Abstract
AbstractHyperspectral imaging (HSI) method was applied to rapidly and nondestructively predict total phenolic content inFlos Lonicerae. The least squares support vector machine (LS‐SVM) and partial least squares regression (PLSR) models were developed on the basis of full wavelengths data and characteristic wavelengths data chosen by six wavelengths selection ways. The results clarified that standard normal variable (SNV) was the optimal pretreatment method, and the nonlinear LS‐SVM model based on the characteristic wavelengths chosen by the combination (CARS‐SPA) of competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) yielded the best prediction performance for the total phenolic content. The overall results demonstrated that the proposed CARS‐SPA was a good method for selecting characteristic wavelengths to enhance prediction performance of HSI, and nonlinear LS‐SVM was more appropriate than linear PLSR for the prediction of total phenolic content inFlos Lonicerae.Practical applicationsFlos Loniceraeis a well‐known traditional Chinese medicinal herb, and also the raw material of various medicines and herbal tea. The demand forFlos Loniceraeis increasing. Therefore, monitoring the quality ofFlos Loniceraeis very important to consumers and industry ofFlos Lonicerae. The phenolic compounds content inFlos Loniceraeis one of the key internal quality factors. The traditional methods (such as spectrophotometric techniques or high‐performance liquid chromatography) for detecting total phenolic content are time‐consuming, labor‐consuming, and destructive. Hyperspectral imaging (HSI) is a nondestructive and reliable method for quality inspection. The results showed that SNV was determined as the best method for pretreatment, and the nonlinear CARS‐SPA‐LS‐SVM model was developed for total phenolic content detection with high precision. It could be verified that HSI technology is effective and promising for rapid determination of total phenolic content and quality control ofFlos Loniceraeas well as other agricultural products.
Published Version
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