基于支持向量机对云南常见野生食用牛肝菌中红外光谱的种类鉴别

Species Identification of Common Wild Edible Bolete in Yunnan by Fourier Transform Mid-infrared Spectroscopy Coupled with Support Vector Machine

利用傅里叶变换中红外光谱鉴别云南野生牛肝菌种类,明确不同数据挖掘方法对模型分类性能的影响,为云南省食用菌的鉴别和质量控制提供参考依据。扫描云南8种827个常见野生牛肝菌样本的中红外光谱,分析光谱特征,结合支持向量机建立判别模型,并利用预处理、提取特征变量及两者组合等方法挖掘光谱信息,比较各模型分类性能,找出野生牛肝菌种类鉴别的最优方法。结果表明:原始数据存在大量噪音和干扰信息,降低模型分类性能;不同数据挖掘方法均能不同程度去除非有效信息,提高模型分类性能;预处理组合特征变量挖掘光谱信息能力最强,对模型分类性能提高最大。预处理组合特征变量对模型信息挖掘能力最强,结合支持向量机建立判别模型,模型拟合好,分类精度高,适用范围广,实现了8种野生牛肝菌的准确、快速鉴别,可以为野生牛肝菌种类鉴别提供参考。

In an effort to provide reference for the identification and quality control of edible fungi in Yunnan,Fourier transform mid-infrared spectroscopy combined with support vector machine(SVM)was used to identify different wild bolete species in Yunnan.The influence of different data mining methods on model classification performance was determined.Infrared spectra of 8 species with 827 samples of eight common wild bolete species were acquired and analyzed for spectral characteristics,and a discriminant model was established using SVM.Spectral information mining was performed by preprocessing,feature variable extraction or their combination,and the classification performance of the models developed was compared with each other to find the optimal method for species identification of wild bolete.The results showed that there was a large amount of noise and interference information in the original data,which reduced the classification performance of the model.All tested data mining methods could remove non-effective information but in different extents,improving model classification performance.Preprocessing combined with characteristic variables extraction had the highest ability to mine spectral information,providing the best model classification performance.The SVM model developed had excellent goodness of fit with high classification accuracy and wide applicability,allowing the accurate and quick identification of the eight wild bolete species.