Total phenolic content prediction in Flos Lonicerae using hyperspectral imaging combined with wavelengths selection methods

Abstract

Hyperspectral imaging (HSI) method was applied to rapidly and nondestructively predict total phenolic content in Flos 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 in Flos Lonicerae. Practical applications Flos Lonicerae is a well‐known traditional Chinese medicinal herb, and also the raw material of various medicines and herbal tea. The demand for Flos Lonicerae is increasing. Therefore, monitoring the quality of Flos Lonicerae is very important to consumers and industry of Flos Lonicerae. The phenolic compounds content in Flos Lonicerae is 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 of Flos Lonicerae as well as other agricultural products.

Effects of radiation temperature on dehydration and moisture migration in banana slices during far‐infrared radiation drying

“…Torki‐Harchegani, Ghanbarian, Maghsoodi, and Moheb (2017) clarified that high temperatures can promote heat transfer from the thermal source to samples and then increase water evaporation rates. However, increments in temperature during drying may result in the increased production of brown pigments due to Maillard reactions, causing undesirable changes in the surface color of materials (Liu, Wang, Gao, & Xie, 2019; Nadian, Rafiee, & Golzarian, 2016). In addition, excessively high temperatures can cause nutrient loss, leading to reduced nutritional value (Rodríguez, Llabrés, Simal, Femenia, & Rosselló, 2014; Zhang, Liu, Sun, Bai, & Chen, 2020).…”

Quantitative analysis and visualization of moisture and anthocyanins content in purple sweet potato by Vis–NIR hyperspectral imaging

“…Further step to reduce the data set by eliminating uninformative wavelengths and retaining key qualitative wavelengths was taken by employing a competitive adaptive reweighted sampling (CARS) algorithm proposed by (Li et al., 2009). The CARS algorithm sequentially performs a broad range of computational processes which result in the selection of a wavelength denoted as an individual species after each sequence while considering the other wavelengths as its natural “environment” (Liu et al., 2019). The process continues until species (wavelengths) considered as fit for purpose are retained while minimally useful ones are discarded.…”

Drying characteristics and optimization of ultrasound‐strengthened cold air drying combined with sequential far‐infrared radiation drying on potato

“…The total phenol content (TPC) of potato samples was determined by the Folin–Ciocalteu colorimetric method (Kim, Jeong, & Lee, 2003; Liu, Wang, Gao, & Xie, 2019). The VC content of the samples was determined by 2,4‐dinitrophenylhydrazine colorimetric method (Yi et al., 2018).…”