Application of Least Square Twins Support Vector Machine in Near Infrared Spectrometry

作者：Song Xiang Zhong; Chen Chang Zhou; Min Shun Geng; He Xiong Kui; Li Zheng; Mi Jin Rui; Zhang Lu Da^*

来源：Chinese Journal of Analytical Chemistry, 2012, 40(6): 950-954.

DOI：10.3724/SP.J.1096.2012.11054

• Least squares twin support vector machine
• Near infrared spectrum
• Chemometrics
• Rhubarb

A method for rapid identification of the traditional Chinese herb rhubarb based on near infrared (NIR) spectroscopy was described. The identification model was established by the least square twins support vector machine (LSTSVM) algorithm with MATLAB. 98 rhubarb samples were used for the investigation. To establish NIR-LSTSVM identification model, the samples were divided into training set with 60 samples and testing set with 38 samples randomly. The parameters of the model were optimized by the leave 1/5 out cross validation method for the training set. And then the optimal recognition model was established by using the selected optimal parameters and near infrared spectra. The identification rate for the testing set was 97.4%. The result indicated that it is an effective method for rapid identification of rhubarb. In addition, recognition models were established by using the above method while the rhubarb samples were randomly divided into training set and testing set six times, and the average identification rate was 93.4%. The result showed that this method presented good robustness.

• 出版日期2012-6
• 单位中国农业大学