| Sumario: | Bread wheat (Triticum aestivum L.) plays a vital role in global food security and processing. Understanding the rheological properties of dough is crucial in the food industry and wheat breeding programs to select high-quality varieties. Traditional tests such as Farinograph and Extensograph are essential, but labor-intensive and impractical for high-throughput screening. Near-infrared spectroscopy is a rapid and cost-effective alternative to grain quality assessment. This study aimed to develop calibration models for key rheological properties of dough in wheat using a dataset of 1082 representative samples. Various spectral pre-processing, variable selection, and regression algorithms have been employed for model calibration. The partial least squares regression model for Farinograph water absorption demonstrated strong predictive capabilities (R2c = 0.92, R2v = 0.90, and RPD = 3.20), while qualitative analysis was feasible for other characteristics with high accuracy from 80.23 % to 94.27 %. The developed NIR models provide an efficient method for evaluating wheat quality in food processing and wheat breeding.
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