利用多光譜成像系統(tǒng)區(qū)分杭白菊栽培種和測(cè)定木犀草素含量

    杭白菊是重要的可食用中藥，因其主要活性成份為木犀草素，其具有自由基清除和抗氧化性能，可改善健康。研究人員利用多光譜成像法進(jìn)行了杭白菊栽培種區(qū)分和木犀草素含量測(cè)定的研究。支持向量機(jī)法（ (LS-SVM)）用來區(qū)分杭白菊栽培種。從樣品獲得的光譜學(xué)和形態(tài)學(xué)特征數(shù)據(jù)用做前三個(gè)主要成份（99.61%）， LS-SVM模型在預(yù)測(cè)集的區(qū)分精度可達(dá)98%。另外，偏小二乘法PLS和 LS-SVM 用于預(yù)測(cè)木犀草素含量，預(yù)測(cè)結(jié)果Rp分別為 0.949和0.965； RMSEP 分別為0.387和0.314 mg g−1。所有結(jié)果顯示，將MSI多光譜成像技術(shù)與化學(xué)計(jì)量法結(jié)合的方法，可能是一種快速、無損區(qū)分杭白菊栽培種和測(cè)定木犀草素含量的有效方法。該文章利用了VideometerLab 多光譜測(cè)量系統(tǒng)進(jìn)行了研究。
   Chrysanthemum morifolium Ramat. (Chr) is a notable medicinal and edible crop that stimulates health owing to its radical-scavenging and antioxidant properties due to its main active flavonoid luteolin. The performance of discriminating between Chr c*rs and determining luteolin content in Chr using multispectral imaging (MSI) was investigated. A combination of MSI with principal component analysis (PCA) and least squares-support vector machines (LS-SVM) was applied to classify Chr c*rs. PCA derived from the spectral and morphological features data of the samples explained 99.61% for summing up the first three principal components and the LS-SVM model achieved 98% discrimination accuracy in the prediction set. Additionally, partial least squares (PLS) and LS-SVM models were obtained to predict performance for luteolin content determination, with Rp of 0.949 and 0.965, and RMSEP of 0.387 and 0.314 mg g−1, respectively. All results demonstrated that the combination of MSI system with chemometrics methods could be a rapid and non-destructive method to discriminate between Chr c*rs and determine luteolin content in Chr.