基于近红外光谱的北京市全株玉米原料康奈尔净碳水化合物-蛋白质体系组分分析与预测

本试验旨在基于康奈尔净碳水化合物-蛋白质体系(CNCPS)建立北京市全株玉米原料营养成分数据库,并利用近红外光谱(NIRS)方法建立其营养价值预测模型。试验采集北京市18个牧场89份全株玉米原料样品,测定其营养成分,利用CNCPS 6.5计算各样品碳水化合物(CHO)和蛋白质组成。定标集和验证集根据4∶1的配比关系,分别选用71份和18份全株玉米原料样品作为定标集和验证集评价NIRS模型。结果显示:1) NIRS分析技术对全株玉米原料常规营养成分、CNCPS中蛋白质组成和CHO组成均具有较好的预测能力,且精确度较高。2)干物质(DM)、粗灰分(Ash)、粗蛋白质(CP)、粗脂肪(EE)、中性洗涤纤维(NDF)、酸性洗涤纤维(ADF)、酸性洗涤木质素(ADL)、淀粉(Starch)、中性洗涤不溶蛋白质(NDIP)、酸性洗涤不溶蛋白质(ADIP)、可溶性蛋白质(SP)、CHO、非纤维性碳水化合物(NFC)、可溶性纤维(CB2)、可消化纤维(CB3)、不消化纤维(CC)、可溶性真蛋白质(PA2)、难溶性真蛋白质(PB1)、纤维结合蛋白质(PB2)和非降解蛋白质(PC)的定标决定系数(1-VR)均>0.80,验证决定系数(RSQv)均≥0.84,这些模型均可用于日常快速检测分析。DM、Ash、EE、NDF、ADF、ADL、Starch、NDIP、CHO、NFC、CB2、CB3、PC和PB1的NIRS模型参数均采用二阶导数处理,CP、SP、ADIP、CC、PA2和PB2的NIRS模型参数均采用标准正态变量+二阶导数处理。综上所述,本研究提供了全株玉米原料的基础化学分析数据,并通过NIRS分析技术建立了主要营养成分的快速预测模型,有利于养殖场青贮前对全株玉米原料质量的快速评估。     

近红外技术在牧草方面的应用

通过对近红外光谱法的基本原理、特点、近红外光谱仪的发展及其在国外牧草应用上的研究情况作一阐述,来推动近红外技术在我国牧草研究中的应用.     

近红外反射光谱法（NIRS）在甘薯品质育种上的应用

以常规化学法为对照法,应用NIRS法对40个甘薯样品进行了品质分析。两种方法所测甘薯化学成分含量间的相关系数和变异系数如下：水分分别为0.979和2.4%,粗蛋白分别为0.966和3.38%,淀粉分别为0.969和1.91%,粗纤维分别为0.957和3.74%,还原糖分别为0.922和4.12%。NIRS法测定甘薯主要品质指标可取得和常规品质分析法相近似的精度水平。 NIRS法是一种光物理化学仪器法,它不需称样,不需用化学试剂处理样品,普通操作者30秒内便可同时测得样品中一种或几种成分含量。在鉴定和筛选成批甘薯育种材料上显示出很大应用潜力。     

近红外反射光谱测定玉米完整籽粒蛋白质和淀粉含量的研究

 以128份常用普通玉米自交系及杂交种的混合籽粒样品为材料,采用偏最小二乘(PLS)回归法,对近红外反射光谱(NIRS)测定玉米完整籽粒蛋白质、淀粉含量的可行性和方法进行了研究。结果表明,采用一阶导数+多元散射校正预处理、谱区为10000～4000cm-1和一阶导数+直线扣减预处理、谱区为9000～4000cm-1,分别建立的蛋白质、淀粉含量的校正模型,其校正和预测效果最佳。其校正决定系数(R2cal)均大于0.97,交叉验证和外部验证决定系数(R2cv、R2val)为0.92～0.95,各项误差(RMSEE     

Genetic variability of growth and wood chemical properties in a clonal population of Eucalyptus urophylla × Eucalyptus grandis in the Congo

Thirty-four Eucalyptus urophylla × Eucalyptus grandis hybrids were evaluated with a view to selecting for improved growth and wood-quality traits for plantations in the Congo. Height, circumference at breast height and volume were measured at 12, 27, 37, 49 and 60 months. Lignin content, the syringyl/guaiacyl ratio and total extractives content were predicted by near-infrared spectroscopy using wood powder samples collected from trees at breast height. While wood chemical properties were stable and under strong genetic control, growth traits were not. The genetic correlation between lignin content and growth was weak and negative, whereas the environmental correlation was also weak but positive. The genetic improvement of E. urophylla × E. grandis clones, based on growth features, leads to a limited decrease in lignin content and syringyl content and to a limited increase in extractives content.     

