Figure 1 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Schematic representation of how the spectroscopy, the sample and the chemometrics are interconnected to define the application.
Source publication
Vibrational spectroscopy techniques are the most used techniques in the routine analysis of foods. This technique is widely utilised to measure and monitor the proximate chemical composition (e.g., protein, dry matter, fat and fibre) in an array of agricultural commodities, food ingredients and products. Developments in optics, instrumentation and...
Citations
... It could be considered a promising technology to be utilized by plant breeders as well as in a variety of industries where rapid screening of numerous samples for antioxidant and/or phenolic content is required [9]. Several studies have reported the ability of near infrared (NIR) spectroscopy which works in the spectral range 13000-4000 cm −1 , to predict, with the help of multivariate calibration methods, the presence of different bioactive compounds in tea such as catechins, caffeine, free amino acids and theaflavins [10][11][12][13][14][15]; this technique has been also used to discriminate different tea varieties, also in relation with the geographical origins, and to identify tea grades and tea processing degree [10,[16][17][18]. Conversely, to the best of our knowledge, only a few studies can be found in the literature regarding the use of FTIR spectroscopy in the MIR region (spectral range 4000-400 cm −1 ) to investigate tea leaves and tea brews and to highlight differences due to processing, cultivation site and extraction methods [4,16,[18][19][20][21][22]. ...
ATR-FTIR (Attenuated Total Reflectance Fourier Transform InfraRed) spectroscopy, combined with chemometric, represents a rapid and reliable approach to obtain information about the macromolecular composition of food and plant materials. With a single measurement, the chemical fingerprint of the analyzed sample is rapidly obtained. Hence, this technique was used for investigating 13 differently processed tea leaves (green, black and white) all grown and processed in European tea gardens, and their vacuum-dried tea brews, prepared using both hot and cold water, to observe how the components differ from tea leaf to the in-cup infusion. Spectra were collected in the 1800–600 cm−1 region and were submitted to Principal Component Analysis (PCA). The comparison of the spectral profiles of leaves and hot and cold infusions of tea from the same country, emphasizes how they differ in relation to the different spectral regions. Differences were also noted among the different countries. Furthermore, the changes observed (e.g., at ~1340 cm−1) due to catechin content, confirm the antioxidant properties of these teas. Overall, this experimental approach could be relevant for rapid analysis of various tea types and could pave the way for the industrial discrimination of teas and of their health properties without the need of time-consuming, lab chemical assays.
... High R 2 value itself does not necessarily mean high predictability of the model. Cozzolino (2022) pointed out that each sample should be only used once in model development and inclusion of technical replicates may lead to extremely higher R 2 and RPD values but overestimated model performance. Li et al. (2019) collected five kinds and three grades of teas with 9 repetitions (in total, 3 *5 *9 = 135 samples) and collected their Vis-NIR spectra (400-2498 nm). ...
... R 2 v= 0.97 for β-carotene. It should be pointed out that 9 replicates of each sample were used in model development, a procedure that would probably lead to overestimation of the model performance since almost identical samples were used 9 times in data processing (Cozzolino, 2022). Similarly, Shawky & Selim (2019) collected NIR spectra (1250-2500 nm) from 72 citrus peel samples (8 varieties, 9 replicates) and quantified the specific bioactive compounds hesperidin and diosmin. ...
... The potential for employing spectral measurement approaches in the quality control of fruits and vegetables is growing enormously (Escaŕate et al., 2022). The reason is that these approaches are non-destructive, fast and accurate, capable for both quantitative and qualitative analysis, thereby requiring minimal sample preparation (Cozzolino, 2022). We divided nondestructive spectral measurements into two categories: (1) spectralbased approaches (FTIR, NIR, and Raman spectroscopy) and (2) imaging-based approaches (HSI), as shown in Figure 1. ...
The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables.
