some information on cheese composition, and Lei and Sun (2019) looked at different nondestructive techniques for evaluating the quality attributes of cheese, including NIRS. A collection of performance of validated NIR applications used routinely for quality control of solid, semi-solid, and liquid samples in dairy industry laboratories and by instruments vendors have been published recently by the International Dairy Federation (Niemöller and Holroyd, 2019). ...
Substantial research has been carried out on rapid, nondestructive, and inexpensive techniques for predicting cheese composition using spectroscopy in the visible and near-infrared radiation range. Moreover, in recent years, new portable and handheld spectrometers have been used to predict chemical composition from spectra captured directly on the cheese surface in dairies, storage facilities, and food plants, removing the need to collect, transport, and process cheese samples. For this review, we selected 71 papers (mainly dealing with prediction of the chemical composition of cheese) and summarized their results, focusing our attention on the major sources of variation in prediction accuracy related to cheese variability, spectrometer and spectra characteristics, and chemometrics techniques. The average coefficient of determination obtained from the validation samples ranged from 86 to 90% for predicting the moisture, fat, and protein contents of cheese, but was lower for predicting NaCl content and cheese pH (79 and 56%, respectively). There was wide variability with respect to all traits in the results of the various studies (standard deviation: 9–30%). This review draws attention to the need for more robust equations for predicting cheese composition in different situations; the calibration data set should consist of representative cheese samples to avoid bias due to an overly specific field of application and ensure the results are not biased for a particular category of cheese. Different spectrometers have different accuracies, which do not seem to depend on the spectrum extension. Furthermore, specific areas of the spectrum—the visible, infrared-A, or infrared-B range—may yield similar results to broad-range spectra; this is because several signals related to cheese composition are distributed along the spectrum. Small, portable instruments have been shown to be viable alternatives to large bench-top instruments. Last, chemometrics (spectra pre-treatment and prediction models) play an important role, especially with regard to difficult-to-predict traits. A proper, fully independent, validation strategy is essential to avoid overoptimistic results.
... Nowadays, fluorescence spectroscopy is being of great interest for scientific community. Some reviews found in the literature show the use of fluorescence techniques in different kinds of foods (Hassoun et al. 2019;Lei and Sun 2019;Shaikh and O'Donnell 2017). ...
The hydrophilic and lipophilic antioxidant activities due to the main bioactive components present in Spanish tomato paste samples were studied, using standardized and fluorescent methods. After extraction, phenolic antioxidants (Folin-Ciocalteu method) and total antioxidant activity (TEAC assay) were evaluated, examining differences between hydrophilic and lipophilic extracts corresponding to different samples. Total fluorescence spectra of extracts (excitation-emission matrices, EEMs) were recorded in the front-face mode at two different ranges: 210–300 nm/310–390 nm, and 295–350 nm/380–480 nm, for excitation and emission, respectively, in the hydrophilic extracts. In the lipophilic extracts, the first range was 230–283 nm/290–340 nm, while the second range was 315–383 nm/390–500 nm for excitation and emission, respectively. EEMs from a set of 22 samples were analyzed by the second-order multivariate technique Parallel Factor Analysis (PARAFAC). Tentative assignation of the different components to the various fluorophores of tomato was tried, based on literature. Correlation between the antioxidant activity and score values retrieved for different components in PARAFAC model was obtained. The possibility of using EEMs-PARAFAC to evaluate antioxidant activity of hydrophilic and lipophilic compounds in these samples was examined, obtaining good results in accordance with the Folin-Ciocalteu and TEAC assays.
... Cheese is produced around the world in a wide range of flavours, varieties, textures, and shapes, which can be used as a final product in human diet, and as an important ingredient in various foods. With consumer's continuing demand for quality cheese as well as increasing challenge from production facing the industry, nondestructive techniques are increasingly used to evaluate cheese quality [5]. ...
One of the possible ways for the qualitative assessment of indicators is the qualimetric approach. To model multi-component products (processed cheese), a qualimetric assessment was used for the selected quality parameters, de-pending on the nutritional and biological value of the raw material. Based on the optimal choice of raw materials and the ratio of ingredients, we obtained formulations, the nutrients of which meet the biomedical requirements in terms of quan-tity and quality. The article provides data for the optimisation of the recipe composition of the nutritional mixture for processed cheeses. Optimisation was carried out on the basis of calculating quality indicators, taking into account the general chemical composition, the content of vitamins, minerals and the daily intake of individual substances. According to the results of the studies, the optimal formulation of the nutritional mixture for processed cheese is option 1. For this mixture, the generalised quality indicator has the highest value (0.289). It received such a high value due to the group in-dicators: “General chemical composition”, “Content of vitamins and macronutrients”. Nutrient mixture according to op-tion 2 in the processed cheese recipe is optimal in terms of chemical composition, vitamins and microelements. Nutrient mixture according to option 3 is optimal only in terms of the content of microelements.
... In this regard, the possibility of using computer vision systems (CVS) to measure these color parameters has recently attracted the attention of researchers (Hadimani & Mittal, 2019;Minz & Saini, 2019;Tomasević et al., 2019). Also the applications of CVS, such as digital image processing, in combination with mathematical models for the determination of other food properties (e.g., fat content and viscosity) can be considered as a future technology in the food processing industry, considering the potential benefits and recent progress in this area of the research (Lei & Sun, 2019;Taheri-Garavand et al., 2019). The current study aims to investigate the possibility of milk concentration by RW technology and to understand the effects of temperature and pressure of RW process on some physical properties of the product such as browning index (BI). ...
This study is the 2nd part of ongoing research on developing a refractive window (RW) milk concentrator. The response surface methodology (RSM)-computer vision approach provided a platform to maximize process productivity, minimize energy consumption, and optimize microbiological, sensory, and color parameters. The results were compared with the conventional concentration (CC). Experimental conditions were temperatures of 50, 60, and 70 °C and pressures of 0.4, 0.6, and 0.8 bar. According to the results, the RW product had a better sensory and microbiological characteristic than CC. The optimum process conditions in terms of shelf-life and sensory properties were the pressure of 0.45 bar and temperature of 66 °C. The productivity of RW was higher than that of CC by 110% (2.3 L/h vs. 1.1 L/h). Besides, RW products got a higher score on sensory assessment in comparison with CC. RW significantly increased the shelf-life and decreased the peroxide value.
... Figure 2 shows that there is an increase in the use of these techniques as PAT in the milk and dairy industry in general, but also in the monitoring of heat treatment in particular. Several review articles on non-destructive techniques for monitoring food processing have been published recently (Loudiyi et al., 2020;Lei and Sun, 2019;Hassoun et al., 2020), but no review focusing on the potential of spectral techniques to assess changes in the quality of milk and dairy products during heat treatments has been found. ...
In the food industry, thermal treatments are generally an essential step to increase the shelf life of the products. This is especially true for milk and dairy products in which heat treatments help to eliminate pathogenic organisms, minimize microbiological development, and improve some sensory properties. However, they can also induce biochemical, physico-chemical, and sensory changes in foods, and then adversely affect the final quality of the products. To assess the quality of milk and dairy products during heating, some non-destructive techniques exist. In this article, the application of spectroscopic non-destructive techniques (fluorescence, infrared, NMR) is analyzed to point out the pertinence by using them as tools to monitor milk and dairy product quality changes during heating. An overview of the last studies on the effect of different conventional and emerging methods of milk and dairy product heating on biochemical, physico-chemical, and sensory quality is also presented, as well as the perspectives of research in this topic.
... On the other hand, various imaging and spectroscopic technologies have emerged as alternative tools for the food industry, particularly, in recent years, hyperspectral imaging technology has gained significant importance as a rapid and noninvasive method for detecting large-scale samples (Liu et al., 2017). Therefore, several reviews of the application of HSI have been published, covering the prediction of the textural properties (Lei & Sun, 2019;Ma et al., 2019;Wang et al., 2016;Xiong et al., 2014) and colour features (ElMasry, ElMasry & Nakauchi, 2016;Wu & Sun, 2013b) of food products. However, no review is available focusing on a wide range of the sensory properties of food products evaluated by HSI. ...
Background
Sensory analysis is the evaluation of the signals received through senses of sight, sound, taste, smell, and touch. The traditional methods for the analysis of sensory properties including trained sensory panel, colourimeters and texture analysers are invasive, laborious, and small-scale procedures. Hyperspectral imaging systems (HSI) has emerged as a less time-consuming and non-destructive method to determine the sensory properties of a diverse range of food products.
Scope and approach
This review provides a comprehensive overview of recent application developments since 2010 for identifying sensory properties including colour, defects, texture, flavour, freshness, and maturity in various food products by HSI.
Key findings and conclusions
The Vis-NIR (400–1000 nm) hyperspectral imaging is most used for the assessment of sensory properties. Moreover, the commonly applied multivariate analysis in the sensory evaluation by HSI is linear regression algorithms (PLSR and MLR), but non-linear analysis achieves better performance for the prediction of sensory features. Regression models are applied for determining the texture, colour, flavour, and freshness of food products while classification models are used to detect the defects and maturity by which the most accurate results are obtained. Furthermore, the combination of algorithms and the integration of spectral and spatial information yield more accurate results in the assessment of sensory features. Although there are still some limitations to overcome for the improvement of the HSI system, studies on the application of HSI in the real-time monitoring of sensory properties have proved a great potential for industrial applications.
... Different techniques are available nowadays to explore the microstructure of dairy products in detail (e.g., computer tomography, x-ray imaging, magnetic resonance imaging). For more information, one may refer to the recent review published by Lei and Sun [104]. ...
