基于LIBS的山药饮片产地溯源研究

Research on Origin Traceability of Rhizoma Dioscoreae Based on LIBS

山药为薯蓣科植物薯蓣的根茎,其中的多糖、多酚、皂苷、黏蛋白和维生素C等成分使山药具有抗肿瘤、抗氧化、抗炎症、降血糖和降血脂等作用。不同产地的山药由于生长条件存在差异,致使药用成分含量显著不同,结合独特的炮制工艺,进而导致市场价格差别大,所以山药饮片的产地识别至关重要。为对山药饮片进行产地溯源,本文基于激光诱导击穿光谱(LIBS)技术提出多元散射矫正-改进遗传算法-支持向量机(MSC-IGA-SVM)模型对山药产地进行精确识别。使用八个不同产地的山药饮片进行LIBS实验,八种产地的山药饮片磨粉过筛后制成粉末压片,通过采集山药饮片的LIBS光谱,分别使用单一分类器与使用光谱预处理、特征提取及模式识别算法的模型对光谱的识别结果进行对比。将光谱信号按2∶1的比例划分为训练集和测试集,使用5次交叉验证K-邻近算法(KNN)模型的测试集准确率作为预处理参数优化的评价指标。各类药材的平均光谱整体趋势一致,所含谱峰基本相同,但因产地不同导致峰值强度各不相同,道地山药对一些金属元素(K,Na,Ca,Mg,Al)的富集能力大于非道地产区山药,其中,K元素特征谱线(769.90 nm)的峰值最高,即山药饮片中K元素含量最多,相关研究表明山药根茎对K元素的富集能力最强。选取35条关键谱线进行分析,在识别种类多、识别难度大的情况下,改进遗传算法(IGA)比主成分分析(PCA)更能清楚辨别光谱中的非线性关系,同时受噪声的影响更小。MSC-IGA-SVM模型的产地溯源效果最好。MSC-IGA-SVM模型的交叉验证集准确率为96.9%,测试集的准确率为97.32%,与直接使用原信号建立的最好模型支持向量机(SVM)(96.43%)相比,测试集准确率提高了0.87%。同时,MSC-IGA-SVM模型将输入变量的维度减少了99.93%。结果表明,LIBS技术结合MSC-IGA-SVM模型能够快速、准确对山药饮片进行产地溯源。

Rhizoma Dioscoreae contains polysaccharides,polyphenols,saponins,mucins and vitamin C,which have anti-tumor,anti-oxidant,anti-inflammatory,hypoglycemic,and hypolipidemic effects.Due to the differences in growth conditions of Rhizoma Dioscoreae from different origins,resulting in significantly different contents of medicinal ingredients,combined with unique processing technology,which in turn lead to large differences in market prices,it is crucial to identify the origin of Rhizoma Dioscoreae Tablets.In order to trace the origin of Rhizoma Dioscoreae Tablets,this paper proposed a Multiplicative signal correction-improved genetic algorithm-support vector machine(MSC-IGA-SVM)model based on Laser-induced breakdown spectroscopy(LIBS)technique for accurate identification of Rhizoma Dioscoreae origin.In the paper,LIBS experiments were conducted using eight Rhizoma Dioscoreae Tablets of different origins.The Rhizoma Dioscoreae Tablets of eight origins were ground and sieved to make powder pressed tablets.The recognition results of the spectra were compared by collecting LIBS spectra of Rhizoma Dioscoreae Tablets using a single classifier and a model using spectral preprocessing,feature extraction and pattern recognition algorithms,respectively.In the research,the spectral signals were divided into training and test sets in the ratio of 2∶1,and the test set accuracy of the K-Nearest Neighbor(KNN)model using five cross-validations was used as an evaluation index for the optimization of preprocessing parameters.The overall trend of the average spectra of all herbs was consistent.The contained spectral peaks were the same,but the peak intensities varied due to different origins,and the enrichment ability of some metal elements(K,Na,Ca,Mg,Al)was greater for Rhizoma Dioscoreae growed in the Dao-di Areas than for those not growed in the Dao-di Areas,among which,the peak of the characteristic spectral line of element K(769.90 nm)was the highest,i.e.,the Rhizoma Dioscoreae Tablets contained the most element K.Related studies showed that the root of Rhizoma Dioscoreae has the strongest enrichment capacity for element K.Thirty-five key spectral lines were selected for analysis.Improved Genetic Algorithm(IGA)could discriminate the nonlinear relationships in the spectra more clearly than Principal Component Analysis(PCA)in the case of many identification species and difficult identification while being less affected by noise.The MSC-IGA-SVM model had the best origin traceability.The accuracy of the MSC-IGA-SVM model was 96.9%for the cross-validation set,and the accuracy of the test set was 97.32%,which was 0.87%higher than the best model Support Vector Machine(SVM)built directly using the original signal(96.43%)for the test set.Meanwhile,the MSC-IGA-SVM model reduced the dimensionality of the input variables by 99.93%.The results showed that the origin of Rhizoma Dioscoreae Tablets could be traced by the LIBS technique combined with the MSC-IGA-SVM model quickly and accurately.