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Journal of Analytical Methods in Chemistry
Volume 2017 (2017), Article ID 4315678, 9 pages
https://doi.org/10.1155/2017/4315678
Research Article

The Application of FT-IR Spectroscopy for Quality Control of Flours Obtained from Polish Producers

1Department of Chemistry, Faculty of Food Sciences, Warsaw University of Life Sciences, Nowoursynowska 159 C, 02-787 Warsaw, Poland
2Department of Food Technology, Faculty of Food Sciences, Warsaw University of Life Sciences, Nowoursynowska 159 C, 02-787 Warsaw, Poland

Correspondence should be addressed to Katarzyna Sujka; ue.airetni@akjus.k

Received 4 October 2016; Revised 2 December 2016; Accepted 6 December 2016; Published 22 January 2017

Academic Editor: Miguel de la Guardia

Copyright © 2017 Katarzyna Sujka et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Samples of wheat, spelt, rye, and triticale flours produced by different Polish mills were studied by both classic chemical methods and FT-IR MIR spectroscopy. An attempt was made to statistically correlate FT-IR spectral data with reference data with regard to content of various components, for example, proteins, fats, ash, and fatty acids as well as properties such as moisture, falling number, and energetic value. This correlation resulted in calibrated and validated statistical models for versatile evaluation of unknown flour samples. The calibration data set was used to construct calibration models with use of the CSR and the PLS with the leave one-out, cross-validation techniques. The calibrated models were validated with a validation data set. The results obtained confirmed that application of statistical models based on MIR spectral data is a robust, accurate, precise, rapid, inexpensive, and convenient methodology for determination of flour characteristics, as well as for detection of content of selected flour ingredients. The obtained models’ characteristics were as follows: , PRESS = 2.14; , PRESS = 0.69; , PRESS = 1.27; , PRESS = 0.76, for content of proteins, lipids, ash, and moisture level, respectively. Best results of CSR models were obtained for protein, ash, and crude fat (; 0.82; and 0.78, resp.).