近红外光谱法对头孢氨苄粉末药品的非破坏定量分析

采用近红外漫反射光谱法对头孢氨苄粉末药品中主要成分头孢氨苄进行快速、无损定量分析.采用偏最小二乘法建立近红外光谱信息与待测组分含量间的最佳数学校正模型.对3种光谱(SNV光谱、一阶导数、二阶导光谱)的预测结果进行了比较,讨论了光谱的预处理方法和主成分数对偏最小二乘法定量预测能力的影响,并对预测集样品进行预测.     

近红外光谱法快速检测烟草中部分香气物的应用研究

应用傅立叶变换近红外漫反射光谱仪,从500个样品中按高、中、低含量挑选出具代表性的150～172个烟草样品建立了近红外光谱与烟草中的苹果酸、柠檬酸和石油醚提取物成分含量间的数学模型,用建立的模型对36个样品进行预测,结果表明,各成分近红外预测值与实测值之间的平均偏差:苹果酸为0.090,柠檬酸为0.040,石油醚提取物为0.124;且近红外预测值与化学法不存在显著性差异,近红外光谱分析技术可初步用于烟草部分香气成分的快速定量分析.     

基于独立分量和神经网络的近红外多组分分析方法

采用小波变换对光谱数据进行压缩,用独立分量分析(ICA)方法提取近红外光谱数据矩阵的独立成分和相应的混合矩阵,再用BP神经网络对混合矩阵和实测浓度矩阵进行建模,提出了基于独立分量分析-神经网络回归(ICA-NNR)的近红外分析建模方法。进一步研究了独立分量数和网络中间隐层的神经元数对模型性能的影响,经优化后的ICA-NNR模型在相关系数与均方根误差两个指标上均优于直接用光谱矩阵作为输入所建立的模型。本方法用于玉米中水分、淀粉、蛋白质3种主要成分含量的同时测定,检验样品集的化学检测值与近红外预测值的相关系数分别达到:淀粉r=0.971,蛋白质r=0.976,水分r=0.975。     

ICA方法与NIR技术用于药片中活性成分含量的测定

用独立分量分析(ICA)方法提取药片近红外光谱数据矩阵的独立成分和相应的混合矩阵, 再用BP神经网络对混合矩阵和药片中活性成分的浓度矩阵进行建模, 提出了新的药片活性成分含量测定的基于独立分量分析-神经网络回归(ICA-NNR)的近红外光谱分析方法. 通过分析独立分量数和网络中间隐层的神经元数对模型性能的影响, 分别建立三类药片定量分析的最优模型. 该方法用于实测的三类药片中活性成分含量的测定, 测试样品集的化学检测值与近红外预测值的相关系数分别达到0.962, 0.980及0.979. 结果表明, 基于ICA-NNR的近红外光谱分析方法对制药业的药片进行定量分析是可行的.     

PLS-BP法近红外光谱技术同时测定鲜乳中四种主成分

基于近红外光谱技术,将偏最小二乘法(Partial Least Squares,PLS)和单隐层的反向传播网络(Back-Propagation Network,BP)联用并测定了鲜乳中4种主成分含量.用PLS法将原始数据压缩为主成分,取前3个主成分的14个数据输入网络,以Kolmogorov定理为依据,经过实验确定中间层的神经元个数为29,初始训练迭代次数为1000,建立了脂肪、蛋白质、乳糖、牛乳总固体4种主成分含量的预测校正模型.PLS-BP模型对样品4个组分含量的预测决定系数(R2)分别为:0.961、0.974、0.951、0.997;本研究为近红外光谱技术在鲜乳多组分快速检测提供了新思路.     

