Chen Lulu, Yang Siyue, Nan Zhuan, Li Yanping, Ma Jianlong, Ding Jianbao, Lv Yi, Yang Jin
School of Chemistry and Chemical Engineering, North Minzu University, Yinchuan 750021, China.
Department of Statistical Sciences, University of Toronto, Toronto M5T 1P5, Canada.
Heliyon. 2023 Jun 9;9(6):e17115. doi: 10.1016/j.heliyon.2023.e17115. eCollection 2023 Jun.
Due to the similar chemical structures and physicochemical properties, it is challenging to distinguish dextran, maltodextrin, and soluble starch from the polysaccharide products of plant origin, such as polysaccharides (LBPs). Using the first-order derivatives of Fourier-transformed infrared spectroscopy (FTIR, wave range 1800-400 cm), this study proposed a two-step pipeline to identify dextran, maltodextrin, and soluble starch from adulterated LBPs samples qualitatively and quantitatively. We applied principal component analysis (PCA) to reduce the dimensionality of FTIR features. For the qualitative step, a set of machine learning models, including logistic regression, support vector machine (SVM), Naïve Bayes, and partial least squares (PLS), were used to classify the adulterants. For the quantitative step, linear regression, LASSO, random forest, and PLS were used to predict the concentration of LBPs adulterants. The results showed that logistic regression and SVM are suitable for classifying adulterants, and random forests is superior for predicting adulterant concentrations. This would be the first attempt to discriminate the adulterants from the polysaccharide's product of plant origin. The proposed two-step methods can be easily extended to other applications for the quantitative and qualitative detection of samples from adulterants with similar chemical structures.
由于化学结构和物理化学性质相似，从植物源多糖产品（如多糖（枸杞多糖））中区分葡聚糖、麦芽糊精和可溶性淀粉具有挑战性。本研究利用傅里叶变换红外光谱（FTIR，波数范围1800 - 400 cm）的一阶导数，提出了一种两步法流程，用于定性和定量鉴定掺假枸杞多糖样品中的葡聚糖、麦芽糊精和可溶性淀粉。我们应用主成分分析（PCA）来降低FTIR特征的维度。在定性步骤中，使用了一组机器学习模型，包括逻辑回归、支持向量机（SVM）、朴素贝叶斯和偏最小二乘法（PLS）对掺假物进行分类。在定量步骤中，使用线性回归、LASSO、随机森林和PLS来预测枸杞多糖掺假物的浓度。结果表明，逻辑回归和SVM适用于掺假物分类，随机森林在预测掺假物浓度方面表现更优。这将是首次尝试从植物源多糖产品中鉴别掺假物。所提出的两步法可轻松扩展到其他应用，用于对具有相似化学结构的掺假物样品进行定量和定性检测。
