Detection of soluble solid content in apples based on hyperspectral technology combined with deep learning algorithm

Abstract

Soluble solid content (SSC) is the most important attribute related to the quality and price of apples. The objective of this study was to detect the SSC of “Fuji” apple using hyperspectral imaging (HSI) combined with a deep learning algorithm. A novel, output-related deep learning algorithm, stacked weighted auto-encoders (SWAE) was proposed to extract output-related features based on pixel-level spectra of each sample. Relevant variables were identified and assigned to different weights by correlation analysis with the output variable. To quantitatively predict the SSC under the constraint of preferential reconstruction for quality-related variables, the deep features containing information about the SSC quality prediction were extracted, and then these features were input into gray wolf optimization-support vector regression (GWO-SVR) to quantitatively predict SSC. Furthermore, successive projection algorithm (SPA) and competitive adaptive reweighed sampling (CARS) were adopted as traditional feature selection algorithms to build GWO-SVR models for predicting the SSC in apples and comparing them with the deep learning models. The results showed that the proposed SWAE-GWO-SVR model yielded satisfactory performance with R p 2 of 0.9436, and RMSEP of 0.1328 °Brix, which demonstrate that deep learning combined with HSI can facilitate the nondestructive assessment of the internal quality attributes of fruits. Practical applications Traditional methods for detecting the soluble solid content (SSC) in apples are destructive and laborious. Therefore, the hyperspectral imaging (HSI) technique combined with deep learning was used to determine the SSC in apples in a rapid and nondestructive manner. The results indicated that combined method is feasible for SSC prediction. Thus, HSI combined with deep learning is considered as a promising method for detecting the SSC in apples.