采用多光程长建模方法检测血液成分含量

为了提高近红外光谱血液成分含量分析模型的预测精度,利用多个光程长(optical path length,OPL)共同参与建模的方法进行血糖等6种血液成分的定量分析。通过微米位移机构实现不同光程长血液光谱的测量,由全自动生化分析仪给出生化成分分析结果,并出具化验单。采用偏最小二乘法(PLS2)进行血液的近红外光谱建模及预测。由于血液光谱存在显著的非线性特征,不同光程长的血液样本的等效吸收系数不同,同一波长不同光程长(0.20~1.25 mm)测得的血液光谱互不相关。主动把非线性特性作为一种测量手段引入,不再利用单个的最佳光程长建模,而是用各个血液组分对应的多个最佳光程长的近红外光谱同时参与建立校正模型,进行血液成分的分析预测。研究结果表明,多光程长建模方法用于血液成分含量分析,可提高血液成分校正模型的预测精度。     

近红外光谱法快速测定再造烟叶成品小片中主要化学成分

提出了近红外光谱法快速测定再造烟叶成品小片中烟碱、总糖、还原糖、总氮、钾、氯等6种主要化学成分的方法。直接采集再造烟叶成品小片,结合偏最小二乘回归算法建立了近红外光谱的分析模型。结果表明:再造烟叶成品小片的近红外光谱能真实、有效地表征待测样品的内在化学物质组成与含量信息;除总氮外,其余5种成分的再造烟叶成品小片近红外光谱分析模型的相关系数均大于0.90;烟碱、总糖、还原糖、总氮、钾、氯等6种成分的预测误差分别为0.024 3,0.399 1,0.270 3,0.059 9,0.050 3,0.031 1。小片光谱分析模型效果与粉末光谱模型较接近,可以替代粉末模型用于再造烟叶成品小片化学成分含量的测定。     

原料乳中蛋白质与脂肪的近红外光谱快速定量研究

本文对快速无损检测原料乳中蛋白质与脂肪含量的近红外光谱(NIRS)技术进行了研究。对采集的250组蛋白质及脂肪含量不同的原料乳近红外光谱进行马氏距离(Mahalanobis Distance)剔除异常光谱,结合主成分分析(Principal Component Analysis,PCA),筛选出最佳建模光谱区间,采用反向传播神经网络(Back Propagation Neutral Network,BPNN)建立原料乳中蛋白含量与脂肪含量的定量模型,获得了较好的预测结果,预测模型R2分别为0.9883、0.9878,预测均方根差(RMSEP)分别为1.83%、1.85%。研究结果表明,通过合理选择光谱范围及建模方法,可得到预测精度与稳定性均较高的近红外光谱定量模型,适用于原料乳中蛋白质与脂肪含量的测定。     

近红外光谱结合新型模型传递方法用于糖料的在线质量监控

当采用近红外光谱技术对糖香料的生产过程进行在线质量监控时,糖香料的温度变化严重影响近红外光谱校正模型的预测性能,使其对糖浆样本中主要成分预测结果的平均均方根误差从2.4%增大到29.2%。本研究将近红外光谱技术与载荷空间标准化新型模型传递方法相结合,有效消除了温度变化对近红外光谱校正模型定量分析结果的影响,使其对糖浆样本中主要成分预测结果的平均均方根误差维持在3.8%的水平,实现了利用近红外光谱技术对糖香料的质量进行快速准确的监测和控制。本研究的研究结果为糖料的配制和使用提供了技术保障。     

基于主成分分析和小波神经网络的近红外多组分建模研究

将小麦叶片原始光谱经过预处理后,采用主成分分析(PCA)对数据进行降维,取前3个主成分输入小波神经网络,建立了基于主成分分析和小波神经网络的近红外多组分预测模型(WNN);进一步研究了小波基函数个数的选取(WNN隐层节点数)对小波神经网络模型性能的影响,并将WNN模型与偏最小二乘法(PLS)和传统的反向传播神经网络(BPNN)模型进行了比较.结果表明,所建立的WNN模型能用于同时预测小麦叶片全氮和可溶性总糖两种组分含量,其预测均方根误差(RMSEP)分别为0.101%和0.089%,预测相关系数(R)分别为0.980和0.967.另外,在收敛速度和预测精度上,WNN模型明显优于BPNN和PLS模型,从而为将小波神经网络用于近红外光谱的多组分定量分析奠定了基础.     

