A chemometric evaluation of the underlying physical and chemical patterns that support near infrared spectroscopy of barley seeds as a tool for explorative classification of endosperm genes and gene combinations

Abstract

Near infrared spectroscopic (NIR; 1100–2500 nm), chemical and genetic data were combined to study the pleiotropic secondary effects of mutant genes on milled samples in a barley seed model. NIR and chemical data were both effective in classifying gene and gene combinations by Principal Component Analysis (PCA). Risø mutants R-13, R-29 high (1→3, 1→4)-β-glucan, low starch and R-1508 (high lysine, reduced starch), near isogeneic controls and normal lines and recombinants were studied. Based on proteome analysis results, six anti-microbial proteins were followed during endosperm development revealing pleiotropic gene effects in expression timing that supporting the gene classification. To verify that NIR spectroscopy data represents a physio–chemical fingerprint of the barley seed, physical and chemical spectral components were partially separated by Multiple Scatter Correction and their genetic classification ability verified. Wavelength bands with known water binding and (1→3, 1→4)-β-glucan assignments were successfully predicted by partial least squares regression giving insight into how NIR-data works in classification. Highly reproducible gene-specific, covariate, pleiotropic classification patterns from NIR and chemical data were demonstrated in PCAs and by visual inspection of NIR spectra. Thus PCA classification of NIR-data gives the classical genetic concept, ‘pleiotropy’, a new operational definition as a fingerprint from a spectroscopic representation of the phenome carrying genetic, physical and chemical information. It is concluded that barley seed phenotyping by NIR and chemometrics is a new, reliable tool for characterising the pleiotropic effects of mutant gene combinations and other genotypes in selecting barley for quality in plant breeding.