An advanced multivariate approach for processing X-ray fluorescence spectral and hyperspectral data from non-invasive in situ analyses on painted surfaces

In the last decades, in situ non-invasive analytical techniques have been widely used for the analysis of paintings. These techniques are useful to extensively map the surface in a non-invasive way, in order to identify the most representative areas to be sampled. When spectroscopic investigations, such as X ray fluorescence (XRF), are conducted, they usually imply the acquisition of a huge amount of measurements. Subsequently, all these data should be processed in situ, in order to immediately support the sampling strategies. To this aim, an appropriate and fast strategy for multivariate treatment of XRF spectral and hyperspectral data sets is presented, able to account for inter-correlation among variables, which is an issue of high importance for elemental analyses. The main advantage of the approach is that XRF spectral profiles are analysed directly, without computation of derived parameters, by means of principal component analysis (PCA). This procedure allows a fast interpretation of results that can be accomplished in situ. Particular attention was paid to the selection of proper spectral pre-treatments to be applied on data together with the use of several chemometric tools (peak alignment, spectra normalisation and exploratory analysis) aimed at improving the interpretation of XRF results. In addition, the application of multivariate exploratory analysis on XRF hyperspectral maps was studied by using an interactive brushing procedure. The multivariate approach was validated on data obtained from the analysis of the famous Renaissance panel painting “The Ideal City”, exhibited in Palazzo Ducale of Urbino, Italy.     

基于稀疏模型的气相色谱-质谱数据解析及其在严重重叠峰解析中的应用

提出一种气相色谱-质谱（GC-MS）数据解析算法。以色谱峰顶点处的质谱作为待测谱,在谱库中检索一定量相关参考谱,求解关于各纯组分色谱响应值的方程。质谱检索采取分步策略,先利用质谱碎片规律建立高速索引进行粗选,然后使用强峰高概率出峰准则和耐挤压性准则排除无关质谱。为求解色谱响应值方程,提出基于稀疏模型的回归算法,相比传统算法,稀疏算法利于提取待测谱的主要结构,避免"过拟合"。实验结果表明,该质谱检索算法具有较高的精度和规模较小的剩余参考谱集,而所提稀疏模型算法可有效解析严重重叠峰。该算法可作为GC-MS重叠峰解析,特别是严重重叠峰解析的一种有效解决方案。     

毛细管电泳法测定肝素和低分子量肝素平均硫酸化程度

肝素和低分子量肝素（LMWHs）作为临床上常用的抗凝血药物,其抗凝血活性与硫酸化程度（SD）密切相关。然而,肝素类药物的生产需经历一系列复杂的工艺过程,在制备和储存过程中,肝素的硫酸基团容易水解丢失,影响抗凝血活性,这将直接影响肝素药物的使用安全性。为保证产品质量,需要发展一种快速检测肝素硫酸化程度的技术,以监测原料质量和工艺条件的稳定性。虽然已有一些测定肝素硫酸化程度的报道,但这些方法均有局限性,不适用于肝素生产的质量控制。为此,开发了一种基于毛细管电泳技术（CE）检测肝素和低分子量肝素的平均硫酸化程度的方法。首先,用肝素酶混合液彻底消化未分级肝素（UHF）和低分子量肝素,然后用毛细管电泳分离酶解得到的所有寡糖和二糖构建模块,并对它们进行定性和定量分析。随后,根据每种寡糖和二糖的峰面积及其硫酸酯基团的数量,便可计算出每个构成肝素二糖单元硫酸化程度的平均值。使用该方法对来自两个生产商各4个批次依诺肝素（低分子量肝素）和5个批次肝素原料进行检测,并计算了各批次样品的相对标准偏差（RSD）,对不同厂家生产的依诺肝素平均硫酸化程度进行了比较,验证了该方法的实用性。该方法灵敏度高,准确可靠,分析速度快,在肝素类药物生产过程的质量控制中具有良好的应用潜力。     

Effects of matrix-assisted laser desorption/ionization experimental conditions on quantitative compositional analysis of ethylene oxide/propylene oxide copolymers

