Rapid Nondestructive Analysis of Intact Canola Seeds Using a Handheld Near-Infrared Spectrometer

Abstract

The near-infrared (NIR) models for canola quality were developed with samples from Canadian canola seeds harvested in 2016 and 2017. All calibration models were first tested on a 2017 external validation sample set. The handheld NIR spectrometer used in this study has a limited wavelength range 908.1–1676.2 nm; however, the validation results showed that it could be used to predict several important parameters that defined canola seed quality. Final testing was performed using calibration models with the least number of factors on a second external canola validation sample set (2018 harvest). Some calibration models showed excellent stability and predictive powers with R2val values of 0.94–0.99 (i.e., oil, protein, oleic acid and iodine value) and low SEPs for both external validation sample sets. The α-linolenic acid model had an R2val of 0.93 when applied to the 2017 external validation set, the correlation fell slightly to 0.88 when applied to the 2018 external validation sample set, potentially indicating a slight instability in the model. The prediction model for total glucosinolate was not very good, but still could be used to segregate the samples into low or high glucosinolate samples. Finally, the predictive models for chlorophyll and total saturates were unusable. The chlorophyll model was very unstable, likely due to the instrument's limited wavelength range.