Bai Zongxiu, Gu Jianfeng, Zhu Rongguang, Yao Xuedong, Kang Lichao, Ge Jianbing
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
Key Laboratory of the Ministry of Agriculture and Rural Affairs, Shihezi University, Shihezi 832003, China.
Foods. 2022 Sep 23;11(19):2977. doi: 10.3390/foods11192977.
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 × 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.
单探头近红外光谱（NIRS）通常使用不同的光谱信息进行建模，但关于其对模型性能影响的报道较少。基于尺寸自适应在线近红外光谱信息和二维传统神经网络（CNN），本研究对纯羊肉、猪肉、鸭肉以及掺杂猪肉/鸭肉的掺假羊肉碎样本进行了分类。分别探讨了光谱信息、卷积核大小和分类器对模型性能的影响。结果表明，光谱信息对模型准确率有很大影响，对于相同的验证集，最大差异可达12.06%。卷积核大小和分类器对模型准确率影响较小，但对分类速度有显著影响。对于所有数据集，采用每个方向平均光谱信息的CNN模型、极限学习机（ELM）分类器和7×7卷积核时，准确率高于99.56%。考虑到快速性和实用性，本研究为掺假羊肉的在线分类提供了一种快速准确的方法。
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