Prediction of quality traits in dry pepper powder using visible and near-infrared spectroscopy

Fruit quality phenotyping is a bottleneck in plant breeding. The present work aimed to investigate the applicability of visible (Vis) and near-infrared (NIR) spectroscopy for quality evaluation in dry red chili powder. We constructed prediction models for the American Spice Trade Association (ASTA)-colour and the Scoville Heat Unit (SHU)-pungency pepper traits using spectroscopy and multivariate statistical techniques. Predictive partial least squares (PLS) models were successfully achieved with high correlations (r) between the predicted and reference values for calibration and validation (r = 0.955 and 0.928 for ASTA-colour; r = 0.941 and 0.918 for SHU-pungency). Spectroscopy data from visible and short-wave radiation (Vis-SWNIR) provided the most robust (residual predictive deviation value) model for ASTA-colour (RPD = 2.84) and long-wave radiation (LWNIR) for SHU-pungency (RPD = 2.48). Spectral categories for wavelength range selection, variable importance for effective wavelength selection, and root mean press-statistic for factor selection were important criteria for PLS. Trait variance and distribution were also important criteria for the predictive capacity and power of the models. In conclusion, non-invasive spectroscopy was a promising tool in our study for dry red chili quality phenotyping.