正交信号校正用于傅里叶变换红外光谱的模型传递

利用正交信号校正（OSC）实现了4组分气体混合物的PLS模型,在两台傅里叶变换红外光谱（FTIR）仪上的传递,并与直接标准化（DS）、分段直接标准化（PDS）、多元分散校正（MSC）和有限脉冲响应（FIR）的传递效果进行了比较,确立了以源机的校正模型直接对目标机数据进行预测的传递方式。经过OSC校正,预测均方根误差（RMSEP）为10^-3左右。OSC可以有效地减小测量仪器间的差异,同时使PLS模型的潜变量个数降为4,使模型简单化。与DS和PDS相比,OSC不需要同一样品在两台仪器上测量,在支集大小为3时,得到一致的预测准确度,表现出稳健性。MSC和FIR处理的RMSEP为10^-1左右,效果远差于OSC。     

Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm⁻¹, followed by peak normalization at 850 cm⁻¹ and preprocessing by MSC.     

基于OSC–WT–PLS近红外光谱法测定烟草中芸香苷

采用正交信号校正(OSC)结合小波变换(WT)对烟草光谱进行光谱预处理,将预处理后的烟草光谱结合偏最小二乘法(PLS)建立了烟草光谱对芸香苷的预测模型。利用OSC滤除光谱中与芸香苷含量无关的光谱信息,确定OSC提取的最佳主成分数为7,再选择WT中的最佳小波基函数bior1.1对OSC预处理后的光谱进行压缩及进一步滤噪,然后进行PLS建模,OSC–WT–PLS所建模型决定系数r~2=0.874,校正标准偏差RMSEC=0.85,预测均方根误差RMSEP=0.743,交互验证系数Q_(ext)~2=0.887。结果表明,用OSC–WT–PLS可滤除光谱信息中与待测样品含量无关的信息、减少光谱数据量,降低建立模型的复杂度、提高建模速度及模型的预测能力、准确度。     

基于正交信号校正的秸秆青贮饲料粗蛋白近红外分析模型传递方法

探讨了基于不同数据预处理方法的正交信号校正在秸杆饲料近红外光谱模型传递中的应用.以141个秸杆青贮饲料样品为研究对象,以其粗蛋白含量为目标参数,研究了基于无处理、局部中心化、全局中心化和Z-score标准化预处理方法的正交信号校正,在源仪器(SPECTRUM ONE NTS)和目标仪器1(ANTA-RIS)与目标仪器2(FOSS 6500)之间的模型传递效果.实验表明:对于两台傅里叶变换型近红外光谱仪,采用局部中心化、全局中心化和Z-score标准化预处理方法的正交信号校正均可成功实现模型传递,其中局部中心化和全局中心化法的作用效果基本一致,且优于Z-score标准化法.对于傅立叶变换和光栅型近红外光谱仪,全局中心化的作用效果明显优于其它3组处理效果,且只有全局中心化预处理的正交信号校正传递后的模型可用于实际预测.     

近红外光谱测定人参与西洋参的主要皂甙总量

采用近红外光谱测定人参与西洋参的主要皂甙总量.采集人参与西洋参的漫反射光谱,分别对光谱进行正交信号校正(OSC)与常规预处理,建立了对应的偏最小二乘(PLS)回归模型.与常规最优预处理方法相比,OSC能很好地消除人参与西洋参的品种差异,显著提高了光谱与皂甙含量的相关系数,同时降低了PLS建模因子数,提高了模型的稳健性与...     

Correction of Temperature Variation with Independent Water Samples to Predict Soluble Solids Content of Kiwifruit Juice Using NIR Spectroscopy

Using the framework of aquaphotomics, we have sought to understand the changes within the water structure of kiwifruit juice occurring with changes in temperature. The study focuses on the first (1300–1600 nm) and second (870–1100 nm) overtone regions of the OH stretch of water and examines temperature differences between 20, 25, and 30 °C. Spectral data were collected using a Fourier transform–near-infrared spectrometer with 1 mm and 10 mm transmission cells for measurements in the first and second overtone region, respectively. Water wavelengths affected by temperature variation were identified. Aquagrams (water spectral patterns) highlight slightly different responses in the first and second overtone regions. The influence of increasing temperature on the peak absorbance of the juice was largely a lateral wavelength shift in the first overtone region and a vertical amplitude shift in the second overtone region of water. With the same data set, we investigated the use of external parameter orthogonalisation (EPO) and extended multiple scatter correction (EMSC) pre-processing to assist in building temperature-independent partial least square regression models for predicting soluble solids concentration (SSC) of kiwifruit juice. The interference component selected for correction was the first principal component loading measured using pure water samples taken at the same three temperatures (20, 25, and 30 °C). The results show that the EMSC method reduced SSC prediction bias from 0.77 to 0.1 °Brix in the first overtone region of water. Using the EPO method significantly reduced the prediction bias from 0.51 to 0.04 °Brix, when applying a model made at one temperature (30 °C) to measurements made at another temperature (20 °C) in the second overtone region of water.     

