Application of benchtop NIR spectroscopy coupled with multivariate analysis for rapid prediction of antioxidant properties of walnut (Juglans regia)

Benchtop near-infrared (NIR) spectroscopy coupled with multivariate analysis was used for the classification and prediction of antioxidant properties of walnut. Total phenolic content (TPC), total flavonoid content (TFC), ABTS assay and FRAP assay were performed spectrophotometrically. The synergy interval partial least square coupled competitive adaptive reweighted sampling (Si-CARS-PLS) was used for the prediction. A decent discrimination using principal component analysis (PCA) was observed by mean of spectroscopic and antioxidant properties data with total cumulative variance of 99.26% (PC1 = 95.07%, PC2 = 2.98%, PC3 = 1.21%) and 96.60% (PC1 = 64.28%, PC2 = 32.32%) respectively. The Si-CARS-PLS yielded optimal performance, RP = 0.9616, RPD = 3.807 for TPC, RP = 0.9657, RPD = 3.367 for TFC, RP = 0.9683, RPD = 2.728 for ABTS assay, and RP = 0.914, RPD = 2.669 for FRAP assay. These findings revealed that NIR integrated with Si-CARS-PLS could be used for the prediction of antioxidant properties of walnut.

Automated summary

Benchtop near-infrared (NIR) spectroscopy combined with multivariate analysis was employed for the classification and prediction of antioxidant properties of walnut. The study revealed that decent discrimination was achieved using principal component analysis (PCA), and Si-CARS-PLS showed optimal performance for predicting various antioxidant properties. The findings suggest that NIR integrated with Si-CARS-PLS could be a useful tool for predicting the antioxidant properties of walnut.