Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins

Liu, Feng; Wu, Bangyu; Zhu, Susu; He, Yong; Zhang, Chu

doi:10.3389/fnut.2021.680357

Cited by 61 publications

(21 citation statements)

References 123 publications

(167 reference statements)

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“…Hyperspectral imaging is a rapidly growing area of research in the food science sector, particularly for the determination of food quality and safety [ 163 , 164 , 165 , 166 ], but also for authentication purposes [ 167 , 168 ]. This technique can collect near-infrared spectra from each pixel in a photograph (creating a ‘hypercube’ dataset), allowing for analysis of the spatial variation of the analyte, in addition to its mean concentration in the sample.…”

Section: Scientific Effort (2016–2020)mentioning

confidence: 99%

The Use of Infrared Spectroscopy for the Quantification of Bioactive Compounds in Food: A Review

et al. 2023

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Infrared spectroscopy (wavelengths ranging from 750–25,000 nm) offers a rapid means of assessing the chemical composition of a wide range of sample types, both for qualitative and quantitative analyses. Its use in the food industry has increased significantly over the past five decades and it is now an accepted analytical technique for the routine analysis of certain analytes. Furthermore, it is commonly used for routine screening and quality control purposes in numerous industry settings, albeit not typically for the analysis of bioactive compounds. Using the Scopus database, a systematic search of literature of the five years between 2016 and 2020 identified 45 studies using near-infrared and 17 studies using mid-infrared spectroscopy for the quantification of bioactive compounds in food products. The most common bioactive compounds assessed were polyphenols, anthocyanins, carotenoids and ascorbic acid. Numerous factors affect the accuracy of the developed model, including the analyte class and concentration, matrix type, instrument geometry, wavelength selection and spectral processing/pre-processing methods. Additionally, only a few studies were validated on independently sourced samples. Nevertheless, the results demonstrate some promise of infrared spectroscopy for the rapid estimation of a wide range of bioactive compounds in food matrices.

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Section: Scientific Effort (2016–2020)mentioning

confidence: 99%

The Use of Infrared Spectroscopy for the Quantification of Bioactive Compounds in Food: A Review

et al. 2023

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“…Hence, subjecting all the models to the voting technique improves the classification performance. It is also worth noting that deep learning and other machine learning approaches have recently been successful in various food-related spectroscopic data analysis ( Zhu et al, 2021 ; Zheng et al, 2014 ; Feng et al, 2021 ; Wang et al, 2021 ; Liang et al, 2020 ; Yan et al, 2021 ; He et al, 2021 ); thus, we expect that the considered machine learning and deep learning methods will work-well in the classification of various adulterant honey samples using NMR spectroscopy in the proposed context as well. As schematically shown in Fig.…”

Section: Resultsmentioning

confidence: 93%

Identifying type of sugar adulterants in honey: Combined application of NMR spectroscopy and supervised machine learning classification

Rachineni¹,

Kakita

Awasthi³

et al. 2022

Current Research in Food Science

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Nuclear magnetic resonance (NMR) is a powerful analytical tool which can be used for authenticating honey, at chemical constituent levels by enabling identification and quantification of the spectral patterns. However, it is still challenging, as it may be a person-centric analysis or a time-consuming process to analyze many honey samples in a limited time. Hence, automating the NMR spectral analysis of honey with the supervised machine learning models accelerates the analysis process and especially food chemistry researcher or food industry with non-NMR experts would benefit immensely from such advancements. Here, we have successfully demonstrated this technology by considering three major sugar adulterants, i.e., brown rice syrup, corn syrup, and jaggery syrup, in honey at varying concentrations. The necessary supervised machine learning classification analysis is performed by using logistic regression, deep learning-based neural network, and light gradient boosting machines schemes.

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“…Recently, the quickly nondestructive measurement of the SSC, firmness, color or maturity of fruits have been demonstrated (Afonso et al, 2022; Feng et al, 2021; Garillos‐Manliguez & Chiang, 2021; Li et al, 2016; Lleó et al, 2011). The above research, the hyperspectral imaging technology has always been adopted to quickly nondestructively predict the internal quality of fruits, that is, apple (Fan et al, 2016; Lan et al, 2021; Zhang, Xu, et al, 2019), pear (Wang, Li, & Dai, 2022; Yu et al, 2018; Zhang, Shang, et al, 2019), strawberry (Su et al, 2021; Weng et al, 2020), plum (Li, Cobo‐Medina, et al, 2018; Meng et al, 2021), pomelo (Chen et al, 2021), orange (Riccioli et al, 2021; Zhang et al, 2020), kiwifruit (Guo et al, 2015; Hu et al, 2017), banana (Xie et al, 2018), hami melons, (Sun et al, 2016) and so on.…”

Section: Introductionmentioning

confidence: 99%

Quality attributes prediction and maturity discrimination of kiwifruits by hyperspectral imaging and chemometric algorithms

Shang

Tan

Feng

et al. 2023

J Food Process Engineering

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The nondestructive detection of the quality attributes and maturity of kiwifruits by using hyperspectral imaging technique and chemometric algorithms were investigated. Two models of the partial least square regression and principal components regression were built up based on full variables. The multiple linear regression (MLR) was designed to develop the simplified detection models based on feature variables chosen by the successive projection algorithm and competitive adaptive reweighted sampling (CARS). Results showed that the optimal CARS‐MLR model was designed to determine the quality attributes with RPD above 2.2. Particularly, about SSC (R2P = 0.896, RMSEP = 0.628%, RPD = 3.121), about firmness (R2P = 0.871, RMSEP = 1.065 kg/cm2, RPD = 2.809). The partial least square discriminant analysis and simplified k nearest neighbor models were designed to discriminate the kiwifruits maturity stages with a classification accuracy of 93.3% and 98.3%. This research demonstrates that the hyperspectral imaging technology coupled with chemometric algorithms is promising for the quality assessment and maturity discrimination of kiwifruits. Practical applications The quality and maturity of kiwifruits have a direct relationship with the indicators of the SSC, firmness and color. Traditional methods for predicting the quality and maturity of kiwifruits are destructive, time‐consuming, and unusually affected by subjective factors. Hyperspectral imaging technique has the advantages of nondestructive, rapid, non‐pollution, and so on. The results demonstrated that the hyperspectral imaging technique coupled with chemometric algorithms can predict the quality and maturity of kiwifruits, which provides a theoretical basis to develop a real‐time detection system to predict the quality and maturity of fruits.

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Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins

Cited by 61 publications

References 123 publications

The Use of Infrared Spectroscopy for the Quantification of Bioactive Compounds in Food: A Review

The Use of Infrared Spectroscopy for the Quantification of Bioactive Compounds in Food: A Review

Identifying type of sugar adulterants in honey: Combined application of NMR spectroscopy and supervised machine learning classification

Quality attributes prediction and maturity discrimination of kiwifruits by hyperspectral imaging and chemometric algorithms

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