菟丝子中总黄酮的闪式提取工艺及近红外透射光谱含量分析方法

利用闪式提取器对菟丝子中的总黄酮进行组织破碎提取,确定菟丝子中总黄酮闪式提取的最佳提取工艺为:料液比1∶8(mg∶mL),以50%的乙醇为溶剂提取8min。采用主成分分析(PCA)和偏最小二乘法(PLS)回归分析法建立了菟丝子提取液中总黄酮含量近红外透射光谱定量分析模型,该模型校正集的相关系数为0.99985,校正集标准偏差(RMSEC)为0.0685,预测集标准偏差(RMSEP)为0.160,交叉验证结果的相关系数为0.99966,交叉验证标准差(RMSECV)为0.102。利用该模型预测和紫外分光光度法测量的总黄酮含量相差很小,且模型具有良好的稳定性。     

近红外光谱法快速测定高含量精制甘油中的甘油含量

采用偏最小二乘法(PLS)建立了快速测定高含量精制甘油中甘油含量的近红外光谱校正模型,该模型主因子数为4,相关系数(R2)为99.12%,校正标准偏差(RMSECV)为0.027;以预测集对模型进行验证,结果表明,R2为99.17%,预测标准偏差(RMSEP)为0.023,对同一样品预测值的相对标准偏差(RSD)为0.04%。     

近红外光谱技术快速测定黄连女贞子提取过程中2种成分含量

目的:建立一种用近红外光谱技术快速测定女贞子黄连提取过程指标成分含量的方法。方法:运用偏最小二乘法结合多种光谱预处理方法及波长选择方法建立近红外光谱与女贞子黄连提取液指标成分(盐酸小檗碱、盐酸巴马汀)含量之间校正模型,通过交互检验标准偏、校正标准偏差、决定系数和主因子数优选校正模型,并对未知样本进行预测分析。结果:一提液中盐酸小檗碱、盐酸巴马汀的校正模型相关系数分别为99.46、98.30,二提液中三盐酸小檗碱、盐酸巴马汀的校正模型相关系数分别为99.16、97.86。验证集的预测值与真实值含量接近。结论:该方法操作简便、快速无损、准确可靠,可用于女贞子黄连提取过程指标成分含量的快速监测。     

单基发射药中钝感剂组分含量的快速检测方法

为解决传统分析方法测定单基发射药中钝感剂(樟脑)含量存在时间长、工作量大等问题,建立了一种采用近红外光谱法快速测定单基发射药中钝感剂(樟脑)组分含量的新方法;通过对比单基发射药药粒样品及钝感剂光谱图特点,确定了钝感剂组分最佳建模光谱范围为8 300~8 510cm~(-1);并对样品光谱图进行了预处理,对比了多个不同光谱预处理方法的效果,确定出最佳光谱预处理方法是多元散射校正(MSC)+一阶导数的组合;采用偏最小二乘法建立了钝感剂的近红外模型,并对模型的预测能力进行了验证。结果表明,钝感剂的模型校正相关系数(R_c~2)和验证相关系数(R_p~2)分别为0.972 3和0.973 5,验证的校正标准偏差(RMSEC)和交互验证的校正标准偏差(RMSECV)分别为0.163 6和0.150 8;预测均方根误差(RMSEP)为0.182 7,验证集标准偏差与预测标准偏差的比值(RPD)为6.87;将该模型应用到单基发射药中樟脑含量的检测,可使预测值极差和标准偏差均低于0.2%,表明该方法能实现单基发射药中钝感剂组分含量的快速检测。     

人造革基布纤维含量近红外光谱法专属定量分析模型的建立

研究了近红外光谱法在人造革基布纤维含量定量分析中的应用。通过分析样品近红外光谱的主成分,选择校正和验证样品集,选用偏最小二乘法(PLS),建立人造革基布纤维含量专属定量分析模型。结果表明:在9 017.5~4 396.9 cm~(-1)的波数范围内,选用9个主成分数建立了专属定量分析模型,模型的校正均方根误差(RMSEC)为0.678、相关系数(R_c~2)为0.999 5;预测均方根误差(RMSEP)为0.705、相关系数(R_v~2)为0.998 9,残差范围为-1.5~1.4;专属定量分析模型具有较好的预测准确性和方法重复性,可实现人造革基布纤维含量的快速测定。     

近红外光谱法快速检测腐竹中硼砂的含量

选取市售腐竹作为研究对象，采集样品的近红外光谱，用国标分光光度法测定样品中硼砂的含量作为标准值，结合偏最小二乘法，建立了腐竹中硼砂含量的近红外光谱快速检测模型。结果表明，采用二阶导数谱，在7 150.75～5 924.25 cm^-1光谱区间，经多元散射校正、五点平滑处理建立的模型为较优模型。该模型的校正集线性相关系数(R_c)值为0.994 1,校正集均方根偏差(RMSEC)值为0.050 8,验证集线性相关系数(R_p)值为0.980 6,验证集均方根偏差(RMSEP)值为0.114,主因子数(factors)为10,相对分析误差(RPD)为5.1。模型有较好的稳定性，外部验证结果准确，可以用于腐竹中硼砂含量的快速检测。     

