Pure component spectral recovery and constrained matrix factorizations: concepts and applications

We present new ideas underlying a self‐modelling factor analytical method which allows to extract pure component spectra and the associated concentration profiles from a set of spectroscopic measurements. The usefulness of the method is demonstrated and compared with established tools for model problems and for a system from catalytic hydroformylation by Rhodium complexes both with overlapping component spectra. Self‐modelling methods tend to minimize the overlap of the recovered spectra, which can result in an unwanted distortion of the spectra and concentration profiles. For strongly overlapping spectra a penalty condition on a specific singular value of the absorptivity matrix factor and a global decomposition approach are appropriate tools to construct improved factorizations. Copyright © 2010 John Wiley & Sons, Ltd.     

Local chemometrics for samples and variables: optimizing calibration and standardization processes

The ability of five sample selection methods for local chemometrics and three variable selection algorithms were compared for the development and the transfer of whole soybeans protein and oil near infrared prediction models. Two new methods based on a similarity index considering Euclidian distance among Fourier coefficients were introduced and tested against more common approaches (locally weighted regression, LOCAL). Genetic algorithms were also challenged with the development of models based on particle swarm optimization (PSO). A modification to the original PSO model was introduced. Sample and variable selection methods, as well as their combinations, were tested in the transfer of models in intra‐ and inter‐brand situations using two Foss Infratecs and two Bruins OmegAnalyzerGs. For each brand, a master was designated and its models transferred onto the second unit of its network and the two units of the second brand. Calibration models were proven transferable from brand to brand with similar or better precisions than when all instruments were calibrated on their own calibration sets (relative predictive determinant (RPD) improving from 10.42 to 12.76 and 12.39 in intra‐brand standardization for Infratec network with local and variable selection methods respectively). These methods provided contrasted results depending on the instrument, the parameter, and the variability of interest. Copyright © 2010 John Wiley & Sons, Ltd.     

PLS-DA Model for the Evaluation of Attention Deficit and Hyperactivity Disorder in Children and Adolescents through Blood Serum FTIR Spectra

Attention deficit and hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood. It affects ~10% of the world’s population of children, and about 30–50% of those diagnosed in childhood continue to show ADHD symptoms later, with 2–5% of adults having the condition. Current diagnosis of ADHD is based on the clinical evaluation of the patient, and on interviews performed by clinicians with parents and teachers of the children, which, together with the fact that it shares common symptoms and frequent comorbidities with other neurodevelopmental disorders, makes the accurate and timely diagnosis of the disorder a difficult task. Despite the large effort to identify reliable biomarkers that can be used in a clinical environment to support clinical diagnosis, this goal has never been achieved hitherto. In the present study, infrared spectroscopy was used together with multivariate statistical methods (hierarchical clustering and partial least-squares discriminant analysis) to develop a model based on the spectra of blood serum samples that is able to distinguish ADHD patients from healthy individuals. The developed model used an approach where the whole infrared spectrum (in the 3700–900 cm⁻¹ range) was taken as a holistic imprint of the biochemical blood serum environment (spectroscopic biomarker), overcoming the need for the search of any particular chemical substance associated with the disorder (molecular biomarker). The developed model is based on a sensitive and reliable technique, which is cheap and fast, thus appearing promising to use as a complementary diagnostic tool in the clinical environment.     

Fast determination of ginsenosides in ginseng by high‐performance liquid chromatography with chemometric resolution

Ginseng is a well‐known traditional Chinese medicinal herb, and ginsenosides are its major active components. A method for the fast determination of ginsenosides in ginseng samples by high‐performance liquid chromatography was developed and used for the quantitative analysis of four ginsenosides in three different ginseng samples. In this method, instead of time‐consuming gradient elution, isocratic elution was used to speed up the analysis. Under strong isocratic elution, all the ginsenosides are eluted in 2.3 min. Although the measured signal is composed of overlapped peaks with the interferences and background, the signal of ginsenosides can be extracted by chemometric resolution. A non‐negative immune algorithm was employed to obtain the chromatographic information of the target components from the data. Compared with conventional chemometric approaches, the method can perform the extraction for one‐dimensional overlapping signals. The method was validated by the determination of four ginsenosides in three different ginseng samples. The recoveries of the spiked samples were in the range of 94.08–107.3%.     

Harnessing the complexity of metabolomic data with chemometrics

Because of the ever‐increasing number of signals that can be measured within a single run by modern platforms in analytical chemistry, life sciences datasets become not only gradually larger but also more intricate in their structures. Challenges related to making use of this wealth of data include extracting relevant elements within massive amounts of signals possibly spread across different tables, reducing dimensionality, summarising dynamic information in a comprehensible way and displaying it for interpretation purposes. Metabolomics constitutes a representative example of fast‐moving research fields taking advantage of recent technological advances to provide extensive sample monitoring. Because of the wide chemical diversity of metabolites, several analytical setups are required to provide a broad coverage of complex samples. The integration and visualisation of multiple highly multivariate datasets constitute key issues for effective analysis leading to valuable biological or chemical knowledge. Additionally, high‐order data structures arise from experimental setups involving time‐resolved measurements. These data are intrinsically multiway, and classical statistical tools cannot be applied without altering their organisation with the risk of information loss. Dedicated modelling algorithms, able to cope with the inherent properties of these metabolomic datasets, are therefore mandatory for harnessing their complexity and provide relevant information. In that perspective, chemometrics has a central role to play. Copyright © 2013 John Wiley & Sons, Ltd.     