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry has the potential to become a valuable tool for the compositional analysis of copolymers. For a copolymer composed of structurally very similar building blocks with minor chain length changes, one would expect the relative peak intensities observed in the MALDI mass spectra to reflect its composition, at least within a narrow mass range. However, we show that variations in experimental conditions in MALDI can have a significant effect on the mass spectral appearance of a copolymer. The effects of concentration, laser power, type of matrices and solvents on mass spectra of an ethylene oxide/propylene oxide copolymer are illustrated. These somewhat surprising results show that great care needs to be exercised when interpreting copolymer spectra for compositional analysis, even for copolymers with structurally similar monomers. This work also points out that further studies are needed to better understand and optimize spectral acquisition conditions for reliable copolymer compositional analysis by MALDI.     

傅里叶变换中红外光纤光谱法用于腮腺肿瘤的检测

采用红外光谱仪与中红外光纤、衰减全反射(ATR)探头联用对40例腮腺肿瘤患者的腺体体表皮肤进行了测定, 并以病人术后肿瘤标本病理观察结果为判断标准, 对良性及恶性肿瘤光谱进行了判别, 发现光谱法判别结果与病理诊断结果基本一致. 实验结果表明, 正常腮腺、良性和恶性腮腺肿瘤体表皮肤的FTIR 光谱中存在显著的差异, 用相关吸收峰的峰形、峰位和峰强可以表征这些差异. 研究进一步发现, 对于不同类型的恶性肿瘤, 其体表组织的红外光谱主要差别在于1500—1000 cm-1区域内峰形及峰位的改变. 根据这一区域的差异可以将恶性肿瘤分为两大类: 一类恶性肿瘤具有上述的光谱特征(如腺泡细胞癌、嗜酸细胞腺癌和腺样囊性癌等), 另一类恶性肿瘤因为类型的不同在1500—1000 cm-1区域内峰形及峰位发生了很大的变化, 不同于上述的光谱特征.     

Probing beer aging chemistry by nuclear magnetic resonance and multivariate analysis

This paper describes the use of nuclear magnetic resonance (NMR) spectroscopy, in tandem with multivariate analysis (MVA), for monitoring the chemical changes occurring in a lager beer exposed to forced aging (at 45 °C for up to 18 days). To evaluate the resulting compositional variations, both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and a clear aging trend was observed. Inspection of PLS-DA loadings and peak integration enabled the changing compounds to be identified, revealing the importance of well known markers such as 5-hydroxymethylfurfural (5-HMF) as well as a range of other relevant compounds: amino acids, higher alcohols, organic acids, dextrins and some still unassigned spin systems. In addition, the multivariate analysis method of 2D correlation analysis was applied to the NMR data enabling the relevant compound variations to be confirmed and inter-compound correlations to be assessed, some reflecting common metabolic/chemical pathways and, therefore, offering improved insight into the chemical aspects of beer aging.     

Vibrational spectroscopic investigation of stereoregularity effects on syndiotactic polypropylene structure and morphology

Vibrational spectroscopy is used to sensitively detect specific morphologies and microstructures present in metallocene-catalyzed syndiotactic polypropylenes (sPP). Six materials, ranging in racemic triad content from 26 to 96% rr, are studied. Changes in high-resolution infrared (IR) and Raman spectra of melt-slow-cooled films are observed as the degree of syndiotacticity varies. Three different types of peak behavior are observed: splitting, wavenumber shift, and change in peak intensity. An overall trend toward greater molecular order (e.g. ordered chain conformations, increased crystallinity) is observed as syndiotacticity increases. By combining results with supporting evidence from X-ray diffraction and IR linear dichroism experiments of highly syndiotactic sPP, new peak assignments are proposed for tacticity-sensitive vibrational bands. Some very interesting spectral behavior is observed for material of intermediate stereoregularity (49% rr). Previously unobserved peaks appear in X-ray diffractograms and IR absorbance spectra, suggesting the presence of an as-yet unidentified “transitional” structure—perhaps a disordered modification of crystalline Form I. This moderate level of syndiotacticty appears to be a critical point or threshold below which sPP chains are unable to adopt characteristic helical or planar zigzag conformations. Results from this work provide a more thorough understanding of stereochemical effects on vibrational spectra, which will be very useful in the interpretation of ongoing IR linear dichroism studies of newly available semi-syndiotactic (semi-sPP) materials.     

