Wheat flour characterization using NIR and spectral filter based on Ant Colony Optimization

The key objective for process optimization is to obtain higher productivity and profit in chemical or bio-chemical process. To achieve this, we must apply control techniques that closely correlate with our ability to characterize this process. Within this context, optical sensors associated with chemometrical modeling are considered a natural choice due to their low response time as well as their non-intrusive and high sensibility characteristics. Usually, chemometrical modeling is based on PCR (Principal Component Regression) and PLS (Partial Least Squares). However, since optical techniques are highly sensible and bio-chemical mediums are highly complex, these methodologies can be replaced by using chemometrical modeling based on Pure Spectra Components (PSCM). Our study applies PCR, PLS and PSCM for protein prediction in flour samples measured with near infrared reflectance (NIR), comparing the three methodologies for on-line sensor project. We also outline the development of a spectral filter based on PSCM associated with Ant Colony Optimization. The results lead to our conclusion that the use of optical techniques works best when PSCM analysis is applied, as it allows the development of a spectral sensor for protein quantification in flour samples with less than twenty NIR wavelengths evaluated, selected from a total of 1150. The filtering tool showed favorable results in condensing relevant information from NIR spectral data, increasing R2 from sample prediction by almost 60% for PCR models and 40% for PLS models, using 10% and 20% of full spectral data, confirming the viability of filtering methods.