Information‐theoretic chemometric analyses of Raman data for chemical reaction studies

A methodology of multivariate chemometric techniques based on the information‐theoretic approach was applied for elucidating chemical reaction information from a Raman data array R _m×ν that arises from in situ reaction monitoring. This reaction‐induced dynamic dataset R _m×ν can be contaminated by random cosmic ray spikes found in the midst of characteristic spectral variations associated with the disappearance or emergence of Raman active reactants, intermediates and products. Such spurious cosmic spikes were identified and removed using a novel and fast numerical approach based on maximum and minimum spectral entropy principles while preserving the genuine reaction‐induced spectral variations. Subsequently, the band‐target entropy minimization (BTEM) algorithm, a minimum spectral entropy based self‐modeling curve resolution technique, was applied to recover the pure component spectra of Raman active chemical species. Information gain through the chemometric analyses was calculated using information entropies with base 2 logarithm. This sequence of information‐theoretic chemometric analyses (or transinformations) was successfully tested on the reaction spectral data obtained from alcoholysis of acetic anhydride, which contains four Raman active chemical species. It is envisioned that this series of multivariate statistical analyses will be useful in chemical reaction studies and process analytical technology (PAT) applications that utilize in situ Raman spectroscopy to monitor transient dynamic changes in chemical concentrations, and also in Raman microscopy/imaging data containing spatial variations. Copyright © 2010 John Wiley & Sons, Ltd.     

On the interpretation of the Raman spectra of Maya Blue: a review on the literature data

The Raman spectroscopy of Maya Blue (MB), a nanostructured organic–inorganic hybrid material, has received considerable attention. A re‐evaluation of the Raman literature data for indigo dye, genuine MB samples and model specimens obtained by the binding of indigo to phyllosilicate clays, such as palygorskite and sepiolite, using chemometric analysis of normalised spectra is reported. Available data present features in support of the following ideas: (1) dehydroindigo accompanies indigo in MB; (2) different topological isomers, i.e. dye molecules attached to different coordinative sites of the palygorskite framework, are involved in MB; and (3) different procedures were probably used for preparing MB in ancient Mesoamerican civilisations. Copyright © 2010 John Wiley & Sons, Ltd.     

基于共聚焦拉曼光谱技术的苹果轻微损伤早期判别分析

苹果在采摘、分拣、储存和运输过程中容易受到挤压、振动和碰撞而损伤,轻微损伤早期肉眼很难识别,轻微损伤部位易被病原微生物入侵而导致自身和周围水果腐烂,因此,苹果轻微损伤的早期快速准确地判别能有效地降低经济损失,对苹果的采后处理和储存具有重要意义。本研究应用拉曼光谱结合化学计量学方法对苹果早期轻微损伤进行快速识别。采用Savitzky-Golay(SG)卷积对原始拉曼光谱进行平滑去噪,用自适应迭代重加权惩罚最小二乘(airPLS)算法进行基线校正,用非线性的支持向量机(SVM)回归算法建立分类判别模型,采用KS法划分训练集和验证集后,基于线性和多项式核函数建立SVM分类模型的分类准确率可达到97.8%。结果表明,拉曼光谱技术结合化学计量学方法可快速识别苹果的早期轻微损伤,展示了拉曼光谱技术用于判别苹果早期轻微损伤的应用前景。     

Raman spectroscopic investigation of frozen and deparaffinized tissue sections of pediatric tumors: neuroblastoma and ganglioneuroma

