Ability of near infrared spectroscopy and chemometrics to measure the phytic acid content in maize flour

Abstract

Phytic acid is one of the important biochemical components in maize as in many plant species. Near infrared spectroscopy has a potential for determination of the phytic acid content in the maize grain. However, there are a limited number of studies on the determination of phytic acid in maize. Also, the effect of chemometric methods on the success of near infrared spectroscopy calibration models for phytic acid content has not been investigated sufficiently yet. To fill these gaps, we create a total of 360 different prediction models and evaluate the effect of chemometric methods on prediction robustness. To develop calibration models, 4 derivatives, 5 pretreatments, 9 wavelength selection methods were used, and partial least squares regression and support vector machines regression methods were applied. Model reliability was evaluated by external validation. Results revealed that spectral pretreatment and wavelength selection methods improve model prediction results. In general, support vector machines yielded more successful results than partial least squares models in detecting phytic acid. The best model was the combination of first derivative + standard normal variate + interval partial least squares combined with support vector regression. While creating calibration models for phytic acid detection, it was concluded that the use of appropriate chemometric methods increases the success of the model.