制浆材综纤维素和聚戊糖含量的近红外预测

用传统方法测定了156个制浆材样品的综纤维素和聚戊糖含量并采集了样品的近红外光谱。对原始光谱进行多元散射校正后,运用偏最小二乘法和交互验证的方法,确定最佳主成分数分别为9和10并建立样品综纤维素和聚戊糖含量的校正模型。独立验证中两个模型的决定系数R_(val)~2分别为0.903 4、0.940 1,预测均方根误差(RMSEP)分别为0.69%、0.78%,相对分析误差(RPD)值分别为3.22、4.09,绝对偏差(AD)分别为-1.00%~1.20%、-1.39%~1.31%,两个校正模型较好地预测了验证集样品的综纤维素和聚戊糖含量,基本满足制浆造纸工业中快速测定的需求。     

近红外光谱法测定异氰酸酯树脂中游离甲苯二异氰酸酯(TDI)

建立使用近红外光谱(NIR)技术研究快速测定异氰酸酯树脂中游离甲苯二异氰酸酯(TDI)方法。收集异氰酸酯树脂样品,使用气相色谱法(GC/FID)测定游离TDI含量,并采集其近红外光谱,采用偏最小二乘法(PLS)建立NIR光谱与游离TDI含量的线性关系。在建模过程中,以均值中心化数据增强、Norris平滑和二阶导数算法对光谱数据进行预处理,主因子数为4,定量分析波段为5700~5743cm~(-1)、5764~5805cm~(-1)、5843~5898和5921~5978cm~(-1)。模型RMSEC、RMSEP和RMSECV分别为0.0448%、0.0472%和0.0485%;校正集、验证集和交叉验证集的相关系数R2分别为0.9696、0.9720和0.9643。该模型预测效果良好,方法简便、快速、准确,适用于异氰酸酯树脂中游离TDI含量的快速检测。     

近红外光谱技术结合支持向量回归法测定烯草酮的含量

采用近红外光谱(NIR)技术结合支持向量回归法(SVR)建立了烯草酮乳油的定量分析方法。通过添加烯草酮原药、烯草酮助剂到二甲苯溶剂来配制不同浓度的校正集,采用SVR法建立了烯草酮的定量分析模型,模型的决定系数(R2)、校正集均方根误差(RMSEC)、检验集均方根误差(RMSEV)、预测集均方根误差(RMSEP)分别为1.0000、0.0260、0.0569和0.0550。结果表明,近红外光谱技术结合支持向量回归法可以准确地定量分析乳油中烯草酮的含量,方法简单、快捷,在农药质量检测中具有实际应用价值。     

近红外光谱技术结合支持向量回归法测定非法对硫磷的含量

[目的]采用近红外光谱(NIR)技术结合支持向量回归法(SVR)建立乳油中非法对硫磷的定量分析方法.[方法]通过向1.8％阿维菌素乳油中加入对硫磷原药和二甲苯溶剂来配制不同质量分数的校正集,采用SVR法建立非法对硫磷的定量分析模型.[结果]模型的决定系数(R2)、校正集均方根误差(RMSEC)、检验集均方根误差(RMSEV)、预测集均方根误差(RMSEP)分别为0.9994、0.4377、0.5065、0.7482.[结论]该法可以定量测定乳油中非法对硫磷的含量.     

Measurement of oil in whole flaxseed by near-infrared reflectance spectroscopy

A near-infrared reflectance (NIR) Infralyzer 500 was calibrated for determination of oil with samples of ground and whole flaxseed grown over three years. Wavelength selection by the computer software interfaced with the Infralyzer, analytical and regression statistic data, such as standard deviation of laboratory analysis (SD_x), correlation coefficient, standard error of estimate (SEE), standard error of prediction (SEP), and the SD_x/SEP ratio showed that calibration of the instrument with whole flaxseed was equal in precision to that obtained with the ground flaxseed. Growth location or seed moisture content had no effect on oil content of whole flaxseed determined by the NIR. The whole seed calibration allowed rapid, nondestructive screening for oil in flaxseed at greatly reduced cost.     

Multivariate model for the prediction of soluble condensed tannins in crude extracts of polyphenols from canola and rapeseed hulls

The feasibility of using ultraviolet spectrophotometry to develop multivariate models for prediction of soluble condensed tannins (SCT) content in crude polyphenols extracts from canola and rapeseed hulls was investigated. The polyphenols were extracted from hulls using 70% (vol/vol) aqueous acetone. Partial least squares regression was used to correlate the spectral data of the crude polyphenols in methanol between 265–295 nm with the SCT content in hulls. Both the proanthocyanidin (P) and the vanillin (V) assays were used to provide reference data for creating the models. The predictive ability of the models is good, as indicated by the RPD values [the ratio of the standard deviation of data to the standard error of calibration (SEC) of above 5. Additionally, the SEC values suggest that P is superior to V in predicting the SCT content of hulls using this method.     