Raman spectroscopy is a nondestructive technique that can provide information at the molecular level about the biochemicals in tissues. We have investigated the cellular regions in neuroblastoma and ganglioneuroma using Raman spectroscopy and compared their spectral characteristics with those of the corresponding normal adrenal gland. Thin sections from both the frozen and the corresponding formalin‐fixed paraffin‐processed (FFPP) tissues were studied in conjunction with the pathological examination of the tissues. Investigation of the spectral data shows that the normal adrenal gland tissues have higher levels of carotenoids, lipids, and cholesterol compared with the neuroblastoma and ganglioneuroma frozen tissues. However, in comparison with the frozen tissues, the FFPP tissues show a significant alteration of several biochemicals, including the complete removal of carotenoids, lipids, and cholesterol in the adrenal tissues. A quantitative analysis using chemometric methods of principal component analysis and discriminant function analysis of the Raman spectral data obtained from the frozen tissues show a clear‐cut classification among pathological groups with high sensitivity and specificity. We have validated the classification results of the FFPP tissues against a training set data obtained from the archived FFPP tissues of nine other patients. The validation process correctly identified and grouped the data with the training set of normal adrenal gland (>97% of the time) and neuroblastoma (100% of the time) tissues, whereas the validation was not so strong for ganglioneuroma. This study shows that Raman spectroscopy combined with chemometric methods can be successfully used to distinguish neuroblastoma and ganglioneuroma at cellular level in frozen tissue sections. This study also shows that formalin fixation and paraffinization/deparaffinization of tissues can alter their biochemical composition. Copyright © 2012 John Wiley & Sons, Ltd.     

Composition and authentication of virgin olive oil from French PDO regions by chemometric treatment of Raman spectra

The search for the origin and the authentication of virgin olive oils (VOOs) is a problem of topical interest. VOOs with the protected designation of origin (PDO) label can be subject to fraudulent practices owing to high price commanded by these oils. This work evaluates the capability of confocal Raman spectroscopy combined with chemometric treatments for determining the fatty acid and triacylglycerol (TAG) compositions of VOOs and for authenticating the PDO labels of six French VOOs. Fatty acid and TAG compositions were determined by chemometric analysis of the Raman spectra using the partial least square (PLS) algorithms. Predictions have provided good results on the high percentage compounds, but for the fatty acids at very low percentages the predicted results were very poor. The PLS‐discriminant analysis model on Raman spectra was able to correctly classify 92.3% of French PDOs and 100% of PDO samples made with only one principal cultivar. However, it was noticeable that the first regression coefficient (which explains the greatest amount of variation of the data) obtained for each PDO could be compared one by one with the first regression coefficient obtained for the quantitative analysis of fatty acids and TAGs. The first regression coefficients obtained for PDOs differed widely from each other. Their study allowed interpreting the PDOs according to their chemical composition. This work confirms that fatty acid and TAG percentages may be used to authenticate French VOOs into PDOs. Copyright © 2011 John Wiley & Sons, Ltd.     

基于拉曼光谱的苹果中农药残留种类识别及浓度预测的研究

应用拉曼光谱技术结合化学计量学方法能有效的实现果蔬中农药残留的定性定量分析。本研究借助实验室自主研发的拉曼光谱检测系统,对苹果中溴氰菊酯和啶虫脒的快速无损识别和检测进行了探索。定性分析时将拉曼峰574 和843 cm-1分别作为识别溴氰菊酯和啶虫脒的拉曼指纹,当苹果中的溴氰菊酯和啶虫脒残留的含量分别为0.78和0.15 mg·kg-1时,两种农药的特征峰仍清晰可见。定量分析首先对光谱进行多种预处理（Savitzky-Golay平滑、一阶导、二阶导、基线校准、标准正态变量变换）,结合偏最小二乘法分别建立苹果中溴氰菊酯和啶虫脒含量的定量模型。结果表明,采用8次多项式拟合进行基线校准的预处理方法效果最好,对于溴氰菊酯,偏最小二乘模型预测值与气相色谱法测定值的相关系数和预测均方根误差分别为0.94和0.55 mg·kg-1,对于啶虫脒,其偏最小二乘模型的相关系数与预测均方根误差分别为0.85和0.12 mg·kg-1。本研究证实了利用拉曼技术对苹果农残进行无损检测的可行性,使用该方法进行检测时,在光谱测定前不需要进行前处理,光谱测定后样品无任何损伤,该技术实现了果蔬农残的现场检测,可在检测部门、果蔬加工企业、超市、市场等场所得到推广使用,为果蔬品质安全提供了一种无损、快速和环保的检测方法。     