The yield and quality of cheese are associated with the composition, physicochemical, sensory, rheological, and microbiological properties of milk and with the technology applied to the milk before and/or during cheese processing. This review describes the most important research on cheeses obtained from processing mixtures of different milk species and discusses the effect of milk mixtures (i.e., species and mixture ratios) on composition, physicochemical, sensory, rheological, and microbiological properties of cheeses. More specifically, the present review paper will gather and focus only on studies that have provided a clear comparison between cheeses produced from a mixture of two milk species to cheeses produced from only one species.
The production of cheese, a beloved culinary delight worldwide, faces challenges in maintaining consistent product quality and operational efficiency. One crucial stage in this process is determining the precise cutting time during curd formation, which significantly impacts the quality of the cheese. Misjudging this timing can lead to the production of inferior products, harming a company’s reputation and revenue. Conventional methods often fall short of accurately assessing variations in coagulation conditions due to the inherent potential for human error. To address this issue, we propose an anomaly-detection-based approach. In this approach, we treat the class representing curd formation as the anomaly to be identified. Our proposed solution involves utilizing a one-class, fully convolutional data description network, which we compared against several state-of-the-art methods to detect deviations from the standard coagulation patterns. Encouragingly, our results show F1 scores of up to 0.92, indicating the effectiveness of our approach.
Safety and quality aspects of food products have always been critical issues for the food production and processing industries. Since conventional quality measurements are laborious, time-consuming, and expensive, it is vital to develop new, fast, non-invasive, cost-effective, and direct techniques to eliminate those challenges. Recently, non-destructive techniques have been applied in the food sector to improve the quality and safety of foodstuffs. The aim of this review is an effort to list non-destructive techniques (X-ray, computer tomography, ultraviolet-visible spectroscopy, hyperspectral imaging, infrared, Raman, terahertz, nuclear magnetic resonance, magnetic resonance imaging, and ultrasound imaging) based on the electromagnetic spectrum and discuss their principle and application in the food sector. This review provides an in-depth assessment of the different non-destructive techniques used for the quality and safety analysis of foodstuffs. We also discussed comprehensively about advantages, disadvantages, challenges, and opportunities for the application of each technique and recommended some solutions and developments for future trends.
Origin authentication methods are pivotal in counteracting frauds and provide evidence for certification systems. For these reasons, geographical origin authentication methods are used to ensure product origin. This study focused on the origin authentication (i.e. at the producer level) of a typical mountain cheese origin using various approaches, including shotgun metagenomics, volatilome, near infrared spectroscopy, stable isotopes, and elemental analyses. DNA-based analysis revealed that viral communities achieved a higher classification accuracy rate (97.4 ± 2.6 %) than bacterial communities (96.1 ± 4.0 %). Non-starter lactic acid bacteria and phages specific to each origin were identified. Volatile organic compounds exhibited potential clusters according to cheese origin, with a classification accuracy rate of 90.0 ± 11.1 %. Near-infrared spectroscopy showed lower discriminative power for cheese authentication, yielding only a 76.0 ± 31.6 % classification accuracy rate. Model performances were influenced by specific regions of the infrared spectrum, possibly associated with fat content, lipid profile and protein characteristics. Furthermore, we analyzed the elemental composition of mountain Caciotta cheese and identified significant differences in elements related to dairy equipment, macronutrients, and rare earth elements among different origins. The combination of elements and isotopes showed a decrease in authentication performance (97.0 ± 3.1 %) compared to the original element models, which were found to achieve the best classification accuracy rate (99.0 ± 0.01 %). Overall, our findings emphasize the potential of multi-omics techniques in cheese origin authentication and highlight the complexity of factors influencing cheese composition and hence typicity.
This study developed a novel method for monitoring cheese contamination with Clostridium spores non-invasively using hyperspectral imaging (HSI). The ability of HSI to quantify Clostridium metabolites was investigated with control cheese and cheese manufactured with milk contaminated with Clostridium tyrobutyricum, Clostridium butyricum and Clostridium sporogenes. Microbial count, HSI and SPME-GC-MS data were obtained over 10 weeks of storage. The developed method using HSI successfully quantified butyric acid (R2=0.91, RPD=3.38) a major compound of Clostridium metabolism in cheese. This study creates a new venue to monitor the spatial and temporal development of late blowing defect (LBD) in cheese using fast and non-invasive measurement.
Dairy products are worldwide spread and have great commercial importance. Rapid and reliable analysis of cheese would be highly desirable both for the manufacturers and consumers. The results of experiments, related to the application of near-infrared spectroscopy for cheese quality estimation will be presented. Several kinds of Bulgarian white brine cheese - natural from cow milk, imitation products with vegetable oil, and cheese with different water content were investigated. Fatty acids composition of samples was determined by using gas chromatography and moisture content by the oven-dry method. Spectra of all tested samples were obtained with a scanning NIRQuest 512 (Ocean Optics, Inc.) instrument in the range of 900-1700 nm using a reflection fiber-optics probe. PLS models were developed for quantitative determination and SIMCA for classification. The misclassification rate of the SIMCA model for discrimination of natural cheese and imitation products with vegetable oil was 2.9%. Quantitative determination of water content based on NIR spectra showed high accuracy, Models for classification of cheese samples into 3 groups according to water content achieved 5.64% misclassification rate for the independent test set. Results showed the potential of near-infrared spectroscopy as a non-destructive and rapid screening tool for assessing cheese quality and detecting adulteration.
Ripening is a very important process that contributes to cheese quality, as its characteristics are determined by the biochemical changes that occur during this period. Therefore, monitoring ripening time is a fundamental task to market a quality product in a timely manner. However, it is difficult to accurately determine the degree of cheese ripeness. Although some scientific methods have also been proposed in the literature, the conventional methods adopted in dairy industries are typically based on visual and weight control. This study proposes a novel approach aimed at automatically monitoring the cheese ripening based on the analysis of cheese images acquired by a photo camera. Both computer vision and machine learning techniques have been used to deal with this task. The study is based on a dataset of 195 images (specifically collected from an Italian dairy industry), which represent Pecorino cheese forms at four degrees of ripeness. All stages but the one labeled as “day 18”, which has 45 images, consist of 50 images. These images have been handled with image processing techniques and then classified according to the degree of ripening, i.e., 18, 22, 24, and 30 days. A 5-fold cross-validation strategy was used to empirically evaluate the performance of the models. During this phase, each training fold was augmented online. This strategy allowed to use 624 images for training, leaving 39 original images per fold for testing. Experimental results have demonstrated the validity of the approach, showing good performance for most of the trained models.
Prato cheese is the second most commercialized cheese in Brazil and its full-fat variety has very high fat content. The aim of this study was to evaluate the degree of perception and preference of untrained consumers to full-fat and low-fat Prato cheese. For such, eight Prato cheese sample (four full-fat and four low-fat version) weighing 500g each, were submitted to microbiological, physical and sensorial evaluations. The microbiological evaluation consisted of determining the Most Probable Number of coliforms at 35 and 45°C as well as the presence of Salmonella sp. In the physical analyses, we found the values of pH, shear force and performed a color analysis. The sensory analysis was performed from the Preference Test. Both cheese varieties had coliform counts at 35 and 45°C within the thresholds established by current legislation; and there was absence of Salmonella sp. In addition, the physical characteristics were within the expected for the type of cheese evaluated. As for the sensory evaluation, full-fat Prato cheese was preferred for the appearance (52%) and aroma (55%) attributes, however the low-fat variety was better scored for the taste (57%), texture (57%) and overall acceptance (55%). The tasters were able to differentiate the samples, and the low-fat Prato cheese was more widely accepted.
Raman confocal microscopy is an increasingly useful technique when applied to food samples. It has the unique ability to interrogate the chemical structure, aligned with the same confocality capability that is available when using standard confocal microscopy. In this research, we used Raman confocal microscopy to investigate the effects of maturation on the microstructure of cheddar cheese. Statistical analysis of the spectral data showed significant protein structural changes as the cheese matured. The results illustrate the potential of Raman confocal microscopy in conjunction with spectroscopy and imaging to understand chemical changes in complex dairy matrices. Raman confocal microscopy images and associated Raman spectral data of Cheddar cheese.
Most foods that are consumed daily are prone to food adulterant. Food adulterants are classified into several main groups. One of the most important cases of food adulterants is to increase the quality of raw or prepared foods by intentionally or accidentally adding non-food to them. The removal or replacement of valuable food components with relatively cheaper ones that significantly affect the original nutritional value and the addition of any poisonous or deleterious substances that are hazardous to the health of the consumer are also cases of food adulterants. In the recent years, food adulterations have steadily increased and become a major challenge for consumers, taking into account all economic, social, health, and religious aspects. Due to the importance of recognizing the authenticity of each food before it becomes available for public consumption, various methods including physical, chemical, biochemical, etc., are used to evaluate them. Meanwhile, electrochemical sensors, with their unique features such as simplicity, ease of operation, high sensitivity, and selectivity, have become a promising choice for evaluating food authenticity.KeywordsFood adulterantBiosensorsElectrochemical methods
Anthocyanins (ACNs) are plant polyphenols that have received increased attention recently mainly due to their potential health benefits and applications as functional food ingredients. This has also created an interest in the development and validation of several non-destructive techniques of ACN assessments in several food samples. Non-destructive and conventional techniques play an important role in the assessment of ACNs in agricultural and food products. Although conventional methods appear to be more accurate and specific in their analysis, they are also associated with higher costs, the destruction of samples, time-consuming, and require specialized laboratory equipment. In this review article, we present the latest findings relating to the use of several spectroscopic techniques (fluorescence, Raman, Nuclear magnetic resonance spectroscopy, Fourier-transform infrared spectroscopy, and near-infrared spectroscopy), hyperspectral imaging, chemometric-based machine learning, and artificial intelligence applications for assessing the ACN content in agricultural and food products. Furthermore, we also propose technical and future advancements of the established techniques with the need for further developments and technique amalgamations.