Chemistry in Times of Artificial Intelligence

Chemists have to a large extent gained their knowledge by doing experiments and thus gather data. By putting various data together and then analyzing them, chemists have fostered their understanding of chemistry. Since the 1960s, computer methods have been developed to perform this process from data to information to knowledge. Simultaneously, methods were developed for assisting chemists in solving their fundamental questions such as the prediction of chemical, physical, or biological properties, the design of organic syntheses, and the elucidation of the structure of molecules. This eventually led to a discipline of its own: chemoinformatics. Chemoinformatics has found important applications in the fields of drug discovery, analytical chemistry, organic chemistry, agrichemical research, food science, regulatory science, material science, and process control. From its inception, chemoinformatics has utilized methods from artificial intelligence, an approach that has recently gained more momentum.     

Cellulose Dissolution in Mixtures of Ionic Liquids and Dimethyl Sulfoxide: A Quantitative Assessment of the Relative Importance of Temperature and Composition of the Binary Solvent

We studied the dissolution of microcrystalline cellulose (MCC) in binary mixtures of dimethyl sulfoxide (DMSO) and the ionic liquids: allylbenzyldimethylammonium acetate; 1-(2-methoxyethyl)-3-methylimidazolium acetate; 1,8-diazabicyclo [5.4.0]undec-7-ene-8-ium acetate; tetramethylguanidinium acetate. Using chemometrics, we determined the dependence of the mass fraction (in %) of dissolved cellulose (MCC-m%) on the temperature, T = 40, 60, and 80 °C, and the mole fraction of DMSO, χ_DMSO = 0.4, 0.6, and 0.8. We derived equations that quantified the dependence of MCC-m% on T and χ_DMSO. Cellulose dissolution increased as a function of increasing both variables; the contribution of χ_DMSO was larger than that of T in some cases. Solvent empirical polarity was qualitatively employed to rationalize the cellulose dissolution efficiency of the solvent. Using the solvatochromic probe 2,6-dichloro-4-(2,4,6-triphenylpyridinium-1-yl)phenolate (WB), we calculated the empirical polarity E_T(WB) of cellobiose (a model for MCC) in ionic liquid (IL)–DMSO mixtures. The E_T(WB) correlated perfectly with T (fixed χ_DMSO) and with χ_DMSO (fixed T). These results show that there is ground for using medium empirical polarity to assess cellulose dissolution efficiency. We calculated values of MCC-m% under conditions other than those employed to generate the statistical model and determined the corresponding MCC-m% experimentally. The excellent agreement between both values shows the robustness of the statistical model and the usefulness of our approach to predict cellulose dissolution, thus saving time, labor, and material.     

Rapid Determination of Metabolites in Bio‐fluid Samples by Raman Spectroscopy and Optimum Combinations of Chemometric Methods

The application of Raman spectroscopic techniques combined with multivariate chemometrics signal processing promise new means for the rapid multidimensional analysis of metabolites non‐destructively, with little or no sample preparation and little sensitivity to water. However, Rayleigh scattering, fluorescence and uncontrolled variance present substantial challenges for the accurate quantitative analysis of metabolites at physiological levels in biologically varying samples. Effective strategies include the application of chemometrics pretreatments for reducing Raman spectral interference. However, the arbitrary application of individual or combined pretreatment procedures can significantly alter the outcome of a measurement, thereby complicating spectral analysis. This paper evaluates and compares six signal pretreatment methods for correcting the baseline variances, together with three variable selection methods for eliminating uninformative variables, all within the context of multivariate calibration models based on partial least squares (PLS) regression. Raman spectra of 90 artificial bio‐fluid samples with eight urine metabolites at near‐physiological concentrations were used to test these models. The combination of multiplicative scatter correction (MSC), continuous wavelet transform (CWT), randomization test (RT) and PLS modeling presented the best performance for all the metabolites. The correlation coefficient (R) between predicted and prepared concentration reached as high as 0.96.     

人工神经网络-微分脉冲伏安法同时测定尿液中的多巴胺、尿酸及抗坏血酸

研究了多巴胺、尿酸和抗坏血酸在玻碳电极上的伏安行为.在pH 5.7的Britton - Robinson缓冲溶液中,采用微分脉冲伏安法进行电化学扫描,3种化合物均有良好的氧化峰,但其波谱重叠严重,常规伏安法难以同时测定.采用化学计量学方法中的偏最小二乘法(PLS)、主成分回归法(PCR)、径向基人工神经网络法(RBF-...     

Application of Vibrational Spectroscopy in Combination with Chemometrics Techniques for Authentication of Herbal Medicine

Abstract: One of the emerging issues in herbal medicine is its authenticity. The substitution of highly valuable materials in herbal medicine with lower ones is common practice in the herbal medicine industry. This practice can cause serious effects or be harmful to human health; therefore, the authentication of herbal medicine using analytical techniques is necessary. Due to its capability as fingerprint technique, vibrational spectroscopy (infrared and Raman), especially in combination with several chemometrics techniques, is the method of choice for authentication purposes. This review highlights the use of mid-infrared, near-infrared, and Raman spectroscopy for authentication of herbal medicine, either in raw materials or in final products.