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.
Food Chem
; 442: 138408, 2024 Jun 01.
Article
in En
| MEDLINE
| ID: mdl-38241985
ABSTRACT
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 < 0.05) from the 118 trait factors that can distinguish PCR from different origins. These factors include color, texture, and odor traits. By integrating multivariate statistical Analysis with the BP neural network algorithm, a novel artificial intelligence algorithm was developed and optimized for traceability of PCR origin. This algorithm achieved a 100% discrimination rate in differentiating PCR samples from various origins. This study offers a valuable reference and data support for developing intelligent algorithms that utilize data fusion from multiple intelligent sensory technologies to achieve rapid traceability of food origins.
Key words
Full text:
1
Database:
MEDLINE
Traditional Medicines:
Medicinas_tradicionales_de_asia
/
Medicina_china
Main subject:
Drugs, Chinese Herbal
/
Citrus
Type of study:
Prognostic_studies
Language:
En
Journal:
Food Chem
Year:
2024
Type:
Article
Affiliation country:
China