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Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Non-destructive determination of taste-related substances using near-infrared spectroscopy (NIRS) is of great significance for effectively evaluating tea quality. In the present research work, NIRS (400–2400 nm) were correlated with tea polyphenol (TP), free amino acid (FAA), caffeine (CAFF), and total sugar (TS) content in 187 tea samples by processing of spectra using continuous wavelet transform (CWT). Some effective variable selection algorithms (variable combination population analysis and iterative retained information variable algorithm (VCPA-IRIV), competitive adaptive reweighted sampling (CARS), variable iterative space shrinkage approach (VISSA), bootstrapping soft shrinkage (BOSS), and genetic algorithm (GA) were used for the quantification of taste-related substances of tea samples. The developed models were established and evaluated for the content of taste substances in several varieties of tea, including PLS and the potential of some machine learning models such as Gaussian process regression, support vector regression, and random forest regression. The efficiency of the developed model was significantly enhanced with the use of CWT-BOSS-PLS for monitoring the state of each component. More than 99.34% of variables was reduced. The predicted R values for TP, FAA, CAFF, and TS were 0.6891, 0.8385, 0.6810, and 0.8638 with accuracy improved by 6%, 3%, 45%, and 8%. Overall, this study provides an important support method for the practical application of content analysis for tea taste components. [ABSTRACT FROM AUTHOR]

The grade of tea is closely related to tea quality, so the identification of tea grade is an important task. In order to improve the identification capability of the tea grade system, a fuzzy maximum uncertainty linear discriminant analysis (FMLDA) methodology was proposed based on maximum uncertainty linear discriminant analysis (MLDA). Based on FMLDA, a tea grade recognition system was established for the grade recognition of Chunmee tea. The process of this system is as follows: firstly, the near-infrared (NIR) spectra of Chunmee tea were collected using a Fourier transform NIR spectrometer. Next, the spectra were preprocessed using standard normal variables (SNV). Then, direct linear discriminant analysis (DLDA), maximum uncertainty linear discriminant analysis (MLDA), and FMLDA were used for feature extraction of the spectra, respectively. Finally, the k-nearest neighbor (KNN) classifier was applied to classify the spectra. The k in KNN and the fuzzy coefficient, m, were discussed in the experiment. The experimental results showed that when k = 1 and m = 2.7 or 2.8, the accuracy of the FMLDA could reach 98.15%, which was better than the other two feature extraction methods. Therefore, FMLDA combined with NIR technology is an effective method in the identification of tea grade. [ABSTRACT FROM AUTHOR]

The continuous evolution of deep learning has garnered significant attention in spectroscopy. This study focuses on identifying wheat flour, presenting a more efficient and accurate method by combining two-dimensional correlation spectroscopy (2D-COS) and deep learning techniques. A data set of 316 near-infrared (NIR) spectral samples of four types of wheat flour was collected. By applying three disparate 2D-COS techniques, i.e., synchronous, asynchronous, and integrated, we crafted 948 2D-COS images. These images, obtained by transforming the original one-dimensional spectra into 2D representations, offer richer information for deep learning analysis. The study introduced an 18-layer residual network incorporating a convolutional attention mechanism, specifically tailored for the 2D-COS analysis of wheat flour, aimed at enhancing the model's discriminative capabilities by refining the residual neural network's structure. Achieving an unprecedented recognition accuracy of 100% through methodical optimization and rigorous training on the synchronous 2D-COS data set of wheat flour, the proposed model is a testament to the efficacy of deep learning in spectroscopic analysis. To further exhibit the confluence of 2D-COS with deep learning, t-distributed stochastic neighbor embedding was employed to visualize the distinctive 2D-COS features within the deep learning architecture. Additionally, the model's performance was juxtaposed with prevailing NIR spectral recognition methods, including random forest, gradient boosting decision tree, and artificial neural network. This comparison cemented the proposed approach's superiority in wheat flour categorization. The findings of this study not only introduce a novel and efficient solution for wheat flour quality analysis but also underscore the significant potential of deep learning techniques in spectroscopy applications. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Biological activities of limonoid include antitumor, antioxidant, anti-inflammatory, etc., and its content in citrus seeds is particularly high with a low utilization rate, so it is an important potential source of limonoid. The miniature near-infrared spectra device provides a high-throughput, low-cost, and field analysis technique for the detection of limonoid from it. In this paper, the quantitative determination of limonin, nomilin, and obacunone in citrus seeds by a NIR instrument, and a precise benchtop NIR instrument were studied. UHPLC-MS/MS was applied as the reference analysis method. The root mean square error (RMSE), determination coefficient (R2), and the ratio of performance to deviation (RPD) were used to monitor the model. R2p, RPD, and RMSEp of the best model based on benchtop TANGO FT-NIR for limonin, nomilin, and obacunone were 0.8920/3.10/0.0141, 0.9250/3.72/0.0058, and 0.9081/3.36/0.0040, respectively, representing reliable results. The miniature device NIR-S-G1 was also good in the model for nomilin (0.9075/3.35/0.0097) and obacunone (0.8860/3.02/0.0050). This result indicated that the miniaturized NIR-S-G1 has a similar ability to TANGO FT-NIR in detecting nomilin and obacunone in citrus seeds. Their experimental NIR absorption was detected to better understand the characteristics of the model. This step, combined with the waveband selection algorithm and the two-dimensional correlation spectroscopy (2D-COS) analysis, explained the main factor that affects the difference in analytical performance between the two spectrometers: detection range and instrument resolution. [ABSTRACT FROM AUTHOR]

