Simplifying Wheat Quality Assessment: Using Near-Infrared Spectroscopy and Analysis of Variance Simultaneous Component Analysis to Study Regional and Annual Effects

Abstract

Assessing the quality of wheat, one of humanity’s most important crops, in a straightforward manner, is essential. In this study, analysis of variance (ANOVA) simultaneous component analysis (ASCA) paired with near-infrared spectroscopy (NIRS) was used as an easy-to-implement and environmentally friendly tool for this purpose. The capabilities of combining NIRS with ASCA were demonstrated by studying the effects of sampling site and year on the quality of 180 Austrian wheat samples across four sites over 3 years. It was found that the year, sample site, and their combination significantly (p < 0.001) affect the NIR spectra of wheat. NIR spectral preprocessing tools, usually employed in chemometric workflows, notably influence the results obtained by ASCA, particularly in terms of the variance attributed to annual and regional effects. The influence of the year was identified as the dominant factor, followed by region and the combined effect of year and sampling site. Interpretation of the loading plots obtained by ASCA demonstrates that wheat components such as proteins, carbohydrates, moisture, or fat contribute to annual and regional differences. Additionally, the protein, starch, moisture, fat, fiber, and ash contents of wheat samples obtained using a NIR-based calibration were found to be significantly influenced by year, sampling site, or their combination using ANOVA. This study shows that the combination of ASCA with NIRS simplifies NIR-based quality assessment of wheat without the need for time- and chemical-consuming calibration development.