title: [Study on Application of NIR Spectral Information Screening in Identification of Maca Origin].
abstract: Medicinal and edible plant Maca is rich in various nutrients and owns great medicinal value. Based on near infrared diffuse reflectance spectra, 139 Maca samples collected from Peru and Yunnan were used to identify their geographical origins. Multiplication signal correction (MSC) coupled with second derivative (SD) and Norris derivative filter (ND) was employed in spectral pretreatment. Spectrum range (7,500-4,061 cm⁻¹) was chosen by spectrum standard deviation. Combined with principal component analysis-mahalanobis distance (PCA-MD), the appropriate number of principal components was selected as 5. Based on the spectrum range and the number of principal components selected, two abnormal samples were eliminated by modular group iterative singular sample diagnosis method. Then, four methods were used to filter spectral variable information, competitive adaptive reweighted sampling (CARS), monte carlo-uninformative variable elimination (MC-UVE), genetic algorithm (GA) and subwindow permutation analysis (SPA). The spectral variable information filtered was evaluated by model population analysis (MPA). The results showed that RMSECV(SPA) > RMSECV(CARS) > RMSECV(MC-UVE) > RMSECV(GA), were 2. 14, 2. 05, 2. 02, and 1. 98, and the spectral variables were 250, 240, 250 and 70, respectively. According to the spectral variable filtered, partial least squares discriminant analysis (PLS-DA) was used to build the model, with random selection of 97 samples as training set, and the other 40 samples as validation set. The results showed that, R²: GA > MC-UVE > CARS > SPA, RMSEC and RMSEP: GA < MC-UVE < CARS <SPA For the spectral information selected by the four methods, GA, MC-UVE, CARS and SPA, the model prediction accuracy were 95.0%, 92.5%, 90.0% and 85.0%, respectively. Compared with the four methods, we could know that the origin discriminant models built based on spectra information filtered by the four methods possess good estimated performance. Among them, the model built based on the spectra information filtered by GA was the best, which could more accurately identify different regions Maca. The method was aimed to lay the foundation for traditional Chinese medicine identification and quality evaluation.
authors.forename: Yuan-zhong
authors.initials: YZ
authors.lastname: Wang
authors.forename: Yan-li
authors.initials: YL
authors.lastname: Zhao
authors.forename: Ji
authors.initials: J
authors.lastname: Zhang
authors.forename: Hang
authors.initials: H
authors.lastname: Jin
mesh_terms: D000465:Algorithms; D002681:China; D016018:Least-Squares Analysis; D029686:Lepidium / Q000737:chemistry*; D008962:Models, Theoretical; D009010:Monte Carlo Method; D010568:Peru; D010946:Plants, Medicinal / Q000737:chemistry*; D025341:Principal Component Analysis; D019265:Spectroscopy, Near-Infrared