玉米茎秆纤维组分近红外反射光谱模型的建立

以200份玉米自交系作为试验材料,利用近红外反射光谱技术建立3种茎秆组分的近红外光谱模型,研究更快速、准确地测定玉米茎秆中木质素、纤维素和半纤维素的含量的方法。结果表明,在4 017.94～8 053.28、4 017.94～8 067.89和4 027.08～8 928.20谱区内建立的测定玉米茎秆木质素、纤维素和半纤维素含量的近红外光谱模型效果最好。利用偏最小二乘回归法建立校正模型,木质素、纤维素和半纤维素的校正相关系数分别为0.932 9、0.925 1和0.926 5,校正标准差分别为1.57、1.68和1.18。选取30份玉米茎秆样品作为检验集对模型进行验证,木质素、纤维素和半纤维素的外部相关系数分别为0.938 9、0.891 1和0.905 0,其预测标准差分别为1.57、2.14和1.49。同样选取30份茎秆样品对模型进行交叉验证,其相关系数分别为0.897 3、0.944 2和0.891 8,交叉验证标准差分别为1.87、2.32和1.43。研究结果表明,所建模型质量较好,能快速、准确测量玉米茎秆木质素、纤维素和半纤维素含量。     

近红外光谱法快速测定苎麻果胶含量的研究与探讨

本文旨在探讨应用近红外光谱法快速测定苎麻中果胶含量的可行性.对62个苎麻样品进行近红外光谱扫描及果胶含量的测定,采用偏最小二乘法(PLS)建立近红外光谱信息与果胶含量的校正模型,该模型的相关系数为0.904.利用该模型对16个验证集样品进行果胶含量的预测,预测标准差为0.21.结果表明,该方法用于快速测定苎麻果胶含量是可行的.     

近红外反射光谱测定玉米完整子粒蛋白质和淀粉含量的校正模型

以玉米完整子粒为实验材料,采用偏最小二乘回归法建立近红外反射光谱测定其蛋白质和淀粉含量的数学模型,光谱预处理结果表明采用矢量归一化和一阶导数+多元散射校正分别建立蛋白质含量和淀粉含量的校正模型效果最佳,外部验证结果证明校正后模型预测结果与化学值之间的相关系数分别达到0.946 9和0.924 0。     

苎麻纤维素含量近红外预测模型的建立与探讨

本文旨在探讨用近红外光谱技术建立预测模型快速测定苎麻中纤维紊含量的可行性.以80个样品组成校正集采用偏最小二乘法(PLS)建立近红外光谱信息与纤维素含量之间的定量预测模型,该模型的相关系数为0.9726.利用该模型对22个样品的纤维素进行预测,预测值与化学测定值之间的平均相对误差为1.17%.结果表明,利用近红外光谱技术建立预测模型可以快速测定苎麻纤维素含量,对苎麻育种和优质资源的筛选利用具有重大意义.     

近红外光谱法快速测定苎麻粗蛋白含量

文中探讨了应用近红外光谱仪测定苎麻粗蛋白的可行性。以50个样品组成校正集,采用偏最小二乘法(PLS)建立了近红外光谱信息与粗蛋白含量的校正模型,该模型的相关系数为0.98。用该模型对10个样品的粗蛋白进行预测,结果表明,苎麻粗蛋白含量的化学测定值与近红外光谱模型预测值之间存在较好的相关性,预测值与化学值之间的平均相对误差为3.54%。可见,用近红外光谱分析建立苎麻粗蛋白预测模型并测定苎麻粗蛋白含量是可行的。     

无效变量消除法在油菜籽芥酸近红外无损速测中的应用

探索改善油菜籽芥酸近红外预测模型准确度与精密度的方法,利用无效变量消除法(UVE),对135个油菜籽样品近红外光谱信号进行筛选,并利用筛选后的光谱对油菜籽芥酸含量进行偏最小二乘法交叉验证。结果表明,UVE法筛选变量后建立的芥酸校正模型对未知样品预测结果的准确度和速度显著优于全波长参与建立的芥酸校正模型。散射校正加一阶导数对光谱预处理,UVE法筛选变量,偏最小二乘法交叉验证建立的校正模型效果最好,其预测值与标准值的相关系数R达到0.92,交叉验证预测均方差为2.2。因此,用UVE进行波长选择后建立的近红外模型,能准确快速地对油菜籽芥酸含量进行定量分析。     