Chemometric quality control of chromatographic purity

It is common practice in chromatographic purity analysis of pharmaceutical manufacturing processes to assess the quality of peak integration combined by visual investigation of the chromatogram. This traditional method of visual chromatographic comparison is simple, but is very subjective, laborious and seldom very quantitative. For high-purity drugs it would be particularly difficult to detect the occurrence of an unknown impurity co-eluting with the target compound, which is present in excess compared to any impurity. We hypothesize that this can be achieved through Multivariate Statistical Process Control (MSPC) based on principal component analysis (PCA) modeling. In order to obtain the lowest detection limit, different chromatographic data preprocessing methods such as time alignment, baseline correction and scaling are applied. Historical high performance liquid chromatography (HPLC) chromatograms from a biopharmaceutical in-process analysis are used to build a normal operation condition (NOC) PCA model. Chromatograms added simulated 0.1% impurities with varied resolutions are exposed to the NOC model and monitored with MSPC charts. This study demonstrates that MSPC based on PCA applied on chromatographic purity analysis is a powerful tool for monitoring subtle changes in the chromatographic pattern, providing clear diagnostics of subtly deviating chromatograms. The procedure described in this study can be implemented and operated as the HPLC analysis runs according to the process analytical technology (PAT) concept aiming for real-time release.     

Near infrared spectroscopic imaging assessment of cartilage composition: Validation with mid infrared imaging spectroscopy

Disease or injury to articular cartilage results in loss of extracellular matrix components which can lead to the development of osteoarthritis (OA). To better understand the process of disease development, there is a need for evaluation of changes in cartilage composition without the requirement of extensive sample preparation. Near infrared (NIR) spectroscopy is a chemical investigative technique based on molecular vibrations that is increasingly used as an assessment tool for studying cartilage composition. However, the assignment of specific molecular vibrations to absorbance bands in the NIR spectrum of cartilage, which arise from overtones and combinations of primary absorbances in the mid infrared (MIR) spectral region, has been challenging. In contrast, MIR spectroscopic assessment of cartilage is well-established, with many studies validating the assignment of specific bands present in MIR spectra to specific molecular vibrations. In the current study, NIR imaging spectroscopic data were obtained for compositional analysis of tissues that served as an in vitro model of OA. MIR spectroscopic data obtained from the identical tissue regions were used as the gold-standard for collagen and proteoglycan (PG) content. MIR spectroscopy in transmittance mode typically requires a much shorter pathlength through the sample (≤10 microns thick) compared to NIR spectroscopy (millimeters). Thus, this study first addressed the linearity of small absorbance bands in the MIR region with increasing tissue thickness, suitable for obtaining a signal in both the MIR and NIR regions. It was found that the linearity of specific, small MIR absorbance bands attributable to the collagen and PG components of cartilage (at 1336 and 856 cm⁻¹, respectively) are maintained through a thickness of 60 μm, which was also suitable for NIR data collection. MIR and NIR spectral data were then collected from 60 μm thick samples of cartilage degraded with chondroitinase ABC as a model of OA. Partial least squares (PLS) regression using NIR spectra as input predicted the MIR-determined compositional parameters of PG/collagen within 6% of actual values. These results indicate that NIR spectral data can be used to assess molecular changes that occur with cartilage degradation, and further, the data provide a foundation for future clinical studies where NIR fiber optic probes can be used to assess the progression of cartilage degradation.     

