Research on Rapid and Non‐Destructive Detection of Prune Brix Based on Visible/Near‐Infrared Spectroscopy Technology

Prunes, known for their sweet aroma and smooth texture, are rich in vitamins and minerals, making them both a delicious and nutritious food. Brix is a key indicator of prune quality; however, rapid and nondestructive measurement remains a challenge. About this research, visible/near‐infrared (Vis/NIR) spectral data from various prune types were collected and preprocessed using Standard Normal Variate (SNV) to reduce noise and scattering effects. Subsequently, the competitive adaptive reweighted sampling (CARS) algorithm was employed to select feature wavelengths indicative of the brix in prunes. Three predictive models, Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Least Squares Support Vector Machine (LSSVM), were developed for prune Brix estimation. Among these, the SVR model, using CARS‐selected wavelengths, achieved the highest predictive accuracy, with R 2 (Coefficient of Determination) = 0.8625, RPD (Residual Prediction Deviation) = 2.7229, and RMSE (Root Mean Square Error) = 1.6801. Validation tests further confirmed these results, with an R 2 (Coefficient of Determination) of 0.8608, RPD (Residual Prediction Deviation) of 2.7069, and RMSE (Root Mean Square Error) of 1.69, demonstrating the reliability of the proposed method. This study presents an effective approach for the rapid, nondestructive detection of prune brix levels, providing a valuable technical foundation for future development of fast detection instruments.