Rapid discrimination of Brassica napus varieties using visible and Near-infrared (Vis-NIR) spectroscopy

Abstract

Brassica napus is an oilseed plant that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels. The number of varieties/cultivars is high for the species, owing to their higher level of economic use. The aim of this study is to assess the use of visible-near infrared (Vis-NIR) spectroscopy in combination with multiple chemometric methods that have been explored for the discrimination of eight Brassica napus varieties in Korea. In this study, the spectra from leaves of the eight B. napus varieties were measured in the Vis-NIR spectra in the range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. The spectral data were preprocessed with three different preprocessing methods and eight different chemometric analyses were used for effective discrimination. After the outlier detection, the samples were split into two sets, one serving as a calibration set and the remaining one as a validation set. When using multiple preprocessing and chemometric methods for the discrimination, the maximum classification accuracy was witnessed in the combination of standard normal variate and support vector machine up to 98.2 %. The use of Savitzky-Golay filter smoothing as a preprocessing method had the best and most satisfactory discrimination of all other chemometric methods. The results suggest that the use of handheld Vis-NIR spectroscopy in combination with chemometric approaches can be used as an effective tool for the discrimination of B. napus varieties in the field.

Keywords

• Visible-near infrared
• Spectroscopy
• Brassica napus
• Chemometrics
• Deep learning
• Preprocessing