测定吡虫啉农药制剂中有效成分的3种HPLC不同定量方法比较

采用高效液相色谱法测定吡虫啉农药制剂产品中有效成分含量,并对单点校正法、标准曲线法和标准加入法3种不同方法进行分析,比较了3种方法的差异性。利用Excel对3种定量方法的标准偏差、基质效应和测定结果进行F检验和t检验。结果表明,在显著性水平α=0.05时,单点校正法与标准曲线法、标准加入法标准偏差有明显差别,标准曲线法和标准加入法之间无显著性差异。对可湿性粉剂产品,基质效应无法忽略,3种方法测定结果间均具有显著性差别;对乳油产品,标准曲线法和标准加入法测定结果无明显差别,但单点校正法与其他2种方法具有显著性差异。建议对可湿性粉剂产品选择标准加入法定量,乳油产品选择标准曲线法定量。     

Oil and water analysis of sunflower seed by near-infrared reflectance spectroscopy

The applicability of NIR for oil and moisture analyses of sunflower seed was determined using a NIR spectrocomputer system. The method was compared with the wide-line NMR method for oil analysis and with the A.O.C.S. oven method for moisture analysis. The NIR was calibrated with 120 samples for oil (96 for calibration, 24 for prediction) and 63 samples for moisture (55 for calibration, 8 for prediction). Twenty-two sunflower seed samples were analyzed for oil and moisture by NIR and by methods used by industry. The oil contents of the samples by NMR and NIR were not significantly different. The overall mean oil contents and mean of the standard deviations for the samples were: NMR, 44.2%±0.35% and NIR, 44.34%±0.74%. A significant difference was found between the moisture values obtained by the oven-drying method and NIR. The average standard deviation for moisture by NIR was 0.57% compared with 0.07% for the oven-drying method. The variability of the oil content in one of the commercial seed samples was 1.52% oil as determined by NMR and 2.52% as determined by NIR. The advantages and disadvantages of both methods are discussed.     

Nondestructive single-kernel oil determination of maize by near-infrared transmission spectroscopy

The utility of near-infrared transmission spectroscopy (NITS) for the nondestructive prediction of oil content in single maize kernels was explored. Calibration models were developed from spectral information gathered between 850 and 1050 nm. Nuclear magnetic resonance (NMR) spectroscopy was employed as a reference method to determine the actual oil content of samples used for calibration development and testing. Various positionings of the kernels in the light path and calibration math treatments were explored. The best NITS calibration yielded a 1.2% standard error of cross validation, which was over four times the standard error of NMR reproducibility. Although not as accurate as NMR, NITS does have utility in selecting kernels with the highest oil content from a segregating population.     

Prediction of properties of diesel/biodiesel blends by infrared spectroscopy and multivariate calibration

Partial least squares models (PLS) using near and middle infrared spectrometry were developed to predict quality parameters of diesel/biodiesel blends (density, sulphur content and distillation temperatures). Practical aspects are discussed, such as calibration set composition; model efficiency using different infrared regions and spectrometers; and the calibration transfer problem. The root mean square errors of prediction, employing both regions and equipment, were comparable with the reproducibility of the corresponding standard method for the properties investigated. Calibration transfer between the two instruments, using direct standardization (DS), yielded prediction errors comparable to those obtained with complete recalibration of the secondary instrument.     

傅立叶近红外光谱法测定汽油中苯含量

介绍了傅立叶红外光谱测定汽油中苯含量的方法.以标准分析方法为参照,利用傅立叶红外光谱仪测定汽油的红外光谱,采用一阶微分和偏最小二乘法对汽油中的苯含量建立校正模型.该模型测定未知样品汽油苯含量的结果与标准分析方法的偏差符合标准方法的要求.     

Estimation of Protein and Fatty Acid Composition in Shell-Intact Cottonseed by Near Infrared Reflectance Spectroscopy

Rapid and accurate analysis of cottonseed protein content and the composition of fatty acids (especially, saturated fatty acids) is often required in cotton production and breeding programs. This study aimed to establish a set of effective estimation models for these parameters. Near infrared reflectance spectroscopy (NIRS) calibration equations using partial least-squares regression for protein concentration, oil concentration, and five fatty acids of shell-intact cottonseeds were established based on 90 varieties, and the prediction abilities of the calibration models were verified using 45 other varieties. The prediction abilities of the NIRS calibration equations were basically consistent with external validation results. Each equation was assessed based on the ratio of performance to deviation (RPD_p). Protein content and seed total fatty acid (STA) content had high RPD_p values (3.687 and 3.530, respectively), whereas cottonseed kernel total fatty acid (KTA) content, linoleic acid (18:2), stearic acid (18:0), myristic acid (14:0), and palmitic acid (16:0) exhibited relatively high RPD_p (2.866, 2.836, 2.697, 2.676, and 2.506, respectively). The calibration model for oleic acid (18:1) had a low RPD_p (1.945). The results indicated that NIRS can be used to rapidly determine contents of STA, KTA, protein, stearic acid (18:0), myristic acid (14:0), and palmitic acid (16:0) in shell-intact cottonseed.