Fusion of electronic nose and hyperspectral imaging for mutton freshness detection using input-modified convolution neural network.

Cunchuan Liu, Zhaojie Chu, Shizhuang Weng, Gongqin Zhu, Kaixuan Han, Zixi Zhang, Linsheng Huang, Zede Zhu, Shouguo Zheng

Food chemistry 2022 Aug 15

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Electronic nose (E-nose) and hyperspectral image (HSI) were combined to evaluate mutton total volatile basic nitrogen (TVB-N), which is a comprehensive index of freshness. The response values of 10 E-nose sensors were collected, and seven responsive sensors were screened via histogram statistics. Reflectance spectra and image features were extracted from HSI images, and the effective variables were selected through random frog and Pearson correlation analyses. With multi-source features, an input-modified convolution neural network (IMCNN) was constructed to predict TVB-N. The seven E-nose sensors, spectra of effective wavelengths (EWs), and five important image features were combined with IMCNN to achieve the best result, with the root mean square error, correlation coefficient, and ratio of performance deviation of the prediction set of 3.039 mg/100 g, 0.920, and 3.59, respectively. Hence, the proposed method furnishes an approach to accurately analyze mutton freshness and provide a technical basis for investigation of other meat qualities. Copyright © 2022 Elsevier Ltd. All rights reserved.

Citation

Cunchuan Liu, Zhaojie Chu, Shizhuang Weng, Gongqin Zhu, Kaixuan Han, Zixi Zhang, Linsheng Huang, Zede Zhu, Shouguo Zheng. Fusion of electronic nose and hyperspectral imaging for mutton freshness detection using input-modified convolution neural network. Food chemistry. 2022 Aug 15;385:132651