Development of non-destructive NIRS models to predict oil and major fatty acid contents of Ethiopian sesame

Abstract

Sesame is a crucial oilseed crop that contains vital fatty acids. The objective of this study was to build calibration equations using near-infrared reflectance spectroscopy for quality screening of sesame. A total of 136 sesame samples were scanned in the reflectance mode and their wet chemistry was determined by n-hexane extraction and gas chromatography mass spectroscopy. Models for oil and four fatty acids were developed with 110 samples and had an acceptable value of calibration coefficient of determination (R²c), with suitable one minus the ratio of unexplained variance divided by variance (1-VR) value were found except palmitic acid. The prediction of an external validation with 26 datasets revealed a blameless correlation between reference values and NIRS values based on the coefficient of determination of validation (R²ᵥ) and relative prediction deviation (RPDᵥ). The models for oil, stearic, oleic, and linoleic acids had suitable values of coefficient of determination of validation and relative prediction deviation, which were more than 2.0 and 0.8, respectively. As a result of this research, it was discovered that the NIRS technology could be used to examine the oil and fatty acid contents of sesame seed qualities directly in breeding program, standard agency and commodity exchanges.

Plant phenotyping relevance

ゴマ種子の油分・脂肪酸含量を推定するNIRS校正式を開発し、外部検証まで実施しており、育種で利用可能な植物形質の非破壊測定法が研究の中心である。

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