The classification of iron ore rock plays a crucial role in assisting the mine to develop scientifically sound mining strategies. Given the limitations of traditional manual identification and chemical techniques in terms of cost and detection time, it is crucial to develop an economical, high-precision, and efficient classification method for iron ore rocks. This paper takes an iron mine in Anshan as the research center and aims to establish an iron ore rock classification model by combining the improved extreme learning machine (ELM) with near-infrared spectra (NIRS). The NIRS of iron ore rock is denoised and feature extracted using the Savitzky–Golay filter and competitive adaptive reweighted sampling (CARS) methods, respectively. In this paper, we propose the IAdaBoost algorithm by introducing the sample similarity relationship into the weight-updating strategy of the AdaBoost integration algorithm. The weighted extreme learning machine (WELM) is adopted as the base learner for the IAdaBoost algorithm. Furthermore, we employ a multi-strategy improved gray wolf optimization (IGWO) algorithm to optimize the learning rate and the number of integrated base learners in the IAdaBoost algorithm. Consequently, we propose the IGWO-IAdaBoost-WELM model. Compared to other machine learning models, our proposed model performs better and can classify iron ore rocks consistently and efficiently. Meanwhile, we also used the model to invert the remote sensing images of the iron ore mining area, and the results show that the model can achieve fast, efficient, and large-scale iron ore rock classification, which provides an important reference for mining. [Display omitted] • Combining spectroscopy with machine learning methods to classify iron ore rocks. • The machine learning structure was improved using an integrated algorithm and its parameters were optimized using a multi-strategy improved optimization algorithm. • Inversion of remote sensing images of mining areas using improved machine learning methods enables fast, efficient and large-scale iron ore rock classification, which provides an important reference for mining. [ABSTRACT FROM AUTHOR]

The grade of tea is closely related to tea quality, so the identification of tea grade is an important task. In order to improve the identification capability of the tea grade system, a fuzzy maximum uncertainty linear discriminant analysis (FMLDA) methodology was proposed based on maximum uncertainty linear discriminant analysis (MLDA). Based on FMLDA, a tea grade recognition system was established for the grade recognition of Chunmee tea. The process of this system is as follows: firstly, the near-infrared (NIR) spectra of Chunmee tea were collected using a Fourier transform NIR spectrometer. Next, the spectra were preprocessed using standard normal variables (SNV). Then, direct linear discriminant analysis (DLDA), maximum uncertainty linear discriminant analysis (MLDA), and FMLDA were used for feature extraction of the spectra, respectively. Finally, the k-nearest neighbor (KNN) classifier was applied to classify the spectra. The k in KNN and the fuzzy coefficient, m, were discussed in the experiment. The experimental results showed that when k = 1 and m = 2.7 or 2.8, the accuracy of the FMLDA could reach 98.15%, which was better than the other two feature extraction methods. Therefore, FMLDA combined with NIR technology is an effective method in the identification of tea grade.

