基于近红外光谱技术评价不同等级渣驯的质量

Quality Assessment of Zhaxun with Different Classifications Based on Near Infrared Spectroscopy

目的：采用近红外光谱技术（NIRS）结合统计学手段，探讨渣驯与其代用品的快速鉴别方法以及渣驯质量的评价方法。方法：参考传统外观质量分级将渣驯药材及其代用品分为4个等级，测定不同等级渣驯及代用品中黄腐酸的含量，分析其与渣驯质量分级的相关性；采集近红外光谱，结合主成分分析，探讨质量等级与近红外光谱的定性关系。采用偏最小二乘法（PLS）建立近红外定量分析模型，考察渣驯及其代用品质量快速评价的可行性。结果：黄腐酸含量与渣驯外观质量分级无明确相关性；Ⅳ级（代用品）近红外光谱与其他等级渣驯能显著分开，而I，Ⅱ，Ⅲ等级渣驯的近红外光谱辨析度不高，但具有一定的变化规律；所建立的渣驯中黄腐酸的近红外定量分析模型的决定系数（R。）=80．13％，交互验证误差均方根（RMSECV）=0．047，表明该模型预测效果较好；验证集R。=11．91％，预测误差均方根（RMSEP）=0．12，相对分析误差（RPD）=1．12，说明模型稳健性与分辨力不理想。结论：近红外光谱结合主成分分析能够为渣驯与其代用品的快速鉴别提供参考；黄腐酸不能作为渣驯质量优劣的评价指标，有必要进一步探索渣驯质量标志物，用以评价该药材的质量。

Objective： To explore rapid identification method of Zhaxun and its substitutes and to develop quality assessment method of Zhaxun by near infrared spectroscopy （NIRS） combined with statistical methods. Method： Zhaxun and its substitutes were divided into four grades according to traditional appearance quality classification, the content of fulvic acid in Zhaxun and its substitutes was determined, and its correlation with quality classification was analyzed. The qualitative relationship between quality classification and spectrum was explored by principal component analysis and NIRS. Partial least squares （PLS） was used to establish a quantitative model of NIRS, feasibility of a rapid assessment method for quality of Zhaxun and its substitute was investigated. Result： There was no clear correlation between fulvic acid content and quality classification of Zhaxun; the near infrared spectrum of grade IV （the substitutes） could be significantly separated from other grades, while the resolutions of near infrared spectrum of grade ] , II and III were not high, but it had a certainvariation rules. The determination coefficient （R2） of NIRS model of fulvic acid content in Zhaxun was 80. 13% , its root mean square error of cross validation （RMSECV） was 0. 047, indicating this model had satisfactory predicted results. The R2, root mean square error of prediction （RMSEP） and relative percent deviation （RPD） of validation set were 11.91%, 0.12 and 1.12, respectively; which indicated that the model robustness and resolution were not ideal. Conclusion： NIRS combined with principal component analysis can provide a reference for the rapid identification of Zhaxun and its substitutes. Fulvic acid content can not be used as a criterion for determining quality of Zhaxun. It is necessary to further explore the quality markers to evaluate the quality of Zhaxun.