基于中红外光谱分析技术的香菇产地识别研究  

Producing Area Identification of Letinus Edodes Using Mid-Infrared Spectroscopy

作　　者：朱哲燕[1,2] 张初[2] 刘飞[2] 孔汶汶[2] 何勇[2] 

机构地区：[1]浙江经济职业技术学院,浙江杭州310018 [2]浙江大学生工食品学院,浙江杭州310058

出　　处：《光谱学与光谱分析》2014年第3期664-667,共4页Spectroscopy and Spectral Analysis

基　　金：国家高技术研究发展计划(863计划)项目(2011AA100705)资助

摘　　要：采用中红外光谱分析技术对香菇产地进行识别研究，并将相关向量机（relevance vector machine ， RVM ）算法应用于中红外光谱判别分析之中，取得了较好的效果。通过采集香菇粉末的中红外透射光谱，去除光谱噪声明显部分，对剩下的3581～689 cm-1透射谱线采用多元散射校正（multiplicative scatter correc-tion ，MSC）进行预处理，并基于预处理谱线建立了香菇产地识别的偏最小二乘判别分析（partial least squares-discriminant analysis ，PLS-DA）、簇类独立软模式分类（soft independent modeling of class analogy ， SIMCA）、K 最邻近算法（K-nearest neighbor algorithm ， KNN ）、支持向量机（support vector machine ， SVM ）、RVM模型等五种判别分析模型。所有模型的识别正确率均高于80％，KNN ，SVM 和RVM 判别分析模型取得了相近的结果，建模集和预测集识别正确率高于90％。基于全谱的PLS-DA模型的加权回归系数，利用加权回归系数法选取了6个特征波数，并基于特征波数建立了PLS-DA ，KNN ，SVM 和RVM 模型。基于特征波数的PLS-DA模型的建模集和预测集识别正确率均低于80％，而KNN ，SVM和RVM模型的建模集和预测集的识别效果相近，且都高于90％。基于全谱和特征波数的模型中，RV M 算法表现出较好的效果，识别正确率优于90％。结果表明，基于中红外光谱技术能用于香菇产地的识别，特征波数的选择以及RVM算法可以有效的用于中红外光谱判别分析中。本文成功将中红外光谱用于香菇产地识别研究，为香菇品质以及其他农产品品质分析提供了一种新的想法，具有实际意义。In the present study ,Mid-infrared spectroscopy was used to identify the producing area of Letinus edodes ,and rele-vance vector machine （RVM） was put forward to build classification models as a novel classification technique ,and they obtained good performances .The head and the tail of the acquired mid-infrared spectra with the absolute noise were cut off ,and the re-maining spectra in the range of 3 581~689 cm-1 （full spectra） of Letinus edodes were preprocessed by multiplicative scatter cor-rection （MSC） .Five classification techniques ,including partial least Squares-discriminant analysis （PLS-DA） ,soft independent modeling of class analogy （SIMCA） ,K-nearest neighbor algorithm （KNN） ,support vector machine （SVM） and RVM ,were applied to build classification models based on the preprocessed full spectra .All classification models obtained classification accu-racy over 80% ,KNN ,SVM and RVM models based on full spectra obtained similar and good performances with classification accuracy over 90% in both the calibration set and the prediction set .The weighted regression coefficients （Bw） were used to se-lect effective wave numbers of mid-infrared spectra and 6 effective wave numbers in total were selected on the basis of the weigh-ted regression coefficients of PLS-DA model based on full spectra .PLS-DA ,KNN ,SVM and RVM models were built using these effective wave numbers .Compared with the classification models based on full spectra ,PLS-DA models based on effective wave numbers obtained relatively worse results with classification accuracy less than 80% ,and KNN ,SVM and RVM obtained similar results in both calibration set and prediction set with classification accuracy over 90% .RVM performed well with classifi-cation rate over 90% based on full spectra and effective wave numbers .The overall results indicated that producing area of Leti-nus edodes could be identified by mid-infrared spectroscopy ,while wave number selection and the RVM algorithm could be effec-tively used i

关 键 词：中红外光谱 香菇产地 相关向量机 RELEVANCE VECTOR machine (RVM ) 

分 类 号：O433.4[机械工程—光学工程] S464.9[理学—光学]