二维相关红外光谱法与阿胶的真伪鉴别

采用傅里叶变换红外光谱法(FT-IR)和二维相关红外光谱技术(2D-IR)对几种阿胶进行了真伪鉴别。实验结果表明,伪品阿胶和黄明胶与标本东阿阿胶的谱图较为相似,尤其是黄明胶与阿胶极其相似,仅仅在1648cm^-1的酰胺I带的吸收峰和东阿阿胶有9个波数的区别;不同批次和厂家的正品阿胶的红外光谱图更为相似,难以区分开,借助于二维相关红外光谱法,获取了物质的微观结构信息,提高了谱图的分辨率,几种真品阿胶得到了直观有效的鉴别。     

红外光谱法对肉苁蓉径向不同部位的分析与评价

采用傅里叶变换红外光谱、二阶导数谱和二维相关红外光谱技术对肉苁蓉由表及里3个部位的药材粉末及其水提物和醇提物进行了分析与评价研究.结果表明,肉苁蓉不同部位的一维光谱非常相似,三者相似系数分别为0.9605,0.944和0.976;二阶导数谱中峰位和峰强的差异明显.1430～1700 cm-1范围内的二维相关谱中皮部自动峰有3个,而中部及髓部均为4个,更直观的反映出三者的差异.不同部位水提物和醇提物的分析结果进一步明确了肉苁蓉皮部芳香类、环烯醚萜类及糖苷类物质与中部和髓部存在明显不同,而髓部的水溶性多糖、半乳糖醇和苯乙醇苷类物质均高于其它部位.可见红外光谱法结合二维相关红外光谱技术为同种药材不同部位的细微差异分析和评价提供了一种快速、全面和客观的方法和手段.     

段式正交信号校正方法及在小麦近红外光谱数据分析中的应用

针对光谱数据峰宽、局部效应显著、含有噪音、变量个数多及彼此间常存在严重的复共线性等问题,改进和设计一种光谱数据局部校正方法:基于窗口平滑的段式正交信号校正方法,并将之结合偏最小二乘回归,以实现光谱数据的预处理及定量分析。通过NIPALS算法初始化将滤去的正交成分,以近邻分段方式进行逐个波长点的正交信号校正。而后将去噪后的光谱矩阵作为新的自变量阵,通过偏最小二乘回归构建其与性质参变量间的校正模型。通过小麦近红外漫反射光谱数据的应用实验结果表明,本方法正交成分估计稳定,去噪明显,模型的预报性能优于其它方法,PLS成分数减少,模型更加简洁。     

有指导的正交投影技术结合斜率/截距校正法实现小试水分近红外定量模型向中试传递

针对小试制剂过程建立的近红外定量模型难以直接应用于中试或大生产过程中的问题,该文以小试和中试条件下多批次药用糊精流化床制粒过程为载体,在线采集其近红外光谱数据并测定水分含量,建立小试过程水分近红外定量模型,提出并应用有指导的正交投影技术结合斜率/截距校正的模型传递方法跨尺度预测中试样本,使中试两个测试集A和B的水分相对预测误差分别由51.04%和26.64%降至4.90%和3.99%,显著提高了模型预测的准确度。将该结果与无指导的正交投影技术结合斜率/截距校正法以及模型更新相比较,该方法能更加有效地去除待测样本光谱中的干扰信息,适用范围广,为小试建立的模型放大应用到中试甚至大生产过程提供了新方案。     

