chrysanthemum morifoliumramat. (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, withrpof 0.949 and 0.965, and rmsep of 0.387 and 0.314mg 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.