Fusion of electronic nose and hyperspectral imaging for mutton freshness detection using input-modified convolution neural network. (15th August 2022)

• Record Type: Journal Article
• Authors: Liu, Cunchuan; Chu, Zhaojie; Weng, Shizhuang; Zhu, Gongqin; Han, Kaixuan; Zhang, Zixi; Huang, Linsheng; Zhu, Zede; Zheng, Shouguo
• Is Part Of: Food chemistry. Volume 385(2022)
• Publication Date: 2022-08-15
• Subjects: Mutton freshness -- Electronic nose -- Hyperspectral image -- Effective variables -- Deep learning; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2022.132651
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: E-nose and HSI were combined to detect mutton TVB-N. Various features were extracted from E-nose and HSI. IMCNN was constructed to develop TVB-N prediction model. Optimal analysis result was achieved by IMCNN and fusion of features. Abstract: 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.