红外光谱法测定汽油中的甲缩醛

通过对异辛烷、汽油和甲缩醛的红外谱图进行分析,确定了1 145 cm–1处吸收峰作为甲缩醛的红外定量谱带。基于朗伯–比耳定律,分别采用0.1,0.2,0.5 mm光程池,对系列浓度标准溶液进行吸光度的测定,建立校准曲线,适合定量的溶液浓度范围分别为0.2~4.2,0.1~2.2,0.1~0.9 g/L;线性方程分别为Y=0.070X+0.001,Y=0.136X+0.001,Y=0.322X+0.002;相关系数分别为0.999 5,0.999 8,0.999 7。质量浓度在0.1~2.2 g/L之间的试样溶液应优先采用0.2mm光程池,测定结果的相对误差小于5%,重复性相对标准偏差小于2.0%(n=6)。     

红外光谱法分析酵母蛋白质的二级结构

采用红外光谱法分析了酵母蛋白质的二级结构。测定了不同温度下酵母酰胺Ⅲ带的一维红外光谱、二阶导数红外光谱及去卷积红外光谱。结果表明:随着测量温度的升高,酵母中的蛋白质α-螺旋结构的红外吸收强度降低;而β-转角结构、无规卷曲结构和β-折叠结构红外吸收强度均有所增加。还研究了酵母酰胺Ⅲ带的二维红外光谱,以确定酵母中蛋白质红外吸收强度的变化次序,进一步证明了酵母蛋白质的β-折叠结构的热不稳定性。     

Polarization Dependent Time-Resolved Infrared Spectroscopy and Its Applications

Polarization dependent time-resolved infrared (TRIR) spectroscopy has proven to be a useful technique to study the structural dynamics in a photochemical process. The angular information of transient species is obtainable in this measurement, which makes it a valuable technique for the investigation of electron distribution, molecular structure, and conformational dynamics. In this review, we briefly introduce the principles and applications of polarization dependent TRIR spectroscopy. We mainly focused on the following topics: (i) an overview of TRIR spectroscopy, (ii) principles of TRIR spectroscopy and its advantages compared to the other ultrafast techniques, (iii) examples that use polarization dependent TRIR spectroscopy to probe a variety of chemical and dynamical phenomena including protein conformational dynamics, excited state electron localization, and photoisomerization, (iv) the limitations and prospects of TRIR spectroscopy.     

Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach

Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied.     

A Variable Selection Method of Near Infrared Spectroscopy Based on Automatic Weighting Variable Combination Population Analysis

Near-infrared spectroscopy (NIR) is widely used in food quantitative and qualitative analysis. Variable selection technique is a critical step of the spectrum modeling with the development of chemometrics. In this study, a novel variable selection strategy, automatic weighting variable combination population analysis (AWVCPA), is proposed. Firstly, binary matrix sampling (BMS) strategy, which provides each variable the same chance to be selected and generates different variable combinations, is used to produce a population of subsets to construct a population of sub-models. Then, the variable frequency (Fre) and partial least squares regression (Reg), two kinds of information vector (IVs), are weighted to obtain the value of the contribution of each spectral variables, and the influence of two IVs of Rre and Reg is considered to each spectral variable. Finally, it uses the exponentially decreasing function (EDF) to remove the low contribution wavelengths so as to select the characteristic variables. In the case of near infrared spectra of beer and corn, yeast and oil concentration models based on partial least squares (PLS) of prediction are established. Compared with other variable selection methods, the research shows that AWVCPA is the best variable selection strategy in the same situation. It has 72.7% improvement comparing AWVCPA-PLS to PLS and the predicted root mean square error (RMSEP) decreases from 0.5348 to 0.1457 on beer dataset. Also it has 64.7% improvement comparing AWVCPA-PLS to PLS and the RMSEP decreases from 0.0702 to 0.0248 on corn dataset.     

大气中痕量气体污染物的傅里叶变换红外光谱分析

本文从两种类型的长光程气体红外吸收方法的角度，介绍了傅里叶变换红外光谱在大气痕量气体污染物分析中的应用，并评述了它的现状和发展前景。     

离子色谱法结合红外光谱法检验氯化琥珀胆碱

建立了离子色谱法结合红外光谱法检验案件中氯化琥珀胆碱的分析方法。用离子色谱法使离子分离后,采用电导检测器检测,同时结合红外光谱法将检材样品挥干后与溴化钾压片用红外光谱仪检测。结果表明,该方法能从案件检材中检出氯化琥珀胆碱成分,相似度达到96%。所建立的方法操作简单,检测灵敏,结果准确,可用于刑事案件中检材的快速检验。     

