Rapid determination of oil content of single peanut seed by near-infrared hyperspectral imaging

Abstract

Oil content is a crucial indicator for evaluating the quality of peanuts. A rapid and non-destructive method to determine oil content of individual peanut seed can provide robust technical support for breeding high-oil-content peanut varieties. In this study, we established a rapid determination method using near-infrared hyperspectral imaging and chemometrics to assess the oil content of single peanut seed. After selecting key wavelengths through competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and random frog (RF), we constructed an oil content calibration model based on partial least squares regression for single peanut seed. Validation results demonstrated that the correlation coefficient was 0.8393 with a root mean square error of 1.7771 in the calibration set, while it was 0.7915 with a root mean square error of 2.2943 in the independent prediction set. Most samples exhibited relative errors below 5%, confirming the reliability of this model in predicting oil content of single peanut seed.