Submitted to: Journal of Agricultural and Food Chemistry
Publication Type: Peer reviewed journal
Publication Acceptance Date: 7/25/2012
Publication Date: 8/29/2012
Publication URL: www.ars.usda.gov/SP2UserFiles/Place/54300520/464%20Measurement%20of%20single%20soybean%20seed%20attributes%20by%20NIR%20technologies.pdf
Citation: Agelet, L.E., Armstrong, P.R., Clariana, I.R., Hurburgh, C.R. 2012. Measurement of single soybean seed attributes by near infrared technologies. A comparative study. Journal of Agricultural and Food Chemistry. 60(34):8314-8322. DOI: 10.1021/jf3012807. Interpretive Summary: Measurement of single seed composition for soybean breeders allows the selection of seeds with traits attributed to either genetics or agronomics, or both of these influences in combination. Four single-seed near infrared spectrometer systems were evaluated for their ability to predict soybean oil content, protein content and seed weight. Each system used slightly different measurement methods. The ability to predict seed traits varied somewhat by what trait was being measured and which system was used. For most cases the best measurement systems were for those that measured seed traits from several angles facilitated by the seed moving during measurement. This work helps breeders to determine the accuracy that can be obtained with single seed near infrared measurement and what instrumentation system is best suited for the trait they are trying to measure. Ultimately, single seed selection can expedite and reduce costs of varietal development by easily selecting varietal lines with the desired traits.
Technical Abstract: Four near infrared spectrophotometers, and their associated spectral collection methods, were tested and compared for measuring three soybean single seed attributes: weight (g), protein (%), and oil (%). Using partial least squares (PLS) and 4 preprocessing methods, the attribute which was significantly most easily predicted was seed weight, and protein the least easily predicted. The performance of all instruments differed from each other. Performances for oil and protein predictions were correlated with the instrument sampling system with the best predictions using spectra taken from more than one seed angle. This was facilitated by the seed spinning or tumbling during spectral collection as opposed to static sampling methods. From the preprocessing methods utilized, no one method gave the best overall performances but weight measurements were often more successful with raw spectra while protein and oil predictions were often enhanced by SNV and SNV+detrending.