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.