应用近红外光谱法测定苎麻粗纤维含量

以66份苎麻材料为检测对象,常规化学方法测定苎麻粗纤维含量,应用近红外光谱技术采集其光谱,运用偏最小二乘法(PLS)建立了苎麻粗纤维含量的校正模型,该模型的相关系数为0.97,标准偏差为0.78。用16个验证集对所建模型预测效果进行外部检验,预测值与化学值之间存在较好的相关性,其相关系数为0.92,表明近红外光谱法用于苎麻粗纤维含量测定是可行的。     

绿茶茶多酚含量近红外光谱快速无损检测方法研究

应用现代近红外光谱分析技术,对156份绿茶样品直接进行光谱扫描,采用偏最小二乘法（PLS）建立了茶多酚含量的定标模型,并讨论了不同的散射处理、导数处理和平滑处理等光谱预处理方法对模型的影响,最后对最优模型的预测性能进行了验证。原始光谱在经过多元散射校正、二阶导数和8点平滑光谱预处理下的模型较优,其定标标准差（SEC）为1．33％,定标相关系数（RC）为0．932,预测标准差（SEP）为1．61％,预测相关系数（RV）为0．913,预测偏差（Bias）仅为0．375％。结果表明,应用近红外光谱法可以实现绿茶中茶多酚含量的快速无损检测,建立的定标模型能够达到实际应用中的精度要求。     

近红外光谱技术在苎麻壳木质纤维组分及镉含量测定上的使用

苎麻壳是原麻剥制后残留的表皮层,有重要的用途。为高效准确测定苎麻壳中木质纤维素组分和重金属镉含量,提高苎麻壳的利用效率,研究将近红外光谱技术与苎麻壳木质纤维素组分、镉(Cd)含量化学测定值相结合,采用定量偏最小二乘法(QPLS),运用不同的预处理和化学计量学方法建立了苎麻壳的校正模型,验证并筛选出最佳模型。结果表明:苎麻壳半纤维素、木质素、纤维素和Cd含量用散射校正预处理方法最佳,相关系数分别为0.9817、0.9864、0.9966、0.9922;果胶用中心化光谱预处理最佳,相关系数为0.9989。果胶、半纤维素、木质素、纤维素和Cd含量预测模型分别为y=0.9977x+0.0074,y=0.964x+0.3654,y=0.9936x+0.1767,y=0.9932x+0.3056,y=0.9851x+0.039。果胶、Cd、半纤维素的预测值和化学值的绝对误差分别在0.06、0.09、0.50左右,误差小,故可以选择该模型对苎麻的果胶、半纤维素和Cd含量等进行快速准确预测。     

基于近红外光谱仪对彩棉色度的测定技术研究

为建立1种快速检测棕色彩棉纤维色度值的方法,收集150份代表性样本,进行了光谱采集及相应化学色度值测定,采用二阶平滑导数及多元散射校正等方法对光谱预处理,采用偏最小二乘法对试验数据进行回归分析,建立棕色彩棉纤维的色度近红外反射光谱校正模型。结果表明：色度值近红外校正模型相关系数为0.984,校正标准误差0.638,交叉验证标准误差0.813,预测值标准误差为0.589。说明预测集样本达到较好的预测效果。表明应用该模型对棕色彩棉色度值具有很好的预测性,该应用模型可以快速检测棕色彩棉纤维色度。     

Measurement of Gliadin and Glutenin Content of Flour by NIR Spectroscopy

Traditional NIR calibration methods rely on assembling a calibration set of samples and using procedures such as multiple linear regression or partial least squares to develop the calibration. The problem with this methodology is to assemble a calibration set which maximises the diversity of samples represented whilst minimising the intercorrelations between constituents, particularly total protein content and moisture content. The application of NIR measurements of grain has moved beyond simply measuring protein and moisture content. There is now considerable interest in using NIR to measure a range of quality parameters such as Extensograph extensibility and maximum resistance. These parameters are not themselves represented in the NIR spectrum, but are a direct result of the protein composition of the sample. Consequently, a method for predicting the protein composition would be useful. In this paper, we present the results of a comparison of a curve fitting methodology and the more usual partial least squares curve fitting of the component protein spectra, using samples obtained from a wheat breeders» trial. Gliadin and glutenin contents were measured by SE-HPLC and used to develop a partial least squares calibration and the results compared with a curve-fitting methodology. For the situation examined here, the curve fitting methodology did not perform as well as partial least squares calibration. For glutenin, SEP=0·65 for the curve fitting compared to SECV=0·38 for a traditional PLS calibration. However, the results from the curve-fitting are independent of the total protein content and show sufficient discrimination for potential use in sample protein ranking.     

