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S-ovalbumin content is an indicator of egg freshness and has an important impact on the quality of processed foods. The objective of this study is to develop simplified models for monitoring the S-ovalbumin content of eggs during storage using hyperspectral imaging (HSI) and multivariate analysis. The hyperspectral images of egg samples at differen...
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... visible-near infrared (VIS-NIR) HSI system is shown in Figure 1, which has been described in detail in our recent research [25]. The egg sample was horizontally placed on the translation platform and scanned in the transmission mode with a spectral range of 401-1002 nm. ...
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... resulting in a fast reduction in RMSECV. In the refined selection stage, the wavelengths with little or no information were eliminated in a stepwise manner, leading to a slow change in RMSECV from sampling runs 31-64, followed by a rapid increase due to the elimination of some informative wavelengths from the optimal subset (denoted by blue *). Finally, fourteen wavelengths, 615.5, 624.3, 680.0, 687.6, 700.4, 711.8, 734.9, 758.0, 787.6, 837.9, 870.3, 896.2, 953.1, and 963.3 nm were selected by CARS. The feature wavelengths were mainly located at around 620, 700, 740, 836, 880, and 960 nm, which were assigned as follows: electron transitions of lipid; pH and pigment molecule in albumen; the third overtone of O-H in water; the combinations and overtone modes ...
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Moisture content (MC) is one of the important indexes to evaluate maize seed quality. Its accurate prediction is very challenging. In this study, the long-wave near-infrared hyperspectral imaging (LW-NIR-HSI) system was used, and the embryo side (S1) and endosperm side (S2) spectra of each maize seed were extracted, as well as the average spectrum...
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... To obtain the typical spectrum of the sample, the background of the image should be removed from the hyperspectral image [24]. The threshold was determined by histogram, and then the mask was made by binarization. ...
The process of meat postmortem aging is a complex one, in which improved tenderness and aroma coincide with negative effects such as water loss and microbial growth. Determining the optimal postmortem storage time for meat is crucial but also challenging. A new visual monitoring technique based on hyperspectral imaging (HSI) has been proposed to monitor pork aging progress. M. longissimus thoracis from 15 pigs were stored at 4 °C for 12 days while quality indexes and HSI spectra were measured daily. Based on changes in physical and chemical indicators, 100 out of the 180 pieces of meat were selected and classified into rigor mortis, aged, and spoilt meat. Discrete wavelet transform (DWT) technology was used to improve the accuracy of classification. DWT separated approximate and detailed signals from the spectrum, resulting in a significant increase in classification speed and precision. The support vector machine (SVM) model with 70 band spectra achieved remarkable classification accuracy of 97.06%. The study findings revealed that the aging and microbial spoilage process started at the edges of the meat, with varying rates from one pig to another. Using HSI and visualization techniques, it was possible to evaluate and portray the postmortem aging progress and edible safety of pork during storage. This technology has the potential to aid the meat industry in making informed decisions on the optimal storage and cooking times that would preserve the quality of the meat and ensure its safety for consumption.
... Spectral pre-processing is an important step in establishing a spectral detection algorithm in order to reduce errors due to external factors and random noise, and baseline changes [27]. Additionally, influencing the spectrum are factors such as the scattering of the object's surface and the alteration of the optical path. ...
Maturity is a crucial indicator in assessing the quality of tomatoes, and it is closely related to lycopene content. Using hyperspectral imaging, this study aimed to monitor tomato maturity and predict its lycopene content at different maturity stages. Standard normal variable (SNV) transformation was applied to preprocess the hyperspectral data. Then, using competitive adaptive reweighted sampling (CARS), the characteristic wavelengths were selected to simplify the calibration models. Based on the full and characteristic wavelengths, a support vector classifier (SVC) model was developed to determine tomato maturity qualitatively. The results demonstrated that the classification accuracy using the characteristic wavelength led to the obtention of better results with an accuracy of 95.83%. In addition, the support vector regression (SVR) and partial least squares regression (PLSR) models were utilized to predict lycopene content. With a coefficient of determination for prediction (R2P) of 0.9652 and a root mean square error for prediction (RMSEP) of 0.0166 mg/kg, the SVR model exhibited the best quantitative prediction capacity based on the characteristic wavelengths. Following this, a visual distribution map was created to evaluate the lycopene content in tomato fruit intuitively. The results demonstrated the viability of hyperspectral imaging for detecting tomato maturity and quantitatively predicting the lycopene content during storage.
... [22] Screening of feature wavelengths: The Competitive Adaptive Reweighted Sampling (CARS) algorithm begins with the elimination of uninformative variables, and the PLS regression coefficients are used to select the optimal subset of combinations of feature variables representing spectral differences based on the position corresponding to the root-mean-square error of cross-validation (RMSECV). [23] The Shuffled Frog Leaping Algorithm (SFLA) is an algorithm similar to the reversible jump Markov Chain Monte Carlo (RJMCMC), [24] which proceeds in an iterative manner, calculating the probability of each variable being selected in each iteration, with the probability increasing with the importance of the variable, and favoring the variable with the highest probability as the feature variable. The SFLA method combines a particle swarm algorithm with strong global search performance and a meta-algorithm with strong local search capability to produce a robust merit search capability. ...