... The general process of spectral analysis Conventional detection methods are cumbersome, time-consuming and labor-intensive, and are prone to chemical pollution to the environment. Different from the conventional analysis technology, the spectral analysis method is an indirect measurement technology, which applies the regression modeling method in information processing to establish a calibration model based on the spectral data of the sample set and the concentration of the components to be measured, and then indirectly calculates the concentration of the unknown sample (even for multi-component analysis) based on the spectral data of the known sample (Cen and He 2007;Cozzolino 2022; Moros, Garrigues, and de la Guardia 2010). The detection process mainly includes: (1) Designing a representative sample set and measuring the corresponding spectrum; (2) measuring the concentration of the component to be measured by a standard method for the designed sample set and using it as a reference value; ...
Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.2 billion in agriculture markets. As a general-purpose data analytic tool, NN has been applied in several areas of food science, such as food recognition, food supply chain security and omics analysis, and so on. Therefore, given the rapid emergence of NN applications in food safety, this review aims to provide a comprehensive overview of the NN application in food analysis for the first time, focusing on domain-specific applications in food analysis by introducing fundamental methodology, reviewing recent and notable progress, and discussing challenges and potential pitfalls. NN demonstrated that it has a bright future through effective collaboration between food specialist and the broader community in the food field, for example, superiority in food recognition, sensory evaluation, pattern recognition of spectroscopy and chromatography. However, major challenges impeded NN extension including void in the food scientist-friendly interface software package, incomprehensible model behavior, multi-source heterogeneous data, and so on. The breakthrough from other fields proved NN has the potential to offer a revolution in the immediate future.
... The main advantage of spectroscopic methods is the possibility of evaluating, measuring and predicting the properties related to several chemical parameters in a single analysis. Therefore, spectroscopic methods represent a major advance in quality monitoring systems for raw materials and functional/nutraceutical products (Cozzolino 2022). Several studies have reported the importance of spectroscopic methods for predicting total phenolic compounds in fruits. ...
Recent studies have demonstrated the metabolic benefits of phenolic compounds on human health. However, traditional analytical methods used for quantification of total phenolic compounds are time-consuming, laborious, require a high volume of reagents, mostly toxic substances, and involve several steps that can result in systematic and instrumental errors. Spectroscopic techniques have been used as alternatives to these methods for the determination of bioactive compounds directly in the food matrix by minimal sample preparation, without using toxic reagents. Therefore, this overview presents the advantages of nondestructive methods focusing on infrared spectroscopy (IR), for the quantification of total phenolic compounds in fruits. In addition, the main difficulties in applying these spectroscopic techniques are presented, as well as a comparison between the quantification of total phenolic compounds by traditional and IR methods. This review concludes by focusing on model building, highlighting that IR data are mainly processed using the partial least-squares (PLS) regression method to predict total phenolic content. The development of portable and inexpensive IR instruments, combined with multivariate data processing, could give to the consumers a straightforward technology to evaluate the total phenolic content of fruits prior to purchase.
... Many analytical techniques have been employed for that goal, among them Raman spectroscopy [7,12]. As a matter of fact, the application of vibrational spectroscopy techniques to quantify nutraceuticals in fruits and plants has proven successful [13], and Raman spectroscopy has been used for quantitative analysis in the pharmaceutical industry [14,15]. The evolution of Raman as an extremely sensitive analytical tool was further boosted by the discovery of several other Raman phenomena, including Resonance Raman (RR), coherent anti-Stokes Raman scattering (CARS), and surface-enhanced Raman scattering (SERS) [16,17]. ...
The identification and quantification of caffeine is a common need in the food and pharmaceutical industries and lately also in the field of environmental science. For that purpose, Raman spectroscopy has been used as an analytical technique, but the interpretation of the spectra requires reliable and accurate computational protocols, especially as regards the Resonance Raman (RR) variant. Herein, caffeine solutions are sampled using Molecular Dynamics simulations. Upon quantification of the strength of the non-covalent intermolecular interactions such as hydrogen bonding between caffeine and water, UV-Vis, Raman, and RR spectra are computed. The results provide general insights into the hydrogen bonding role in mediating the Raman spectral signals of caffeine in aqueous solution. Also, by analyzing the dependence of RR enhancement on the absorption spectrum of caffeine, it is proposed that the sensitivity of the RR technique could be exploited at excitation wavelengths moderately far from 266 nm, yet achieving very low detection limits in the quantification caffeine content.
