论文标题:作物种子品质研究中近红外光谱分析模型的创建和应用
NIR Spectroscopy Model Construction and Application in the Research of Crop Seed Qualities
论文作者 吴建国
论文导师 石春海；朱军,论文学位 博士,论文专业 作物遗传育种
论文单位 浙江大学,点击次数 641,论文页数 112页File Size6313k
2003-12-01论文网 http://www.lw23.com/lunwen_1169282/ 近红外光谱技术;作物;种子品质;化学计量学;分析模型
Near infrared spectroscopy,crop,seed quality,chemometrics,predication model
近红外光谱(NIR)技术是近年来发展最快的测定技术之一，具有无损、快速、高效、方便等特点，适合于种子品质性状的测定和分析。由于该技术测定的准确性、可靠性、适用范围等都依赖于以常规测定方法为基础建立起来的校正分析模型。NIR分析技术所建立的校正模型具有专一性强的特点，需根据不同的样品种类和状态以及不同的分析内容等逐项开展研究，建立相应的校正分析模型，才能发挥其优越性。因此，该技术应用的关键是建立预测准确、抗干扰能力强的校正分析模型。 本研究的目的是根据NIR分析仪的特点和性能，重点研制和开发种子品质性状的校正分析模型，以便进行快速大批量测定，大幅度降低分析成本，提高分析测试水平和效率以及仪器使用率；同时，结合作物品质育种研究中品质成分分析的实际情况，探讨微量样品、甚至单粒无损测定的技术和方法，开拓和扩大NIR分析仪的应用范围和领域。 本研究选用了水稻、油菜、大麦、玉米等作物种子为研究对象，广泛收集和人工创造不同类型的种子样本材料。进行重要品质性状的常规分析和测定，收集多种样品状态的NIR信息，以此组成NIR分析的原始群体。依据实际应用的需要，利用不同的化学计量学方法对不同的种子样品群体进行NIR模型的创建和应用。主要研究结果有：创建水稻常规品质如直链淀粉含量、胶稠度、碱消值、粘滞度(RVA)的NIR校正分析模型，具有分析准确，可靠、适用性广的特点；发展了蛋白质含量和多种氨基酸指标校正模型，进一步拓展了NIR技术在水稻稻米营养品质分析中应用范围。同时发展了适于遗传育种研究用的油菜籽油分、蛋白质、硫甙和脂肪酸品质测定的分析模型，具有大样本、小用量、整粒无损分析等特点；进一步发展了大麦的蛋白质和葡聚糖快速测定的NIR技术分析模型；构建了能准确测定玉米种子水份和蛋白质的NIR校正模型。在创建各种NIR校正分析模型的同时，探索各主要作物种子品质分析的校正集选择方法和参数以及构建模型时的各参数优化设置，为建立规范的分析方法奠定基础。
In recent years, near infrared (NIR) spectroscopy has made rapid progress as analytical technique. Being capable of making nondestructive, rapid, high efficient and convenient analysis, NIR technique is suitable to analyze seed quality traits.The accuracy, reliability, and application scope of NIR method are depended on the calibration model developed by the database based on routine analytical methods. The calibration model of NIR possesses sample and constituent dependence when is used for predication. To make use of the advantage of NIR spectroscopy, the individual calibration models must be developed according to the corresponding sample status and constituents. Thus, the key work of this technique is to develop the robust and accurate calibration models.The goal of this project was to develop calibration models for seed quality traits based on the characteristics of the NIR instrument, which could be capable of analyzing amount of seed samples, and to reduce the analysis cost greatly and to increase the analysis efficiency and to accelerate the usage frequency of the instrument. Meanwhile, the calibration models for small sample and single grain were constructed according to the requirement of crop breeding and the analytical scope of the NIR instrument, which needed to be enlarged and exploited.The seed samples of crops such as rice, rapeseed, barley and maize were chosen as materials and collected in large scale and various specific seed samples were created. Many important constituents of seed sample were determined and corresponding NIR spectra of various samples were collected, therefore, the original databases for NIR calibration were created. According to the requirement of quality traits studies, thecorresponding calibration models were developed by using various chemometrics methods.The main results were as follows: The NIR calibration models, which possess an accuracy and reliable predication, were constructed for rice common constituents such as amylose content, gel consistency, alkali spreading value and RVA. The further calibration models for rice nutrient quality traits like protein content and various amino acid contents were developed and the scope of the NIR application on rice nutrient assay was enlarged. For predicating the samples used in the genetic and breeding purpose, the models, which had possessed the information including a large database, small sample size and intact seed samples, were created for rapeseed oil, protein, glucosinolate and fatty acids contents. The NIR calibration models for barley protein and glucan contents were developed, simultaneously and created the models which can be used to predicate maize moisture and protein content precisely. When developing the various models, the parameter and selecting methods of calibration set were investigated and chemometrics parameter for models were optimized in order to make a base for constructing future normative analysis methods.
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