大麦籽粒抗性淀粉含量快速测定方法研究

为探索快速高效测定大麦籽粒中抗性淀粉含量的方法，利用衰减全反射中红外（attenuated total reflection mid-infrared spectroscopy,ATR-MIR）和近红外（near-infrared spectroscopy,NIR）光谱技术，分别用3种不同方法进行预处理，建立大麦样品的抗性淀粉含量快速测定红外模型，通过不同预处理预测模型的校正和内部交叉验证结果的比较，依据决定系数（r）和均方根误差（RMSE）筛选出基于ATR-MIR和NIR光谱的最佳预测模型，再对最佳预测模型进行外部验证。结果表明，经基线位移校正+范围归一化（BOC+RN）预处理后的PLS模型为最佳ATR-MIR预测模型；经标准正态变换+Savitzky-Golay法一阶求导（SNV+1thD）的预处理模型为最佳NIR预测模型。用验证集材料对BOC+RN和SNV+1thD最佳预测模型的预测效果进行外部验证，光谱预测值与化学测定值之间没有显著差异，说明两种方法均可以用于大麦籽粒抗性淀粉含量测定；ATR-MIR光谱比NIR光谱具有更好的预测能力。     

大豆主要脂肪酸含量近红外模型的建立

为提高大豆脂肪酸品种的育种进程,使用气相色谱法测量289份大豆籽粒样品的脂肪酸含量,并用近红外谷物品质分析仪NIRFIEX N-500采集大豆样品近红外光谱值,采用主成分回归法将化学试验测定的数值与采集的光谱曲线对应拟合整理分析建立定标模型。每种脂肪酸建立11个定标标准,选取决定系数定标Q值中最高的数值为定标模型。结果显示5种脂肪酸定标模型的适宜Q值分别为:棕榈酸0.823 5、硬脂酸0.854 1、油酸0.819 6、亚油酸0.829 1、亚麻酸0.836 3。最后验证结果表明,近红外法测量值与化学试验测定值误差均在1.5%误差范围之内,证明所建立模型质量较好,具有使用价值,可用于大豆育种材料脂肪酸含量的快速测量。     

NIR determination of potato constituents

Summary The development of a method of NIR (near infrared) spectrometric analysis to measure the quality determining constituents of potatoes and the accuracy of its performance are presented. The results show that it is possible to obtain quantitative information about different constituents from reflectance measurement of homogenized peeled potatoes. The analytical performance of NIR spectrometry is highly dependent on the cultivar being measured. Consequently, the calibration and validation sample sets will have to be expanded to provide greater utility.     

Estimating the amino acid composition in milled rice by near-infrared reflectance spectroscopy

This study was conducted to develop near-infrared reflectance spectroscopy (NIRS) equations to predict the amino acid and nitrogen content of milled rice powder. The samples were scanned by NIRS and analyzed for amino acid composition and total nitrogen by HCl hydrolysis–HPLC methodology and Kjeldahl method, respectively. The NIRS equations of 15 different amino acids, except for cystine, methionine and histidine, showed high coefficients of determination (RSQ=84.8–97.5%) and low standard errors in calibration (SEC) with 3 g samples for NIRS scanning, while the calibration models of cystine and histidine could explain less variation (RSQ with 77.7 and 65.0%). Calibration for methionine was not suitable to estimate methionine because of its very low RSQ (10.2%). The equations for total amino acids and nitrogen also showed high RSQ and lower SEC, respectively. Furthermore, calibration equations developed with only about 500 mg samples showed similar accuracy and reliability to those with the full cup by using the same calibration set. The equations developed for relative contents of total amino acids did not show good, effective calibration and cross-validation. Only eight different amino acids can be predicted using the equations because their RSQs of calibration were higher than 50.6% (50.6–73.9%). The others cannot be estimated with confidence by their relative contents due to lower RSQ in calibration. Moreover, their relative contents can be calculated from their absolute contents estimated by NIRS calibration.     