Hand sorting for different types of waste textiles is time-consuming, laborious and inaccurate. The non-destructive and efficient identification of fibers in waste fabrics is of great significance to the reuse of textile materials. In this paper, 593 samples were selected as the research objects, including polyester, cotton, wool, viscose, nylon, silk, acrylic, polyester/nylon, polyester/cotton, polyester/wool and silk/cotton waste textiles. The near-infrared spectrum of each sample was obtained by a portable near-infrared spectrometer, and the influence of environmental humidity and fabric thickness on the near-infrared spectrum of the sample was discussed to obtain the best test conditions. On this basis, the back propagation artificial neural network (BP-ANN) was applied to the qualitative classification of waste textiles to complete the automatic identification of fabric components in the sorting process. Firstly, a standard sample set was established by waveform clipping and normalization, and a BP-ANN deep web suitable for near-infrared spectroscopy was established. Then the BP network was trained according to the input near-infrared spectrum data of known sample categories and the classification results of the preset 11 types of labels, and the weights and thresholds of each layer were adjusted in the repeated training process. Finally, a 1500 × 100 × 11 network structure was established when the network error was the smallest, and the number of corresponding hidden layer nodes was 100. When the number of training steps was 500, the sum of squared errors reached 0.001, and the model recognition effect was the best. Meanwhile, the validity of the model was verified by inspecting additional 299 samples outside the model, and the recognition accuracy rate of the established model also exceeded 99%, which verified the effectiveness of the model. These results show that this near-infrared qualitative analysis model can more accurately classify and identify waste textiles, especially polyester waste textiles. In addition, it provides a new idea for the recycling and reuse of waste textiles for enterprises. [ABSTRACT FROM AUTHOR]

Although Fourier transform infrared spectroscopy has been widely used in research on the aging of asphalt binders, it is a cumbersome, time-consuming, and environmentally polluting process using toxic solvents to extract and recover asphalt binders from asphalt mixtures for spectral measurement and analysis. However, little effort has been devoted towards its non-destructive spectral measurement, which is important for intelligent monitoring of pavement deterioration conditions. This paper focused on the non-destructive investigation of comparative near-infrared spectra of asphalt mixture specimens designed using the Marshall method after laboratory short- and long-term aging with a portable near-infrared spectrometer. The near-infrared spectra showed that asphalt mixtures from different aging states appeared significantly different fingerprints. The near-infrared fingerprints and aging states were also statistically significantly different according to the analysis of one-way analysis of variance. These distinctive near-infrared fingerprints could be attributed to the varied amounts of chemical functional groups, mainly hydrogen-containing groups during oxidative aging. This study provided a non-destructive and in-situ detection of the aging of asphalt mixtures. • The portable NIR spectrometer is used for the measurement of asphalt mixtures. • Asphalt mixtures from four aging states have unique and distinct NIR fingerprints. • ANOVA indicates a statistical difference in NIR spectra from four aging states. • NIR spectroscopy is suitable for in-situ detection of aging of asphalt mixtures. [ABSTRACT FROM AUTHOR]

Cork spot disorder is a common physiological disease that severely affects the quality and yield of the 'Akizuki' pear. In this study, we propose a novel method, Near-infrared spectra and Visual images with Neural Architecture Search (NV-NAS), to achieve a highly accurate diagnosis of cork spot disorder. This method automatically generates fusion features by combining near-infrared spectral and visual image features and then searches for the best-performing model structure. The best result was obtained with near-infrared spectral features preprocessed by first derivatives (FD) and the visual image features extracted by DenseNet121 in the NV-NAS method. On the validation set, the accuracy was 95.5%, and on the test set, the accuracy was 88.2%. Compared to the modeling results of a single feature, the accuracy improved by 26.3% over spectra and by 11.2% over images on the test set. Additionally, in experiments with models of different depths, deep image features were more conducive to feature fusion, with accuracy improvements of 21.7% and 30.1% compared to features from the middle or bottom layers, respectively. The results indicate that the model built on the fusion feature shows high generalization performance and holds significant reference value for the more accurate diagnosis of the 'Akizuki' pear cork spot disorder. • NV-NAS method effectively diagnoses 'Akizuki' pear cork spot disorder. • The model built by the NV-NAS method demonstrates excellent generalization. • The model with fusion data is better than others by the single spectrum or image. [ABSTRACT FROM AUTHOR]

