A Review of the Discriminant Analysis Methods for Food Quality Based on Near-Infrared Spectroscopy and Pattern Recognition

Zeng, Jing; Guo, Yuan; Han, Yanqing; Li, Zhanming; Yang, Zhi-Xin; Chai, Qinqin; Wu, Wei; Zhang, Yuyu; Chen, Fu

doi:10.3390/molecules26030749

Cited by 47 publications

(28 citation statements)

References 51 publications

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“…In 2021 alone, deep learning neural networks have been used to classify beef freshness from visible-NIR reflectance spectra [60], to analyze NIR HSI to detect the presence of contamination during food packing [61], and to conduct a series of different food quality analyses from NIR spectra [62].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Advanced Optical Technologies in Food Quality and Waste Management

Chauvin¹,

Duran²,

Ng³

et al. 2021

Innovation in the Food Sector Through the Valorization of Food and Agro-Food by-Products

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Food waste is a global problem caused in large part by premature food spoilage. Seafood is especially prone to food waste because it spoils easily. Of the annual 4.7 billion pounds of seafood destined for U.S. markets between 2009 and 2013, 40 to 47 percent ended up as waste. This problem is due in large part to a lack of available technologies to enable rapid, accurate, and reliable valorization of food products from boat or farm to table. Fortunately, recent advancements in spectral sensing technologies and spectroscopic analyses show promise for addressing this problem. Not only could these advancements help to solve hunger issues in impoverished regions of the globe, but they could also benefit the average consumer by enabling intelligent pricing of food products based on projected shelf life. Additional technologies that enforce trust and compliance (e.g., blockchain) could further serve to prevent food fraud by maintaining records of spoilage conditions and other quality validation at all points along the food supply chain and provide improved transparency as regards contract performance and attribution of liability. In this chapter we discuss technologies that have enabled the development of hand-held spectroscopic devices for detecting food spoilage. We also discuss some of the analytical methods used to classify and quantify spoilage based on spectral measurements.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Advanced Optical Technologies in Food Quality and Waste Management

Chauvin¹,

Duran²,

Ng³

et al. 2021

Innovation in the Food Sector Through the Valorization of Food and Agro-Food by-Products

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“…A reliable and e ective qualitative near-infrared spectroscopy discrimination method is critical for excellent model building. At present, NIR spectroscopy combined with pattern recognition technique has become an important type of nondestructive discriminant method [4].…”

Section: Introductionmentioning

confidence: 99%

A Variable Selection Method Based on Fast Nondominated Sorting Genetic Algorithm for Qualitative Discrimination of Near Infrared Spectroscopy

Liu

Yuan

et al. 2022

Journal of Spectroscopy

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A reliable and effective qualitative near-infrared (NIR) spectroscopy discrimination method is critical for excellent model building, yet the performance of models built by these methods is highly dependent on valid feature extraction. The goal of feature selection is to associate the selected variables with the property of interest, which many have done successfully. However, many of selection methods focus only on strong association with the analytes or properties of interest, neglecting correlations between variables. A variable selection method based on a fast nondominated-ranking genetic algorithm (NSGA-II) was proposed in this paper for qualitative discrimination of NIR spectra. The method had two objective functions: (1) maximizing the sum of ratios of interclass variance to intraclass variance, (2) minimizing the sum of correlation coefficients between the selected variables. FT-NIR spectra of a total of 124 tobacco samples from different origins and parts in Guizhou Province, China, were used as the experimental objects, and the part-grade discrimination models of tobacco leaves were established by combining this method with partial least squares-based discriminant analysis (PLS-DA), and compared with PLS-DA model based on the full spectrum. The results showed that the performance of PLS-DA model with the NSGA-II was improved, with a comparable or better correct discrimination rate and reasonable discrimination rate, and could discriminate different parts of the tobacco leaves well. It indicates that the NSGA-II can select a few and effective feature variables to build a high-performance qualitative discrimination model and is proved to be a promising algorithm. In addition, the method is not designed exclusively for spectral data. It is a general strategy that could be used for variable selection for other types of data.

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“…Near-Infrared Spectroscopy (NIRS) is a simple, rapid, and non-destructive method that requires very little sample preparation. Vis-NIR spectroscopy, combined with machine learning algorithms, is being widely used as a reliable and successful scientific instrument in a variety of fields, including agricultural food [6], insect-based food [7], organic-fertilizers [8], whey protein powder [9], petrochemical [10], pharmaceutical [11], environment [12], metabolomics profiling [13], as well as several reviews on recent applications such as [14][15][16][17], etc. NIRS has already been established in the 1960s for cereal analyses [18].…”

Section: Introductionmentioning

confidence: 99%

Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques

Kabir

Guindo

Chen

et al. 2021

Foods

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Millet is a primary food for people living in the dry and semi-dry regions and is dispersed within most parts of Europe, Africa, and Asian countries. As part of the European Union (EU) efforts to establish food originality, there is a global need to create Protected Geographical Indication (PGI) and Protected Designation of Origin (PDO) of crops and agricultural products to ensure the integrity of the food supply. In the present work, Visible and Near-Infrared Spectroscopy (Vis-NIR) combined with machine learning techniques was used to discriminate 16 millet varieties (n = 480) originating from various regions of China. Five different machine learning algorithms, namely, K-nearest neighbor (K-NN), Linear discriminant analysis (LDA), Logistic regression (LR), Random Forest (RF), and Support vector machine (SVM), were used to train the NIR spectra of these millet samples and to assess their discrimination performance. Visible cluster trends were obtained from the Principal Component Analysis (PCA) of the spectral data. Cross-validation was used to optimize the performance of the models. Overall, the F-Score values were as follows: SVM with 99.5%, accompanied by RF with 99.5%, LDA with 99.5%, K-NN with 99.1%, and LR with 98.8%. Both the linear and non-linear algorithms yielded positive results, but the non-linear models appear slightly better. The study revealed that applying Vis-NIR spectroscopy assisted by machine learning technique can be an essential tool for tracing the origins of millet, contributing to a safe authentication method in a quick, relatively cheap, and non-destructive way.

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A Review of the Discriminant Analysis Methods for Food Quality Based on Near-Infrared Spectroscopy and Pattern Recognition

Cited by 47 publications

References 51 publications

Advanced Optical Technologies in Food Quality and Waste Management

Advanced Optical Technologies in Food Quality and Waste Management

A Variable Selection Method Based on Fast Nondominated Sorting Genetic Algorithm for Qualitative Discrimination of Near Infrared Spectroscopy

Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques

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