Two-dimensional visible/near-infrared correlation spectroscopy study of thawing behavior of frozen chicken meats without exposure to air

Deep Learning Combined with Hyperspectral Imaging Technology for Variety Discrimination of Fritillaria thunbergii

Traditional Chinese herbal medicine (TCHM) plays an essential role in the international pharmaceutical industry due to its rich resources and unique curative properties. The flowers, stems, and leaves of Fritillaria contain a wide range of phytochemical compounds, including flavonoids, essential oils, saponins, and alkaloids, which may be useful for medicinal purposes. Fritillaria thunbergii Miq. Bulbs are commonly used in traditional Chinese medicine as expectorants and antitussives. In this paper, a feasibility study is presented that examines the use of hyperspectral imaging integrated with convolutional neural networks (CNN) to distinguish twelve (12) Fritillaria varieties (n = 360). The performance of support vector machines (SVM) and partial least squares-discriminant analysis (PLS-DA) was compared with that of convolutional neural network (CNN). Principal component analysis (PCA) was used to assess the presence of cluster trends in the spectral data. To optimize the performance of the models, cross-validation was used. Among all the discriminant models, CNN was the most accurate with 98.88%, 88.89% in training and test sets, followed by PLS-DA and SVM with 92.59%, 81.94% and 99.65%, 79.17%, respectively. The results obtained in the present study revealed that application of HSI in conjunction with the deep learning technique can be used for classification of Fritillaria thunbergii varieties rapidly and non-destructively.

Near-infrared hyperspectral imaging for detection and visualization of offal adulteration in ground pork

Hyperspectral imaging (HSI) technique was investigated to explore a feasible protocol for detecting the potential offal (lung) adulteration in ground pork. Tested samples (176 adulterated and 2 controls) were first prepared with adulterant of ground lung in range of 0%-100% (w/w) at 10% intervals. After hyperspectral images were acquired and calibrated in reflectance mode (400-1000 nm), full spectra were extracted from identified regions of interests (ROIs) and then transformed into absorbance and Kubelka-Munck spectral units, respectively. Partial least squares regression (PLSR) models based on full spectra showed that raw reflectance spectra with no preprocessings performed best with coefficient of determination (R_p²) of 0.98, root mean square error (RMSEP) of 4.25%, and ratio performance deviation (RPD) of 7.53 in prediction set. To reduce the high dimensionality of spectra, data was further explored using principal component loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC), respectively. The optimal performance of established simplified PLSR model were acquired using eleven featured wavelengths selected by PC loadings with R_p² of 0.98, RMSEP of 4.47% and RPD of 7.16. Finally, the limit of detection (LOD) was calculated to be a satisfactory 7.58%, and readily discernible visualization procedure using preferred simplified PLSR model yielded satisfactory spatial distribution of adulteration situation. Control samples with known distribution were also visualized to successfully prove the validity. Consequently, this research offers an alternative assay for visually and rapidly detecting offal of lung adulteration in ground pork.

Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics

The classification of wheat grain varieties is of great value because its high purity is the yield and quality guarantee. In this study, hyperspectral imaging combined with the chemometric methods was applied to explore and implement the varieties classification of wheat seeds. The hyperspectral images of all the samples covering 874–1734 nm bands were collected. Exploratory analysis was first carried out while using principal component analysis (PCA) and linear discrimination analysis (LDA). Spectral preprocessing methods including standard normal variate (SNV), multiplicative scatter correction (MSC), and wavelet transform (WT) were introduced, and their effects on discriminant models were studied to eliminate the interference of instrumental and environmental factors. PCA loading, successive projections algorithm (SPA), and random frog (RF) were applied to extract feature wavelengths for redundancy elimination owing to the possibility of existing redundant spectral information. Classification models were developed based on full wavelengths and feature wavelengths using LDA, support vector machine (SVM), and extreme learning machine (ELM). This optimal model was finally utilized to generate visualization map to observe the classification performance intuitively. When comparing with other models, ELM based on full wavelengths achieved the best accuracy up to 91.3%. The overall results suggested that hyperspectral imaging was a potential tool for the rapid and accurate identification of wheat varieties, which could be conducted in large-scale seeds classification and quality detection in modern seed industry.

Application of near-infrared spectroscopy for frozen-thawed characterization of cuttlefish (Sepia officinalis)

Few works to date have reported the application of near-infrared spectroscopy (NIRS) for the characterization and authentication of cephalopods. This study investigated the feasibility of a portable NIRS instrument for the non-destructive freshness evaluation of fresh (F) and frozen-thawed (FT) cuttlefish (Sepia officinalis). Samples were examined by chemical, microbiological and sensorial analyses, during 13 days of conservation at 3 °C. The spectral data were collected on lateral mantle of cuttlefish, and different partial least squares discriminant analyses (PLS-DA) were applied for classification purposes. The interpretation of spectra was also investigated by applying the specific water coordinates, using aquaphotomics. Few significant differences in the wet chemistry and microbiological data were detected between F and FT during storage. The quality index method and microbiological analyses suggested similar behavior between F and FT samples until to near 9 days of shelf life. PLS-DA models with the spectral range 900-1650 nm achieved a classification precision of 0.91 between F and FT, while the performances for the prediction of storage days were less effective. The results of aquaphotomics plotted in aquagrams were suitable for the interpretation of the main physicochemical changes of cuttlefish throughout the shelf life. The water coordinates suggested a different molecular conformation of water species in the FT than F samples, with more free water molecules and a lower amount of bound species and the water solvation shell, respectively.

Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis

Hyperspectral imaging was used to identify and to visualize the coffee bean varieties. Spectral preprocessing of pixel-wise spectra was conducted by different methods, including moving average smoothing (MA), wavelet transform (WT) and empirical mode decomposition (EMD). Meanwhile, spatial preprocessing of the gray-scale image at each wavelength was conducted by median filter (MF). Support vector machine (SVM) models using full sample average spectra and pixel-wise spectra, and the selected optimal wavelengths by second derivative spectra all achieved classification accuracy over 80%. Primarily, the SVM models using pixel-wise spectra were used to predict the sample average spectra, and these models obtained over 80% of the classification accuracy. Secondly, the SVM models using sample average spectra were used to predict pixel-wise spectra, but achieved with lower than 50% of classification accuracy. The results indicated that WT and EMD were suitable for pixel-wise spectra preprocessing. The use of pixel-wise spectra could extend the calibration set, and resulted in the good prediction results for pixel-wise spectra and sample average spectra. The overall results indicated the effectiveness of using spectral preprocessing and the adoption of pixel-wise spectra. The results provided an alternative way of data processing for applications of hyperspectral imaging in food industry.

Consumer Attitudes Toward Storing and Thawing Chicken and Effects of the Common Thawing Practices on Some Quality Characteristics of Frozen Chicken

In this study, a survey was conducted to both evaluate the consumers’ general attitudes for purchasing and storing the raw chicken and determine the thawing practices used for defrosting frozen chicken at home. About 75% of the consumers indicated purchasing chicken meat at least once a week or more. Furthermore, the majority (82.16%) of those who stored at least a portion of the raw chicken stated freezing the raw chicken meat at home. Freezing the chicken meat was considered to have no effect on the quality by 43.49% of the consumers while 56.51% thought that freezing had either negative or positive effects on the quality. The survey study indicated that top five most commonly used thawing practices included thawing on the kitchen counter, thawing in the refrigerator, thawing in the warm water, thawing in the microwave, and thawing under tap water. In addition, an experimental study was conducted to determine the effects of these most commonly used thawing practices on some quality characteristics of the chicken meat including pH, drip loss, cooking loss, color analysis and textural profile analysis. Although, L* value for thawing on the kitchen counter was the lowest, after cooking, none of the thawing treatments have a significant effect on the color values. Thawing in the microwave produced the highest drip loss of 3.47% while the lowest drip loss of 0.62% was observed with thawing in the refrigerator. On the other hand, thawing in the microwave and refrigerator caused the lowest cooking loss values of 18.29% and 18.53%, respectively. Nevertheless, there were no significant differences among textural parameter values of the defrosted and then cooked samples using the home based thawing practices, indicating similar quality characteristics among the samples.

Freeze-thaw and cooking effects on broiler breast fillets with extreme initial L* values

Five hundred broiler males were grown to 56 d and processed under common terms. Front halves were deboned 24 h postmortem to obtain breast fillets, and CIELAB light reflectance was measured on the skin side of each fillet 24 h later. All fillets were bagged and frozen (-20 degrees C) for 5 mo. Then the fillets exhibiting the lowest (dark), median (normal), and highest (pale) L* values 48 h postmortem were thawed (3 d at 4 degrees C) and cooked (internal temperature 80 degrees C). Thawing reduced the L* value in the pale fillets and increased it in the dark ones, and cooking further increased L* value and reduced the differences in L*, a*, and b* between groups. Thawing and cooking losses were not affected by initial L* value until they were combined. Total losses increased with initial L*, which was in parallel with a lower increase in thickness after cooking.

Detection of septicemia in chicken livers by spectroscopy,

To establish a procedure for differentiating normal chickens from chickens with septicemia/toxemia (septox) by machine inspection under the Hazard Analysis and Critical Control Point-Based Inspection Models Project, spectral measurements of 300 chicken livers, of which half were normal and half were condemned due to septox conditions, were collected and analyzed. Neural network classification of the spectral data after principal component analysis (PCA) indicated that normal and septox livers were correctly differentiated by spectroscopy at a rate of 96%. Analysis of the data established 100% correlation between the spectroscopic identification and the subset of samples, both normal and septox, that were histopathologically diagnosed. In an attempt to establish the microbiological etiology of the diseased livers, isolates from 30 livers indicated that the poultry carcasses were contaminated mostly with coliforms present in the environment, hindering the isolation of pathogenic microorganisms. Therefore, to establish the cause of diseased livers, a strictly aseptic environment and procedure for sample collection is required.