近红外光谱法非破坏性测定绿豆籽粒粗蛋白质含量的研究

为了找到一种快速、简单的测定绿豆品质的方法,利用瑞典波通(Perten)公司生产的DA7200二极管阵列近红外光谱仪,以来自我国绿豆主产区的77份绿豆资源为试验材料,对样品进行光谱扫描,并测定了直链淀粉含量的参比数据。结果表明:定标集和检验集样品的蛋白质含量预测值与化学测定值之间均呈极显著的正相关,相关系数分别为0.977 2和0.963 1;所建定标模型具有较高的预测精度。本研究利用近红外光谱仪对完整绿豆粗蛋白质的分析,可直接用于育种材料选择以及突变体筛选和种质资源的评价等研究。     

近红外光谱技术在农产品品质检测中的应用

近红外光谱技术是一种新型的无损检测技术,在许多领域都得到了很好的应用。本文从农产品中各种物质成分含量预测、分类鉴别、腐烂鉴别、实时监测几个方面综述了近红外光谱技术在农产品品质检测上的应用,并对其在仪器硬件的研究和开发、化学计量学方法的探索与研究以及快速在线检测方法的研究等方面的发展趋势进行了展望。     

近红外光谱技术在天然草地牧草营养价值评价中的应用及展望

近红外光谱技术(NIRS)是近几十年迅速发展的测试分析技术,由于准确、高效、无损等检测优势,在牧草营养价值评价领域得到广泛应用,但是在天然草地牧草方面应用较少。快速、实时评价天然草地牧草营养价值,为研究天然草地营养供给和营养载畜量提供基础数据,对草地畜牧业生产具有重要意义。文章阐述了近红外光谱技术的基本原理和特点,介绍了直接法和间接法评价牧草营养价值,分别从常规营养成分、矿物元素、抗营养成分、营养物质消化率4个层次综述近红外光谱技术在2个方法中的应用,并做出展望,以期建立基于NIRS技术的天然草地牧草营养价值数据库,为天然草地的科学管理和合理利用发挥重要作用。     

花生自然风干种子油酸、亚油酸和棕榈酸含量的近红外分析模型构建

利用不同来源、不同种皮颜色的大粒型和小粒型材料,构建了花生自然风干种子油酸、亚油酸和棕榈酸含量的近红外定量分析模型。经优化,最佳光谱预处理方法均为“一阶导数+矢量归一化法”,油酸含量谱区范围为8717.1~5446.3cm-1（厘米波数）,维数为9,模型的决定系数（R2）为89.16,均方差（RMSECV）为2.62;花生种子亚油酸含量谱区范围为9,666~5,785.7cm-1,维数为9,模型R2为90.85,RMSECV为2.00;花生种子棕榈酸含量谱区范围为8,717.1~5,446.3cm-1,维数为8,模型R2为79.21,RMSECV为0.525。该模型可用于花生品质育种。     

Potential of Near- and Mid-infrared Spectroscopy in Biofuel Production

For many decades, near-infrared spectroscopy (NIRS) has been used to determine the composition of animal feedstuffs and grains. More recently, mid-infrared spectroscopy (mid-IR) has also been examined for similar determinations. These spectroscopic methods offer the potential for rapid and accurate determination of organic constituents, such as fiber components and protein, of forages, by-products, and grains at reduced cost and greatly increased speed (minutes instead of hours or days). Because they are nonchemical in nature, they result in a large reduction (90% or more) in the chemical wastes associated with standard chemical-based assay methods. The same components of interest for biofuel production (cellulose, hemicellulose, lignin, starch, protein, oil, etc.) are those that have already been determined by NIRS/mid-IR for evaluating grains and animal feedstuffs. Therefore, these techniques would appear to be a natural match for evaluating feedstocks for biofuels, and the literature shows that efforts in this direction are being successfully tested and instituted. For this discussion, an overview of where such efforts are and the potential for NIRS/mid-IR in producing biofuels will be covered. For example, while there are similarities between the needs of the biofuels industry and the analysis of animal feedstuffs, there are also both practical and technical differences between the two that will likely impact how NIRS/mid-IR is developed for biofuels. As an example, grain analysis for protein is performed on a large scale by government agencies such as the Canadian Grain Commission and U.S. Grain Inspection Service, while at least in the United States, animal feedstuff analysis is performed by state or independent laboratories for individual farmers. For biofuels, this might well result in most analysis being performed by the large corporations converting the feedstocks to biofuels, as opposed to the individual producer having analysis performed at an independent laboratory. Similarly for animal feedstuffs, measurements of fiber (neutral detergent fiber or NDF, acid detergent fiber or ADF, and lignin) and protein are carried out. These fiber measurements often consist of more than one type of fiber component with some being computed by difference (hemicellulose = NDF – ADF) and are empirical at best. Whether such empirical estimates will be sufficient for assessing biofuels or whether new spectroscopic methods for directly measuring the components of interest (cellulose, etc.) will need to be developed is a question to be answered when components other than starch for ethanol or oil for biodiesel become common.