The composition-explicit distillation curve technique: Relating chemical analysis and physical properties of complex fluids

The analysis of complex fluids such as crude oils, fuels, vegetable oils and mixed waste streams poses significant challenges arising primarily from the multiplicity of components, the different properties of the components (polarity, polarizability, etc.) and matrix properties. We have recently introduced an analytical strategy that simplifies many of these analyses, and provides the added potential of linking compositional information with physical property information. This aspect can be used to facilitate equation of state development for the complex fluids. In addition to chemical characterization, the approach provides the ability to calculate thermodynamic properties for such complex heterogeneous streams. The technique is based on the advanced distillation curve (ADC) metrology, which separates a complex fluid by distillation into fractions that are sampled, and for which thermodynamically consistent temperatures are measured at atmospheric pressure. The collected sample fractions can be analyzed by any method that is appropriate. The analytical methods we have applied include gas chromatography (with flame ionization, mass spectrometric and sulfur chemiluminescence detection), thin layer chromatography, FTIR, corrosivity analysis, neutron activation analysis and cold neutron prompt gamma activation analysis. By far, the most widely used analytical technique we have used with the ADC is gas chromatography. This has enabled us to study finished fuels (gasoline, diesel fuels, aviation fuels, rocket propellants), crude oils (including a crude oil made from swine manure) and waste oils streams (used automotive and transformer oils). In this special issue of the Journal of Chromatography, specifically dedicated to extraction technologies, we describe the essential features of the advanced distillation curve metrology as an analytical strategy for complex fluids.     

Peak Profile Analysis in High Field Asymmetric Wave Ion Mobility Spectrometry

High field asymmetric wave ion mobility spectrometry (FAIMS) is a powerful tool to detect and characterize gas-phase ions, while the unsolvable partial differential equation of ions moving in ion drift tube poses a big challenge to FAIMS spectral peak analysis. In this work, a universal and effective model of FAIMS spectral peak profile has been proposed by introducing ion trajectory and loss height. With this model, the influence of the structure of ion drift tube, dispersion voltages, compensation voltages, and carrier gas flow rate on the FAIMS spectral peak characteristics like peak shape, full width at half maximum and peak height is analyzed and discussed. The results show that the influence of different factors on the FAIMS spectral peak profile can be qualitatively described by the model which agrees with the experimental data.     

基于无基底扣除的数据趋势累积谱峰检测算法

谱峰的检测分析在色谱技术研究中具有十分重要的作用,但在色谱数据采集、传输的过程中,不同程度的噪声干扰给谱峰检测带来了极大的困难。目前传统的谱峰检测算法普遍通过基底扣除的方式对谱峰的形态进行预定义,将谱峰分为单峰、重叠峰等多个种类。针对不同种类的谱峰采用不同的检测方法,这就导致了传统的谱峰检测算法具有高复杂度、低自动化程度以及容易失真等缺点。因此,该文从另一个角度出发提出了一种新型的谱峰检测算法。该算法取消基底扣除以及谱峰分类这一步骤,直接在源数据曲线的基础上进行谱峰检测,主要分为离散差分、趋势累积以及遍历寻峰3个步骤。首先通过信号量表征数据升降趋势;然后进行数据趋势累积,根据累积总和定位谱峰,采用三点定位的方式,即峰起点、极值点和峰终点描述一个谱峰的位置;最后根据遍历排序的方式进行谱峰的筛选。此外,通过谱峰扣除的方式得到曲线基底部分。采用C语言设计编写了算法程序,并对多个动态比表面积分析仪测定的色谱图进行了检测分析,结果显示使用该算法可以精准区分谱峰部分与基底部分,受数据曲线毛刺、震荡等噪声干扰很小,谱峰的三点定位十分准确,且不受其复杂形态的影响,具有很强的普适性。与其他算法相比,该算法定位准确,结构清晰,具有较好的稳定性以及可靠性。该文报道了无基底扣除以及趋势累积等新型谱峰检测思想在吸脱附色谱曲线中的应用,证明了其在吸脱附色谱峰检测中的有效性和良好的应用前景。     

Chromatography of functional polymers: A new approach to the characterization of reactive polymers obtained by chemical modification