The dairy products sector is an important part of the food industry, and their consumption is expected to grow in the next 10 years. Therefore, the authentication of these products in a faster and precise way is required for the sake of public health. This review proposes the use of near‐infrared techniques for the detection of food fraud in dairy products as they are faster, nondestructive, environmentally friendly, do not require sample preparation, and allow multiconstituent analysis. First, we have described frequent forms of food fraud in dairy products and the application of traditional techniques for their detection, highlighting gaps and counterproductive characteristics for the actual global food chain, as longer sample preparation time and use of reagents. Then, the application of near‐infrared spectroscopy and hyperspectral imaging for the detection of food fraud mainly in cheese, butter, and yogurt are described. As these techniques depend on model development, the coverage of different dairy products by the literature will promote the identification of food fraud in a faster and reliable way.
Microscopy is often used to assist the development of cheese products, but manufacturers can benefit from a much broader application of these techniques to assess structure formation during processing and structural changes during storage. Microscopy can be used to benchmark processes, optimize process variables, and identify critical control points for process control. Microscopy can also assist the reverse engineering of desired product properties and help troubleshoot production problems to improve cheese quality. This approach can be extended using quantitative analysis, which enables further comparisons between structural features and functional measures used within industry, such as cheese meltability, shreddability, and stretchability, potentially allowing prediction and control of these properties. This review covers advances in the analysis of cheese microstructure, including new techniques, and outlines how these can be applied to understand and improve cheese manufacture.
Infrared spectroscopy has been shown to be efficient in cheese authentication due to the advantages of high sensitivity and speed of analysis, especially when associated with chemometrics. This review discusses approaches on the authenticity, the principles of near‐ and middle‐infrared techniques and the importance of chemometrics for cheese authentication. The spectroscopic techniques proved to be promising for the cheese geographical origin identification, analysis of adulterants and monitoring of maturation stages. The application of principal component analysis, partial least squares and linear discriminant analysis associated with spectroscopy has provided powerful tools for the cheese authentication. Infrared spectroscopic techniques and chemometry to cheese authentication.
The presence of impurities in food grains greatly impact their grades and quality as well as their overall acceptance. Additionally, bulk grains storage is subject to deterioration and losses through factors that could be biological (insects, fungi, mites, mould, rodents) or environmental (temperature, moisture and relative humidity). Several techniques ranging from manual methods (human sensory) to microbiological and chemical tests have been employed in carrying out quality assessments to control deterioration in stored bulk grains. However, it is often challenging, time-consuming and expensive to discriminately identify grain contaminants in store using the traditional methods, especially when they are physically and sometimes visually similar. The hyperspectral imaging, an emerging technique that combines the technology of conventional imaging (computer vision), radiometry and spectroscopy to simultaneously obtain both spatial and spectral information from a sample is potentially a viable approach. Studies on the application of this technology to food grain quality inspection during bulk storage appears to be scanty. Herein a comprehensive review on the applicability of hyperspectral imagining for food grain quality assessment is presented and its potential for application in grain quality inspection, evaluation and control during bulk storage is provided. Specifically, the work presents the basic theory and principles of hyperspectral imaging, and its recent uses in quality and safety inspection of agro-food products. It highlights and discusses the potential and possible approaches in the application of the technology for food grains quality inspection, evaluation and control during bulk storage. Particularly, hyperspectral imaging systems utilizing line or area scanning were considered capable and proposed for application in bulk grain storage quality inspection. Existing literature showed that most studies on the application of hyperspectral imaging for grain quality assessment were majorly focused on wheats. Future research should consider other grains.
This is the first book for some years that provides a comprehensive overview of food oral processing. It includes fundamental chapters at the beginning of each section to aid the understanding of the later more specific oral processing chapters. The field is rapidly developing, and the systems researched in the context of food oral processing become increasingly complex and therefore the fundamental sections include information on how to build complex food systems.
The main coverage includes the biomechanics of swallowing, the biophysics of mouthfeel and texture as well as the biochemistry of flavours and how food microstructures can be manipulated. It contains up-to-date research findings, looking at consumer preferences and the response to these preferences by food process technologists and those developing new foods.
The book will be of interest to postgraduate students and researchers in academia and industry who may be from very diverse backgrounds ranging from food process engineers to functional food developers and professionals concerned with swallowing and taste disorders.
Food safety and quality are of utmost importance for consumers, suppliers, and regulators. The methodologies for determining the food quality are majorly classified as destructive and non-destructive techniques. The destructive techniques used for determining the food quality are labor-intensive, time-consuming, cost-intensive, and biased, while non-destructive techniques are gaining popularity due to ease of operation, reliability, and real-time results. Non-destructive methods play an important role in food industries due to its inherent nature. Both external and internal quality can be determined by non-destructive methods and rapidly help in sorting the superior quality of food products. Currently, different non-destructive methods such as electromagnetic, optical, mechanical, and dynamic are gaining popularity due to ease of operation, reliability, and faster turnover. The major drawback of non-destructive methods is the high equipment cost and the use of various instruments to analyze various parameters. But even then, these methods help to ensure customer satisfaction of products as it helps in providing good quality products without rupturing it. This chapter gives broader concept of non-destructive method used in quality assessment of food products and their applications for food evaluation and quality.
Food is one of the most important substances for survival, it contains all the components that are required for nutrition to maintain a healthy life. Nowadays, due to tremendous advancement, synthetic and natural supplements are added to food items and beverages in huge amount. The synthetic or artificial colors need a strong accreditation from the administrative bodies for their consumption [1–4].
When color preservatives overshoot the sanctioned limit it gradually causes hyperactivity in children. It is mandatory to oversee food quality and the supplements being added to the food for consumption. Also, natural color is rapidly being replaced with artificial or synthetic colors [5].
It is very crucial and critical to understand the effects of each preservation methods and their handling procedure on various foods because each step of storage, handling, processing, and distribution affects the various characteristics of food which could be desirable/undesirable.
Adulteration of food is a demonstration of deliberately corrupting the nature of nourishment offered available to be purchased either by the admixture or substitution of second rate substances and which antagonistically influence nature of food sources, yet in addition their coincidental pollution during the time of development, reaping, capacity, preparing, transport, and dispersion [6–8].
Keywords :
Dehydration is one of the most widely used food processing techniques, which is sophisticated in nature. Rapid and accurate prediction of dehydration performance and its effects on product quality is still a difficult task. Traditional analytical methods for evaluating food dehydration processes are laborious, time-consuming and destructive, and they are not suitable for online applications. On the other hand, vibrational spectral techniques coupled with chemometrics have emerged as a rapid and noninvasive tool with excellent potential for online evaluation and control of the dehydration process to improve final dried food quality. In the current review, the fundamental of food dehydration and five types of vibrational spectral techniques, and spectral data processing methods are introduced. Critical overtones bands related to dehydration attributes in the near-infrared (NIR) region and the state-of-the-art applications of vibrational spectral analyses in evaluating food quality attributes as affected by dehydration processes are summarized. Research investigations since 2010 on using vibrational spectral technologies combined with chemometrics to continuously monitor food quality attributes during dehydration processes are also covered in this review.
The present study is focused on the evaluation of the effect of grater type and fat content of the pulp on the spectral response obtained by near infrared hyperspectral imaging (NIR-HSI), when this technique is used to determine the rind percentage in Parmigiano Reggiano (P-R) cheese.
To this aim, grated P-R cheese samples were prepared considering all the possible combinations between three levels of rind amount (8%, 18% and 28%), two levels of fat content of the pulp and two different grater types, and the corresponding hyperspectral images were acquired in the 900–1700 nm spectral range. In a first step, the average spectrum (AS) was calculated from each hyperspectral image, and the corresponding dataset was analysed by means of Analysis of Variance Simultaneous Component Analysis (ASCA) to assess the effect of the three considered factors and their two-way interactions on the spectral response. Then, the hyperspectral images were converted into Common Space Hyperspectrograms (CSH), which are signals obtained by merging in sequence the frequency distribution curves of quantities calculated from a Principal Component Analysis (PCA) model common to the whole hyperspectral image dataset. ASCA was also applied to the CSH dataset, in order to evaluate the effect of the considered factors on this kind of signals.
Generally, all the three factors resulted to have a significant effect, but with a different extent according to the method used to analyse the hyperspectral images. Indeed, while fat content of the pulp and rind percentage showed a comparable effect on the spectral response of AS dataset, in the case of CSH signals rind percentage had a greater effect compared to the other main factors. However, CSH were also more sensitive to differences ascribable to the natural variability between diverse Parmigiano Reggiano cheese samples.
The spectrophotometric method for measuring protein content can be used to evaluate the degree of proteolysis in cheeses. At a wavelength of 280 nm, tryptophan and tyrosine are absorbed, a high amount of them is found in casein, the main protein of cheese mass. It was found that the value of the absorbance coefficient of the solution of proteins extracted from flavoring additives with cheese flavor (FA) and cheeses depends on the degree of proteolysis of proteins in the cheese mass and differs in FA and different types of cheeses. The highest absorbance coefficient is observed in the FA samples A1%1cm = 10.30, in which from 65 to 81% of the protein is converted into a soluble state. In cheeses, the degree of proteolysis is from 23 to 33%, and the absorbance coefficient of solution is from 1.1 to 2.4 (with the exception of Cheddar cheese), which indicates an incomplete transition of amino acids absorbing radiation at 280 nm into the extract released from cheeses. Using the spectrophotometric method, the results of measuring the content of soluble protein in cheeses and FA, strictly correlating with the results achieved by the Kjeldahl method (R ² > 0.81), can be obtained. To get reliable results of evaluating the content of water-soluble protein in cheeses, it is necessary to carry out measurements on a sample of cheeses belonging to the same species group, having the same specificity of proteolysis and slightly different absorbance coefficient between samples within the instance.
