Development of non‐destructive models to predict oil content and fatty acid composition of Gomenzer (Ethiopian mustard) using near‐infrared reflectance spectroscopy

Abstract

Background ‟Gomenzer” or Ethiopian mustard is the potential oilseed crop that is economically important. Ethiopian mustard and rapeseed quality breeding has focused on altering the fatty acid contents of seed oil to create novel genotypes with different oil characteristics. The goal of this research was to establish calibration equations' using a method called near‐infrared reflectance spectroscopy with modified partial least squares (MPLS) regression. Result The spectra of 180 mustard samples were collected and their oil and fatty acid compositions were determined by n‐hexane extraction and GC–MS methods respectively. With 130 samples, calibration equations were developed for oil and fatty acid compositions. All developed equation had an acceptable value of R 2c, RPDc, and also had suitable 1‐VR values. Prediction of an external validation with 50 datasets revealed a blameless correlation between reference values and NIRS values based on the R 2v in prediction, SEP, and the ratio of SD to SEP (RPDv) for oil, oleic, linoleic, linolenic, and erucic acids. These had suitable values of RPDv and R 2v, with the range of 2.0 to 8.8 and 0.79 to 0.99 respectively. Conclusion The developed models gave meaningful quantitative data, however, the equations generated for palmitic, stearic, and eicosenoic acids were insufficient for data analysis. Hence, the developed NIRS model, which was satisfactory, could be used to assess and evaluate the oil and fatty acid content for the unknown mustard samples on a regular basis in the oilseed breeding quality program.