Prediction of soluble solid content in Nanfeng mandarin by combining hyperspectral imaging and effective wavelength selection

Abstract

Soluble solid content (SSC) is among primary evaluation indicators of fruit quality and a key factor influencing consumer purchasing decisions. The research utilized hyperspectral imaging (380–1030 nm) to forecast SSC in Nanfeng mandarin. After a series of preprocessing methods, partial least squares regression (PLSR) and least squares support vector machine (LSSVM) were adopted to build prediction models. Combining multiplicative scatter correction and Savitzky-Golay smoothing was more effective compared to other preprocessing methods. Effective wavelengths (EWs) were selected by using bootstrapping soft shrinkage (BOSS), competitive adaptive reweighted sampling (CARS), iteratively retaining informative variables (IRIV) and their combinations. The BOSS-CARS-PLSR model performs optimally in prediction with the Rp2, RMSEP and RPD being 0.9376, 0.3986 and 4.0542, respectively. Additionally, the spatial distribution of the SSC in Nanfeng mandarin was visualized using the optimal model. Results show that combining hyperspectral imaging and EWs selection offers a rapid and intuitive approach that can non-destructively evaluate internal quality of Nanfeng mandarin.