Classification of cereal flour species using Raman spectroscopy in combination with spectra quality control and multivariate statistical analysis

As an important staple food, grain is subject to substitution and adulteration with cheaper cereals. Therefore, verification of authenticity is an issue for flour. In this work, Raman spectroscopy was used as non-invasive technique to discriminate the species barley, rye, spelt and wheat. In total, 129 samples with different origins and varieties from two harvest years were used as training data. Principal component analysis and partial leastsquares discriminant analysis showed a clear differentiation of the species (accuracies >98 %). Quality control of anomalous spectra improved the classification accuracy with Mahalanobis distance and Hotelling's T2 and Q residuals statistics proving superior to manual selection. Validation with 86 independent samples including an additional harvest year per species corroborated the feasibility of discrimination (accuracy: 88 %) and shows the impact of the variety and harvest year. The discrimination of the spectra was primarily based on starch, protein and arabinoxylan signals.

Automated summary

Raman spectroscopy was successfully used to distinguish between barley, rye, spelt and wheat with high accuracy in this study. Principal component analysis and partial least squares discriminant analysis were effective in differentiating between the species. Quality control of anomalous spectra improved classification accuracy.