Protein and Oil Composition Predictions of Single Soybeans by Transmission Raman Spectroscopy

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• 作者：Matthew V. Schulmerich ; Michael J. Walsh ; Matthew K. Gelber ; Rong Kong ; Matthew R. Kole ; Sandra K. Harrison ; John McKinney ; Dennis Thompson ; Linda S. Kull ; Rohit Bhargava
• 刊名：Journal of Agricultural and Food Chemistry
• 出版年：2012
• 出版时间：August 22, 2012
• 年：2012
• 卷：60
• 期：33
• 页码：8097-8102
• 全文大小：392K
• 年卷期：v.60,no.33(August 22, 2012)
• ISSN：1520-5118

文摘

The soybean industry requires rapid, accurate, and precise technologies for the analyses of seed/grain constituents. While the current gold standard for nondestructive quantification of economically and nutritionally important soybean components is near-infrared spectroscopy (NIRS), emerging technology may provide viable alternatives and lead to next generation instrumentation for grain compositional analysis. In principle, Raman spectroscopy provides the necessary chemical information to generate models for predicting the concentration of soybean constituents. In this communication, we explore the use of transmission Raman spectroscopy (TRS) for nondestructive soybean measurements. We show that TRS uses the light scattering properties of soybeans to effectively homogenize the heterogeneous bulk of a soybean for representative sampling. Working with over 1000 individual intact soybean seeds, we developed a simple partial least-squares model for predicting oil and protein content nondestructively. We find TRS to have a root-mean-standard error of prediction (RMSEP) of 0.89% for oil measurements and 0.92% for protein measurements. In both calibration and validation sets, the predicative capabilities of the model were similar to the error in the reference methods.r>

Keywords:

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