Near-infrared reflectance spectroscopy model predictive of cadmium concentration in peanut kernels.

Peanut is a major food crop prone to enrichment in toxic cadmium (Cd). To control Cd levels in peanut, this study aimed to develop a near infrared spectroscopy (NIRS) model for determining Cd concentration in peanut kernels as an alternative to traditional wet chemistry techniques, which can be expensive, time-consuming, and destructive. Near-infrared (NIR) diffuse reflectance spectra of 110 bulk peanut kernel samples were collected, and Cd concentration of the kernel samples was determined by inductively coupled plasma mass spectrometry. A robust quantitative NIRS prediction model for Cd concentration in peanut kernels was developed for the first time. In the calibration set, the best model had a high coefficient of determination (R_cal² = 0.9194) and a low root mean square error of cross-validation (RMSECV = 0.0388). In the prediction set of 105 additional peanut kernel samples not involved in the model development, the coefficient of determination (R_p²) was as high as 0.9539 and the root mean square error of prediction was as low as 0.0341. This study provides a rapid and low-cost screening tool for low-Cd breeding and Cd management in peanut. [ABSTRACT FROM AUTHOR]