Bulk Protein and Oil Prediction in Soybeans Using Transmission Raman Spectroscopy: A Comparison of Approaches to Optimize Accuracy
Bulk Protein and Oil Prediction in Soybeans Using Transmission Raman Spectroscopy: A Comparison of Approaches to Optimize Accuracy
复制标题
使用透射拉曼光谱预测大豆中的大量蛋白质和油：优化精度方法的比较
DOI：
10.1177/0003702818815642
复制
发表时间：
2019
影响因子：
3.5
通讯作者：
R. Bhargava
中科院分区：
文献类型：
--
作者：
Rajveer Singh;T. Wróbel;Prabuddha Mukherjee;M. Gryka;Matthew R Kole;Sandra K Harrison;R. Bhargava
蛋白质和脂肪含量的快速测量对于各种用途都很重要，从收获时对大豆的分拣到豆粕生产过程中的反馈。在这项研究中，我们的目标是开发一种简单的方案，允许使用透射拉曼光谱(TRS)对大豆成分进行快速和稳健的定量预测。为了开发这种方法，我们系统地改变了测量过程的各种元素，以提供一个多样化的试验台。首先，我们利用了内部构建的台式TRS仪器，以便可以快速部署和分析合适的光学配置，以收集单个大豆颗粒的实验数据。其次，我们还利用蛋白质含量较低(33.97%)、中等(36.98%)和高蛋白(41.23%)的三个不同大豆品种对其发育过程进行了测试。第三，每个品种的样品都是用全豆和三种不同的样品处理(即豆粉、全粉和磨粉)制备的。在每种情况下，我们使用偏最小二乘(PLS)回归和评估的基于谱度量的多元线性回归(METRIME-MLR)方法对获得的数据进行建模，以建立稳健的预测模型。与所有处理类型的大宗蛋白质和油脂的相应经典偏最小二乘回归模型相比，度量-MLR模型显示出较低的均方根误差(RMSEP)，因此具有更好的预测能力。比较不同的制样方法，全粉处理的RMSEP值较低，因此米粉处理的米氏最大似然比模型是大豆蛋白质和油脂预测的最佳方法，其RMSEP值分别为1.15 ± 0.04(R2 = 0.87)和0.80 ± 0.02(R2 = 0.87)。这些预测比相应的近红外光谱测量(即粮食行业的二级黄金标准)几乎高出两到三倍(即较低的RMSEP)。对于整个大豆的含量预测，在公制-最大似然比方法中，结合单个谷物的物理属性，散装蛋白质的预测提高了22%，散装油的预测相对温和(最高可达∼5%)。将度量-MLR建模方法(在谷物分析领域中很少见)与样本处理的独特组合导致了预测模型的改进；建议使用单个谷物的物理属性作为提高预测精度的新措施。
Rapid measurements of protein and oil content are important for a variety of uses, from sorting of soybeans at the point of harvest to feedback during soybean meal production. In this study, our goal is to develop a simple protocol to permit rapid and robust quantitative prediction of soybean constituents using transmission Raman spectroscopy (TRS). To develop this approach, we systematically varied the various elements of the measurement process to provide a diverse test bed. First, we utilized an in-house-built benchtop TRS instrument such that suitable optical configurations could be rapidly deployed and analyzed for experimental data collection for individual soybean grains. Second, we also utilized three different soybean varieties with relatively low (33.97%), medium (36.98%), and high protein (41.23%) contents to test the development process. Third, samples from each variety were prepared using whole bean and three different sample treatments (i.e., ground bean, whole meal, and ground meal). In each case, we modeled the data obtained using partial least squares (PLS) regression and assessed spectral metric-based multiple linear regression (metric-MLR) approaches to build robust prediction models. The metric-MLR models showed lower root mean square errors (RMSEPs), and hence better prediction, compared to corresponding classical PLS regression models for both bulk protein and oil for all treatment types. Comparing different sample preparation approaches, a lower RMSEPs was observed for whole meal treatment and thus the metric-MLR modeling with ground meal treatment was considered to be optimal protocol for bulk protein and oil prediction in soybean, with RMSEP values of 1.15 ± 0.04 (R2 = 0.87) and 0.80 ± 0.02 (R2 = 0.87) for bulk protein and oil, respectively. These predictions were nearly two- to threefold better (i.e., lower RMSEPs) than the corresponding NIR spectroscopy measurements (i.e., secondary gold standards in grain industry). For content prediction in whole soybean, incorporating physical attributes of individual grains in metric-MLR approach show up to 22% improvement in bulk protein and a relatively mild (up to ∼5%) improvement in bulk oil prediction. The unique combination of metric-MLR modeling approach (which is rare in the field of grain analysis) and sample treatments resulted in improved prediction models; using the physical attributes of individual grains is suggested as a novel measure for improving accuracy in prediction.