Non-Destructive Prediction of Sugar Content in ‘Bingtang’ Oranges Using Visible/Near-Infrared Hyperspectral Imaging and Machine Learning Models

Yunnan ‘Bingtang’ oranges possess significant market competitiveness due to their unique taste and high sugar content. However, the limited efficiency of traditional quality inspection methods severely hampers the industrial grading process. Sugar content (Brix), as a core indicator of internal quality, is also a key factor determining consumer repurchase rates. This study focuses on the characteristic ‘Bingtang’ orange variety “Yunguan Orange” in the Longling Chu Orange Demonstration Base in Yunnan Province. Hyperspectral imaging data and corresponding sugar content values were collected during three critical stages from fruit set to harvest, and sugar content prediction research was conducted within the 400–1000 nm spectral range. Based on the extraction of regions of interest (ROI), Multispectral Scattering Correction (MSC) coupled with the Savitzky-Golay first-order derivative (SG-1st Deriv) method was employed to preprocess the raw spectral data (RAW). This study focused on investigating the performance of the Competitive Adaptive Weighted Sampling (CARS) algorithm across six prediction models. Raw full spectra (RAW) and the Sequential Projection Algorithm (SPA) were adopted as comparative schemes in ablation experiments to systematically analyze the influence of different feature extraction strategies. Experimental results showed that the coefficient of determination (R2) for most models exceeded 0.8 on the test set, while the root mean square error (RMSE) remained below 1.0. The findings indicate that under the current experimental conditions, this system can realize acceptable quantitative analysis for tested Yunguan oranges and greatly lower the overall model complexity. It clarifies the compatibility of the CARS algorithm with various prediction models, and systematically integrates three innovations of multi-stage sampling, SPA/CARS comparison and multi-type model evaluation, providing critical theoretical support and technical parameters for the development of low-cost, real-time online sorting equipment.