Development of prediction models for rapid analysis of glucosinolate content in canola meal using near-infrared spectroscopy

Canola meal (CM) is a valuable protein source for monogastric animals. Despite its effective use as a protein supplement, variation in amino acid digestibility, the presence of glucosinolates (GLS), and high dietary fibre content can limit its inclusion levels in poultry and swine diets. The nutritive value of canola meal often varies due to differences in its chemical composition, which is directly related to the seed processing conditions, particularly the heat and moisture treatments. Excessive heat treatment during processing can result in the destruction of heat sensitive amino acids turning them to biologically unavailable derivatives, and at the same time it can contribute to the decomposition of glucosinolates. Therefore, the glucosinolate contents can serve as an indirect indicator of protein damage in canola meal. As conducting direct wet chemistry analysis on a rapid and large scale might not be feasible, near infrared (NIR) spectroscopy can be used as a rapid, reliable, user-friendly, and environmentally friendly technique to predict components of the chemical composition of canola meal. Therefore, the aim of the project was to determine if NIR models could be developed to predict Canadian canola meal quality, including the measurement of total glucosinolates content. Canola meal and expeller-cold pressed canola samples were collected from various crushing plants over multiple years and were used to develop the NIR prediction models to predict glucosinolates, crude protein, and fat contents in pellet and mash canola meal. Reference methods were used to analyse each sample, which was scanned three times on multiple near-infrared spectrometers. The average scan of each sample was associated with the wet-chemistry data to develop the NIR models, and the instrument’s software was used to perform the statistical analysis for model development. The total glucosinolates content in canola meal was not uniformly distributed across the concentration range. Majority of samples from the conventional pre-press solvent extracted process exhibited very low levels of total glucosinolates, whereas a limited number of samples had high total glucosinolates. Three prediction models were developed for the prediction of glucosinolate contents, crude protein and ether extract. Poor correlation was observed in Model I for glucosinolates prediction in canola meal (R2 = 0.0005), and for glucosinolates and crude protein prediction in expeller-cold pressed canola (ECPC), respectively. (R2 = 0.49 and 0.61). Excellent correlation was observed in Model I for ether extract prediction in ECPC (R2 = 0.95). Adjustments to Model I resulted in greatly improved correlations for glucosinolates, crude protein and ether extract in Models II for prediction of mash canola meal (R2 = 0.87, 0.91, 0.74), and Models III for predictions of pellet canola meal (R2 = 0.91, 0.83, 0.82). Given its precise predictions, each of the three models has components which can be utilized for qualitative analysis in the feed industry. Model I can be used for ether extract prediction in ECPC. Model II can be used for glucosinolates and crude protein prediction in mash canola meal. Model III can be used for glucosinolates, crude protein, and ether extract prediction in pellet canola meal. Overall, these studies underscore the significant potential of NIRS technology within the feed industry as an effective and efficient tool for assessing canola meal quality by predicting glucosinolate, crude protein, and ether extract contents.

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