Evaluation of food quality in industrial production is a major interest. Fast, reliable, cost-effective and easy-to-use applications are required for on-line inspection of food products. With the recent advances in the field, time domain nuclear magnetic resonance (TD NMR) and magnetic resonance imaging (MRI) techniques have been shown to provide rapid, non-invasive characterization of foods alternative to labor intensive and invasive conventional methods. The basic principle of TD NMR is analysis of the signal coming from the excited sample whereas MRI is able to visualize the internal structure of the samples without any disruption. This review refers to the most recent advances in the TD NMR and MRI based food quality applications. Some new quality applications of TD-NMR have also been addressed.
Discriminating traditional Coalho cheese by origin is important to ensure authenticity. The middle and near infrared spectroscopy and analytical measures associated with different chemometric approaches were used to characterise and discriminate Coalho cheese from different mesoregions of the State of Bahia, Brazil. Principal Component Analysis and Linear Discriminant Analysis were able to show the difference between cheeses. where better discrimination was observed using near infrared spectroscopy, which formed distinct groups by region and correct classification up to 90.00%. The infrared spectroscopy, especially the near infrared, showed to be very useful in the quick discrimination of cheeses according to their origin.
The application of non-destructive process analytical technologies in the area of food science got a lot of attention the past years. In this work we used hyperspectral imaging to detect mould on milk agar and cheese. Principal component analysis is applied to hyperspectral data to localise and visualise mycelia on the samples' surface. It is also shown that the PCA loadings obtained from a set of training samples can be applied to hyperspectral data from new test samples to detect the presence of mould on these. For both the agar and cheeselets, the first three principal components contained more than 99
%
of the total variance. The spatial projection of the second principal component highlights the presence of mould on cheeselets. The proposed analysis methods can be adopted in industry to detect mould on cheeselets at an early stage and with further testing this application may also be extended to other food products.
El queso paria es un queso madurado semiduro producido principalmente en Puno, Arequipa y Cusco, Perú. Actualmente, cuenta con gran potencial de exportación; sin embargo, aún es carente de parámetros de calidad completos. Los objetivos de este trabajo fueron evaluar las características fisicoquímicas y el contenido de bacterias ácido-lácticas (BAL) en tres marcas de queso paria, adquiridas en Arequipa (Perú). Los parámetros fisicoquímicos evaluados fueron: pH, humedad, sólidos totales (ST), actividad de agua (aw), cloruro de sodio (NaCl), grasas y proteínas. Las marcas de queso paria presentaron características organolépticas similares a los reportados en la NTP y los valores fisicoquímicos fueron; pH de 5,15 a 6,11; humedad de 42,94 a 45,33 g%; ST de 54,67 a 57,06 g%; aw de 0,93 a 0,96; NaCl de 1,74 a 3,07 g%; grasas de 27,33 a 31,0 g% y proteínas de 20,58 a 23,41g%. El contenido de BAL varió de 8,06 a 8,56 Log ufc/g. Se encontraron diferencias significativas entre las marcas de queso para pH, aw, NaCl, grasas y contenido de BAL. El contenido de BAL presentó una correlación directa con NaCl, mientras, una correlación inversa con el pH y el contenido de grasas; por lo que estos parámetros fisicoquímicos pueden ser utilizados para un control indirecto del contenido de BAL en los quesos estudiados.
In this work, surface-enhanced Raman spectroscopy (SERS) was used with silver colloid substrate for rapid detection of omethoate (an organophosphate pesticide) in standard solution and peach extract. The findings demonstrated that the characteristic wavenumber of the pesticide could be precisely identified using the SERS method. The calibration curve was plotted between concentrations and Raman intensities of the target peak at 1649 cm⁻¹ for the peach extract and at 1647 cm⁻¹ for the standard solution. The coefficients of determination (R²) of 0.9829 and 0.98 were obtained for standard solution and for peach extract, respectively. The calculated limits of detection for omethoate in standard solution and in peach extracts were 0.001 mg L⁻¹ and 0.01 mg kg⁻¹, respectively. This study revealed that the proposed method could be used for the analysis of trace contaminants like omethoate in multifaceted food matrices.
Moisture content (MC) and color are two important quality parameters of beef during microwave heating process. This study examined the effects of microwave heating time (0–75 s) on MC, color, and myoglobins of beef samples. The results showed that heating time significantly influenced the MC, color (L*, a*), and percentage of related myoglobins. The suitability of hyperspectral imaging (HSI) (400–1000 nm) was investigated to correlate the mean spectra of beef samples and the color and MC values during microwave treatment. After the use of pre-processing methods and optimum wavelengths selection, the SG-SPA-LS-SVM prediction model for MC (R2P = 0.869, RMSEP = 1.304, and RPD = 2.724) and the SG-RC-MLR model for a* (R2P = 0.890, RMSEP = 0.735, and RPD = 2.733) were established. The models were then used to develop the distribution maps of MC and a* values, respectively, showing that both MC and a* at the center of the meat slices were higher than those at the edge, corresponding to the temperature distribution during microwave heating. The results demonstrated the ability of HSI system for monitoring the changes of some quality parameters during microwave heating.
The non-uniformity of microwave sterilization usually causes incomplete inactivation of microorganism (especially foodborne pathogens) and potential food safety issue. Detecting the non-uniformity of load distribution using hyperspectral imaging (HSI) (400–1050 nm) in tandem with multivariate analysis was investigated and infrared (IR) thermal imaging technique was used to assist the evaluation. The best simplified model of least-squares support vector machines (LS-SVM) obtained based on ten optimal wavelengths showed an R²p of 0.905, RMSEP of 0.404 log CFU/mL and RPD of 2.62, while the analysis of temperatures detected by IR thermal camera showed that the temperatures of mid-way along each edge were obviously higher than the center, and heating time of microwave had a significant effect on mean temperature (p < 0.01) and temperature coefficient of variation (COV) values (p < 0.01). Moreover, the visualization of L. monocytogenes load distribution was acquired by transferring the quantitative model of LS-SVM to each pixel in the image at different heating times, with the results being well consistent with those of temperature distribution by IR thermal camera. Therefore HSI technique could be used to monitor the non-uniformity of microwave sterilization processing.
The evaluation of cheese texture during the ripening phase usually involves invasive and destructive methods, as well as specialized equipment, which are nonadvantageous characteristics for routine testing. Therefore, new noninvasive technologies for measuring texture properties are being studied. In this paper, forty Swiss-type cheese samples were prepared and carried to the ripening stage. During this process, hyperspectral images (HSI) were obtained in reflectance mode in the range of 400-1000 nm. The hardness of Swiss-type cheese was measured using texture profile analysis. The relationship between the spectral profiles and hardness values was modeled with two types of regression models, partial least squares regression (PLSR) and artificial neural networks (ANN). For both PLSR and ANN, two models were created: the first used all the wavelengths and the second selected relevant wavelengths. The ANN models showed slightly better performance than the PLSR models. Therefore, the proposed technique (HSI + ANN) can be used to predict the texture properties of Swiss-type cheeses throughout the ripening period.
An ultrasensitive method for the kanamycin (KANA) detection in milk sample using surface-enhanced Raman spectroscopy-based aptasensor was employed in the current study. Double strand DNA binding bimetallic [email protected] nanoparticles were developed as a sensing platform. Probe DNAs were first embedded on the surface of gold nanoparticles by the end-modified thiol, and after silver shell encapsulating, KANA aptamer DNAs with the Raman reporter Cy3 were then hybridized with probe DNAs by complementary base pairing. Results showed that with increase in the KANA concentration, the Raman intensity of Cy3 decreased. Besides achieving selectivity, an ultralow detection limit of 0.90 pg/mL, a broad linear relationship ranging from 10 μg/mL to 100 ng/mL in aqueous reagent and satisfactory recoveries of 90.4–112% in liquid whole milk were obtained. The result of actual sample proved that this aptasensor was promising in trace determination of KANA residue.
Fast sampling and multicomponent detections are important in the analysis of pesticide residues detection. In this work, surface-enhanced Raman scattering (SERS) method based on silver-coated gold nanoparticles ([email protected] NPs) was used to simultaneously detect multi-class pesticide residues such as thiacloprid (carbamate), profenofos (organophosphate) and oxamyl (neonicotinoid) in standard solution and peach fruit. The [email protected] NPs with 26 nm Au core size and 6 nm Ag shell thickness exhibited significant Raman enhancement, especially by the creation of hot spots through NPs aggregation induced by the connection between [email protected] NPs and target molecules. The findings demonstrated that the characteristic wavenumber of the pesticides (thiacloprid, profenofos, and oxamyl) could be precisely identified using the SERS method. Compared with earlier studies, the current approach was rapid, inexpensive and without lengthy sample pretreatment. Moreover, the results revealed that the limit of detection (LOD) was 0.1 mg/kg for thiacloprid obtained in the peach extract with determination coefficient (R²) of 0.986. Additionally, LOD for both profenofos and oxamyl was 0.01 mg/kg with a determination coefficient (R²) of 0.985 and 0.988, respectively. Good recovery percentage (78.6–162.0%) showed the high SERS activity with better accuracy for the detection of the thiacloprid, profenofos, and oxamyl in peach. The results of this study could offer a promising SERS platform for simultaneous detection of other contaminants such as thiacloprid, profenofos and oxamyl in multifaceted food matrices.
Thiabendazole (TBZ) is a kind of pesticide that is widely used in agriculture, and its residue may pose a threat to human health. In order to measure TBZ residues in food samples, a surface-enhanced Raman spectroscopy (SERS) method combined with a homogeneous and reusable gold nanorods (GNR) array substrate was proposed. GNR with a high uniformity was synthesized and then applied to the self-assembly of a GNR vertically aligned array. The relative standard deviation (RSD) of the array for SERS could reach 15.4%, and the array could be reused for more than seven times through the treatment of plasma etching. A logarithmic correlation between TBZ concentration and Raman intensity was obtained, with the best determination coefficient (R²) and the corresponding limit of detection (LOD) of 0.991 and 0.037 mg/L in methanol solution, and 0.980 and 0.06 ppm in apple samples, respectively. The recoveries of TBZ in apple samples ranged from 76% to 107%. This study provided a rapid and sensitive approach for detecting TBZ in apples based on SERS coupled with GNR array substrate, showing great potential for analyzing other trace contaminants in food matrices.