High-performance liquid chromatography (HPLC) has been used to complement size-exclusion (gel permeation) chromatography (SEC) for the characterization of functional polymers. Whereas SEC is unable to detect compositional changes, HPLC in an appropriate interacting medium can provide detailed information on compositional changes occurring during chemical modification of a polymer. The method has been demonstrated using a normal-phase column consisting of porous monodisperse 10 μm poly(2,3-dihydroxypropyl methacrylate-co-ethylene dimethacrylate) beads that have a homogeneous coverage of aliphatic hydroxyl groups for the analysis of brominated poly(isobutylene-co-4-methylstyrene). Differences of well below 1 mol % of bromomethylstyrene units are easily detected and quantified. © 1997 John Wiley & Sons, Inc. J Polym Sci A: Polym Chem 35: 1173–1180, 1997     

1H‐HRMAS NMR study of cold smoked Atlantic salmon (Salmo salar) treated with E‐beam

¹H High resolution magic angle spinning (HRMAS) NMR spectroscopic data in combination with principal components analysis and analysis of variance were used to differentiate between irradiated and non‐irradiated cold‐smoked Atlantic salmon (Salmo salar). NMR profiling was obtained, with a spectral acquisition time of less than 8 min, from a small sample size of intact white salmon muscle, by non‐destructive analysis that includes a very simple and rapid sample preparation step. Results obtained enable the use of creatine, trimethylamine oxide and the sum of phosphorylcholine and glycerophosphorylcholine as diagnostic compounds to detect irradiation treatment. This study shows the potential of ¹H‐HRMAS to be a rapid method for investigating compositional changes due to food processing as well as to confirm the presence or absence of some bioactive compounds in irradiated samples. Copyright © 2013 John Wiley & Sons, Ltd.     

Multivariate analysis of non-homogeneous peaks in liquid chromatography

Summary The effects of concentration, separation and spectral similarity as factors influencing the accuracy of iterative target testing factor analysis (ITT-FA) are investigated for three component systems by the application of analysis of variance (ANOVAR). ANOVAR is applied over a range of peak separations to map the changing effects of the three factors with increasing overlap. Two error responses were measured and analysed, (a) Relative cluster error (RCE) a measure of the error over all peaks in a cluster and (b) Relative peak error (RPE) the error of an individual peak. Multicomponent analysis (MCA) a method requiringa priori spectral information, is used as a referee method for ITT-FA.     

Using near-infrared overtone regions to determine biodiesel content and adulteration of diesel/biodiesel blends with vegetable oils

This work evaluates the use of near-infrared (NIR) overtone regions to determine biodiesel content, as well potential adulteration with vegetable oil, in diesel/biodiesel blends. For this purpose, NIR spectra (12,000–6300 cm⁻¹) were obtained using three different optical path lengths: 10 mm, 20 mm and 50 mm. Two strategies of regression with variable selection were evaluated: partial least squares (PLS) with significant regression coefficients selected by Jack-Knife algorithm (PLS/JK) and multiple linear regression (MLR) with wavenumber selection by successive projections algorithm (MLR/SPA). For comparison, the results obtained by using PLS full-spectrum models are also presented. In addition, the performance of models using NIR (1.0 mm optical path length, 9000–4000 cm⁻¹) and MIR (UATR – universal attenuated total reflectance, 4000–650 cm⁻¹) spectral regions was also investigated. The results demonstrated the potential of overtone regions with MLR/SPA regression strategy to determine biodiesel content in diesel/biodiesel blends, considering the possible presence of raw oil as a contaminant. This strategy is simple, fast and uses a fewer number of spectral variables. Considering this, the overtone regions can be useful to develop low cost instruments for quality control of diesel/biodiesel blends, considering the lower cost of optical components for this spectral region.     

