Classification of artificially aged lentil seeds using Fourier transform near-infrared spectroscopy

Abstract

Seed deterioration is among the main causes of loss in physiological potential. This study aimed to investigate the potential of Fourier transform near-infrared (FT-NIR) spectroscopy to classify lentil seeds subjected to artificial aging at 41 ºC and 100 % of relative humidity for 24, 48, 72, 96 and 120 hours, with unaged seeds used as control. After obtaining the spectra, the seeds were submitted to germination tests, and the percentages of normal, vigorous and weak seedlings, as well as dead seeds, were recorded. From the spectral data, models were obtained using the Partial Least Squares - Discriminant Analysis (PLS-DA) method, separating 70 % of the data for training and 30 % for validation. Pre-processing using the second derivative of Savitzky-Golay (SG) and the combination of the second derivative of SG + multiplicative scatter correction resulted in accuracy and kappavalues in training of 0.94 and 0.92, respectively. The FT-NIR spectroscopy showed to be a promising tool for classifying artificially aged lentil seeds, considering their physiological potential.

Keywords

• lens culinaris
• seed physiological potential
• artificial intelligence