Near-infrared spectroscopy (NIR) for classifying the physiological quality of canola seeds

The use of high-quality seeds is essential in production systems. NIR spectroscopy, combined with chemometric methods, is a promising, rapid, nondestructive, and easy-to-use tool for assessing seed physiological potential. This study aimed to analyze the feasibility of NIR spectroscopy coupled with chemometric methods to detect differences in the physiological quality of canola seeds. Seeds from eight lots of the Nuola 300, Hyola 575, and Diamond cultivars underwent initial characterization tests, seedling length tests, and X-ray analysis. For spectral acquisition, random seed samples were selected, with 12 readings taken for each cultivar. The cultivars were categorized as high (C1) and low (C2) vigor. The spectra were preprocessed using methods such as Multiplicative Scattering Correction (MSC), Standard Normal Variance (SNV), and the first and second Savitzky-Golay derivatives. These data were subsequently used to build classification models using Partial Least Squares Discriminant Analysis (PLS-DA). The model demonstrated high accuracy and kappa coefficients, with the SNV method being the best suited for the dataset. The wavelength regions between 1,004-1,064 nm and 1,698-1,907 nm were the most relevant for distinguishing seed quality levels. Analysis of near-infrared (NIR) spectra, subjected to preprocessing based on derivative methods and dispersion correction, demonstrated high efficiency in detecting variations in the physiological quality levels of canola seeds.