An adaptive strategy for selecting representative calibration samples in the continuous wavelet domain for near-infrared spectral analysis

Sample selection is often used to improve the cost-effectiveness of near-infrared (NIR) spectral analysis. When raw NIR spectra are used, however, it is not easy to select appropriate samples, because of background interference and noise. In this paper, a novel adaptive strategy based on selection of representative NIR spectra in the continuous wavelet transform (CWT) domain is described. After pretreatment with the CWT, an extension of the Kennard–Stone (EKS) algorithm was used to adaptively select the most representative NIR spectra, which were then submitted to expensive chemical measurement and multivariate calibration. With the samples selected, a PLS model was finally built for prediction. It is of great interest to find that selection of representative samples in the CWT domain, rather than raw spectra, not only effectively eliminates background interference and noise but also further reduces the number of samples required for a good calibration, resulting in a high-quality regression model that is similar to the model obtained by use of all the samples. The results indicate that the proposed method can effectively enhance the cost-effectiveness of NIR spectral analysis. The strategy proposed here can also be applied to different analytical data for multivariate calibration.     

Information theory applied to feature selection of binary-coded infrared spectra for automated interpretation by retrieval of reference data

A method is described for feature selection from infrared spectra, intended for identification of organic compounds by computer-aided retrieval of reference data contained in small files. Complete discrimination of the binary-coded spectra is achieved by selecting a minimum number of spectral features; the information content is used as the selection criterion. The selection procedure is applied to five data sets (saturated and unsaturated hydrocarbons, alcohols, ethers and aldehydes/ketones) involving some 400 spectra. Each spectrum is uniquely coded by using about 10% of the 140 spectral features (binary-coded peak positions) available originally. For the intensity, a threshold of 50% appears to be applicable in some cases. For coding the frequency or wavelength parameter, wavenumbers (cm^-1) are preferred to wavelengths (mm). The method takes into account the a priori probabilities of spectral features and their correlations. Results of a retrieval program for a few “unknown” spectra are given.     

基于液相色谱与质谱联用的代谢组学及磷脂轮廓分析在糖代谢异常研究中的应用

糖代谢异常由于其发病率的升高和影响人类的生活质量而日益受到科学研究者的关注。实验中利用液相色谱与质谱(LC-MS)联用技术对糖代谢异常分别进行了代谢组学和磷脂轮廓分析,研究了糖代谢异常中的两个阶段——空腹血糖受损(IFG)和初诊糖尿病(NDD)的代谢差异情况。首先从LC-MS采集到血浆中代谢组学分析及磷脂轮廓分析的原始谱图,通过软件的峰匹配等步骤得到峰表,之后利用多种统计分析方法进行数据分析,通过正交校正的偏最小二乘法(OSC-PLS)对样品进行分型,根据模型的变量重要因子(VIP)、显著性差异检验结果等筛选出差异性代谢物。结果显示: NDD组比IFG组与对照组(N组)比较存在更明显的代谢差异,发生变化的化合物主要为游离脂肪酸、溶血磷脂酰胆碱、磷脂酰乙醇胺、鞘磷脂和磷脂酰胆碱等。     

Swarm intelligence based wavelet coefficient feature selection for mass spectral classification: An application to proteomics data

This paper introduces the ant colony algorithm, a novel swarm intelligence based optimization method, to select appropriate wavelet coefficients from mass spectral data as a new feature selection method for ovarian cancer diagnostics. By determining the proper parameters for the ant colony algorithm (ACA) based searching algorithm, we perform the feature searching process for 100 times with the number of selected features fixed at 5. The results of this study show: (1) the classification accuracy based on the five selected wavelet coefficients can reach up to 100% for all the training, validating and independent testing sets; (2) the eight most popular selected wavelet coefficients of the 100 runs can provide 100% accuracy for the training set, 100% accuracy for the validating set, and 98.8% accuracy for the independent testing set, which suggests the robustness and accuracy of the proposed feature selection method; and (3) the mass spectral data corresponding to the eight popular wavelet coefficients can be located by reverse wavelet transformation and these located mass spectral data still maintain high classification accuracies (100% for the training set, 97.6% for the validating set, and 98.8% for the testing set) and also provide sufficient physical and medical meaning for future ovarian cancer mechanism studies. Furthermore, the corresponding mass spectral data (potential biomarkers) are in good agreement with other studies which have used the same sample set. Together these results suggest this feature extraction strategy will benefit the development of intelligent and real-time spectroscopy instrumentation based diagnosis and monitoring systems.