This study aimed to determine the integrated freshness index (IFI) of eggs using Vis-NIR spectroscopy and optimized support vector regression, which gave the first insight into the freshness quality of eggs from the biochemical essence of quality changes. In this work, Vis-NIR transmission spectra of brown-shell and pink-shell egg samples were analyzed between 500 nm and 900 nm. Standard normal variables (SNV) were used to normalize the spectral data, and the Shuffled Frog Leaping Algorithm (SFLA) and Competitive Adaptive Reweighted Sampling (CARS) were used to choose the optimal wavelengths. The quantitative analysis model of IFI was developed using a support vector regression (SVR) that was optimized using Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). After conducting a comparative analysis, it was determined that the GA-SVR model based on 63 wavelengths screened by the SFLA best predicted IFI with a training set coefficient of determination (Rc²) of 0.900, root means square error (RMSEC) of 0.005, a prediction set coefficient of determination (Rp²) of 0.816, root mean square error (RMSEP) of 0.012 and relative analysis error (RPD) of 2.077. The results demonstrate that the model can be used to simultaneously perform nondestructive detection of two distinct egg IFI variants, suggesting broader applicability and enhanced model reliability.
... The S-ovalbumin content of EW was measured by the method of Yao et al. (2022), with slightly modifications. The EW (1.5 g) was mixed with 25 mL phosphate buffer (0.01 M, pH 7) for 5 min using a magnetic stirrer. ...
The changes in refrigeration storage after defrosting treatments on functional properties and structure of egg white (EW) were investigated in this study. The foaming properties and surface hydrophobicity decreased significantly (P < 0.05), whereas the free thiol content of EW increased during prolonged refrigeration storage after defrosting treatments. The values of emulsifying activity index were slightly higher in EM subjected to refrigeration storage for 20–30 days after defrosting treatments (P < 0.05). The texture profiles showed that the cooked EW gel subjected to refrigeration storage for 20 days after defrosting treatments, reached the minimum hardness, springiness and gumminess values vs. that of Control and Group 1. Additionally, scanning electron microscope (SEM) reflected that more and larger protein aggregates as well as irregular structure in EM during prolonged refrigeration storage after defrosting treatments. The increase in free sulfhydryl group and decrease in surface hydrophobicity tended to cause protein oxidation, aggregation and degradation, leading to deterioration of functional properties in EW following refrigeration storage after defrosting treatments. The results of this study indicate that it is important to use the EW of Control and Group 1 without any detrimental effects on functional and morphology properties in food processing industry.
3D-CNNs have demonstrated their capability to capture intricate non-linear relationships within Hyperspectral Images (HSIs). However, the computational complexity of 3D CNNs often leads to slower processing speeds, limited generalization, and susceptibility to overfitting. In response to these challenges, this study introduces the concept of depthwise separable convolutions using (2+1)D convolutions as an alternative to traditional 3D convolutions for Hyperspectral Image Classification (HSIC). The study observes that (2+1)D convolutions can effectively approximate the complex relationships represented by 3D convolutions while requiring fewer convolutional operations, thereby reducing the computational overhead associated with classification. Experimental results obtained from benchmark HSI datasets, including Indian Pines, Botswana, Pavia University, and Salinas, demonstrate that the proposed model yields results that are comparable to those achieved by various state-of-the-art models in the existing literature. The source code is available on GitHub github.com/mahmad00/Extreme-Xception-Net-for-HSIC.
Eggs that are produced using organic methods retail at higher prices than those produced using conventional methods, but they cannot be differentiated reliably using visual methods. Eggs can therefore be fraudulently mislabeled in order to increase their wholesale and retail prices. The objective of this research was therefore to test near-infrared hyperspectral imaging (NIR-HSI) to identify whether an egg has been produced using organic or conventional methods. A total of 210 organic and 210 conventional fresh eggs were individually scanned using NIR-HSI to obtain absorbance spectra for discrimination analysis. The physical properties of each egg were also measured non-destructively in order to analyze the performance of discrimination compared with those of the NIR-HSI spectral data. Principal component analysis (PCA) showed variation for PC1 and PC2 of 57% and 23% and 94% and 4% based on physical properties and the spectral data, respectively. The best results of the classification using NIR-HSI spectral data obtained an accuracy of 96.03% and an error rate of 3.97% via partial least squares–discriminant analysis (PLS-DA), indicating the possibility that NIR-HSI could be successfully used to rapidly, reliably, and non-destructively differentiate between eggs that had been produced using organic methods from eggs that had been produced using conventional methods.