Raman spectroscopy of complex defined media: biopharmaceutical applications

We demonstrate the detection of glucose and lactate concentrations with high accuracy in the supernatants of Chinese hamster ovary (CHO) cell culture, grown in shake flasks in batch fermentation mode, using Raman spectroscopy and explicit model‐based classical least squares (CLS) algorithm. A deterministic Raman spectral library of pure components was created by acquiring Raman spectra from credible nutrient media constituents and CHO cell culture metabolites. Only analytes present with concentration above the instrument detection limit were included in this library. Residuals obtained after CLS analyses were used to identify missing components and to generate a revised library. An algorithmic sieve was thus construed to obtain an appropriate Raman spectral library from a complex chemical mixture that is well‐defined but an industrial secret. High performance liquid chromatography (HPLC) was used to provide reference glucose and lactate concentrations. [Correction added on 30 April 2015, after first online publication: the last three sentences of the abstract (previously stated in the abstract) have been deleted]. Copyright © 2015 John Wiley & Sons, Ltd.     

Experimental study of the Raman strain rosette based on the carbon nanotube strain sensor

This work presents a new technique named Raman strain rosette for the micro‐strain measurement of both Raman active and Raman inactive materials. The technique is based on the theoretical model of the carbon nanotube (CNT) strain sensor that applies the resonance and polarization Raman properties of CNTs and calculates the synthetic contributions of uniformly dispersed CNTs to the entire Raman spectrum. In our work, the proposed technique is applied in different experiments on the Raman inactive materials, such as step‐by‐step uniaxial tensile and Raman mapping around a circular hole. The experimental results reached by the Raman strain rosette are consistent with the actual values as a whole. This study verifies that the Raman strain rosette is applicable to quantitative measurement of all the in‐plane components of the strain tensor (including both normal and shear strains) by three polarized Raman detections for each sampling spot on a microscale. The technique is further applicable to achieving the strain fields through Raman mapping. Copyright © 2010 John Wiley & Sons, Ltd.     

表面增强拉曼光谱检测鸡蛋蛋清内三聚氰胺的定量方法研究

三聚氰胺对人体有害,鸡蛋内三聚氰胺定量检测非常有必要。以鸡蛋蛋清为研究对象,应用表面增强拉曼光谱技术结合化学计量学方法对蛋清内三聚氰胺进行了定量检测。首先采用人工饲养蛋鸡的方法获取含有三聚氰胺的样品鸡蛋。然后使用便携式拉曼光谱检测仪(Opto Trace RamTracer-200)和拉曼增强试剂测定蛋清的表面增强拉曼光谱,同时利用气相色谱质谱技术测定相应蛋清中三聚氰胺的含量。利用Raman Analyzer对拉曼光谱基线进行校正。应用相关系数法从表面增强拉曼光谱中选取320个光谱变量作为输入变量,建立偏最小二乘定量校正模型;并应用谱峰分解法建立谱峰分解定量校正模型。两种模型建立过程中均选定90个样本做为模型校正集,44个样本做为模型验证集,两种模型都有较好的预测效果。偏最小二乘定量校正模型预测值与气相色谱质谱联用法(GC-MS)测定值的决定系数R2为0.856,预测均方根误差RMSEP为1.547;谱峰分解定量校正模型R2为0.947,RMSEP为0.893。实验结果表明,该方法能有效定量检测鸡蛋内三聚氰胺,检测一个样本仅需15 min,为蛋品的三聚氰胺检测提供了一种新途径。     

