Combined NIR/MIR analysis: A novel method for the classification of complex substances such as Illicium verum Hook. F. and its adulterants

A novel combined near- and mid-infrared (NIR and MIR) spectroscopic method has been researched and developed for the analysis of complex substances such as the Traditional Chinese Medicine (TCM), Illicium verum Hook. F. (IVHF), and its noxious adulterant, Iuicium lanceolatum A.C. Smith (ILACS). Three types of spectral matrix were submitted for classification with the use of the linear discriminant analysis (LDA) method. The data were pretreated with either the successive projections algorithm (SPA) or the discrete wavelet transform (DWT) method. The SPA method performed somewhat better, principally because it required less spectral features for its pretreatment model. Thus, NIR or MIR matrix as well as the combined NIR/MIR one, were pretreated by the SPA method, and then analysed by LDA. This approach enabled the prediction and classification of the IVHF, ILACS and mixed samples. The MIR spectral data produced somewhat better classification rates than the NIR data. However, the best results were obtained from the combined NIR/MIR data matrix with 95–100% correct classifications for calibration, validation and prediction. Principal component analysis (PCA) of the three types of spectral data supported the results obtained with the LDA classification method.

Bayesian predictive modeling and comparison of oil samples

Statistical comparison of oil samples is an integral part of oil spill identification, which deals with the process of linking an oil spill with its source of origin. In current practice, a frequentist hypothesis test is often used to evaluate evidence in support of a match between a spill and a source sample. As frequentist tests are only able to evaluate evidence against a hypothesis but not in support of it, we argue that this leads to unsound statistical reasoning. Moreover, currently only verbal conclusions on a very coarse scale can be made about the match between two samples, whereas a finer quantitative assessment would often be preferred. To address these issues, we propose a Bayesian predictive approach for evaluating the similarity between the chemical compositions of two oil samples. We derive the underlying statistical model from some basic assumptions on modeling assays in analytical chemistry, and to further facilitate and improve numerical evaluations, we develop analytical expressions for the key elements of Bayesian inference for this model. The approach is illustrated with both simulated and real data and is shown to have appealing properties in comparison with both standard frequentist and Bayesian approaches

Novel Mass Spectrometric Analysis Methods for Anabolic Androgenic Steroids in Sports Drug Testing

The feasibility of different modern analytical techniques for the mass spectrometric detection of anabolic androgenic steroids (AAS) in human urine was examined in order to enhance the prevalent analytics and to find reasonable strategies for effective sports drug testing. A comparative study of the sensitivity and specificity between gas chromatography (GC) combined with low (LRMS) and high resolution mass spectrometry (HRMS) in screening of AAS was carried out with four metabolites of methandienone. Measurements were done in selected ion monitoring mode with HRMS using a mass resolution of 5000. With HRMS the detection limits were considerably lower than with LRMS, enabling detection of steroids at low 0.2-0.5 ng/ml levels. However, also with HRMS, the biological background hampered the detection of some steroids. The applicability of liquid-phase microextraction (LPME) was studied with metabolites of fluoxymesterone, 4-chlorodehydromethyltestosterone, stanozolol and danazol. Factors affecting the extraction process were studied and a novel LPME method with in-fiber silylation was developed and validated for GC/MS analysis of the danazol metabolite. The method allowed precise, selective and sensitive analysis of the metabolite and enabled simultaneous filtration, extraction, enrichment and derivatization of the analyte from urine without any other steps in sample preparation. Liquid chromatographic/tandem mass spectrometric (LC/MS/MS) methods utilizing electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI) and atmospheric pressure photoionization (APPI) were developed and applied for detection of oxandrolone and metabolites of stanozolol and 4-chlorodehydromethyltestosterone in urine. All methods exhibited high sensitivity and specificity. ESI showed, however, the best applicability, and a LC/ESI-MS/MS method for routine screening of nine 17-alkyl-substituted AAS was thus developed enabling fast and precise measurement of all analytes with detection limits below 2 ng/ml. The potential of chemometrics to resolve complex GC/MS data was demonstrated with samples prepared for AAS screening. Acquired full scan spectral data (m/z 40-700) were processed by the OSCAR algorithm (Optimization by Stepwise Constraints of Alternating Regression). The deconvolution process was able to dig out from a GC/MS run more than the double number of components as compared with the number of visible chromatographic peaks. Severely overlapping components, as well as components hidden in the chromatographic background could be isolated successfully. All studied techniques proved to be useful analytical tools to improve detection of AAS in urine. Superiority of different procedures is, however, compound-dependent and different techniques complement each other.