Integration of independent component analysis with near infrared spectroscopy for evaluation of rice freshness

The storage time and conditions of rice has an enormous effect on its appearance, flavor, and quality of the nutrients; and the acidity of rice usually increases with prolonged storage. Therefore, evaluation of freshness is an important issue for rice quality. In this study, the NIR (near infrared) spectra combined with independent component analysis (ICA) technique was used to evaluate the rice freshness. A total of 180 white rice samples were collected from 6 crop seasons for the purpose of developing an ICA-NIR based procedure for rice freshness as quantified by pH values. Values of pH were determined by a BTB-MR (bromothymol blue – methyl red) method. The best calibration model of white rice was developed using the smoothed first derivative spectra, five ICs and cross-validation; the results indicated that r² (coefficient of determination) = 0.924, and in units of pH, SEC (standard error of calibration) = 0.145, SEP (standard error of prediction) = 0.146, bias = 0.001, and RPD (residual predictive deviation) = 3.65. Freshness of white rice could be distinguished either visually by a 3-dimensional diagram composed from ICs 2, 3 and 4, or statistically by a calibration model. The results show that ICA with NIR has the potential to be adopted as an effective method for evaluating rice freshness.     

不同施氮方式对壤质草甸土土壤pH值及全氮磷钾含量的影响

试验测定越冬前后耕层土壤的pH值、全氮、全磷和全钾的变化,分析氮素优化减量、秸秆还田和缓控释尿素的应用对耕层土壤pH值和全氮磷钾的变化趋势。结果表明,季节性冻融和冬季雨雪作用能够提高耕层土壤的pH值,降低耕层土壤的氮素含量,由于玉米根系分泌钾和微生物矿化的双重作用,耕层土壤全钾含量有明显增加;秸秆还田能够提高氮磷钾的有效性和流动性,改善土壤供肥能力,促进玉米吸收养分;缓控释尿素能够持续降低耕层土壤的pH值,促进磷素的吸收。     

Prediction of milled rice grades using Fourier transform near-infrared spectroscopy and artificial neural networks

This paper describes a method to rapidly and objectively predict the grades of milled rice according to the surface lipid content (SLC), which was determined by using near-infrared (NIR) spectroscopy. Sixty-six rice varieties were milled to different degrees. Then each sample was graded by a three-member panel. After the NIR spectra for each sample were collected over the wavenumber range of 11,000–4000 cm⁻¹, the SLC of each sample was measured according to the official method. The calibration equations relating the Fourier Transform Near-infrared (FT-NIR) spectra to the measured SLC were developed based on the partial least square (PLS) regression. The best model gave the root mean square error of the prediction (RMSEP) of 0.0248% and the determination coefficients of 0.9905. If the relationships between the grades and the SLC predicted by the developed NIR model were described with the linear and the logarithmic regression equations, the correct prediction percents (CCP) were 75.76% and 83.33%, respectively. When the back propagation artificial neural network (BP-ANN) model was developed to estimate the grades according to SLC, the resultant CCP was 95.45%, indicating that the milled rice grades could be predicted by the proposed BP-ANN model with satisfactory accuracy.     

胡麻测土施用氮肥的研究

通过相关性研究和校验试验表明,内蒙西部平原地区冲积土有效氮的测定,采用1.8N NaoH水解氮的方法是较好的。通过校验试验确定了土壤的肥力指标。根据土壤碱解氮测定值即可获得胡麻氮肥的建议施肥量。     

“稻+鱼+再生稻”模式对稻田土壤氮、磷、钾养分含量的影响

设置头季稻+鱼(或泥鳅)+再生稻的栽培模式,水稻品种选用黄华占和Y两优800,分析两种模式下土壤中全量氮、磷、钾含量与速效氮、磷、钾含量表现,结果表明:(1)稻鱼模式和稻鳅模式可增加土壤中全效养分含量,全氮、全磷、全钾含量在整个生育期内维持在一个相对稳定的状态,其中以稻鱼处理效果更明显,总体高于对照田.头季稻与再生季两个生育季内全氮、全磷处于相对稳定的状态,全钾含量则在再生季略有下降;(2)稻鱼模式和稻鳅模式提高了土壤中速效养分含量,孕穗期、灌浆期、成熟期含量比分蘖期时有所提升,稻鱼模式下土壤速效养分含量整体表现要高于稻鳅模式.两个水稻品种再生季的土壤速效养分含量变化规律性不明显,但整体维持在相对稳定水平.