基于时频谱特征的白酒品质分类方法研究

为了建立快速、准确的白酒品质鉴别方法,利用机器学习方法对不同品质的白酒建模。为了提取不同品质白酒的特征,使用离子迁移谱对不同品质白酒进行分析,构建了基于白酒离子迁移谱信号的特征向量,并对不同品质的白酒进行了识别与分类。白酒样本的离子迁移谱信号通过利用美国Excellims公司GA2100型电喷雾-离子迁移谱仪（ESI-IMS）采集获得,每一个离子迁移谱信号是强度随时间变化的时间序列信号;提取了原始数据离子迁移谱的时域特征谱峰。为了获得更全面的特征,对离子迁移谱数据进行了傅里叶变换并提取频域内的特征谱峰。同时为了表述信号变化的特征,计算了离子迁移谱的谱熵和过零率,构建N×9维的特征向量矩阵;使用主成分分析(PCA)和线性判别分析(LDA)分别对上述获得的特征进行了特征降维,其中使用PCA对特征向量矩阵降维后的前三维特征对整体特征的累计贡献率达到了95%,而使用LDA对特征向量矩阵降维后的前两维特征对整体特征的累计贡献率就达到了95%。因此,选择了LDA作为特征降维方法;最后,利用机器学习中的非线性分类器支持向量机(SVM)对白酒离子迁移谱数据进行分类研究。实验结果表明,在真酒和添加酒精的白酒二分类中,SVM方法正确分类率达到100%;而在真酒和分别添加10%,20%,30%,40%和50%酒精浓度的五种假酒的六分类中,SVM方法正确分类率达到99.7%。比较了逻辑回归(LRM)分类、模糊C均值分类(FCM)和K近邻分类(KNN)对白酒样本离子迁移谱分类实验结果。研究表明,对于离子迁移谱非常接近的真酒和添加酒精的白酒,基于频谱特征向量的SVM方法能够准确的区分开来,为白酒的品质鉴别提供了一种新的检测方法。     

Different classification algorithms and serum surface enhanced Raman spectroscopy for noninvasive discrimination of gastric diseases

In this study, surface enhanced Raman spectroscopy (SERS) was used to investigate the spectral characteristics of blood serum for the purpose of diagnosing stomach diseases. SERS spectral data was collected from patients with atrophic gastritis, both pre‐operation and post‐operation gastric cancer, and from healthy individuals. Visual differences in the SERS spectra were observed between the four groups which indicate corresponding biomolecule concentration changes in blood. To further investigate the diagnostic ability of human serum, the spectral data was analyzed with three chemometric processes. These three methods extracted features and classified from the spectral data. Principal component analysis (PCA) was first performed to reduce the dimensionality of the original spectral data. Then, the classification methods support vector machine (SVM), linear discriminant analysis (LDA) and classification and regression tree (CART) were used for the evaluation of diagnostic ability. Accuracies of 96.5%, 88.8% and 87.1% were obtained for PCA‐SVM, PCA‐LDA and PCA‐CART, respectively. Copyright © 2016 John Wiley & Sons, Ltd.     

Quantitative analysis of sugar composition in honey using 532‐nm excitation Raman and Raman optical activity spectra

Raman spectroscopy based on the 1064‐nm laser excitation was suggested as a handy non‐invasive technique allowing to quickly determine sugar content in honey and similar food products. In the present study, the green 532‐nm laser radiation is explored instead as it provides higher‐quality spectra in a shorter time. The sample fluorescence was quenched by purification with activated carbon. For control mixture decomposition of Raman spectra to standard subspectra led to a typical error of the sugar content of 3%. Raman optical activity (ROA) spectra that could be measured at the shorter excitation wavelength as well provided a lower accuracy (~8%) than the Raman spectra because of instrumental sensitivity and noise limitations. The results show that Raman spectroscopy provides elegant and reliable means for fast analyses of sugar‐based food products. Copyright © 2016 John Wiley & Sons, Ltd.     