Near infrared spectroscopy as a rapid tool to measure volatile aroma compounds in Riesling wine: Possibilities and limits

Volatile chemical compounds responsible for the aroma of wine are derived from a number of different biochemical and chemical pathways. These chemical compounds are formed during grape berry metabolism, crushing of the berries, fermentation processes (i.e. yeast and malolactic bacteria) and also from the ageing and storage of wine. Not surprisingly, there are a large number of chemical classes of compounds found in wine which are present at varying concentrations (ng L-1 to mg L-1), exhibit differing potencies, and have a broad range of volatilities and boiling points. The aim of this work was to investigate the potential use of near infrared (NIR) spectroscopy combined with chemometrics as a rapid and low-cost technique to measure volatile compounds in Riesling wines. Samples of commercial Riesling wine were analyzed using an NIR instrument and volatile compounds by gas chromatography (GC) coupled with selected ion monitoring mass spectrometry. Correlation between the NIR and GC data were developed using partial least-squares (PLS) regression with full cross validation (leave one out). Coefficients of determination in cross validation (R 2) and the standard error in cross validation (SECV) were 0.74 (SECV: 313.6 μg L−1) for esters, 0.90 (SECV: 20.9 μg L−1) for monoterpenes and 0.80 (SECV: 1658 ?g L-1) for short-chain fatty acids. This study has shown that volatile chemical compounds present in wine can be measured by NIR spectroscopy. Further development with larger data sets will be required to test the predictive ability of the NIR calibration models developed.

MOE and MOR assessment technologies for improving graded recovery of exotic pines in Australia.

This project was designed to provide the structural softwood processing industry with the basis for improved green and dry grading to allow maximise MGP grade yields, consistent product performance and reduced processing costs. To achieve this, advanced statistical techniques were used in conjunction with state-of-the-art property measurement systems. Specifically, the project aimed to make two significant steps forward for the Australian structural softwood industry: • assessment of technologies, both existing and novel, that may lead to selection of a consistent, reliable and accurate device for the log yard and green mill. The purpose is to more accurately identify and reject material that will not make a minimum grade of MGP10 downstream; • improved correlation of grading MOE and MOR parameters in the dry mill using new analytical methods and a combination of devices. The three populations tested were stiffness-limited radiata pine, strength-limited radiata pine and Caribbean pine. Resonance tests were conducted on logs prior to sawmilling, and on boards. Raw data from existing in-line systems were captured for the green and dry boards. The dataset was analysed using classical and advanced statistical tools to provide correlations between data sets and to develop efficient strength and stiffness prediction equations. Stiffness and strength prediction algorithms were developed from raw and combined parameters. Parameters were analysed for comparison of prediction capabilities using in-line parameters, off-line parameters and a combination of in-line and off-line parameters. The results show that acoustic resonance techniques have potential for log assessment, to sort for low stiffness and/or low strength, depending on the resource. From the log measurements, a strong correlation was found between the average static MOE of the dried boards within a log and the predicted value. These results have application in segregating logs into structural and non-structural uses. Some commercial technologies are already available for this application such as Hitman LG640. For green boards it was found that in-line and laboratory acoustic devices can provide a good prediction of dry static MOE and moderate prediction for MOR.There is high potential for segregating boards at this stage of processing. Grading after the log breakdown can improve significantly the effectiveness of the mill. Subsequently, reductions in non-structural volumes can be achieved. Depending on the resource it can be expected that a 5 to 8 % reduction in non structural boards won’t be dried with an associated saving of $70 to 85/m3. For dry boards, vibration and a standard Metriguard CLT/HCLT provided a similar level of prediction on stiffness limited resource. However, Metriguard provides a better strength prediction in strength limited resources (due to this equipment’s ability to measure local characteristics). The combination of grading equipment specifically for stiffness related predictors (Metriguard or vibration) with defect detection systems (optical or X-ray scanner) provides a higher level of prediction, especially for MOR. Several commercial technologies are already available for acoustic grading on board such those from Microtec, Luxscan, Falcon engineering or Dynalyse AB for example. Differing combinations of equipment, and their strategic location within the processing chain, can dramatically improve the efficiency of the mill, the level of which will vary depending of the resource. For example, an initial acoustic sorting on green boards combined with an optical scanner associated with an acoustic system for grading dry board can result in a large reduction of the proportion of low value low non-structural produced. The application of classical MLR on several predictors proved to be effective, in particular for MOR predictions. However, the usage of a modern statistics approach(chemometrics tools) such as PLS proved to be more efficient for improving the level of prediction. Compared to existing technologies, the results of the project indicate a good improvement potential for grading in the green mill, ahead of kiln drying and subsequent cost-adding processes. The next stage is the development and refinement of systems for this purpose.

