2009
DOI: 10.1094/cchem-86-5-0556
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High‐Throughput Near‐Infrared Reflectance Spectroscopy for Predicting Quantitative and Qualitative Composition Phenotypes of Individual Maize Kernels

Abstract: Near‐infrared reflectance (NIR) spectroscopy can be used for fast and reliable prediction of organic compounds in complex biological samples. We used a recently developed NIR spectroscopy instrument to predict starch, protein, oil, and weight of individual maize (Zea mays) seeds. The starch, protein, and oil calibrations have reliability equal or better to bulk grain NIR analyzers. We also show that the instrument can differentiate quantitative and qualitative seed composition mutants from normal siblings with… Show more

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Cited by 84 publications
(92 citation statements)
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“…This is in contrast to rice, where a single mutant lacking function of the pdk1 homolog caused an opaque phenotype (24). Single-kernel near infrared reflectance (NIR) spectra predicted starch, protein, and oil content, as well as density, in the segregating populations (27,28). No consistent differences between wild-type and mutant siblings were observed regarding composition, although NIR spectra differences correlated with elevated density in pdk2-DsEx4 homozygous kernels (SI Appendix, Table S1).…”
Section: Resultsmentioning
confidence: 95%
“…This is in contrast to rice, where a single mutant lacking function of the pdk1 homolog caused an opaque phenotype (24). Single-kernel near infrared reflectance (NIR) spectra predicted starch, protein, and oil content, as well as density, in the segregating populations (27,28). No consistent differences between wild-type and mutant siblings were observed regarding composition, although NIR spectra differences correlated with elevated density in pdk2-DsEx4 homozygous kernels (SI Appendix, Table S1).…”
Section: Resultsmentioning
confidence: 95%
“…Seed weights were collected from an automated microbalance as described by Spielbauer et al (2009). Kernels were ground to a fine meal using a mortar and pestle.…”
Section: Quantitative Kernel Phenotypesmentioning
confidence: 99%
“…As can be seen from the interaction plot ( Figure 6), SNV accompanied with other combinations of preprocessing decrease the predictive ability of weight calibrations. This preprocessing method also affects calibrations developed with absolute units, 20 which suggests that SNV attenuates the effects of seed size and thus negatively affects the prediction of seed weight. Conversely, SNV tends to improve oil and protein prediction for composition expressed in percent thanks to the removal of unnecessary spectral information induced by seed size.…”
Section: Journal Of Agricultural and Food Chemistrymentioning
confidence: 99%