基于机器学习的红外光谱数据鉴别中药材性能方法

A machine learning methods for identifying the properties of Chinese medicinal materials from infrared spectrum data

中药材种类不同,近红外和中红外光谱特征也有很大差异.由于无机元素和有机物质等化学成分不同,所以即使同种中药材产地不同,在近红外和中红外光谱辐照下标记效果也会显示不同的光谱特性,这些特性可用于对中药材进行分类和产地识别.借助MATLAB软件和SPSS分类工具K-均值聚类算法对中药材进行无监督机器学习,从而对中药材进行分类.同时,运用SPSS神经网络多层感知器和Python语言提供的随机森林算法,将数据集的70%作为训练集,30%作为验证集,进行监督机器学习模型训练,从而对中药材产地进行鉴别预测.

There are great differences in the characteristics of near-infrared and mid-infrared spectra of different kinds of traditional Chinese medicine.Due to the different chemical components such as inorganic elements and organic substances,even if the origin of the same traditional Chinese medicine is different,the labeling effect under near-infrared and mid-infrared spectral irradiation will have different spectral characteristics which can be used to classify Chinese herbal medicine and identify the origin of Chinese herbal medicine.With the help of MATLAB software tool and K-means clustering algorithm in SPSS classification tool,unsupervised machine learning is carried out on traditional Chinese medicine to classify traditional Chinese medicine;Using SPSS neural network multilayer perceptron and the random forest algorithm provided by Python language,70%of the data set is used as the training set and 30%as the verification set to train the supervised machine learning model which is finally used to identify and predict the origin of traditional Chinese medicine.