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Aslam R, Sharma SR, Kaur J, Panayampadan AS, Dar OI. A systematic account of food adulteration and recent trends in the non-destructive analysis of food fraud detection. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01846-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
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Amsaraj R, Ambade ND, Mutturi S. Variable selection coupled to PLS2, ANN and SVM for simultaneous detection of multiple adulterants in milk using spectral data. Int Dairy J 2021. [DOI: 10.1016/j.idairyj.2021.105172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Liu Y, Zhou S, Han W, Li C, Liu W, Qiu Z, Chen H. Detection of Adulteration in Infant Formula Based on Ensemble Convolutional Neural Network and Near-Infrared Spectroscopy. Foods 2021; 10:foods10040785. [PMID: 33917308 PMCID: PMC8067368 DOI: 10.3390/foods10040785] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 03/17/2021] [Accepted: 03/22/2021] [Indexed: 11/16/2022] Open
Abstract
Adulteration in dairy products has received world-wide attention, and at the same time, near infrared (NIR) spectroscopy has proven to be a promising tool for adulteration detection given its advantages of real-time response and non-destructive analysis. Regardless, the accurate and robust NIR model for adulteration detection is hard to achieve in practice. Convolutional neural network (CNN), as a promising deep learning architecture, is difficult to apply to such chemometrics tasks due to the high risk of overfitting, despite the breakthroughs it has made in other fields. In this paper, the ensemble learning method based on CNN estimators was developed to address the overfitting and random initialization problems of CNN and applied to the determination of two infant formula adulterants, namely hydrolyzed leather protein (HLP) and melamine. Moreover, a probabilistic wavelength selection method based on the attention mechanism was proposed for the purpose of finding the best trade-off between the accuracy and the diversity of the sub-models in ensemble learning. The overall results demonstrate that the proposed method yielded superiority regression performance over the comparison methods for both studied data sets, and determination coefficients (R2) of 0.961 and 0.995 were obtained for the HLP and the melamine data sets, respectively.
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He Y, Bai X, Xiao Q, Liu F, Zhou L, Zhang C. Detection of adulteration in food based on nondestructive analysis techniques: a review. Crit Rev Food Sci Nutr 2020; 61:2351-2371. [PMID: 32543218 DOI: 10.1080/10408398.2020.1777526] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
In recent years, people pay more and more attention to food quality and safety, which are significantly relating to human health. Food adulteration is a world-wide concerned issue relating to food quality and safety, and it is difficult to be detected. Modern detection techniques (high performance liquid chromatography, gas chromatography-mass spectrometer, etc.) can accurately identify the types and concentrations of adulterants in different food types. However, the characteristics as expensive, low efficient and complex sample preparation and operation limit the use of these techniques. The rapid, nondestructive and accurate detection techniques of food adulteration is of great and urgent demand. This paper introduced the principles, advantages and disadvantages of the nondestructive analysis techniques and reviewed the applications of these techniques in food adulteration screen in recent years. Differences among these techniques, differences on data interpretation and future prospects were also discussed.
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Affiliation(s)
- Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Xiulin Bai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Qinlin Xiao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Lei Zhou
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Chu Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
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