【作者】 李勇；

【导师】 魏益民；

【作者基本信息】 西北农林科技大学 ， 粮食油脂及植物蛋白工程， 2005， 硕士

【摘要】 近红外光谱分析技术(NIR)作为“绿色检测技术”的代表,具有高效、快速、低成本、无破坏性和绿色环保等特点。近红外检测技术的基础依赖于所建立的校正分析模型,该模型具有很强的专一性,需根据样品不同的种类、状态以及不同的分析内容逐项开展研究,分别建立相应的模型,才能发挥其优越性。建立NIR 校正分析模型需要消耗大量的时间、人力、物力和财力,因此,NIR 校正分析模型是近红外分析中最重要的资源之一。而模型的稳健性决定模型预测时的抗干扰能力和适用范围,从而影响分析结果的准确性、可靠性。因此,研究如何建立近红外稳健分析模型的方法对近红外光谱分析技术的广泛应用有着非常现实的意义,同时也是实现近红外模型传递的前提和基础。本研究以小麦、水稻、玉米、大豆等作物种子为研究对象,广泛搜集不同品种和人工创造不同的扫描背景,进行重要品质性状的常规分析和测定,采集样品的近红外光谱信息,利用化学计量学方法对作物品质指标进行近红外模型的创建;考察影响近红外光谱分析模型稳健性的因素;并以小麦水分含量为研究对象,研究建立准确、可靠、抗干扰能力强的近红外稳健分析模型的方法,并且以主成分空间对模型的稳健性进行分析和评价;探讨了近红外光谱技术分析单粒作物品质的可行性,得出下述结论: 1、利用近红外光谱分析技术分析谷物的水分含量、蛋白质含量、脂肪含量、淀粉含量、赖氨酸含量和小麦湿面筋含量的预测值与实际参考值具有较高的决定系数和较低的均方根误差。其中小麦水分含量校正分析模型的决定系数达到0.99以上,均方根误差低于0.2%。分析糙米胶稠度和碱消值效果不够理想,决定系数均小于0.60。　2、待测样品的水分含量、温度、粒度、状态对近红外光谱的形状和分析结果的准确性均产生较大影响。在5kGy 的剂量下,辐照处理对小麦蛋白质含量的近红外预测值无显著影响。　3、光谱预处理对建立稳健分析模型无明显效果;有效频率区间的选取、全局定标模型的应用对建立稳健分析模型均有效,其中建立全局校正分析模型效果最佳(内校正法)。用主成分空间分布可以对模型的稳健性进行评价,空间分布越大,模型适用性越宽。　4、建立作物单籽粒品质分析模型时,籽粒的放置位置影响分析结果的准确性。可通过对单粒样品的多次扫描或从不同方向测定样品光谱,取平均值的方法来提高模型的预测效果。更多还原

【Abstract】 The technology of Near-Infrared spectroscopy (NIR) analysis have the characters of high efficiency, quickly, low cost and nondestructive. The accuracy, reliability and application scope of NIR method depend on the calibration model developed by the databases. NIR calibration model have a very specificity, and it was set up according to the corresponding sample status and constituents. And the robustness of NIR calibration model decides the result accuracy, reliability and application scope. It is very important to set up a set of robust calibration models in application of NIR technology, and it is the basic work of transferring the NIR calibration models. The goal of this project is to setting up calibration model of wheat, rice, corn and soybean; Examining the factors which can effect the robustness of calibration models; Studying on the method to set up a well robust calibration models; Investigating the method of creating NIR calibration of single kernel seeds. The conclusions as follows: 1、It has a good coefficency and low SECV between NIR prediction value and true value in testing moisture, protein, fat, starch and gluten of wheat by NIR technology. The coefficency of wheat moisture reach 0.99, and the SECV is lower than 0.2. It is not a good result when predict the value of gel consistency and alkali spreading value. 2、The moisture, temperature, granular and status of the sample have great effects on the results and the shape of NIR. Irradiating processing have no eminent effect on NIR prediction value of wheat protein. 3、Preprocessing of NIR spectroscopy have no effect on setting up robust calibration; It is very well method to set up robust calibration with selecting a suitable scope of wavelength and taking global calibration. 4、The accuracy can be improved after getting the means of samples spectroscopy, which have been obtained by repeated scan and all directions test.更多还原