Rapid and accurate measurement of polyphenol oxidase (PPO) activity is important in the food industry as PPOs play a vital role in catalyzing enzymatic reactions. In this study, the possibility of using surface-enhanced Raman scattering (SERS) approach based on the reduction in SERS intensity of catechol in reaction medium for accurate determination of PPO activity in fruit and vegetables was investigated. Within a certain catechol concentration, when a purified PPO solution was analyzed, the reduction in SERS intensity (ΔI) was linear to PPO activity (Ec) in a wide range of 500-50000 U/L, and a linear regression equation of logΔI/Δt = 0.6223 logEc + 0.8072, with a correlation coefficient of 0.9689 and a limit of detection of 224.65 U/L was obtained. The method was used for detecting PPO activity in real apple and potato samples, and the results were compared with those obtained from colorimetric assay, demonstrating that the proposed method could be successfully used for detecting PPO activity in food samples.
The residual of pesticides in fruit and vegetables is one of the major food safety concerns for consumers. There is a demand for easy and rapid analytical methods to sense pesticide residues in foods. In this study, a core-shell Au@Ag nanoparticles aggregates (Au@AgNAs) based surface-enhanced Raman scattering (SERS) method was developed to detect trace amount of difenoconazole. Results suggested that by targeting the characteristic peaks at 700 and 808 cm-1, the logarithmic SERS signal intensities and logarithmic difenoconazole concentrations in the range from 5 × 10-7 to 2.5 × 10-5 M showed linear relationship with the coefficient of determination (R2) of 0.990 and 0.985, and limit of detection (LOD) values of 5.01 × 10-8 and 2.8 × 10-8 M, respectively. The Quick Easy Cheap Effective Rugged and Safe (QuEChERS) sample preparation method was used to extract difenoconazole in grapes for SERS measurements. The LOD of difenoconazole in grapes using this developed method was as low as 48 μg/kg, which was significantly lower than the maximum residue limit (MRL) values prescribed by European Union and China. This study demonstrated that the Au@AgNAs-based SERS method can be used as a simple, rapid and sensitive approach for sensing trace contaminants in food.
Recent research on the development and application of process analytical technology (PAT) for cheese manufacture is reviewed in this article. PAT is a framework for innovative process manufacturing and quality assurance, which has been widely investigated for dairy processing applications, where particular processing challenges arise due to the variations in the physiochemical properties of milk. Cheese manufacturers are increasingly considering the adoption of a PAT approach to facilitate manufacture of cheese with enhanced product quality, safety and process efficiency. However, to date adoption of PAT in the dairy industry has been limited due to challenges associated with development and validation of calibration models, instrument variability, sanitary design and compatibility with processing environments. New technical developments in PAT tools, advances in chemometric modelling, robust data management tools and improved understanding of critical product and process parameters will facilitate further adoption of a PAT approach in cheese manufacture. Implementation and adoption of PAT approach ‐ ‘design, analyse and control’ of processes to ensure final product quality.
Emulsion is a commonly investigated bioactive loaded delivery system. The bioactive content and its location in oil phase primarily determine the quality and chemical stability of emulsion. In this study, Raman microspectroscopy was used to quantify α-tocopherol and to visualize its distribution in oil-in-water emulsion stabilized by whey protein isolates. Results suggested that α-tocopherol contents (25–300 g/kg) in corn oil with the integrating Raman intensity at 481.9 and 588.6 cm⁻¹ showed determination coefficient (R²) of 0.98 and 0.99, and limit of detection of 5.1 and 21.2 g/kg, respectively. For detecting α-tocopherol in emulsions, the relative standard deviation values from Raman method using intensities at 481.9 cm⁻¹ and 588.6 cm⁻¹ were in the ranges of 4%–16% and 2%–6%, respectively. The developed Raman method provided correlative results with those of HPLC method (R² = 0.99). Moreover, Raman chemical imaging depicted the non-homogeneous TOC distribution within oil droplets, where TOC had the trend of migrating to the interface of oil and water. This study provided a novel approach for functional emulsion analysis, which may serve the basis for designing stable and controllable release of emulsion systems in future.
Common Dimension (ComDim) chemometrics method for multi-block data analysis was employed to evaluate the impact of different added salts and ripening times on physicochemical, color, dynamic low amplitude oscillatory rheology, texture profile, and molecular structure (fluorescence and MIR spectroscopies) of five Cantal-type cheeses. Firstly, Independent Components Analysis (ICA) was applied separately on fluorescence and MIR spectra in order to extract the relevant signal source and the associated proportions related to molecular structure characteristics. ComDim was then applied on the 31 data tables corresponding to the proportion of ICA signals obtained for spectral methods and the global analysis of cheeses by the other techniques. The ComDim results indicated that generally cheeses made with 50% NaCl or with 75:25% NaCl/KCl exhibit the equivalent characteristics in structural, textural, meltabiliy and color properties. The proposed methodology demonstrates the applicability of ComDim for the characterization of samples when different techniques describe the same samples.
Background
The irrational usage of chemical substances including pesticides and drugs in agricultural and food production is a significant food safety issue due to its residues. Therefore, the detection of harmful residues in foods is an indispensable step for guaranteeing the consumer's health. Conventional methods, such as HPLC, GC-MS and LC-MS are accurate enough, but they fail to meet the requirements of the modern industry for rapid and on-line detection. Novel reliable techniques should thus be developed as alternatives.
Scope and approach
In this review, fundamentals of surface-enhanced Raman spectroscopy (SERS) is introduced. Recent advances in its usage for detecting harmful chemical residues in agricultural products including pesticides, antibiotics and β2-adrenergic agonists are discussed by two typical ways of detection improvement, and the advantages of SERS are addressed. Finally, future trends to routine use of SERS applications in harmful residues are presented.
Key findings and conclusions
SERS is a promising detection technique for the detection of common harmful chemical residues with merits of simple sampling, rapid data collection and non-destructiveness. Despite rapid developments in the technology, there is much studies should be done before SERS could be used as a daily tool for the industry.
In this assay, a simple method for rapid determination of alternariol (AOH) in pear fruit based on surface-enhanced Raman scattering (SERS) was developed and validated. Silver nanoparticles (AgNPs) were used as SERS substrate. A new strategy was adopted to circumvent the issue of the affinity of AOH for metallic surface and the surface of AgNPs was modified using pyridine in order to improve the sensitivity of detection. Quantitative analysis was performed in AOH solutions at concentrations over a range of 3.16-316.0 μg/L, and the limit of detection was 1.30 μg/L. The novel method was also applied to detect AOH residues in pear fruit purchased from market and those were artificially inoculated with A. alternata. AOH was not found in any of the fresh fruit, while contamination with AOH was found in the rotten fruit and the inoculated fruit. Finally, the SERS method was cross validated against HPLC. These results revealed that SERS method should have great potential utility in rapid detection of AOH in pear fruit and other agricultural products.
The salting process involves complex phenomena that affect the overall quality of cheese due to its effect on water activity and induced biochemical changes. The permittivity of cheese was analysed throughout the cheese salting treatment in order to relate it to water and salt transport. The salting treatment was carried out using 25% (w/w) sodium chloride brine at 4 °C. The samples were immersed in a vessel containing the osmotic solution with continuous stirring, for 0, 10, 20, 30, 40, 50, 60, 90, 120, 180, 240, 360, 480, 720, 900 and 1440 min. Samples were subsequently equilibrated in an isothermal chamber at 4 °C for 24 h. Mass, volume, surface water activity, moisture, ion content and permittivity were determined in fresh and salted samples. Permittivity was measured from 500 MHz to 20 GHz, using an open-ended coaxial probe connected to a Vector Network Analyser. The results showed that measurements at 20 GHz explain the water loss and water flux in the overall product. The state of the electrolytes in cheese can be followed using the ionic conductivity at 500 MHz. A coupled measurement of permittivity at 20 GHz and 500 MHz can predict the chemical species involved in the cheese salting process, and its structural changes. In conclusion, the measurement of permittivity in the microwave range can be used to monitor the salting cheese process.
Background
Food safety and quality have gained much attention in recent years and the capability to evaluate food quality and safety in a sensitive, rapid, and reliable manner is of great importance in the food industry. Therefore, surface-enhanced Raman scattering (SERS) with the advantages of excellent sensitivity, high selectivity, non-destructive nature and significant enhancement to identify the target has demonstrated a great potential for quick detection of chemical contaminants, chemical constitutes, and pathogens in food samples.
Scope and approach
The enhancement of Raman signals for SERS is not only related to the interactions between substrates and samples but also the functionalization of substrates to gain SERS active substrates. In the present review, different types of substrates are briefly discussed, functionalization techniques for SERS active substrates are discussed, and applications of functionalized SERS substrate in food samples are presented.
Conclusions and key findings
It is evident that functionalization techniques for improving SERS substrates have given encouraging outcomes, which provides possibility for identifying multiple target analytes within a complex matrix, and thus could be used as a powerful analytical tool in real-world applications in food safety analysis as well as for enhancing food quality surveillance.
