Abstract

Maca (Lepidium meyenii Walp.) is a cruciferous edible and medicinal plant rich in nutrients. As maca demand in the international market is gradually increasing, dishonest people have been using low-priced alternatives to either adulterate or falsify maca and increase their profit. Existing methods to identify and quantify adulterated maca are laborious, expensive, destructive, time-consuming, and environmentally unfriendly. Thus, it is imperative to develop a method to overcome these problems to effectively authenticate maca products. We combine near infrared spectroscopy with chemometrics to classify and quantify maca powder adulteration by turnip and radish powder. Different maca samples were adulterated with turnip and radish powder individually at different percentages (5–95%). Specifically, discriminant analysis based on a support vector machine provides a classification accuracy of 100%, allowing near infrared spectroscopy to be used to distinguish maca powder adulteration. Furthermore, to calibrate a regression model, we evaluated the partial least squares (PLS), interval PLS, and synergy interval PLS (siPLS). The siPLS models were determined as the best models for the quantification of maca powder adulterated with turnip and radish powder, in which the correlation coefficients were both 0.97 with root-mean-square error of prediction values of 5.79% and 5.85% for the two models, respectively. The combination of near infrared spectroscopy and chemometrics can provide a fast, simple, and environment-friendly analytical method for identifying and quantifying the properties of maca.

© 2020 The Author(s)

Rapid detection of cellulose and hemicellulose contents of corn stover based on near-infrared spectroscopy combined with chemometrics

Na Wang, Longwei Li, Jinming Liu, Jianfei Shi, Yang Lu, Bo Zhang, Yong Sun, and Wenzhe Li
Appl. Opt. 60(15) 4282-4290 (2021)

Rapid detection of carbon-nitrogen ratio for anaerobic fermentation feedstocks using near-infrared spectroscopy combined with BiPLS and GSA

Jinming Liu, Nan Li, Feng Zhen, Yonghua Xu, Wenzhe Li, and Yong Sun
Appl. Opt. 58(18) 5090-5097 (2019)

Rapid detection of talc content in flour based on near-infrared spectroscopy combined with feature wavelength selection

Changhao Bao, Changhao Zeng, Jinming Liu, and Dongjie Zhang
Appl. Opt. 61(19) 5790-5798 (2022)

Nondestructive detection model of soluble solids content of an apple using visible/near-infrared spectroscopy combined with CARS and MPGA

Yong Chen, Chaoyuan Cui, and Yun Wu
Appl. Opt. 60(27) 8400-8407 (2021)

Combination of near-infrared spectroscopy with Wasserstein generative adversarial networks for rapidly detecting raw material quality for formula products

Xiaowei Xin, Junhua Jia, Shunpeng Pang, Ruotong Hu, Huili Gong, Xiaoyan Gao, and Xiangqian Ding
Opt. Express 32(4) 5529-5549 (2024)