Comparison of chemometric methods in the analysis of pharmaceuticals with hyperspectral Raman imaging

Chemical imaging method of vibrational spectroscopy, which provides both spectral and spatial information, creates a three‐dimensional (3D) dataset with a huge amount of data. When the components of the sample are unknown or their reference spectra are not available, the classical least squares (CLS) method cannot be applied to create visualized distribution maps. Raman image datasets can be evaluated even in such cases using multivariate (chemometric) methods for extracting the needed hidden information. The capability of chemometrics‐assisted Raman mapping is evaluated through the analysis of pharmaceutical tablets (considered as unknown) with the aim of estimating the pure component spectra based on the collected Raman image. Six chemometric methods, namely, principal component analysis (PCA), maximum autocorrelation factors (MAF), sample–sample 2D correlation spectroscopy (SS2D), self‐modeling mixture analysis (SMMA), multivariate curve resolution–alternating least squares (MCR‐ALS), and positive matrix factorization (PMF), were compared. SMMA was found to be the best choice to determine the number of components. MCR‐ALS and PMF provided the pure component spectra with the highest quality. MCR‐ALS was found to be superior to PMF in the estimation of Raman scores (which correspond to the concentrations) and yielded almost the same results as CLS (using the real reference spectra). Thus, the combination of Raman mapping and chemometrics could be successfully used to characterize unknown pharmaceuticals, identify their ingredients, and obtain information about their structures. This may be useful in the struggles against illegal and counterfeit products and also in the field of pharmaceutical industry when contaminants are to be identified. Copyright © 2011 John Wiley & Sons, Ltd.     

Rapid detection of gasoline by a portable Raman spectrometer and chemometrics

Identification of the gasoline purity is important for quality control and detection of gasoline adulteration. Principal component analysis and Raman spectroscopy were used to authenticate gasoline adulterated with methyl tert‐butyl ether (MTBE) and benzene. Gasoline could be clearly distinguished from gasoline adulterated with MTBE and benzene by a plot of the first principal component (x‐axis) against the second principal component (y‐axis). And the radial basis function neural network was used for quantitative prediction of the volume percentages of MTBE and benzene in gasoline based on Raman Spectra. The correlation coefficient (r) and mean absolute percentage error between predictive values and spiked values were 0.9907 and 0.9934 and 15.73 and 8.19%, respectively. Moreover, the Raman spectra of the samples were obtained with a portable Raman spectrometer. Therefore, the method is simple, effective, fast, does not require sample pre‐processing, and is promising for rapid gasoline detection. Copyright © 2012 John Wiley & Sons, Ltd.     

基于Raman光谱和支持向量机回归的古井贡酒年份鉴别方法

白酒年份的快速准确鉴定是白酒品质分析的重点和难点问题之一。实现白酒年份酒的快速、准确的鉴别,对促进白酒行业的健康发展、维护消费者的合法权益具有重要意义。光谱分析法结合模式识别技术是实现白酒品质快速鉴别的首选方法之一,而Raman光谱由于其受水的影响很小且很少或不需要样品前处理,在白酒分析中具有广阔的发展空间。因此,采用Raman光谱和支持向量机回归(SVR)建立数据分析模型,用于不同年份白酒的年份鉴定和同一年份不同贮存时间的白酒年份鉴定。该研究创新之处主要包括如下三个方面：(1)应用Raman光谱对白酒品质进行分析,在分析方法的应用上具有一定的创新之处。(2)研究白酒的年份鉴定问题,在研究对象的选择上,具有一定的创新之处。(3)建立基于回归框架的白酒年份与年份指数对应关系,实现白酒年份识别及预测,不仅可以有效鉴别白酒年份,同时可用于鉴别白酒贮存时间,因此,在分析方法的确定和应用上,具有一定的创新之处。实验中采用古井贡5年、8年、16年及26年系列年份酒进行了实证分析,数据分析实验结果表明,所建立的基于Raman光谱和SVR的白酒年份鉴别分析流程和方法,对鉴别不同年份的白酒,以及同一年份不同贮存时间的白酒样品(包括对数据库内已有样本年份的鉴别,以及对数据库内没有的盲样的年份预测),均取得较好的应用效果,相比于其他常用回归分析方法具有明显的优越性,可以为白酒年份酒分析提供一定的技术支持。     

