Accurate prediction of soluble solid content in dried Hami jujube using SWIR hyperspectral imaging with comparative analysis of models

Soluble solid content (SSC) is an important factor in determining jujube fruit's nutritional and marketable values. In this study, the potential of using short-wave infrared (SWIR) hyperspectral imaging (HSI) technique with the spectral range of 1000-2500 nm coupled with multiple regression models for predicting SSC of dried Hami jujube was explored. The influence of three detection positions on the SSC prediction result was analyzed. Results indicated that the influence of detection position on the HSI data affected the prediction accuracy of SSC, and the stalk-side down was the optimal detection position. Then a custom convolutional neural network (CNN) model was constructed and compared with linear partial least squares regression (PLSR) and non-linear support vector machines regression (SVR). Competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), and iteratively retains informative variables (IRIV) algorithms were used for selecting effective variables, respectively. Based on the full spectra and selected spectra, different models were built and compared to determine the best calibration strategy. Among all the models, the performance obtained by the CNN model based on full spectral was satisfactory. The determination coefficient of prediction (R-p(2)), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were 0.857, 0.563 and 2.648, respectively. This research demonstrated that SWIR hyperspectral imaging combined with appropriate regression models could be applied to nondestructively predict the SSC of dried Hami jujubes. Furthermore, the CNN method had great potential for chemical compositions determination. This study can provide a valuable reference for the assessment of SSC in other dried fruits with small sizes.