LAUSR

Abstract Mid-infrared spectroscopy coupled with chemometrics was used to identify spectral differences in malt barley associated with region (e.g. rainfall, temperature and sunlight). The observed differences in the MIR spectra confirmed that different environmental conditions of the region they are grown, explain or modulate changes in the chemical composition of the grain. Principal component analysis (PCA) was performed to identify underlying patterns and correlations in the data set. Partial least squares (PLS) regression with cross-validation was applied to develop models to predict the regions using the weather variables, the MIR fingerprint region and a combination of both. The PLS model with the combined data predicted the growing location with the standard error of cross-validation (SECV) of 3.38%, an R2 value of 0.53, a slope of 0.52 and a bias of 0.23. The exploratory approach demonstrated the usefulness of chemometrics to identify differences and similarities based on the MIR spectra. The results provided a solid foundation for hypothesising the overall degree of biochemical similarity and or difference among the samples and a quantifiable method to screen samples for further analysis.