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A method for freshness detection of pork using two-dimensional correlation spectroscopy images combined with dual-branch deep learning.
- Source :
-
Journal of Food Composition & Analysis . May2024, Vol. 129, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- This study proposes a new visible near-infrared spectral analysis method by abandoning the traditional feature engineering-based method. It presents a dual-branch convolutional neural network (CNN) with bilinear pooling, combined with synchronous-asynchronous two-dimensional correlation spectroscopy (2D-COS) images, for quantifying pork freshness. 2D-COS images revealed spectral correlations of pork with different freshness at different bands, effectively separating overlapping bands and amplifying spectral differences. A dual-branch CNN using a bilinear pool integrates synchronous and asynchronous features to capture the interactions between features for quantitative analysis. Results demonstrate that the dual-branch CNN achieves high accuracy (R2p=0.9579 and RMSEP=0.8093 mg/100 g) for Total Volatile Basic Nitrogen (TVB-N) content prediction. The mechanism of TVB-N prediction is explained using Gradient-weighted Class Activation Mapping (Grad-CAM), demonstrating the reliability of the proposed method to replace human experience for feature extraction and modeling analysis. In conclusion, this study proposes a new approach for food inspection tasks that is convenient, efficient and human-expertise-independent. • A spectral analysis method based on 2D-COS images and dual-branch CNN is proposed. • 2D-COS can efficiently separate more spectral features for analysis. • Synchronized asynchronous spectral images are fused by a bilinear pooling approach. • Grad-CAM was used to explain the mechanism of the model in TVB-N prediction. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08891575
- Volume :
- 129
- Database :
- Academic Search Index
- Journal :
- Journal of Food Composition & Analysis
- Publication Type :
- Academic Journal
- Accession number :
- 176010137
- Full Text :
- https://doi.org/10.1016/j.jfca.2024.106144