基于拉曼光谱和机器学习的百合分类识别

百合鳞茎，百合科百合属多年生草本球根植物生长的肥厚鳞片构成的地下变态茎，是一种典型的药食同源作物，含有丰富营养成分的同时还具有抗肿瘤、抗抑郁、降血糖、提高免疫力等保健作用。不同产地百合鳞茎的市场价格差异较大，依赖于人工经验感官的传统评测方法主观性强、确定性差，难以广泛推广在现代生产环节。以化学检验法为主的先进检测方法耗时长、费用高，而且难以满足产地鉴别这一要求。针对百合鳞茎现场快速地产地判断和品质评价的需求，提出了一种使用拉曼光谱和机器学习的百合鳞茎检测方法。拉曼光谱是一种基于非弹性散射的振动光谱，能够做到快速准确的无损检测，将拉曼光谱与机器学习算法相结合，建立了我国分布最为广泛的三种百合鳞茎(兰州百合、宜兴百合和龙牙百合)的产地分类模型，着眼于基质光谱上479, 870, 942和1 606cm^-1等特征峰，提出了一种基于拉曼光谱的成分含量判断产地和评价百合鳞茎品质的无损检测方法。首先采集百合鳞茎样本的光谱，经过光谱数据预处理后，使用人工先验法提取百合鳞茎代表物质并确定特征峰，再使用主成分分析和t-分布随机邻域嵌入方法降维提取光谱数据特征。并将获得的数据特征分别...

Classification and Recognition of Lilies Based on Raman Spectroscopy and Machine Learning

Lily bulbs, the underground metamorphic stems composed of thick scales grown by perennial herbaceous bulbous plants of the lily family Liliaceae, a typical medicinal and edible homologous crop. It is rich in nutrients and has anti-tumor, antidepressant, hypoglycemia, and improves immune functions. The market prices of lily bulbs from different origins are quite different. The traditional evaluation methods that rely on artificial experience and sensory are highly subjective and have poor certainty, making it difficult to be widely used in modern production links. Advanced detection methods based on chemical inspection methods are time-consuming and expensive and it are difficult to meet the requirements for origin identification. Raman spectroscopy is a vibration spectrum based on inelastic scattering, which can achieve fast and accurate non-destructive testing. Combining Raman spectroscopy with machine learning algorithms, a classification model of the three most widely distributed lily bulbs in China (Lanzhou lily, Yixing lily and Longya lily) was established. Observing the characteristic peaks of 479, 870, 942 and 1 606 cm-1 on the matrix spectrum, a non-destructive testing method based on the component content of Raman spectroscopy to determine the place of origin and evaluate the quality of lily bulbs is proposed. First, the traditional method is used to collect the spectrum of the lily bulb sample. After the spectral data is preprocessed, the artificial prior method is used to extract the representative substance of the lily bulb and determine the characteristic peaks. Then the principal component analysis and the t-distribution random neighborhood embedding method are used to reduce the dimensionality. Extract spectral data features. The data features obtained above are applied to support vector machines, decision trees and random forest algorithms. The experimental results show that these classification models all show ideal classification accuracy on the same test set. Among them, the model’s accuracy based on principal component analysis and decision tree algorithm reached 91.7%。The model’s accuracy based on t-distribution random neighborhood embedding and support vector machine is 93.7%, and the accuracy rate of the model combining the principal component analysis and random forest algorithm is as high as 95.8%. In summary, this method can provide on-site rapid identification and identification of the origin of lily bulbs, improve the accuracy of the quality assessment in the modern production process, and provide a reference for the identification of the origin of modern production and the quality analysis of lily bulbs.