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首页> 外文期刊>Cereal Chemistry >Prediction of Triticale Grain Quality Properties, Based on Both Chemical and Indirectly Measured Reference Methods, Using Near-Infrared Spectroscopy
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Prediction of Triticale Grain Quality Properties, Based on Both Chemical and Indirectly Measured Reference Methods, Using Near-Infrared Spectroscopy

机译：基于化学方法和间接测量的参考方法，使用近红外光谱法预测黑小麦的谷物品质特性

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摘要

The increasing demand for triticale as food, feed, and fuel has resulted in the availability of cultivars with different grain quality characteristics. Analyses of triticale composition can ensure that the most appropriate cultivars are obtained and used for the most suitable applications. Near-infrared (NIR) spectroscopy is often used for rapid measurements during quality control and has consequently been investigated as a method for the measurement of protein, moisture, and ash contents, as well as kernel hardness (particle size index [PSI]) and sodium dodecyl sulfate (SDS) sedimentation from both whole grain and ground triticale samples. NIR spectroscopy prediction models calculated using ground samples were generally superior to whole grain models. Protein content was the most effectively modeled quality property; the best ground grain calibration had a ratio of the standard error of test set validation to the standard deviation of the reference data of the test set (RPDtest) of 4.81, standard error of prediction (SEP) of 0.52% (w/w), and r(2) of 0.95. Whole grain protein calibrations were less accurate, with optimum RPDtest of 3.54, SEP of 0.67% (w/w), and r(2) of 0.92. NIR spectroscopy calibrations based on direct chemical reference measurements (protein and moisture contents) were better than those based on indirect measurements (PSI, ash content, and SDS sedimentation). Calibrations based on indirect measurements would, however, still be useful to identify extreme samples.
机译：对黑小麦作为食物，饲料和燃料的需求不断增加，导致了具有不同谷物品质特征的品种的供应。对黑小麦的组成进行分析可以确保获得最合适的品种并将其用于最合适的应用。近红外（NIR）光谱通常用于质量控制过程中的快速测量，因此已作为蛋白质，水分和灰分含量以及籽粒硬度（粒度指数[PSI]）的测量方法进行了研究。从全谷物和磨碎的黑小麦样品中沉淀十二烷基硫酸钠（SDS）。使用地面样品计算出的近红外光谱预测模型通常优于全谷物模型。蛋白质含量是最有效地模拟的质量特性;最佳地面谷物校准的标准是，测试集验证的标准误差与测试集参考数据的标准偏差（RPDtest）的比率为4.81，预测的标准误差（SEP）为0.52％（w / w）， r（2）为0.95。全谷物蛋​​白校准的准确性较差，最佳RPDtest为3.54，SEP为0.67％（w / w），r（2）为0.92。基于直接化学参比测量（蛋白质和水分含量）的NIR光谱校准要优于基于间接测量（PSI，灰分和SDS沉降）的近红外光谱校准。但是，基于间接测量的校准对于识别极端样品仍然有用。

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