Rapid determination of farinograph parameters of wheat flour using data fusion and a forward interval variable selection algorithm

Abstract

Farinograph tests are used to predict the functional properties and quality of wheat flour. However, these tests are time-consuming and labor-intensive. Conventional rapid determination methods based on near-infrared (NIR) spectra showed a limited ability to predict farinograph parameters. The potential of combining NIR and mid-infrared (MIR) spectral regions to predict wheat flour farinograph quality properties (water absorption, dough development time, dough stability, and degree of softening) was investigated. Partial least squares models based on NIR, MIR and fused spectra were calibrated and compared. Two data fusion strategies (low- and mid-level) have been applied to take advantage of the synergistic effect of information obtained from MIR and NIR. Low-level data fusion models showed inferior performance compared to the corresponding MIR and NIR models, whereas mid-level data fusion models combined with a forward interval variable selection algorithm were validated to show good performance. Fusion of the previously selected variables from MIR and NIR spectra improved the prediction accuracy of farinograph parameters, which indicates the superiority of the forward interval variable selection algorithm that will be helpful for the cereal and baking industries.