Colorímetro e regressão linear múltipla na quantificação de cinzas em farinha de trigo

Abstract

Wheat flour is widely consumed and used in various food applications, and the determination of ash content is an essential parameter for its classification and industrial destination. Traditionally, this analysis is performed using established laboratory methods, which are time-consuming and labor-intensive. In this work, an alternative method for determining the ash content in wheat flour was developed, based on color measurements obtained by colorimetry associated with multivariate calibration models using multiple linear regression (MLR). Samples with ash contents ranging from 0.38% to 1.39% were analyzed, using the colorimetric parameters L*, a*, and b* as predictor variables. The calibration and external validation of the model were performed using the merit parameters: mean squared calibration error (RMSEC), validation error (RMSEP), correlation coefficient, and deviation performance ratio (RPD). The results obtained demonstrated satisfactory model performance, with a correlation coefficient of 0.958, RMSEC and RMSEP values around 0.061, and an RPD greater than 4.9. The confidence ellipse indicates, with 95% confidence, that there is no significant difference between the ash determinations obtained using the traditional laboratory method and the developed alternative method, highlighting the accuracy of the proposed method. Thus, the presented method proves to be a fast, low-cost, and sustainable alternative for determining the ash content in wheat flour, contributing to the optimization of quality control processes in the milling industry.