Discrimination of Minced Mutton Adulteration Based on Sized-Adaptive Online NIRS Information and 2D Conventional Neural Network

文献类型： 外文期刊

第一作者： Bai, Zongxiu

作者： Bai, Zongxiu;Gu, Jianfeng;Zhu, Rongguang;Yao, Xuedong;Ge, Jianbing;Bai, Zongxiu;Gu, Jianfeng;Zhu, Rongguang;Yao, Xuedong;Ge, Jianbing;Kang, Lichao

作者机构：

关键词： online NIRS; convolutional neural network; different spectral information; classification; adulterated mutton

期刊名称：FOODS （ 影响因子：5.561； 五年影响因子：5.94 ）

ISSN：

年卷期： 2022 年 11 卷 19 期

页码：

收录情况： SCI

摘要： Single-probe near-infrared spectroscopy (NIRS) usually uses different spectral information for modelling, but there are few reports about its influence on model performance. Based on sized-adaptive online NIRS information and the 2D conventional neural network (CNN), minced samples of pure mutton, pork, duck, and adulterated mutton with pork/duck were classified in this study. The influence of spectral information, convolution kernel sizes, and classifiers on model performance was separately explored. The results showed that spectral information had a great influence on model accuracy, of which the maximum difference could reach up to 12.06% for the same validation set. The convolution kernel sizes and classifiers had little effect on model accuracy but had significant influence on classification speed. For all datasets, the accuracy of the CNN model with mean spectral information per direction, extreme learning machine (ELM) classifier, and 7 x 7 convolution kernel was higher than 99.56%. Considering the rapidity and practicality, this study provides a fast and accurate method for online classification of adulterated mutton.

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