Rapid and simultaneous quantification of phenolic compounds in peanut (Arachis hypogaea L.) seeds using NIR spectroscopy coupled with multivariate calibration.

Phytochemically, peanuts provide significant nutritional value to humans, animals, and the food industry as a whole. This study was conducted to explore the potential for near-infrared (NIR) spectroscopy and effective variable selection algorithms to quantify phenolic compounds in peanut seeds. The phenolics were extracted and then identified and quantified using high-performance liquid chromatography (HPLC). The spectroscopic data were acquired from the peanut samples using a tabletop NIR spectrometer with a wavelength range of 10,000–4000 cm–1. The acquired spectra were preprocessed using a synergistic effect of first- and second-order derivative (FOD and SOD) preprocessing techniques, and multivariate algorithms were used, examined, and evaluated using correlation coefficients of the validation set (R p), root mean square error of prediction (RMSEP), and residual predictive deviations (RPDs). The competitive adaptive reweighted sampling-partial least squares (CARS-PLS) model produced optimum performance for chlorogenic acid (R p = 0.933, RPD = 2.77), kaempferol (R p = 0.928, RPD = 2.68), p -coumaric acid (R p = 0.900, RPD = 2.32), and quercetin (R p = 0.932, RPD = 2.88), respectively. Therefore, this study proved that NIR spectroscopy in combination with CARS-PLS was capable of nondestructively predicting phenolic content in peanut seeds. • Phenolic compounds in peanut seeds were individually quantified using HPLC. • NIR spectroscopy was effectively employed to evaluate phenolic compounds. • Spectral signal pretreatment improved the performance of the PLS. • Variable selection methods can be used to enhance model performance. • CARS-PLS produced optimal results for predicting phenolic contents. [ABSTRACT FROM AUTHOR]