Rapid determination of the chemical compositions of peanut seed (Arachis hypogaea.) Using portable near-infrared spectroscopy

• Developed portable NIR spectroscopy was used for quantification of chemical compositions. • Prediction models was developed with improved accuracy for the prediction of chemical parameters. • Portable NIR system coupled with Si-GA-PLS delivered optimal results. In the present research work, portable near-infrared (NIR) spectroscopy coupled with different types of chemometric algorithms like partial least-squares (PLS) regression and some effective variable selection algorithms, i.e., synergy interval-PLS (Si-PLS), genetic algorithm-PLS (GA-PLS) and synergy interval genetic algorithm-PLS (Si-GA-PLS) were used for the quantification of chemical compositions of peanut seed samples; also the Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) models were applied for discrimination of peanut of different regions. The compositional parameters, i.e., total phenolic content (TPC), fat, protein, fiber, carbohydrate, moisture, ash and pH, were estimated. The results of the developed model estimated by applying correlation coefficients of the calibration (R c ) and prediction (R p ); root mean standard error of cross-validation, RMSECV; root mean square error of prediction, RMSEP and residual predictive deviation, RPD. The efficiency of the developed model was significantly enhanced with the use of Si-PLS, GA-PLS and Si-GA-PLS correlated with the classical PLS model. The results of R p determined for prediction and R c calibration set differ from 0.7473 to 0.9420 and 0.7794 to 0.9623 correspondingly. These results showed that portable NIR spectroscopy coupled with different chemometric algorithms having the potential to be applied for the prediction of the chemical compositions of peanut seed samples.