A Combination of Near-Infrared Hyperspectral Imaging with Two-Dimensional Correlation Analysis for Monitoring the Content of Alanine in Beef

Combining Vis-NIR and NIR hyperspectral imaging techniques with a data fusion strategy for prediction of norfloxacin residues in mutton

Norfloxacin is an antibacterial compound that belongs to the fluoroquinolone family. Currently, hyperspectral imaging (HSI) for the detection of antibiotic residues focuses mostly on individual systems. Attempts to integrate different HSI systems with complementary spectral ranges are still lacking. This study investigates the feasibility of applying data fusion strategies with two HSI techniques (Visible near-infrared and near-infrared) in combination to predict norfloxacin residue levels in mutton. Spectral data from the two spectral techniques were analyzed using partial least squares regression (PLSR), support vector regression (SVR) and stochastic configuration networks (SCN), respectively, and the two data fusion strategies were fused at the data level (low-level fusion) and feature level (middle-level fusion, mid-level fusion). The results indicated that the modeling performance of the two fused datasets was better than that of the individual systems. Mid-level fusion data achieved the best model based on uninformative variable elimination (UVE) combined with SCN, in which the determination coefficient of prediction set (R2p) of 0.9312, (root mean square error of prediction set) RMSEP of 0.3316 and residual prediction deviation (RPD) of 2.7434, in comparison with all others. Therefore, two HSI systems with complementary spectral ranges, combined with data fusion strategies and feature selection, could be used synergistically to improve the detection of norfloxacin residues. This study may provide a valuable reference for the non-destructive detection of antibiotic residues in meat.

Assessment of comprehensive PAH index in roasted Tan lamb by two-dimensional correlation spectroscopy combined with hyperspectral imaging

Thermally processed meat may contain harmful compounds, including polycyclic aromatic hydrocarbons (PAHs). This study constructed, for the first time, the comprehensive PAH index (CPI) concentration (phenanthrene [26.47%], acenaphthene [21.83%], pyrene [18.64%], fluoranthene [17.11%], fluorene [8.49%], and anthracene [7.46%]). A visible near-infrared (Vis-NIR) hyperspectral image (HSI) system was employed to detect CPI in 150 roasted Tan lamb samples. Furthermore, two-dimensional correlation spectra were used to identify spectral features and reveal the order of chemical bond changes under the characteristic peaks at 579-737-631-449 nm. The results indicated that competitive adaptive reweighted sampling-multiple linear regression quantitative prediction model worked the best with calibration set coefficient of determination of 0.9161, calibration set coefficient of root mean square error of 2.3426 µg/kg, R-squared prediction of 0.8469, and root mean square error of prediction of 2.4119 µg/kg. Finally, PAH content distributions were visualized using the best prediction model. This study aimed to propose a feasible method for CPI in roasted Tan lamb detection based on Vis-NIR HSI coupled with multivariate analysis methods.

Novel Developments and Progress in Two-Dimensional Correlation Spectroscopy (2D-COS)

This first of the two-part series of the comprehensive survey review on the progress of the two-dimensional correlation spectroscopy (2D-COS) field during the period 2021-2022, covers books, reviews, tutorials, novel concepts and theories, and patent applications that appeared in the last two years, as well as some inappropriate use or citations of 2D-COS. The overall trend clearly shows that 2D-COS is continually growing and evolving with notable new developments. The technique is well recognized as a powerful analytical tool that provides deep insights into systems in many science fields.

Diverse Applications of Two-Dimensional Correlation Spectroscopy (2D-COS)

This second of the two-part series of a comprehensive survey review provides the diverse applications of two-dimensional correlation spectroscopy (2D-COS) covering different probes, perturbations, and systems in the last two years. Infrared spectroscopy has maintained its top popularity in 2D-COS over the past two years. Fluorescence spectroscopy is the second most frequently used analytical method, which has been heavily applied to the analysis of heavy metal binding, environmental, and solution systems. Various other analytical methods including laser-induced breakdown spectroscopy, dynamic mechanical analysis, differential scanning calorimetry, capillary electrophoresis, seismologic, and so on, have also been reported. In the last two years, concentration, composition, and pH are the main effects of perturbation used in the 2D-COS fields, as well as temperature. Environmental science is especially heavily studied using 2D-COS. This comprehensive survey review shows that 2D-COS undergoes continuous evolution and growth, marked by novel developments and successful applications across diverse scientific fields.

