紫外可见光谱结合化学模式识别对紫苏油的真伪鉴别  

Identification of Perilla Oil Adulteration by Ultraviolet-Visible Spectroscopy Combined with Chemical Pattern Recognition

作　　者：卞希慧[1,3] 刘雨 王瑶 张强 张妍[2] BIAN Xi-hui;LIU Yu;WANG Yao;ZHANG Qiang;ZHANG Yan(Tianjin Key Laboratory of Green Chemical Engineering Process Engineering,School of Chemical Engineering and Technology,Tiangong University,Tianjin 300387,China;Research Center for Analytical Sciences,College of Chemistry,Nankai University,Tianjin 300071,China;NMPA Key Laboratory for Technology Research and Evaluation of Drug Products,Shandong University,Jinan 250012,China)

机构地区：[1]天津工业大学化学工程与技术学院,天津市绿色化工过程工程重点实验室,天津300387 [2]南开大学化学学院分析科学研究中心,天津300071 [3]山东大学国家药品监督管理局药物制剂技术研究与评价重点实验室,山东济南250012

出　　处：《分析测试学报》2025年第2期229-237,共9页Journal of Instrumental Analysis

基　　金：药物制剂技术研究与评价国家药品监督管理局重点实验室开放课题(2022TREDP04,2023TREDP01)。

摘　　要：作为高经济价值且昂贵的非常规植物油,紫苏油易被低价食用油掺假。由于食用油的匀质性及其组成的复杂性,传统鉴别方法难以快速准确地鉴别紫苏油的真伪。该文探索了紫外可见光谱结合化学模式识别对紫苏油真伪鉴别的可行性。首先购买了40个纯紫苏油样品,并将大豆油、棕榈油分别按一定的比例加入到纯紫苏油中配制了51个二元掺伪和63个三元掺伪紫苏油样品。根据鉴别目的,从154个总样品中获得两个数据集,一个是由40个纯紫苏油和114个掺伪紫苏油构成的真伪紫苏油二分类数据集;另一个是由40个纯紫苏油、51个二元掺伪和63个三元掺伪紫苏油构成的真伪紫苏油三分类数据集。然后采用主成分分析(PCA)、簇类独立软模式(SIMCA)、偏最小二乘-判别分析(PLS-DA)和极限学习机(ELM)4种方法,依次对以上两个数据集进行分类。使用混淆矩阵可视化分类结果,并用准确率、精确率、召回率、F1分数对模型性能进行评价。结果表明,对于真伪紫苏油二分类和三分类数据集,PLS-DA均为最佳模型,预测准确率分别为98.04%和100%。因此,紫外可见光谱结合化学模式识别可以实现真伪紫苏油的快速准确鉴别。As an unconventional vegetable oil with high economic value and premium price,perilla oil is vulnerable to adulteration by cheap edible oils.Due to the uniform property and complex compo⁃sition of edible oils,it is challenge to quickly and accurately determine the authenticity of perilla oil using traditional identification methods.In this research,the feasibility of ultraviolet-visible(UV-Vis)spectroscopy in conjunction with chemical pattern recognition techniques were investigated for the authentication of perilla oil.First,40 samples of pure perilla oil were purchased,then soybean oil and palm oil were added to the pure perilla oil in certain proportions to prepare 51 binary adulterat⁃ed and 63 ternary adulterated perilla oil samples.Subsequently,based on different identification pur⁃poses,the total of 154 samples were used as two datasets.One is a genuine and adulterated perilla oil two-classification dataset,which is composed of 40 pure oil and 114 adulterated oil samples.The other is a three-classification dataset of 40 pure oil samples,51 binary adulterated,and 63 ternary adulterated perilla oil samples.Principal component analysis(PCA),soft independent modeling of class analogy(SIMCA),partial least squares-discriminant analysis(PLS-DA)and extreme learning machine(ELM)were compared for two-classification and three-classification datasets.Additionally,confusion matrices,accuracy,precision,recall and F1-score were used to evaluate classification performance.The results show that PLS-DA is the best classification model for two-classification and three-classification datasets with accuracy 98.04%and 100%,respectively.Therefore,UV-Vis spectroscopy combined with chemical pattern recognition can be used to achieve fast and accurate identification of genuine and adulterated perilla oils.

关 键 词：紫苏油 紫外可见光谱 化学模式识别 真伪鉴别 

分 类 号：O657.3[理学—分析化学] R284[理学—化学]