近红外光谱快速分析青贮饲料pH值和发酵产物

采用近红外光谱技术,结合偏最小二乘回归法,研究了142个不同种类的秸秆青贮饲料样品的pH值和发酵产物(乳酸、乙酸、丙酸、丁酸和氨态氮),建立了干燥粉碎和新鲜样品的近红外漫反射光谱定量分析模型以及浸提液样品的近红外透射光谱定量分析模型。研究发现,pH值的近红外漫反射光谱和透射光谱的分析效果均较好,校正模型决定系数R2和验证集样品预测值与化学值的相关关系决定系数r2都大于0.80,并且干燥粉碎、新鲜和浸提液样品的RPD值分别为3.44、2.50和2.27;3种状态样品的乳酸、乙酸、丁酸和氨态氮的定量分析模型精度需进一步提高;R2在0.64~0.85之间;RPD值在1.38~1.93之间;丙酸含量的测定结果较差。方差分析显示,3种状态样品的测定结果之间均无显著性差异(P>0.05)。     

松杉灵芝不同部位的红外光谱分析研究

采用红外光谱三级鉴定法对松杉灵芝菌盖、表皮、菌柄、子实体不同部位的红外光谱图进行了整体分析。结果表明:松杉灵芝的各部位均含萜类、甾醇类、氨基酸、多肽、蛋白质和糖苷类等物质。在松杉灵芝的一维红外光谱中,菌盖的1 649cm-1峰的相对强度明显比表皮、菌柄和子实体强,说明菌盖中所含的氨基酸、多肽、蛋白质物质的量比其它部位高;在二阶导数谱中,四者在1 720~1 600cm-1范围内峰形差别较大,进一步说明松杉灵芝不同部位所含氨基酸、多肽、蛋白质物质不一致;而二维相关红外光谱中,各部位自动峰的数目、峰位置和峰相对强度都有所不同,可推断松杉灵芝各部位本身所含的糖苷类物质是不一致的。     

正交信号校正在正常成人血清1H NMR谱的代谢组分析中的滤噪作用评价

观察、比较正交信号校正(OSC)滤噪前后, 用不同的模式识别方法对正常成人血清代谢组¹H NMR谱进行分析的效果, 以探讨NMR代谢组学技术应用于临床研究和疾病早期诊断的可行性. 78例正常成人在采血前按常规要求禁食8 h, 记录血清一维600 MHz氢谱后, 分别采用主成分分析(PCA)、偏最小二乘法-判别分析(PLS-DA)以及簇类的独立软模式法(SIMCA)对氢谱进行模式识别分析. 结果表明: 虽然采血前并无其它诸如饮食、生活方式、生理周期等方面的严格限制, 采用OSC 滤噪后, PLS-DA能够完全区分不同性别的血清氢谱, 其判别能力优于PCA和SIMCA. 而且采用OSC滤噪与文献报道的未经OSC处理的PLS-DA法获得的与性别分类有关的主要NMR积分区段基本相同. 从OSC去除不同数目的隐变量后所致的PLS-DA模型的性能改变可见: OSC去除两个隐变量时, 前两个隐变量的特征值明显比后面的大; 剩余残差为20.82%, 即去除了79.18%的X变量中与反应变量Y不相关的系统变异. 此时PLS-DA计算所得的隐变量个数为1; 而不使用OSC或用OSC去除一个隐变量时, PLS-DA所得的隐变量个数分别为3和2. 作为PLS-DA模型质量的评价指标, R2X表示PLS-DA模型计算所获得的隐变量反映自变量X的变异的百分比, R2Y则表示隐变量反映因变量Y的变异的百分比, Q2 (cum)为交叉验证后PLS-DA模型所获隐变量能够预测X和Y变异的累计百分比. R2X在OSC去除两个隐变量时达到最低值, 表明此时PLS-DA计算模型包含的系统变异最少; R2Y与Q2 (cum)都达到80%以上并趋于稳定, 说明OSC去除两个隐变量时PLS-DA模型的质量优良. 显然, OSC可去除饮食、环境等因素的影响, 降低临床样本的不均一性, 这对于NMR代谢组学技术应用于临床研究至关重要. OSC滤噪去除的隐变量个数应根据剩余残差、去除隐变量的特征值大小、PLS-DA模型计算所得的隐变量个数和反映模型质量的相关指标加以判断.