The objective of our work was to develop and evaluate the performance of a rapid method for measuring fat, protein, moisture, and salt content of Cheddar cheese using a combination mid-infrared (MIR) transmittance analysis and an in-line conductivity sensor in an MIR milk analyzer. Cheddar cheese was blended with a dissolving solution containing pentasodium triphosphate and disodium metasilicate to achieve a uniform, particle-free dispersion of cheese, which had a fat and protein content similar to milk and could be analyzed using a MIR transmittance milk analyzer. Annatto-colored Cheddar cheese samples (34) from one cheese factory were analyzed using reference chemistry methods for fat (Mojonnier ether extraction), crude protein (Kjeldahl), moisture (oven-drying total solids), and salt (Volhard silver nitrate titration). The same 34 cheese samples were also dissolved using the cheese dissolver solution, and then run through the MIR and used for calibration. The reference testing for fat and crude protein was done on the cheese after dispersion in the dissolver solution. Validation was done using a total of 36 annatto-colored Cheddar cheese samples from 4 cheese factories. The 36 validation cheese samples were also analyzed using near-infrared spectroscopy for fat, moisture, and the coulometric method for salt in each factory where they were produced. The validation cheeses were also tested using the same chemical reference methods that were used for analysis of the calibration samples. Standard error of prediction (SEP) values for moisture and fat on the near-infrared spectroscopy were 0.30 and 0.45, respectively, whereas the MIR produced SEP values of 0.28 and 0.23 for moisture (mean 36.82%) and fat (mean 34.0%), respectively. The MIR also out-performed the coulometric method for salt determination with SEP values of 0.036 and 0.139 at a mean level of salt of 1.8%, respectively. The MIR had an SEP value of 0.19 for estimation at a mean level of 24.0% crude protein, which suggests that MIR could be an easy and effective way for cheese producers to measure protein to determine protein recovery in cheese making.
In this study, five Cantal-type cheeses with different salts (NaCl and KCl) and two ripening times (5 and 15 days) were analyzed for their physicochemical characteristics, their structure at a molecular level and their rheological properties during heating (20 to 60 °C).
The analysis of the molecular structure of cheeses was investigated by MIR spectroscopy coupled with ICA (Independent Components Analysis) and rheological properties by small-amplitude oscillatory rheology.
ICA on physicochemical characteristics showed a good discrimination of the cheeses as a function of their chemical characteristics and ripening time. ICA applied to MIR spectra gave Independent Components (ICs) that were attributed to the molecular characteristics of protein, water and fat. Signal proportions of each IC depicted information regarding changes in those ICs with salts, heating and ripening. In addition, similar fat melting temperatures were obtained, regardless the technique used (oscillatory rheology and MIR) for all cheeses.
This study demonstrated that MIR spectroscopy coupled with ICA is a promising tool to monitor and characterize modification of cheeses at a molecular level depending on temperature, salt content, and ripening time.
The aim of this study was to measure the starch content in adulterated fresh cheese using hyperspectral imaging technique. Adulterated fresh cheese was prepared using concentrations of starch of 0.055-12.705mgg⁻¹ (0.0055-1.2705%); subsequently, hyperspectral imaging in the range of 200-1000nm, distributed in 101 bands were acquired. The modeling of starch content was performed by the method of partial least squares regression (PLSR). A correlation coefficient (R²) of 0.9915 and a Root Mean Square Error of cross-validation (RMSECV) of 0.3979 was obtained. With five latent variables, a correlation coefficient of validation (R²) of 0.8321 and a RMSEP of 1.3515 was obtained for a reduced model.
Background
Surface-enhanced Raman spectroscopy (SERS) techniques are becoming increasingly widespread and accessible for accurate and specific identification of chemical or microbiological contaminants in foodstuffs. However, the consistency and repeatability of SERS-based techniques is a challenge due to complicated detection environments. This drawback can be overcome by integrating the SERS detection into a microfluidic platform, which can provide a continuous flow condition for highly reproducible SERS measurements. Furthermore, the SERS-microfluidic platform can perform elaborating sample pre-treatments, showing great potentials for on-situ analysis of food contaminants.
Scope and approach
This review mainly summarizes the principles and methods of SERS-microfluidic systems, as well as their applications in rapid analysis of food contaminants. Three ways of integrating SERS substrate into microfluidic channels are highlighted. Finally, the main challenges and future efforts in developing SERS-microfluidic systems for on-situ food contaminants detection are discussed.
Key findings and conclusions
SERS-microfluidic platform conforms to the development tendency of modern analytical technology, and has great potentials for rapid analysis of food contaminants.
The ability to quantify and qualify subtle differences between milk powders is very advantageous to industrial manufacturers. Hyperspectral imaging (HSI) combines the spatial attributes of image processing with the chemical diagnostic attributes of spectroscopy, and was evaluated to determine if it could be used to discriminate between milk powders produced in various factories, and of differing functional qualities, such as dispersibility. The results showed that HSI can achieve these aims when multivariate analysis techniques such as Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression are applied. The PCA results showed that the most obvious differences were in the first and second principal components. Strategies to pre-process hyperspectral data, and to optimally automatically detect and remove artefacts in the images were also established. The PLS results showed that the information from HSI can be used to predict with reasonable accuracy the key functional property of dispersibility, and is the first step in a ‘real-time quality’ initiative to establish correlations between hyperspectral images and key quality attributes of milk powder either on, or at-line in close to real-time.
The functionality of Zedu gum as a fat mimetic in low-fat brined cheese was studied. The physicochemical, textural, rheological, microstructural and sensory properties of cheese samples modified with 0.1% and 0.25% of Zedu gum were compared to those of control cheeses (low-fat and full-fat cheeses with no fat mimetic) during ripening. To obtain further information about the cheeses' structure and interactions between macromolecules (casein protein and Zedu gum), other parameters were analysed by differential scanning calorimetry and Fourier transform infrared (FTIR) spectroscopy. Incorporation of Zedu gum into low-fat cheese caused an open microstructure and softer texture in comparison with the control low-fat cheese. The thermal properties and FTIR spectra of the cheeses were influenced by both fat mimetic and ripening time. On days 1 and 60 of ripening time, the lower value of enthalpy of the low-fat cheese with 0.25 g of Zedu gum/kg of milk (AS 0.25) in comparison with control low-fat cheese could have been due to the electrostatic nature of the interactions between Zedu gum and casein protein. On both days, the FTIR spectrum of AS 0.25 showed a well separated absorption at 1746 cm⁻¹ possibly due to the formation of ester groups as a result of the interaction of the carbonyl groups in Zedu gum with the hydroxyl groups of some amino acids in casein.
Fourier transform infrared (FT-IR) and Raman and hyperspectral imaging (HSI) techniques have emerged as reliable analytical methods for effectively characterizing and quantifying quality attributes of different categories of powdery food products (such as milk powder, tea powder, cocoa powder, coffee powder, soybean flour, wheat flour, and chili powder). In addition to the ability for gaining rapid information about food chemical components (such as moisture, protein, and starch), and classifying food quality into different grades, such techniques have also been implemented to determine trace impurities in pure foods and other properties of particulate foods and ingredients with avoidance of extensive sample preparation. Developments of corresponding quality evaluation systems based on FT-IR, Raman, and HSI data that measure food quality parameters and ensure product authentication, would bring about technical and economic benefits to the food industry by enhancing consumer confidence in the quality of its products. Accordingly, a comprehensive review of the mushrooming spectroscopy-based FT-IR, Raman, and HSI literature is carried out in this article. The spectral data collected, the chemometric methods used, and the main findings of recent research studies on quality assessments of powdered materials are discussed and summarized. Providing a review in such a flourishing research field is relevant as a signpost for future study. The conclusion details the promise of how such non-invasive and powerful analytical techniques can be used for rapid and accurate determinations of powder quality attributes in both academical and industrial settings.
The aim of the study was to evaluate the feasibility of near infrared (NIR) transmittance spectroscopy to predict cheese ripeness using the ratio of water-soluble nitrogen (WSN) to total nitrogen (TN) as an index of cheese maturity (WSN/TN). Fifty-two Protected Designation of Origin cow milk cheeses of 5 varieties (Asiago, Grana Padano, Montasio, Parmigiano Reggiano, and Piave) and different ripening times were available for laboratory and chemometric analyses. Reference measures of WSN and TN were matched with cheese spectral information obtained from ground samples by a NIR instrument that operated in transmittance mode for wavelengths from 850 to 1,050 nm. Prediction equations for WSN and TN were developed using (1) cross-validation on the whole data set and (2) external validation on a subset of the entire data. The WSN/TN was calculated as ratio of predicted WSN to predicted TN in cross-validation. The coefficients of determination for WSN and TN were >0.85 both in cross- and external validation. The high accuracy of the prediction equations for WSN and TN could facilitate implementation of NIR transmittance spectroscopy in the dairy industry to objectively, rapidly, and accurately monitor the ripeness of cheese through WSN/TN.
Background
The quality of products depends on their processing. Effective way of monitoring and controlling these processes will ensure the quality and safety of products. Since traditional measurement methods cannot achieve on-line monitoring, imaging spectroscopy, as a fast, accurate and non-destructive detection tool, has been widely used to evaluate quality and safety attributes of foods undergoing various processes.
Scope and Approach
In the current review, detailed applications of hyperspectral imaging (HSI) system in various food processes are outlined, including cooking, drying, chilling, freezing and storage, and salt curing. The study emphasized the ability of HSI technique to detect internal and external quality parameters in different food processes. Also, the advantages and disadvantages of HSI applications on these food processes are discussed.
Key Findings and Conclusions
The literature presented in this review clearly demonstrate that HSI has the ability to inspect and monitor different food manufacturing processes and has the potential to control the quality and safety of the processed foods. Although still with some barriers, it can be expected the HSI systems will find more useful and valuable applications in the future evaluation of food processes.
Background:
Reduction of NaCl content of cheeses has received considerable attention by researches during the past decades due to its health effects. Nonetheless, NaCl reduction is a challenge since it plays an important role in cheese quality, such as structure, texture and functional properties. Serval methods were used to evaluate the effect of NaCl on these attributes. In this study, Cantal-type cheeses with different salts (NaCl and KCl) were analyzed for their structure at a molecular level and rheological properties during heating (20 to 60 °C) and cooling (60 to 20 °C). The structure was investigated by synchronous fluorescence spectroscopy (SFS) and the rheological properties by small-amplitude oscillatory rheology.
