Rapid evaluation of Curcuma origin and quality based on E-eye, flash GC e-nose, and FT-NIR combined with machine learning technologies

• Published in: Food chemistry Vol. 481; p. 143953
• Main Authors: Guo, Qiang, Li, Ming-xuan, Fu, Rao, Wan, Xin, Dong, Wen-hao, Mao, Chun-qin, Bian, Zhen-hua, Ji, De, Lu, Tu-lin, Li, Yu
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 30.07.2025
• Subjects: adulterated products; Chromatography, Gas - instrumentation; Chromatography, Gas - methods; Curcuma; Curcuma - chemistry; curcumin; curry; E-eye; Electronic Nose; Flash GC e-nose; flavor; food chemistry; FT-NIR; ingredients; Machine Learning; Origin division; Plant Extracts - chemistry; PLSR; prediction; Quality Control; Quality evaluation; Spectroscopy, Near-Infrared - methods; RMSET; RMSEP; RPD; E-eye; Quality evaluation; Origin division; FT-NIR; R2p; 2D; 1D; SG; R2t; Machine learning; SNV; MSC; Flash GC e-nose; PLSR

Curcuma, a key ingredient in curry and a popular health supplement, has been subject to adulteration and fraudulent origin labeling. In this study, E-eye, Flash GC e-nose, and FT-NIR, combined with machine learning and multivariate algorithms, were employed for origin identification and quantitative...