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 (Rcal2 = 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 (Rp2) 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.