Results:
ICA gave three Independent Components (IC) that were attributed to coenzyme/Maillard products (IC1), tryptophan (IC2) and vitamin A (IC3). Signal proportions of each IC depicted information regarding the changes in those fluorophores with salts, heating and cooling. In addition, Canonical Correlation Analysis (CCA) of the IC proportions and rheological-measurements related modifications at a molecular level evaluated by fluorescence to cheese texture (0.34< R(2) <0.99).
Conclusion:
This study demonstrated that SFS can monitor and characterize modification of Cantal-type cheeses at a molecular structure, based on the analysis of the fluorescence spectra by ICA and the nature of correlation with the rheological parameters, since CCA depicted that rheological attributes of cheeses observed at the macroscopic level can be derived from fluorescence spectra.
Background
Proteins are essential nutrients required in various body functions and normal human life. However, in the food industry, the application of proteins especially those of plant origin have been limited due to their poor functionality. Although nowadays, diverse modification techniques are usually employed to improve their performance in food products, it is also important that effective methods for monitoring the resultant conformational changes induced during protein modification are developed.
Scope and approach
In this review, the relationship between protein conformation and functionality is briefly discussed. Thereafter, the underlying principles behind five selected spectroscopic techniques i.e. Fourier transform infrared, Raman, circular dichroism, fluorescence, and ultraviolet spectroscopies are introduced and their recent applications for monitoring conformational changes that occur during physical, chemical or enzymatic modification of proteins are addressed. In addition, the advantages and limitations of each spectroscopic technique are comparatively discussed and perspectives on the current situation alongside future trends are highlighted.
Key findings and conclusions
Spectroscopic techniques present an attractive panacea for evaluation of conformational changes during protein modification. Although certain challenges especially with complex food materials require urgent attention thus, more robust spectroscopic solutions should be exploited in the future.
In this paper, fresh pear fruits and those infected by Alternaria alternata (A. alternata) were studied by confocal Raman microspectroscopy (CRM) to illustrate the changes in chemical compositions of cell wall. Firstly, Raman spectra of the cell wall of both fresh and infected fruits were collected with spatial resolution at micron level, and then label-free in situ imaging of chemical compositions in the cell wall were mapped. The results showed that there were significant changes in the signal intensity of cell wall, especially in the later stage of A. alternata infection. After 8 days of infection at room temperature, the signal intensities at 1086 and 871 cm⁻¹, which were associated with cellulose and pectin, were decreased by 58.50% and 58.67%, respectively, revealing changes in the main components of the infected cell wall infected. Meanwhile, the chemical images of the cell wall of both fresh and infected fruits were compared, indicating that A. alternata infection caused the alterations of morphological structure and chemical compositions in the cell wall in a time-dependent manner. Our results confirmed that CRM is a useful tool for the identification of compositional changes in the cell wall caused by fungal infection without the need for any chemical treatment. For the first time, the current research applied CRM in phytopathology for investigating interactive relationship between a pathogen and its host, thus offering a new way for in-depth study of pathogen-host interactions at cellular level.
One of the main objectives of the present study was to investigate by different analytical techniques (physicochemical analysis, dynamic rheology, and synchronous fluorescence spectroscopy) the impact of the NaCl reduction and its substitution by KCl on the molecular structure and fat melting of Cantal-type cheese. Molecular structure changes were investigated on five cheese sample formulations from 20 to 60 °C with five offsets using SF spectroscopy coupled with independent components analysis. Results showed that significant differences were observed for protein, Cl, Ca, Na and K contents of cheeses. Complex viscosity decreased as the temperature increased for the different cheeses. SF spectroscopy provided relevant information related to protein and fat structures with varying salt concentrations and type during melting, allowing investigation of molecular structure changes of the cheeses. In addition, similar fat melting temperatures for each cheese were obtained regardless the technique used (dynamic rheology and fluorescence).
The objective of this study was to investigate the potential of fluorescence spectroscopy to predict rheological characteristics of semi-hard cheeses as yield stress (τL), flow stress (τF), storage modulus (G’) and loss modulus (G”) measured at linear-viscoelastic, yield stress and flow stress oscillation regions. Melting temperatures and chemical composition of the semi-hard cheeses were also predicted using fluorescence spectra. Principal component analysis (PCA) and partial least squares regression (PLSR) were applied to the fluorescence spectra to extract information on the rheological properties, chemical composition, and melting temperatures. τL and τF were predicted with R²=0.90 from the vitamin A emission and excitation spectra, respectively. Melting temperatures, moisture, protein and fat contents were predicted with R²=0.98 from the vitamin A emission spectra. This study demonstrates that fluorescence spectroscopy has potential for the accurate, non-destructive and rapid prediction of cheese rheology at linear-viscoelastic, yield stress and flow stress oscillation regions simultaneously.
Background
Food quality and safety is of great concern to governments, the food industry, as well as consumers. Thus, research has been conducted to develop advanced detection techniques instead of traditional methods to provide rapid, non-destructive food quality and safety evaluation and analysis for the industry.
Scope and approach
As an emerging technology, Raman imaging has been successfully studied in food safety assessment and control. This review provides a comprehensive overview on the applications of Raman imaging in the area of food quality evaluation, assessment of adulterants and contaminants, and detection of pesticides. Other relevant techniques are also reported due to their close relationship with food safety control.
Conclusions and key findings
With these applications, it is evident that Raman imaging has given promising results and thus could be a powerful technique for food quality surveillance as well as for reducing the occurrence of food safety issues.
The feasibility of hyperspectral imaging (HSI) (400-1000 nm) for tracing the chemical spoilage extent of the raw meat used for two kinds of processed meats was investigated. Calibration models established separately for salted and cooked meats using full wavebands showed good results with the determination coefficient in prediction (R²P) of 0.887 and 0.832, respectively. For simplifying the calibration models, two variable selection methods were used and compared. The results showed that genetic algorithm - partial least squares (GA-PLS) with as much continuous wavebands selected as possible always had better performance. The potential of HSI to develop one multispectral system for simultaneously tracing the chemical spoilage extent of the two kinds of processed meats was also studied. Good result with an R²P of 0.854 was obtained using GA-PLS as the dimension reduction method, which was thus used to visualize total volatile base nitrogen (TVB-N) contents corresponding to each pixel of the image.
Controlling the quality of cheese involves monitoring its taste, texture, stability and appearance. One method of doing so involves the analysis of cheese microstructure. This research investigated the use of confocal Raman microscopy for observing the microstructure of processed cheese including the distribution of additives therein. Reference Raman spectra were obtained for several additives commonly present in processed cheese including: trisodium citrate (TSC), sorbic acid, paprika and corn starch. Low and high spatial resolution Raman images were obtained from commercial and custom formulated processed cheeses. These were created using band integrals and principal component analysis scores values. Results of this investigation show that fat, protein, water, TSC, paprika and starch distributions can be imaged effectively using the many Raman imaging methods, including high spatial resolution Raman microscopy. Copyright
Semi-hard cheese samples made from pasteurised 3.5 % and 5 % fat cow’s milk were partly enzyme treated with microbial transglutaminase (mTG). Cheese color and yield was greatly affected by mTG at both fat levels. The cross-linking effect of mTG also led to improved protein content and hardness in cheese. As Texture Profile Analysis (TPA) could only distinguish according to enzyme treatment we studied hyperspectral imaging (HSI) to differentiate also according to fat level and ripening status. HSI was used on a weekly basis during 4-10 weeks period of ripening time. In case of uneven surface, using the spatial information and making proper normalization on the spectra of ROI pixels, the hyperspectral method can provide even better signal-to-noise ratio then the conventional spectrophotometric method. Statistical analysis with Partial Least Squares (PLS) regression and Monte Carlo Cross Validation (MCCV) led to determination of significant wavelengths both for the detection of enzyme treatment (1387 nm) and for fat content (1190, 1234 nm) independent of the actual age of examined semi-hard cheese. Hyperspectral measurement method allowed remote inspection of the product through transparent vacuum foil. The spectrum normalisation allowed the elimination of effects originating from inhomogeneous illumination caused by cheese holes.
This study investigated the feasibility of hyperspectral imaging (HSI) to predict moisture content (MC) values in heated - dehydrated (H-D) and cool - air - dehydrated (C-A-D) pork samples using the calibrated models established based on fresh (F), frozen - thawed (F-T), dry - salting - dehydrated (D-S-D) and wet - salting - dehydrated (W-S-D) pork samples. The full spectra were extracted from the region of interests (ROIs) in the spectral range of 400–1000 nm and the textural variables were extracted by gray-level gradient co-occurrence matrix (GLGCM) method from the first two PC images accounting for 98.73% of the total variance. Moreover, the optimal wavelengths were selected by regression coefficients (RC). Partial least-squares regression (PLSR) predictive model was developed based on the above extracted data and their mutual combination, in which the changes can be correlated with MC - related attributes. The results demonstrated that the PLSR model based on the incorporation of the optimal wavelengths and the textures (OW-T) from F, F-T, D- S -D and W-S-D samples were the best to predict MC in H-D and C-A-D samples with of 0.9489 and RMSEP of 1.4736. Moreover, the generated visualization maps provided a rapid way to screen the MC values unequally distributed in the whole pork samples after diverse processing conditions. Therefore, it is feasible and promising to improve the applicability of the existing MC predictive models for MC prediction in the pork samples by supplementing more samples under different treatments as another prediction sets.