表面增强拉曼光谱准确检测玉米中杀螟硫磷农药残留

杀螟硫磷是一种在农作物上广泛使用的有机磷杀虫剂,常用于玉米上害虫的防治。过量或者不合理施用导致的残留积累关系到食品安全和人体健康。常规检测杀螟硫磷的方法有气相色谱-质谱法、高效液相色谱法,其准确性虽好,但存在需要专业人员介入、样品前处理复杂、检测时间长等缺点。表面增强拉曼光谱(SERS)法具有分析速度快、检测灵敏度高和特异性好等优点,被广泛应用于农产品中痕量残留的快速检测。利用表面增强拉曼光谱结合化学计量学方法实现玉米中杀螟硫磷残留的准确检测。以两步种子生长法合成的纳米金棒作为拉曼增强基底,测量600～1 800 cm-1范围内的拉曼光谱。对比杀螟硫磷乙醇溶液和金棒的光谱,确定杀螟硫磷的特征峰在650,830,1 082,1 241,1 344和1 581 cm-1处。采用简单预处理方法快速提取玉米中的杀螟硫磷残留。将受污染的玉米样品粉碎后,利用乙醇溶剂对残留进行两次提取,每次获取的提取液经离心获得上清液,将上清液合并混匀,在水浴中蒸发浓缩,浓缩后的上清液用于采集SERS光谱。每个浓度制备50个平行样本。各浓度残留提取液中的残留参考值采用色质联用方法测定。对比残留提取液的光谱,1 082,1 241和1 581 cm-1处特征峰强度随残留浓度的降低而迅速变弱甚至消失,650,830和1 344 cm-1处的特征峰直至残留浓度为0.48 μg·mL-1时依然可见。当浓度低至0.37 μg·mL-1时,所测光谱与空白提取液光谱相似。采用主成分分析(PCA)提取不同浓度杀螟硫磷残留光谱的主体信息,其中残留为0.37 μg·mL-1和空白提取液光谱的主成分得分重叠,进而判断SERS方法对玉米中杀螟硫磷残留的检测限可达到0.48 μg·mL-1,低于国家规定的农作物中最大残留限,体现出SERS检测的高灵敏性。选取浓度为14.25 μg·mL-1的50个样本分析其650,830和1344 cm-1处的特征峰强度变化可知,所采集的光谱呈现出较好的重复性,相对标准偏差(RSD)值仅为3.12%。对杀螟硫磷残留的定量分析采用支持向量机回归(SVR)实现,Savitzky-Golay卷积平滑和小波变换(WT)用于本次光谱数据的预处理。校正集和预测集样本的划分采用Kennard-Stone算法实现,模型的性能采用校正均方根误差(RMSEC)、校正集决定系数(R2_c)、预测均方根误差(RMSEP)和预测集决定系数(R2_p)评估。最优模型为SVR结合WT所构建的,具有最小的预测误差,其中校正集的RMSEC=0.103 2 μg·mL-1,R2_ｃ=0.999 74,预测集的RMSEP=0.134 1 μg·mL-1,R2_p=0.999 60。同时,最优模型的预测值与色质联用法所测值基本一致,其预测回收率为95.31%～100.66%。以上表明,SERS结合化学计量学方法检测玉米中杀螟硫磷残留是准确可行的,且有望推广到农作物中多种农药残留的检测,为农产品的安全检测提供一种新思路。     

Numerical simulations of confocal Raman spectroscopic depth profiles of materials: a photon scattering approach

