Edible Oil Classification Based on Molecular Spectra Analysis With SIMCA-SVDD Method
ZHAO Zhong1*, LI Bin1, WU Yan-xian1, YUAN Hong-fu2
1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
2. College of Materials Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
摘要： 食用油是日常生活中的必需品。市场上食用油在成分、营养价值及价格上有很大的不同。为避免欺诈行为，亟需建立一套有效的市场销售的食用油品质分类方法。常规的食用油检测方法速度慢而且需要复杂的实验室预处理过程。分子光谱从分子水平上反映了物质的组成与结构信息，分子光谱分析速度快而且是无损监测，因此分子光谱分析结合化学计量学的方法正成为食用油分类方法的趋势。SIMCA（Soft Independent Modeling of Class Analogy）是应用广泛的分子光谱分析方法，然而在SIMCA中使用欧氏距离于对基于PCA和F检验提取的特征进行分类，难以区分不规则的特征空间。由于食用油样本分子光谱差别细微，通常难以用SIMCA方法进行分类。SVDD（Support Vector Domain Description）算法是一类基于支持域的非线性单类分类方法，SVDD利用求解凸二次规划得出一个尽可能包含所有目标样本的最小超球体进行分类。本文提出了一种基于SIMCA-SVDD方法的分子光谱分析方法并用于食用油的快速分类。为鉴别不同种类的食用油，在ATR-FTIR光谱仪上扫描四种食用油的红外光谱。应用SIMCA方法提取分类特T2和Q，由于提取的特征T2和Q分布的不规则性，不同于SIMCA中的欧氏距离，本文采用SVDD用于对提取的不规则特征进行分类。由于SVDD能通过映射函数将分类特征映射到高维空间，因此可以通过求解凸二次规划来训练最优的分类超球面对分类特征进行分类。采用本文所提的SIMCA-SVDD方法及传统的SIMCA方法，对同样的样本进行了对比实验。对比实验证实了本文所提的SIMCA-SVDD方法具有比传统的SIMCA方法更好的分类结果，所提的方法为实现基于分子光谱进行食用油快速分类提供了一条新的途径。
关键词：食用油；分子光谱；SIMCA；欧氏距离；SVDD
Abstract：Edible oil is a necessity in daily life. The nutritional value and price of different types of edible oils on the market vary a lot. Because of the spurious activities in the market, it is necessary to establish effective detection methods to classify the quality of the edible oils in the market. Traditional edible oil classification methods are usually time-consuming and requiring complex pre-treatment in the lab. Molecular spectroscopy can elucidate the sample information of both compositions and properties at the molecular level, and molecular spectra analysis has the advantages of fast speed detection and non-destructive testing for edible oil classification. Molecular spectra analysis combined with the chemometrics is becoming a popular method for rapid classification of edible oil. SIMCA (Soft Independent Modeling of Class Analogy) is widely applied to molecular spectra analysis. However, the Euclidean distance is used in SIMCA to classify the extracted features with PCA and F test. Therefore it is difficult to classify the irregular feature spaces. When the molecular spectral differences among the different types of samples are tiny such as edible oils, it is usually difficult to identify them with the traditional SIMCA method. SVDD（Support Vector Domain Description）algorithm is a support domain method for solving the one-class classification problem. SVDD can get a hypersphere to include as many objective samples as possible by solving the convex quadratic programming problem. In this work, a method of molecular spectra analysis based on SIMCA-SVDD method for rapid classification of edible oils is proposed. In order to accomplish recognition of the different types of edible oils, the attenuated total reflectance infrared spectra of four types of edible oil are scanned on ATR-FTIR. SIMCA is applied to extract the classification features T2 and Q. Since the extracted edible oil classification features T2 and Q distribute irregularly, instead of classification with Euclidean distance in SIMCA, Support Vector Domain Description (SVDD) is applied in this work to classify the extracted features. Since SVDD can map the extracted classification features to high dimensional space by mapping functions, then an optimal classification hypersphere can be trained to classify the irregular distributing feature spaces by solving the convex quadratic programming problem. Comparative experiments to identify the same molecular spectra samples with the proposed SIMCA-SVDD method and the SIMCA method have also been done. Comparative experiment results have verified that the classification results with the proposed SIMCA-SVDD method are obviously better than that with SIMCA. The proposed SIMCA-SVDD method has provided a new way to classify the edible oil rapidly based on molecular spectra analysis.
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