Development of a rapid X-ray fluorescence method for protein determination in soybean grains.

Abstract

X-ray fluorescence (XRF) is a well-established technique for elemental determination. This study evaluates the ability of XRF to quantify soybean protein content based on elemental composition, particularly sulfur emission. Univariate linear regression, multiple linear regression, and partial least squares regression (PLS) were compared. Two scenarios were considered: scenario A used 108 soybean samples for calibration and 54 for validation; scenario B expanded the protein content range of scenario A, including 32 new samples of soybean mixed with concentrates. PLS showed the best performance in validation, with R 2 of 0.73 and 0.89 in scenarios A and B, respectively. The results indicate that protein quantification by XRF has relative prediction errors below 3.1 %. The developed methods provide an alternative for monitoring soybean protein content, suitable for screening applications such as integrating XRF sensors on soybean harvesters.

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