A new comprehensive quantitative index for the assessment of essential amino acid quality in beef using Vis-NIR hyperspectral imaging combined with LSTM

The quality of beef is usually predicted by measuring a single index rather than a comprehensive index. To precisely determine the essential amino acid (EAA) contents in 360 beef samples, the feasibility of optimized spectral detection techniques based on the comprehensive EAA index (CEI) and comprehensive weight index (CWI) constructed by factor analysis was explored. Two-dimensional correlation spectroscopy (2D-COS) was used to analyse the mechanisms of spectral peak shifts in complex disturbance systems with CEI and CWI contents, and 15 sensitive feature variables were extracted to establish a quantitative analysis model of a long short-term memory network (LSTM). The results indicated that 2D-COS had good predictive performance in both CEI-LSTM (R2P of 0.9095 and RPD of 2.76) and CWI-LSTM (R2P of 0.8449 and RPD of 2.45), which reduced data information by 88%. This indicates that utilizing 2D-COS can eliminate collinearity and redundant information among variables while achieving data dimensionality reduction and simplification of calibration models. Furthermore, a spatial distribution map of the comprehensive EAA content was generated by combining the optimal prediction model. This study demonstrated that the comprehensive index method furnishes a new approach to rapidly evaluate EAA content.

Development of a visible to 1600 nm hyperspectral imaging rigid-scope system using supercontinuum light and an acousto-optic tunable filter

In this study, we developed a rigid-scope system that can perform hyperspectral imaging (HSI) between visible and 1600 nm wavelengths using a supercontinuum light source and an acousto-optic tunable filter to emit specific wavelengths. The system optical performance was verified, and the classification ability was investigated. Consequently, it was demonstrated that HSI (490-1600 nm) could be performed. In addition, seven different targets could be classified by the neural network with an accuracy of 99.6%, recall of 93.7%, and specificity of 99.1% when the wavelength range of over 1000 nm (OTN) was extracted from HSI data as train data.

Machine learning identification of edible vegetable oils from fatty acid compositions and hyperspectral images

Hyperspectral imaging analysis combined with machine learning was applied to identify eight edible vegetable oils, and its classification performance was compared with the chemical method based on fatty acid compositions. Furthermore, the degree of adulteration in vegetable oils was quantitatively investigated using machine learning-enabled hyperspectral approaches. The hyperspectral absorbance spectra of palm oil with a high degree of saturation were distinctly different from those of the other liquid oils. The flaxseed and olive oils exhibited the dominant hyperspectral intensities at 1170/1671 and 1212/1415 nm, respectively. Linear discriminant analysis demonstrated that two linear discriminants could explain a significant portion of the total variability, accounting for 96.0% (fatty acid compositions) and 98.9% (hyperspectral images). When the hyperspectral results were used as datasets for three machine learning models (decision tree, random forest, and k-nearest neighbor), several instances to incorrectly classify grapeseed and sunflower oils were detected, while olive, palm, and flaxseed oils were successfully identified. The machine learning models showed a great classification performance that exceeded 98.9% from the hyperspectral images of the vegetable oils, which was comparable to the fatty acid composition-based chemical method in identifying edible vegetable oils. In addition, the random forest model was the most effective in ascertaining adulteration levels in binary oil blends (R2 > 0.992 and RMSE < 2.75).

Nondestructive identification and classification of starch types based on multispectral techniques coupled with chemometrics

Starch is the main source of energy and nutrition. Therefore, some merchants often illegally add cheaper starches to other types of starches or package cheaper starches as higher priced starches to raise the price. In this study, 159 samples of commercially available wheat starch, potato starch, corn starch and sweet potato starch were selected for the identification and classification based on multispectral techniques, including near-infrared (NIR), mid-infrared (MIR) and Raman spectroscopy combined with chemometrics, including pretreatment methods, characteristic wavelength selection methods and classification algorithms. The results indicate that all three spectral techniques can be used to discriminate starch types. The Raman spectroscopy demonstrated superior performance compared to that of NIR and MIR spectroscopy. The accuracy of the models after characteristic wavelength selection is generally superior to that of the full spectrum, and two-dimensional correlation spectroscopy (2D-COS) achieves better model performance than other wavelength selection methods. Among the four classification methods, convolutional neural network (CNN) exhibited the best prediction performance, achieving accuracies of 99.74 %, 97.57 % and 98.65 % in NIR, MIR and Raman spectra, respectively, without pretreatment or characteristic wavelength selection.