TRANSFER OF ANALYTICAL METHODS FOR THE CHARACTERISATION OF NATURAL PRODUCTS TO R&D PROCESSES OF IDOKI

IDOKI SCF Technologies S.L. is a technology-based company, set up on September 2006 in Derio (Biscay) with the main scope of developing extraction and purification processes based on the use of supercritical fluid extraction technology (SFE) in food processing, extraction of natural products and the production of personal care products. IDOKI¿s researchers have been working on many different R&D projects so far, most of them using this technology. However, the optimization of a SFE method for the different matrices cannot be performed unless we have an analytical method for the characterisation of the extracts obtained in each experiment. The analytical methods are also essential for the quality control of the raw materials that are going to be used and also for the final product. This PhD thesis was born to tackle this problem and therefore, it is based on the development of different analytical methods for the characterisation of the extracts and products. The projects that we could include in this thesis were the following: the extraction propolis, the recovery of agroindustrial residues (soy and wine) and the dealcoholisation of wine.On the one hand, for the extraction of propolis, several UV-Vis spectroscopic methods were used in order to measure the antioxidant capacity and the total polyphenol and flavonoid content of the extracts. A SFC method was also developed in order to measure more specific phenolic compounds. On the other hand, for the recovery of agroindustrial residues UV-Vis spectroscopy was used to determine the total polyphenol content and two SFC methods were developed to analyse different phenolic compounds. Extraction methods such as MAE, FUSE and rotary agitation were also evaluated for the characterisation of the raw materials.Finally, for the dealcoholisation of wine, the development of a SBSE-TD-GC-MS and DHS-TD-GC-MS methods for the analysis of aromas and a NIR spectroscopic method for the determination of ethanol content with the help of chemometrics was necessary. Most of these methods are typically used in IDOKI¿s lab as routine analyses apart from others not included in this PhD thesis.

Acoplamento de técnicas espectrométricas com métodos quimiométricos de classificação e calibração multivariada em alimentos