Combination of mid- and near-infrared spectroscopy for the determination of the quality properties of beers

The combination of infrared (MIR) and near-infrared (NIR) spectroscopy has been employed for the determination of important quality parameters of beers, such as original and real extract and alcohol content. A population of 43 samples obtained from the Spanish market and including different types of beer, was evaluated. For each technique, spectra were obtained in triplicate. In the case of NIR a 1 mm pathlength quartz flow cell was used, whereas attenuated total reflectance measurements were used in MIR. Cluster hierarchical analysis was employed to select calibration and validation data sets. The calibration set was composed of 15 samples, thus leaving 28 for validation. A critical evaluation of the prediction capability of multivariate methods established from the combination of NIR and MIR spectra was made. Partial least squares (PLS) and artificial neural networks (ANN) were evaluated for the treatment of data obtained in each individual technique and the combination of both. Different parameters of each methodology were optimized. A slightly better predictive performance was obtained for NIR-MIR combined spectra, and in all the cases ANN performs better than PLS, which may be interpreted from the existence of some non-linearity in the data. The root-mean-sqare-error of prediction (RMSEP) values obtained for the combined NIR-MIR spectra for the determination of real extract, original extract and ethanol were 0.076% w/w, 0.14% w/w and 0.091% v/v.     

GC-MS法检测生物检材中的白酒和啤酒

用GC-MS法分析白酒和啤酒中的成分，统计出白酒、啤酒的各自特征成分并加以比较。对添加不同量白酒、啤酒及白酒与啤酒混合物的胃内容物进行检验，对饮用不同量白酒、啤酒及白酒与啤酒混合物的人员分别采集血样进行检验，统计并总结出胃内容物及血液中白酒、啤酒的区分检验方法。当生物检材中乙醇含量大于0．3mg／mL时，利用该方法可以区分检材中的白酒和啤酒。     

Orthogonal signal correction applied to the classification of wine and molasses vinegar samples by near-infrared spectroscopy. Feasibility study for the detection and quantification of adulterated vinegar samples

The most common fraudulent practice in the vinegar industry is the addition of alcohol of different origins to the base wine used to produce wine vinegar with the objective of reducing manufacturing costs. The mixture is then sold commercially as genuine wine vinegar, thus constituting a fraud to consumers and an unfair practice with respect to the rest of the vinegar sector. A method based on near-infrared spectroscopy has been developed to discriminate between white wine vinegar and alcohol or molasses vinegar. Orthogonal signal correction (OSC) was applied to a set of 96 vinegar NIR spectra from both original and artificial blends made in the laboratory, to remove information unrelated to a specific response. The specific response used to correct the spectra was the extent of adulteration of the vinegar samples. Both raw and corrected NIR spectra were used to develop separate classification models using the potential functions method as a class-modeling technique. The previous models were compared to evaluate the suitability of near-infrared spectroscopy as a rapid method for discrimination between vinegar origin. The transformation of vinegar NIR spectra by means of an orthogonal signal-correction method resulted in notable improvement of the specificity of the constructed classification models. The same orthogonal correction approach was also used to perform a calibration model able to detect and quantify the amount of exogenous alcohol added to the commercial product. This regression model can be used to quantify the extent of adulteration of new vinegar samples.     

离子色谱法同时分析啤酒中的有机酸和无机阴离子

建立了以NaOH-甲醇混合淋洗液,离子交换色谱柱,电导检测器检测,分离啤酒中有机酸和无机阴离子的离子色谱法.结果表明：在20 min内分离出啤酒中的11种主要有机酸与无机阴离子,方法回收率和相对标准偏差分别为96.30%～104.8%和0.02%～4.0%.     

啤酒风味物质及游离脂肪酸的气相色谱研究

啤酒中含量极低、极不稳定的游离脂肪酸(FFAs)是影响啤酒风味的主要因素。实验采用气相色谱技术,对形成啤酒的主要风味(如药物味、水果味、酒精味和哈喇味等),重点是不良风味的风味物质进行了检测。啤酒样品经两次酸化处理,酯化后薄层分离的方法测得游离脂肪酸的含量具有较好的重复性和较高的回收率,以癸酸和亚油酸为代表测得回收率分别为85%和114%。     

Determination of purine contents of alcoholic beverages using high performance liquid chromatography

