NIR pre-selection data using modified changeable size moving window partial least squares and pure spectral chemometrical modeling with ant colony optimization for wheat flour characterization

•Improved chemometrical methods for flour characterization with NIR spectrum.•CSMWPLS modification•Proposed a spectral filter based on ant colony optimization and CSMWPLS.•Analysis of spectrum data based on process variables.

The aim of process optimization is obtaining higher productivity and profit in chemical or bio-chemical process. For that, one must apply control techniques that closely correlate with our ability to characterize a process. Optical sensors associated with chemometric modeling are considered a natural choice for non-intrusive and high sensitivity measurements. This study focus on wheat flour characterization (usual and mandatory action, widely present on the food industry) using near-infrared, comparing two approaches for spectral region selection: modified CSMWPLS and PSCM/ACO. Spectroscopic data is assayed using a combination of CSMWPLS and variable selection algorithm based on ant colony optimization. Protein prediction results are compared with standards PLS, CSMWPLS and PSCM/ACO models. Prediction capability improved 46% using modified CSMWPLS and PSCM/ACO modeling, confirming the efficiency of the proposed characterization methods and chemometric modeling strategy.