Este trabalho de pesquisa descreve três estudos de utilização de métodos quimiométricos para a classificação e caracterização de óleos comestíveis vegetais e seus parâmetros de qualidade através das técnicas de espectrometria de absorção molecular no infravermelho médio com transformada de Fourier e de espectrometria no infravermelho próximo, e o monitoramento da qualidade e estabilidade oxidativa do iogurte usando espectrometria de fluorescência molecular. O primeiro e segundo estudos visam à classificação e caracterização de parâmetros de qualidade de óleos comestíveis vegetais utilizando espectrometria no infravermelho médio com transformada de Fourier (FT-MIR) e no infravermelho próximo (NIR). O algoritmo de Kennard-Stone foi usado para a seleção do conjunto de validação após análise de componentes principais (PCA). A discriminação entre os óleos de canola, girassol, milho e soja foi investigada usando SVM-DA, SIMCA e PLS-DA. A predição dos parâmetros de qualidade, índice de refração e densidade relativa dos óleos, foi investigada usando os métodos de calibração multivariada dos mínimos quadrados parciais (PLS), iPLS e SVM para os dados de FT-MIR e NIR. Vários tipos de pré-processamentos, primeira derivada, correção do sinal multiplicativo (MSC), dados centrados na média, correção do sinal ortogonal (OSC) e variação normal padrão (SNV) foram utilizados, usando a raiz quadrada do erro médio quadrático de validação cruzada (RMSECV) e de predição (RMSEP) como parâmetros de avaliação. A metodologia desenvolvida para determinação de índice de refração e densidade relativa e classificação dos óleos vegetais é rápida e direta. O terceiro estudo visa à avaliação da estabilidade oxidativa e qualidade do iogurte armazenado a 4C submetido à luz direta e mantido no escuro, usando a análise dos fatores paralelos (PARAFAC) na luminescência exibida por três fluoróforos presentes no iogurte, onde pelo menos um deles está fortemente relacionado com as condições de armazenamento. O sinal fluorescente foi identificado pelo espectro de emissão e excitação das substâncias fluorescentes puras, que foram sugeridas serem vitamina A, triptofano e riboflavina. Modelos de regressão baseados nos escores do PARAFAC para a riboflavina foram desenvolvidos usando os escores obtidos no primeiro dia como variável dependente e os escores obtidos durante o armazenamento como variável independente. Foi visível o decaimento da curva analítica com o decurso do tempo da experimentação. Portanto, o teor de riboflavina pode ser considerado um bom indicador para a estabilidade do iogurte. Assim, é possível concluir que a espectroscopia de fluorescência combinada com métodos quimiométricos é um método rápido para monitorar a estabilidade oxidativa e a qualidade do iogurte

Measurement of polycyclic aromatic hydrocarbons in ambient air by fluorescence spectroscopic techniques

250 p. + anexos

化学计量学方法辅助多组分分析

近年来，随着生物学、生命科学、药物学、环境科学等学科的迅猛发展，使得人们对于分析化学的要求也越来越高。由于这些学科一般都涉及对复杂多组分体系的定性、定量及结构分析，传统的单组分分析手段已经无法满足需要，这就迫切需要新的分析理论和技术来解决日益复杂的分析问题。而化学计量学的出现和迅速发展，为广大化学工作者提供了强有力的理论工具和分析手段，使得他们能够根据具体问题的不同而采用适当的分析技术来解决传统方法无法解决的难题。论文工作围绕因子分析类方法和小波分析方法在多组分分析中的应用进行了较系统的研究，主要内容归纳如下：1）应用渐进因子分析法和固定尺寸移动窗口渐进因子分析法对模拟的重叠HPLC-DAD数据进行解析，并对两种方法的解析结果进行比较。2）应用子窗口因子分析法解析模拟的重叠HPLC-DAD数据。3）应用小波分析方法进行数据压缩、平滑和滤噪、背景扣除和基线校正、重叠信号的解析，得到了满意的结果。4）将二维小波变换与子窗口因子分析方法相结合，用于高噪声的模拟HPLC-DAD数据的解析。5）将启发渐进式特征投影法和局部正交投影法分别应用于丹参和川芍提取物的HPLC-DAD重叠峰解析，所得结果可以作为进一步定性定量分析的依据。

化学计量学方法在分子光谱分析中的研究与应用

分子光谱手段是化学分析家们常用的一种获得化合物信息的方法，如红外光谱、拉曼光谱、紫外光谱等等。这些方法在化学常规分析中有着非常广泛的应用。化学计量学（Chemometrics）是应用数学、统计学与计算机的工具与手段设计或者选择最优化学测量方法，并通过解析化学测量数据，最大限度的获取化学及其他相关信息。20世纪70年代以来，随着计算机技术的迅速普及，数学和计算机科学在分析化学中的应用日益广泛。在分子光谱分析中，化学计量学方法被用来给一些复杂体系的样品作定量和定性分析。对于一些成分简单的样品，我们可以直接根据该样品的红外、紫外等光谱来得到分析结果，但是对于复杂的样品，尤其是天然产物，根本无法解释它的红外谱图。因此，如果不采用化学计量学方法，根本无法做出准确的定性定量分析。本文更多的是以近红外（Nearhifrared，NIR）光谱方法为主，结合化学计量学方法，包括模式识别、偏最小二乘（PLS）等，对各种复杂体系，如中药，农产品等，进行定量定性分析。对中药的分析结果表明，分子光谱分析技术完全可以实现对中药样品的鉴别分析，尤其是近红外光谱分析技术，分析更为简单、快速，完全可以取代传统方法。用近红外光谱分析技术对大豆、酒醅各项指标进行分析，结果表明近红外分析技术也可用于在实际工作对大豆、酒醅的分析。研究结果表明，分子光谱方法简单快速。同时借助于各种化学计量学方法，能快速得到准确的分析数据，完全可以替代标准方法。只要在实验初期收集足够的具有代表性的样品，然后采用标准方法得到各种化学分析数据，应用计量学建立模型。在对未知样品进行分析时，只需利用采集的谱图数据和建立的模型，就能实现对样品化学分析数据的准确预测，包括定性和定量分析。相比于常规方法，分子光谱分析方法更加简单、快速，能大大提高分析效率。