• An online NIR system was developed to collect single seed spectrum. • A dynamic white reference acquisition method was developed. • The system was evaluated for protein and insect infestation detection. Maize is one of the most important crops in the world. The protein content and insect infestation significantly influence seed vigor and growth. Therefore, it is vital to detect the protein content and insect infestation seeds rapidly and non-destructively. In this study, a near-infrared (NIR) spectra acquisition device (900–1700 nm) was designed and employed for on-line seed quality detection. Support vector machine (SVM), logistic regression (LR), and partial least square regression discrimination analysis (PLS-DA) were used for insect infestation seeds classification. PLS and least squares-support vector machine (LS-SVM) were adopted for protein detection. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were applied to select the feature wavelengths to reduce the redundant data and identify important information. As for insect infestation seeds, CARS-SPA-LR model achieved the classification using only 7 features with an accuracy of 0.83. In terms of protein prediction, the LS-SVM models obtained the best results for grain protein content (GPC, %) and absolute GPC (Ab_GPC, mg/kernel), respectively. The optimal models only used 22 and 21 feature wavelengths selected by CARS-SPA, with the RMSEP of 3.38 % and 2.38 mg/kernel, and RPD was 2.08 and 2.11, respectively. The results indicated that the NIR on-line acquisition system could be applied for qualitative and quantitative analysis of maize seeds. [ABSTRACT FROM AUTHOR]

[Display omitted] • This work provides a new field analysis method for the accurate identification of Moutan Cortex products. • Handheld near infrared spectroscopy for accurate quantification of Gallic acid, 5-HMF, paeoniflorin and paeonol. • Global models were established to detect various processing states of Moutan Cortex. Now, almost all solutions are moving toward miniaturization of devices. Stir-frying, as a food processing process, presents field analysis challenges to the NIR foundation. Therefore, in this paper, Moutan Cortex, a functional food product that requires strict control of stir-frying degree, with a variety of biological activities such as antioxidant, anti-inflammatory and protection of cardiovascular system, was investigated to realize the qualitative and quantitative analysis of the product on site through miniaturized spectrometer NIR-S-G1. TANGO FT-NIR, a precision benchtop instrument, was applied as a reference to observe the field analysis performance of the instrument. The results show that the handheld device can accurately judge the products of different stir-frying degrees by SVM model with the accuracy rate of 100%. At the same time, it can accurately and quantitatively monitor the four main components in the process. Also combinations of optimization algorithms (Competitive Adaptive Weighted sampling, variable combination population analysis combined with iteratively retaining informative variables and genetic algorithm) and regression models (Partial least squares regression and Support Vector Machine) were investigated to perform a better result. Therefore, this work provided a reference for the field analysis of NIR technology from qualitative and quantitative analysis in this specific analytical application. [ABSTRACT FROM AUTHOR]

[Display omitted] • A review of the existing hybrid methods for analyzing near-infrared spectra is presented. • A systematic classification of developed variable selection algorithms based on hybrid strategy is presented. • Suggestions for connecting two or three different algorithms to create new hybrid methods are provided. • The proposals on the development of new hybrid methods are given. Wavelength (variable) selection is considered as a means of solving the curse of dimensionality, which is an important step in multivariate calibration of near-infrared spectra (NIR) and a topic of interest in NIR spectroscopy. A large number of variable selection methods have been developed. They possess diverse advantages and disadvantages because of their different principles and application scope. To overcome their drawbacks and fully utilize their merits, a hybrid strategy, which combines several methods was proposed, and has been proven effective in the selection of informative variables from high-dimensional NIR spectra. Based on the hybrid strategy, many variable selection methods have been developed. To aid people in learning about the various hybrid methods, this paper systematically classifies these methods and elucidates their principles and features. Further, two forms of developing new hybrid methods are suggested based on the existing single variable selection method. Every single method is classified into the stages of the suggested hybrid strategy based on its theory to enable more convenient and efficient utilization. Finally, several proposals are presented to better apply and promote the development of hybrid methods for analyzing NIR spectra in the future. [ABSTRACT FROM AUTHOR]