Near-Infrared Spectroscopy and Machine Learning for Fast Quality Prediction of Bottle Gourd

Protein and amino acid content are the crucial quality parameters in bottle gourd, and traditional measurement methods for detecting those parameters are complicated, time-consuming, and costly. In this study, we employed NIRS along with machine learning and neural network-based methods to model and predict protein and free amino acids (FAAs) of bottle gourd. Specifically, the content of protein and FAAs were measured through conventional methods. Then a near-infrared analyzer was utilized to obtain the spectral data, which were processed using multiple scattering correction (MSC) and standard normalized variate (SNV). The processed spectral data were further processed using feature importance selection to select the feature bands that had the highest correlation with protein and FAAs, respectively. The models for protein and FAAs estimation were developed using support vector regression (SVR), ridge regression (RR), random forest regression (RFR), and fully connected neural networks (FCNNs). Among them, ridge regression achieved the optimal performance, with determination coefficients (R²) of 0.96 and 0.77 on the protein and FAAs test sets, respectively, and root mean square error (RMSE) values of 0.23 and 0.5, respectively. Based on this, we developed a precise and rapid prediction model for the important quality indices of bottle gourd.