Application of uniform design and genetic algorithm in optimization of reversed-phase chromatographic separation

An optimization method based on uniform design in conjunction with genetic algorithm is described. According to the proposed method, the uniform design technique was applied to the design of starting experiments, which can reduce the number of experiments compared with traditional simultaneous methods, such as simplex. And genetic algorithm was used in optimization procedure, which can improve the rapidity of optimal procedure. The hierarchical chromatographic response function was modified to evaluate the separation equality of a chromatogram. An iterative procedure was adopted to search for the optimal condition to improve the accuracy of predicted retention and the quality of the chromatogram. The optimization procedure was tested in optimization of the chromatographic separation of 11 alkaloids in reversed-phase ion pair chromatography and satisfactory optimal result was obtained. (C) 2003 Elsevier B.V. All rights reserved.

A novel invariant and sequence comparison for DNA

We consider numerical characterization of DNA primary sequence based on the positions of bases (a, t, c, g) and the pairs of bases X, Y in DNA (X, Y=a, t, c, g). This leads to a representation of DNA by a numerical sequence. Then, we extract a novel invariant (molecular connectivity index) from the derived numerical sequences. The suitable invariant can offer a characterization of DNA primary sequence. Finally, we provide an illustration of its utility by making a comparison between ten DNA sequences belonging to beta-globin gene in different species. The evolutionary relationships of ten species we have revealed in this contribution accord with phylogenetic tree properly.

Extended topological indices and prediction of activities of chiral compounds

The activities/properties of two molecules with identical formula but different configuration states of the asymmetric atoms are different. Thus, usually the common topological indices are not suitable. In this study, the chiral topological indices were obtained by extending A(mi) indices suggested by our laboratory and molecular connectivity indices. The modified topologial indices have been used for the studies on D2 for dopamine receptor and a receptor activities of fourteen N-alkylated 3-(3-hydroxypyenyl)-piperidines. It has been observed that selected variables possess low correlations. The results obtained by using multiple regression analysis and artificial neural networks are satisfactory.

A QSAR study of the antiallergic activities of substituted benzamides and their structures

Multiple regression analysis (MRA) and comparative molecular field analysis (CoMFA) have been used in studies of the correlation between the antiallergic activities of substituted benzamides and their structures. The results achieved using Coh IFA based on 3D factors are much better than those obtained using MRA based on mainly 2D structural information. The CoMFA results reveal that the steric effects are more important than the electrostatic effects for the activities of substituted benzamides. (C) 1999 Elsevier Science B.V. All rights reserved.

Classification of valence changes of trivalent rare earth ions in alkaline earth borates using artificial neural networks

The investigations of classification on the valence changes from RE3+ to RE2+ (RE = Eu, Sm, Yb, Tm) in host compounds of alkaline earth berate were performed using artificial neural networks (ANNs). For comparison, the common methods of pattern recognition, such as SIMCA, KNN, Fisher discriminant analysis and stepwise discriminant analysis were adopted. A learning set consisting of 24 host compounds and a test set consisting of 12 host compounds were characterized by eight crystal structure parameters. These parameters were reduced from 8 to 4 by leaps and bounds algorithm. The recognition rates from 87.5 to 95.8% and prediction capabilities from 75.0 to 91.7% were obtained. The results provided by ANN method were better than that achieved by the other four methods. (C) 1999 Elsevier Science B.V. All rights reserved.