Fast and non- destructive multivariate test method to predict bread wheat grain major quality parameters

Abstract

For decades researches have been conducted in Ethiopia to improve wheat productivity and quality. This involved extensive selection of breeding lines based on agronomic and laboratory data from numerous trials. However, generating laboratory data for all these services was both time-consuming and expensive. Hence, the study aimed to develop a very rapid and cost-effective testing method for wheat genotypes and varieties using near-infrared spectroscopy. For this purpose, we collected 155 bread wheat samples from high-growing potential areas of Sinana, Kulumsa, and Holeta. The wet chemistry analysis was done in duplicate following the international standard official methods. The same samples were also scanned using TANGO’S Bruker NIRS machine in duplicate to acquire spectral data. A calibration model was then developed by fitting the reference and spectral data using the partial least square (PLS). The calibration models exhibited a high level of strength for all parameters. The performance of the calibration model for protein (R2 = 0.99; RPD = 9.63), moisture (R2 = 0.96; RPD = 5.29), ash (R2 = 0.98; RPD = 6.39), total gluten (R2 = 0.98; RPD = 8.09), dry gluten (R2 = 0.97; RPD = 5.57), gluten index (R2 = 0.96; RPD = 5.23), and falling number (R2 = 0.97 and RPD = 6.23) were deemed suitable for wheat process and quality control purposes. But the models for moisture and gluten index (R2 = 0.96) were found to be suitable for screening and simple quality control purposes only. Therefore, this study clearly demonstrated that the near-infrared spectroscopy model is a highly reliable and robust method for predicting the key quality traits of bread wheat grains, serving the needs of both breeding programs and industry raw material control.

Keywords

• wheat
• quality
• traits
• NIRS
• calibration
• Validation