Non-Destructive Measurement of Sugar Content in Litchis Using Visible and Near-Infrared Spectroscopy and Fuzzy Stochastic Configuration Network

Abstract

Non-destructive measurement of sugar content in litchis is essential for achieving precise and efficient sorting. This study developed a predictive model for sugar content in litchis based on visible-near-infrared (Vis-NIR) spectral data and intelligent algorithms. To overcome the limitations of existing models, such as poor fitting performance and time-consuming training processes, an efficient fuzzy stochastic configuration network (FSCN) was proposed by integrating the strengths of fuzzy neural networks and stochastic configuration networks. The FSCN incorporates fuzzy membership functions to quantify uncertainties in input features and leverages the incremental learning mechanism of its stochastic configuration layer to dynamically optimize network parameters. This approach enhances the model's capacity to capture the complex nonlinear relationship between sugar content and spectral wavelengths. Furthermore, the effects of 12 spectral preprocessing methods and 4 feature extraction techniques were systematically evaluated. Using 656 spectral data samples from litchis, experimental results demonstrated that the combination of multiplicative scatter correction (MSC) with the successive projections algorithm (SPA) yielded the most effective outcome. The proposed FSCN model exhibited superior predictive performance, achieving an R² of 0.9676, a root mean square error (RMSE) of 0.5487, and a mean absolute error (MAE) of 0.4441. These values surpassed those obtained by partial least squares regression (PLSR), artificial neural networks (ANNs), and support vector regression (SVR). These findings confirm that the FSCN model is well-suited for accurate sugar content detection in practical litchi sorting systems.