Near-infrared reflectance spectroscopy for the prediction of chemical composition in walnut kernel

In the present work, 116 samples were collected and near-infrared reflectance spectroscopy prediction model for determination of moisture, protein, and fat contents of walnut meal were obtained and evaluated. All the samples were analyzed based on the chemical methods. Meanwhile, they were scanned to obtain their near-infrared reflectance spectrum in the wavelength range of 570–1840 nm. Several preprocess treatments including scattering pretreatments, mathematical pretreatments, and aggression methods were optimized to increase the accuracy of the calibration models according to the coefficient of determination for calibration (Rc2) and the cross-validation (one minus the variance ratio, 1-VR), and the standard error of calibration and cross-validation. The results showed modified partial least square loading was the better aggression method to predict the moisture, proteins, and fats in walnut kernel. The calibration equations obtained indicated that near-infrared reflectance spectroscopy had excellent predictive capacity for the three components with Rc2 = 0.965, standard error of calibration = 0.052 for moisture, and Rc2 = 0.967, standard error of calibration = 0.191 for proteins, and Rc2 = 0.979, standard error of calibration = 0.171 for fats, respectively. The external validation further confirmed the robustness and reliability of the near-infrared reflectance spectroscopy prediction models with the correlation coefficient of actual and predicted values (R2) = 0.952, ratio of performance deviation = 4.14, the standard error of prediction =0.058 for moisture, and R2 = 0.977, ratio of performance deviation = 5.55, standard error of prediction = 0.182 for proteins, and R2 = 0.990, ratio of performance deviation = 8.64, standard error of prediction = 0.191 for fats, respectively. Near-infrared reflectance spectroscopy is a reliable technology to predict these constituents in walnuts.