Optimizing BP neural network algorithm for Pericarpium Citri Reticulatae (Chenpi) origin traceability based on computer vision and ultra-fast gas-phase electronic nose data fusion

•Differences in color, texture and odor of PCR samples from different origins.•The optimized BP-NN based on multi-data fusion improves the discrimination rate.•This study provides a new reference for origin traceability of other food products. This study utilized computer vision to extract color and...

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Bibliographic Details
Published in:Food chemistry 2024-06, Vol.442, p.138408-138408, Article 138408
Main Authors: Chen, Peng, Fu, Rao, Shi, Yabo, Liu, Chang, Yang, Chenlu, Su, Yong, Lu, Tulin, Zhou, Peina, He, Weitong, Guo, Qiaosheng, Fei, Chenghao
Format: Article
Language:English
Subjects:
Computer vision
Intelligent algorithm
Multivariate statistics
Pericarpium Citri Reticulate
Traceability
Ultra-fast gas phase electronic nose
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Summary:•Differences in color, texture and odor of PCR samples from different origins.•The optimized BP-NN based on multi-data fusion improves the discrimination rate.•This study provides a new reference for origin traceability of other food products. This study utilized computer vision to extract color and texture features of Pericarpium Citri Reticulatae (PCR). The ultra-fast gas-phase electronic nose (UF-GC-E-nose) technique successfully identified 98 volatile components, including olefins, alcohols, and esters, which significantly contribute to the flavor profile of PCR. Multivariate statistical Analysis was applied to the appearance traits of PCR, identifying 57 potential marker-trait factors (VIP > 1 and P 
ISSN:0308-8146
1873-7072
DOI:10.1016/j.foodchem.2024.138408