Near Infrared and UV-Visible Spectroscopy Coupled with Chemometrics for the Characterization of Flours from Different Starch Origins

Abstract

This work tested near-infrared (NIR) and UV-visible (UV-Vis) spectroscopy coupled with chemometrics to characterize flours from different starch origins. In particular, eighteen starch-containing flours (e.g., type 00 flour, rye, barley, soybean, chestnut, potato, spelt, buckwheat, oat, millet, rice, durum wheat, amaranth, chickpea, sesame, corn, hemp and sunflower flours) were analyzed with a twofold objective: chemically characterizing the investigated flours and laying the groundwork for the development of a fast and suitable method that can identify the botanical source of starch in food. This could ensure ingredient traceability and aid in preventing/detecting food fraud. Untargeted approaches were used for this study, involving the simultaneous acquisition of a large amount of chemical information (UV-Vis on extracted starch and NIR signals on raw flours) coupled with chemometric techniques. UV-VIS spectra were acquired between 225 and 800 nm after sample pretreatment to extract starch. NIR spectra were acquired between 900 and 1700 nm using a poliSPEC NIRe portable instrument on the flours without any kind of pretreatments. An initial exploratory investigation was conducted using principal component analysis and cluster analysis, obtaining interesting preliminary information on patterns among the investigated flours. In particular, the UV-Vis model successfully discerned samples such as potato, chestnut, sunflower, durum wheat, sesame, buckwheat, rice, corn, spelt and 00-type flours. PCA model results obtained from the analysis of NIR spectra also provided comparable results with the UV-Vis model, particularly highlighting the differences observed between hemp and potato flours with soybean flour. Some similarities were identified between other flours, such as barley and millet, rye and oats, and chickpea and amaranth. Therefore, some flour samples underwent surface analysis via scanning electron microscope (SEM) using the Nova NanoSEM 450 to detect distinctive morphology.