Surface-enhanced Raman scattering (SERS) imaging coupling with multivariate analysis in spectral region of 200 to 1800 cm⁻¹ was developed to quantify and visualize thiophanate-methyl (TM) and its metabolite carbendazim residues in red bell pepper (Capsicum annuum L.). Least squares support vector machines (LS-SVM) and support vector machines (SVM) models based on seven optimized characteristic peaks that showed SERS effects of TM and its metabolite carbendazim residues were employed to establish prediction models. SERS spectra with first derivative (1st) and second derivative (2nd) method were subsequently compared and the optimized model of 1st-LS-SVM acquired showed the best performance (RPD = 6.08, R²P = 0.986 and RMSEP = 0.473). The results demonstrated that SERS imaging with multivariate analysis had the potential for rapid determination and visualization of the trace TM and its metabolite carbendazim residues in complex food matrices.
Cheese eyes in traditional Swiss-type cheese are an ‘eye-catcher’ and therefore a key factor for quality. In contrast to the widespread X-ray radiography, in the computed tomography (CT) system, the density difference between gas and the cheese body is imaged three-dimensionally. To quantify the eye volume and distribution in the cheese matrix, three major commercial software tools and a Python script were compared by measuring 12 cheeses with increasing numbers of cheese eyes. The number of cheese eyes was controlled by producing the 12 hard cheeses using an increasing number of plastic balls of 10 and 20 mm diameter. Although the agreement among the different software products was very high (correlation between void volume of the introduced balls and the calculated eye volume ranges between 0.9990 and 0.9999), the accessibility of different properties varies among the different software products, and systematic trends in accuracy could be identified.
Biogenic amine index (BAI) is a sensitive indicator of meat freshness and quality. This study investigated the use of chemometric methods for analyzing hyperspectral imaging (HSI) data between 400 nm and 1000 nm to rapidly and non-destructively determine BAI values in pork. Partial least square regression (PLSR) model established using full wavelengths showed good results. In order to simplify the calibration model, four new PLSR and multiple linear regression (MLR) models based on the two sets of feature-related wavelengths selected by successive projections algorithm (SPA) and regression coefficients (RC) were built and compared. The optimized simplified model (RC-MLR) yielded excellent results with R2P of 0.957 and RMSEP of 4.866 mg/kg, which was thus used to visualize BAI value corresponding to each pixel of the image using pseudo color. In addition, the mechanisms of HSI for BAI determination were discussed. The established models used to determine BAI values were based on physiochemical changes associated with BAI generation in meat rather than direct detection of the BAI contents. The overall results of this study demonstrated that HSI data can be utilized to predict BAI values in pork based on chemometric analysis.
K value is an important freshness indicator of meat. This study investigated the integration of spectral and textural data for enhancing the hyperspectral prediction ability of K value in pork meat. In this study, six feature wavebands (407, 481, 555, 578, 633, and 973 nm) were identified by successive projections algorithm (SPA). Meanwhile, the texture data of the grayscale images at the feature wavebands were extracted by gray level co-occurrence matrix (GLCM). The spectral and textural data were integrated by feature level fusion and the partial least square regression (PLSR) model built based on data fusion yielded excellent results, an improvement of at least 17.5% was obtained in model performance compared to those when either spectral data or textural data were used alone, indicating that data fusion is an effective way to enhance hyperspectral imaging ability for the determination of K values for freshness evaluation in pork meat.
Sixteen experimental semi-hard cheeses, varying in moisture (42.1 to 49.8%), protein (20.2 to 25.9%) and fat (23.7 to 31.1%) content, were manufactured and ripened under controlled conditions. Fluorescence (tryptophan) and mid-infrared (Amide I and II regions) spectra were collected at 1, 21, 51 and 81 days of ripening in order to test the ability of spectroscopy to highlight the molecular changes that occur during this process. The mid-infrared and fluorescence spectral data from the experimental cheeses were analysed firstly by principal component analysis. Secondly, the correlations between the chemical domain and the spectral domains were studied by canonical correlation analysis methods. These analyses showed that each spectroscopic technique provided relevant information related to the cheese protein structure, which was used to discriminate each ripening stage. In addition, some spectral characteristics of ripened cheeses, linked to the initial chemical composition and the initial protein network structure, were detected at the early stage of ripening. Finally, a canonical correlation analysis between the two sets of spectroscopic data was performed and allowed to clearly discriminate each stage of ripening and each cheese at the 4 ripening stages. A molecular interpretation of these results involving the modifications of proteins, minerals and water interactions during ripening was attempted. This result demonstrated the interest of coupling two complementary spectroscopic techniques. Such coupling allowed the description of global characteristics of the investigated samples, which can be used for their characterisation.
Since 2008, the detection of the adulterant melamine (2,4,6-triamino-1,3,5-triazine) in food products has become the subject of research due to several food safety scares. Near-infrared (NIR) hyperspectral imaging offers great potential for food safety and quality research because it combines the features of vibrational spectroscopy and digital imaging. In this study, NIR hyperspectral imaging was investigated for quantitative evaluation of melamine particles in nonfat and whole milk powders. Melamine was mixed into milk powders in a concentration range of 0.02–1.00% (w/w). A NIR hyperspectral imaging system was used to acquire images (938–1654 nm) of melamine powder, whole milk powder, nonfat milk powder, and mixtures of melamine and each of the milk powders. Two optimal bands (1447 nm and 1466 nm) were selected by a linear correlation algorithm with pure milk and pure melamine. Band ratio (B1447/1466) images coupled with a single threshold were used to create resultant images to visualize identification and distribution of the melamine adulterant particles in milk powders. The identification results were verified by spectral feature comparison between separated mean spectra of melamine pixels and milk pixels. Linear correlations (r) were found between the number of pixels identified as containing melamine and melamine concentration in nonfat milk and whole milk powders, which were 0.980 and 0.970 or higher, respectively. The study demonstrated that the combination of NIR hyperspectral imaging and simple band ratioing was promising for rapid quantitative analysis of melamine in milk powders.
Nowadays, near-infrared spectroscopy (NIR) has become one of the most efficient and advanced techniques for food products analysis. Many relevant researches have been conducted in this regard. However, no reviews about the applications of NIR for liquid food analysis are reported. Therefore, this review summarizes the recent research developments of NIR technology in the field of liquid foods, focusing on the detection of quality attributes of various liquid foods including alcoholic beverages (red wines, rice wines and beer), nonalcoholic beverages (juice, fruit vinegars, coffee beverages and cola beverages), dairy products (milk and yogurt) and oils (vegetable oils, camellia oils, peanut oils, virgin olive oils and frying oil). In addition, the classification and authentication detection of adulterations are also covered. It is hoped that the current paper can serve as a reference source for future liquid food analysis by NIR techniques.
Background
Microbial evaluation plays a very important role in food quality evaluation. By providing spectral information relevant to microbial attributes, spectroscopy technology has been introduced and applied for microbial quality evaluation of food products in a rapid and non-destructive way. By mining different range spectral data with appropriate chemometrics, some important microbial quality indicators could be potentially evaluated and quantified.
Scope and approach
In this review, recent progresses and applications of visible/infrared (Vis/IR), Raman and Fluorescence spectroscopy as efficient and promising tools in replacing traditional time-consuming, tedious, and destructive technologies for detecting spoilage microorganisms in various raw and processed food products are described. The challenges and future researches of these spectroscopy techniques are also suggested.
Key findings and conclusions
Although spectroscopy technology shows its prosing and potential in evaluating various microbial parameters, some challenges in terms of spectra pre-processing, model calibration and instrument development are still needed to be faced. Much more works are still required to improve the stability and suitability of spectroscopy before implementation in food industry.
A dedicated setup was developed for simultaneous measurement of pressure and volume in a single eye of semi-hard cheese. A known level of gas pressure was applied to the cheese eye and the resulting eye inflation was monitored using Magnetic Resonance Imaging (MRI). Image analysis methods were developed to measure the eye volume, horizontal and vertical diameters of the eye and the deflected shape of the top surface of the cylinder of cheese under study. Two amounts of pressure were applied to attempt to reproduce a creep-recovery experiment in situ. In the last stage, lowering of pressure was applied in order to investigate time-independent elasticity. The core of the semi-hard cheese was found to show no relevant time-independent elasticity during processing in a 90 h experiment. A low amount of pressure (< 3.5 kPa) was able to inflate already existing eyes in semi-hard cheese within the linear domain.
A visible/near-infrared hyperspectral imaging (HSI) system (400-1000 nm) coupled with wavelet analysis was used to determine the total volatile basic nitrogen (TVB-N) contents of prawns during cold storage. Spectral information was denoised by conducting wavelet analysis and uninformative variable elimination (UVE) algorithm, and then three wavelet features (energy, entropy and modulus maxima) were extracted. Quantitative models were established between the wavelet features and the reference TVB-N contents by using three regression algorithms. As a result, the LS-SVM model with modulus maxima features was considered as the best model for determining the TVB-N contents of prawns, with an excellent 2 P R of 0.9547, RMSEP =0.7213 mg N/100 g and RPD = 4.799. Finally, an image processing algorithm was developed for generating a TVB-N distribution map. This study demonstrated the possibility of applying the HSI imaging system in combination with wavelet analysis to the monitoring of TVB-N values in prawns.
In this work, FT-Raman spectroscopy was explored to evaluate spreadable cheese samples. A partial least squares discriminant analysis was employed to identify the spreadable cheese samples containing starch. To build the models, two types of samples were used: commercial samples and samples manufactured in local industries. The method of supervised classification PLS-DA was employed to classify the samples as adulterated or without starch. Multivariate regression was performed using the partial least squares method to quantify the starch in the spreadable cheese. The limit of detection obtained for the model was 0.34% (w/w) and the limit of quantification was 1.14% (w/w). The reliability of the models was evaluated by determining the confidence interval, which was calculated using the bootstrap re-sampling technique. The results show that the classification models can be used to complement classical analysis and as screening methods.