We present a simple model that uses a novel photon scattering approach to predict the depth profile response obtained when confocal Raman spectroscopy is applied both to silicon and to a number of related polymeric materials of varying optical clarity. This paper first provides an overview of the models proposed to date to demonstrate the evolution in understanding of the confocal Raman response of semi‐transparent materials, based upon geometrical optics. A new model is then described that is based upon the twin notions of a permanent extended Raman illuminated volume and the degree of extinction of the incident and Raman scattered photons from the whole of the illuminated volume as it is gradually moved further into, or defocused above, the sample. The model's predictions are compared with empirical data from previous studies of a range of semi‐crystalline polymers with different scattering properties and, by means of contrast, with that of a silicon sample. We show that, despite its inherent simplicity, the physics this model utilizes is able successfully to predict the form of the depth profile for each material, something that has not been achieved by any model previously proposed, and that the parameters used in the model scale with independent physical measurements. Finally the model is used to account for the fact that useful Raman spectra can be obtained when the laser is focused as much as 40 µm above the sample surface. Copyright © 2007 John Wiley & Sons, Ltd.     

基于激光诱导水拉曼抑制法的油膜厚度测量方法研究

为了实现水体表面油膜厚度的快速测量分析,以266 nm的激光作为探测系统的激发光源,基于激光诱导水拉曼散射光谱检测技术,通过获取不同种类不同厚度油膜存在下水拉曼光谱信息,建立油膜厚度反演模型。采用高斯函数拟合法校正了荧光光谱对拉曼光谱的干扰。然后根据水拉曼抑制法结合非线性最小二乘优化算法,建立油膜厚度反演模型。结果表明：对92#汽油、0#柴油、美孚机油20w-40、壳牌润滑油10w-40、采埃孚变速箱油AG6和原油油膜能探测到的油膜厚度范围为0.19~379.22 μm。采用水拉曼光谱-油膜厚度反演模型预测油膜厚度的平均相对误差在8.14%~15.81%之间。该方法能实现实验室条件下对微米级油膜的测量。     

Residual benzamide contamination in synthetic oligonucleotides observed using UV resonance Raman spectroscopy

Spectroscopic methods based on Raman scattering have for many years employed synthetic oligonucleotides in a broad range of applications, either as probes or as model analytes for biophysical investigations. Benzamide is commonly used as a protecting group in the phosphoramidite synthesis of oligonucleotides and, while standard desalting used after synthesis yields sufficiently pure reagents for most assay reactions or other routine uses of the oligomer, it does not completely remove benzamide. We show that the 1609 cm⁻¹ band of residual benzamide contamination can interfere with certain nucleic acid bands, particularly when using excitation wavelengths near 244 nm where the benzamide band is strongly enhanced. For example, the 1609 cm⁻¹ band of benzamide could obscure (or be mistaken as) a weak vibration attributed to an  NH₂ scissoring. The extent of benzamide contamination in desalted preparations varies considerably among different commercial sources, and hence caution is advised when making direct comparisons of ultraviolet Raman data of oligonucleotides from different sources. Copyright © 2010 John Wiley & Sons, Ltd.     

表面增强拉曼光谱法快速检测脐橙果肉中三唑磷农药残留

采用表面增强拉曼光谱技术结合快速溶剂前处理方法检测脐橙果肉中三唑磷农药残留,应用化学计量学方法建立脐橙果肉中三唑磷农药残留的快速检测模型。以脐橙果肉提取液为基质,采用N-丙基乙二胺、C18和石墨化碳去除果肉中有机酸、色素等荧光物质,配制不同浓度的三唑磷农药溶液,应用不同预处理方法对光谱信号进行预处理,建立偏最小二乘模型。结果表明,以脐橙果肉提取液为基质的三唑磷溶液最低检测浓度低于0.5mg·L-1;归一化预处理后建立的模型预测性能最好,模型对预测集样本的均方根误差为1.38 mg·L-1,相关系数为0.976 6,相对分析误差为(RPD)4.66。预测回收率为95.97%～103.18%,相对误差绝对值在5%以下,表明模型具有较好的预测效果。对4个未知浓度样本进行配对t检验,预测值与真实值无显著差异,说明所建立的方法准确可靠。