Predictive Analysis of Linoleic Acid in Red Meat Employing Advanced Ensemble Models of Bayesian and CNN-Bi-LSTM Decision Layer Fusion Based Hyperspectral Imaging

Rapid non-destructive testing technologies are effectively used to analyze and evaluate the linoleic acid content while processing fresh meat products. In current study, hyperspectral imaging (HSI) technology was combined with deep learning optimization algorithm to model and analyze the linoleic acid content in 252 mixed red meat samples. A comparative study was conducted by experimenting mixed sample data preprocessing methods and feature wavelength extraction methods depending on the distribution of linoleic acid content. Initially, convolutional neural network Bi-directional long short-term memory (CNN-Bi-LSTM) model was constructed to reduce the loss of the fully connected layer extracted feature information and optimize the prediction effect. In addition, the prediction process of overfitting phenomenon in the CNN-Bi-LSTM model was also targeted. The Bayesian-CNN-Bi-LSTM (Bayes-CNN-Bi-LSTM) model was proposed to improve the linoleic acid prediction in red meat through iterative optimization of Gaussian process acceleration function. Results showed that best preprocessing effect was achieved by using the detrending algorithm, while 11 feature wavelengths extracted by variable combination population analysis (VCPA) method effectively contained characteristic group information of linoleic acid. The Bi-directional LSTM (Bi-LSTM) model combined with the feature extraction data set of VCPA method predicted 0.860 Rp2 value of linoleic acid content in red meat. The CNN-Bi-LSTM model achieved an Rp2 of 0.889, and the optimized Bayes-CNN-Bi-LSTM model was constructed to achieve the best prediction with an Rp2 of 0.909. This study provided a reference for the rapid synchronous detection of mixed sample indicators, and a theoretical basis for the development of hyperspectral on-line detection equipment.

ATR-FTIR spectroscopy combined with chemometrics to assess the spectral markers of irradiated baijius and their potential application in irradiation dose control

Although some methods have been proposed for the identification of irradiated baijius, they are often costly, time-consuming, and destructive. It is also unclear what instrumentation can be used to fully characterize the quality changes in irradiated baijius. To address this issue, this study pioneers the use of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy in combination with chemometrics to open up new avenues for characterizing irradiated baijius and their quality control. Principal component analysis, five spectral pre-processing methods (Savitzky-Golay smoothing (S-G), second-order derivative (SD), multiple scattering correction (MSC), S-G + SD and S-G + MSC), five wavelength selection methods (random forest variable importance (RFVI), two-dimensional correlation spectroscopy (2D-COS), variable importance in projection (VIP), ReliefF, and Venn), and three classification models (partial least squares-discriminant analysis (PLS-DA), random forest (RF), and grasshopper optimization algorithm-based support vector machine (GOA-SVM)) were integrated into the analytical framework of ATR-FTIR spectroscopy, aiming to accurately identify baijiu samples according to different irradiation doses and to search for irradiation-induced spectral difference characteristics (spectral markers). The results showed that SD was the best spectral pre-processing method, and RF models constructed using the 20 most competitive and discriminative spectral markers (selected by Venn) could achieve accurate identification of baijiu samples based on irradiation dose (0, 4, 6, and 8 kGy). After Pearson correlation analysis, the five significantly (P＜0.05) changed spectral markers (1596, 2025, 2309, 2329, and 2380 cm-1) were attributed to changes in the content of total acids, alcohols, and aromatic compounds. These findings demonstrate for the first time the potential of ATR-FTIR spectroscopy as a fast, low-cost, and non-destructive tool for the characterization and identification of irradiated baijiu samples. This approach may also offer a promising solution for labeling management of irradiated foods, vintage identification of baijius, and brand protection.

Rapid Nondestructive Testing Technology-Based Biosensors for Food Analysis

Food analysis plays a vital role in ensuring the safety and quality of food products [...].

Combination of hyperspectral imaging and entropy weight method for the comprehensive assessment of antioxidant enzyme activity in Tan mutton

The antioxidant enzymes play the crucial role in inhibiting mutton spoilage. In this study, visible near-infrared (Vis-NIR) hyperspectral imaging (HSI) combined with entropy weight method (EWM) was developed for the first time to evaluate the antioxidant properties of Tan mutton. The comprehensive index of antioxidant enzymes (AECI) consisting of peroxidase (49.34%), catalase (37.97%) and superoxidase (12.69%) was constructed by the EWM. Partial least squares regression, least squares support vector machine and artificial neural networks (ANN) were developed based on characteristic wavelengths extracted by successful projections algorithm, uninformative variable selection, iteratively retains informative variables (IRIV), regression coefficient and competitive adaptive reweighted sampling (CARS). The textural features (TF) were extracted by the gray level co-occurrence matrix and fused with the spectral data to establish models. Visualization of the changes in antioxidant enzyme activity was constructed from the optimal model. In addition, two-dimensional correlation spectra (2D-COS) with AECI as a perturbation variable was used to identify spectral features, revealing chemical bond changes order under the characteristic peaks at 612-799-473-708-559 nm. The results showed that the IRIV-CARS-TF-ANN model performed the best, with prediction set coefficient of determination (R_P²) of 0.8813, which improved 2.12%, 1.11% and 2.77% over the R_P² of full band, IRIV and IRIV-CARS, respectively. It was suggested that fusion data of HSI may effectively predict the activity of antioxidant enzymes in Tan mutton.