The measurement and control of physicochemical properties in beer production processes are essential to guarantee product quality and consumer satisfaction. Total solids concentration is a key quality indicator as it affects product sensory attributes such as flavor, body, and texture. The color of the beer, measured in SRM units (Standard Reference Method), is another determining property in product quality and is associated with the concentration and presence of melanoidins resulting from the malting processes. This study focuses on developing a predictive model that uses SRM color to estimate total solids in beer, providing an efficient and non-invasive tool for the brewing industry.

Samples of various commercial and craft beers were collected, covering a wide range of SRM colors. Color measurements were performed using a spectrophotometer, and total solids were determined by gravimetric analysis. Multiple linear regression techniques were applied to develop the predictive model, integrating SRM color and other relevant variables. The model was validated using an independent dataset.

The analyses showed a strong correlation between SRM color and total solids in the evaluated beers. The developed multiple linear regression model exhibited a high coefficient of determination (R²) values ​​between 0.886 and 0.997 were obtained for six different styles of beer with concentrations between 0.51 and 8.42% w/w of total solids, indicating significant predictive capacity. Validation with independent data demonstrated the robustness and generalizability of the model to different beer styles.

The predictive model allows for rapid, non-destructive estimation of total solids based on the SRM color of the beer. This offers significant advantages to the industry, such as real-time production monitoring, improved quality control, and the efficiency of monitoring new process technologies such as freeze concentration.