The purine contents of alcoholic beverages were determined in order to utilize them in the dietary care of gout and hyperuricemia. In the management of these diseases, restriction of both alcohol and purine intake are important. The method employed in this study is a quantitative determination of purine contents by HPLC. Alcoholic beverages were hydrolyzed to corresponding purine bases, which were then separated by HPLC, and base peaks were identified using an enzymatic peak‐shift technique. This method is sufficiently accurate and reproducible to examine the purine contents of various alcoholic beverages that patients consume. Purine contents were as follows: spirits, 0.7–26.4 µmol/L; regular beer, 225.0–580.2 µmol/L; low‐malt beer, 193.4–267.9 µmol/L; low‐malt and low‐purine beer, 13.3 µmol/L; other liquors, 13.1–818.3 µmol/L. Some local and low‐alcohol beers were found to contain about 2.5 times more purines than regular beer. As some alcoholic beverages contain considerable amounts of purines, we recommend that excess consumption of these beverages be avoided. These data should be useful in the management of hyperuricemia and gout, not only for patients but also for physicians. Copyright © 2009 John Wiley & Sons, Ltd.     

A near infrared spectroscopic discrimination of noodle flours using a principal-component analysis coupled with chemical information.

Using a portable near infrared (NIR) spectrometer, we discriminated flours for making Japanese noodles (Soba), not only relying on a statistical and mathematical approach, but also on a chemical interpretation of the NIR spectra. In original NIR spectra, the particle-size difference, which results in an undesired systematic variation, was extracted and interpreted as the first-principal component factor by a principal-component analysis. The discrimination of flour materials cannot be satisfied by this factor. However, after a standardized treatment for the original spectra, the particle-size effects were eliminated; alternatively, differences in the chemical contents were extracted as principal-component factors. Using these factors, flour material discrimination was achieved much better. This study suggests a novel idea of utilizing the wavelength contribution ratio spectra for interpreting the factors extracted from the principal-component analysis for the NIR spectra. This report also describes the relationship between the NIR spectra and the chemical-analysis data.     

Variable selection in visible/near infrared spectra for linear and nonlinear calibrations: A case study to determine soluble solids content of beer

Three effective wavelength (EW) selection methods combined with visible/near infrared (Vis/NIR) spectroscopy were investigated to determine the soluble solids content (SSC) of beer, including successive projections algorithm (SPA), regression coefficient analysis (RCA) and independent component analysis (ICA). A total of 360 samples were prepared for the calibration (n = 180), validation (n = 90) and prediction (n = 90) sets. The performance of different preprocessing was compared. Three calibrations using EWs selected by SPA, RCA and ICA were developed, including linear regression of partial least squares analysis (PLS) and multiple linear regression (MLR), and nonlinear regression of least squares-support vector machine (LS-SVM). Ten EWs selected by SPA achieved the optimal linear SPA-MLR model compared with SPA-PLS, RCA-MLR, RCA-PLS, ICA-MLR and ICA-PLS. The correlation coefficient (r) and root mean square error of prediction (RMSEP) by SPA-MLR were 0.9762 and 0.1808, respectively. Moreover, the newly proposed SPA-LS-SVM model obtained almost the same excellent performance with RCA-LS-SVM and ICA-LS-SVM models, and the r value and RMSEP were 0.9818 and 0.1628, respectively. The nonlinear model SPA-LS-SVM outperformed SPA-MLR model. The overall results indicated that SPA was a powerful way for the selection of EWs, and Vis/NIR spectroscopy incorporated to SPA-LS-SVM was successful for the accurate determination of SSC of beer.     

离散小波变换-遗传算法-交互检验法用于近红外光谱数据的高倍压缩与变量筛选

用遗传算法(GA)与交互检验(CV)相结合建立了一种用于对近红外光谱(NIR)数据及其离散小波变换(DWT)系数进行变量筛选的方法，并应用于烟草样品中总挥发碱和总氮的同时测定。结果表明：NIR数据经DWT压缩为原始大小的3．3％时基本没有光谱信息的丢失；有效的变量筛选可以极大地减少模型中的变量个数，降低模型的复杂程度，改善预测的准确度。