Estimating the changes in mechanically expressible oil in terms of content and quality from ohmic heat treated mustard (Brassica juncea) seeds by Vis–NIR–SWIR hyperspectral imaging

Abstract

Designed experiments were conducted to investigate the influence of ohmic heating (OH) at varying electric field strength (EFS) and holding time on the recovery of oil from mustard (Brassica juncea) seeds during mechanical expression. Hyperspectral imaging (HSI) in the visible-near infrared (Vis–NIR, 399–1003 nm) and short-wave infrared (SWIR, 895–1712 nm) ranges was used to visualize the change in oil distribution induced by OH on the mustard seeds. OH treatment led to an increase in expression of oil content by 25% as compared to control samples. Chemometric techniques, including partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR), were employed to analyze spectral data and develop models for predicting the enhancement in expressible oil due to OH treatment and its quality in terms of free fatty acids thereof. PLS-DA differentiated OH treated seeds from the control sample for by Vis–NIR and SWIR HSI at 93.0 and 95.8% accuracy, respectively. The variable selection method (iPLS) identified crucial wavelengths with minimal performance loss for accurate prediction. The PLSR model using SWIR HSI data accurately predicted oil content and fatty acid composition (R² > 0.92), while Vis–NIR predictions exhibited a lower accuracy (R² > 0.73).