Vis/NIR-Based Wireless Sensing for Potatoes

Potato quality is determined by multiple physicochemical indicators, including dry matter content (DC), starch content (SC), and color parameters (lightness L*, redness a*, yellowness b*, and browning index (BI)). Conventional spectrometers are costly, non-portable and lack wireless in-situ monitoring, restricting efficient postharvest quality assessment. Chemical methods are destructive and inefficient for field inspection and high-throughput detection. The primary objective of this study was to develop and validate a low-cost wireless 12-channel visible/near-infrared (Vis/NIR) spectral sensing system, comprising 6 Vis channels and 6 NIR channels, for the real-time non-destructive prediction of six potato quality indicators. After preprocessing the spectral data with mean normalization, a multiple linear regression (MLR) model was established to optimize the prediction performance of quality parameters. The six indicators evaluated were DC, SC, L*, a*, b*, and BI. Statistical analysis and cross-validation were further conducted to quantitatively evaluate the stability and credibility of the prediction model. Among these, the b* parameter demonstrated the most robust predictive performance, achieving a cross-validated coefficient of determination (R2CV) of 0.881. The MLR model was integrated into the sensing hardware to realize synchronous data collection and prediction. This study provides a validated, low-cost, wireless solution for rapid potato quality assessment under controlled conditions, offering a potential alternative to conventional spectrometers and destructive chemical methods.