Problems In Analytical Chemistry Research Articles

Multi-spectroscopic characterization of organic salt components in medicinal plant

The characterization of structure of organic salts in complex mixtures has been a difficult problem in analytical chemistry. In the analysis of Scutellariae Radix (SR), the pharmacopoeia of many countries stipulates that the quality control component is baicalin (≥9% by high performance liquid chromatography (HPLC)). The component with highest response in SR was also baicalin detected by liquid chromatography-mass spectrometry (LC-MS). However, in the attenuated total reflection Fourier transform infrared spectroscopy, the carbonyl peak of glucuronic acid of baicalin did not appear in SR. The results of element analysis, time of flight secondary ion mass spectrometry, matrix assisted laser desorption ionization mass spectrometry and solid-state nuclear magnetic resonance all supported the existence of baicalin magnesium salt. Based on this, this study proposes an analysis strategy guided by infrared spectroscopy and combined with multi-spectroscopy techniques to analyze the structure of organic salt components in medicinal plant. It is meaningful for the research of mechanisms, development of new drugs, and quality control.

METHODS OF APPLICATION OF CALCULATION PROBLEMS STUDYING ANALYTICAL CHEMISTRY

Analytical chemistry is the science of research methods for the qualitative and quantitative composition of substances. Recently, physico-chemical experimental methods of research, characterized by speed, accuracy and high sensitivity, have been widely implemented - these are chromatography, polarography, potentiometry, spectrophotometry, etc. In all these methods, we use various calculation problems.
 The ability to solve calculation problems in analytical chemistry is one of the important components of the professional competence of students, because it is a general theoretical basic discipline in the system of training future pharmacists. The use of calculation problems is one of the constituent parts of pharmaceutical education, along with the study and assimilation of theoretical material and the mastery of experimental techniques. The introduction of calculation problems into the educational process makes it possible to implement the following didactic principles of education: ensuring the independence and activity of students, achieving the unity of knowledge and skills, establishing connections between learning and everyday life. At the same time, it is ensured: the development of the optimal method of approach to the theoretical substantiation of the condition of the problem; consolidation of rules, terms, laws, equations; understanding the connection of physical quantities, polytechnic training of students' knowledge; specification, systematization, strengthening and verification of students' knowledge; the combination of theory and practice, the ability to put one's knowledge into practice.
 The article presents the experience of using calculation problems in analytical chemistry at the Department of Chemistry, Pharmaceutical Analysis and Postgraduate Education for the second-year students of the Faculty of Pharmacy. The main attention is paid to the method of solving problems. The main stages of solving the problem are considered, namely:
 
 Determination of the standard of actions (classification of calculation problem systems, their analysis, solution plan and solution design).
 Trial tasks (semi-independent work on solving problems with proposed algorithms, instructions, some indicative actions).
 Independent work of an educational nature (problems and benchmarks of their solutions are proposed, which students can use after completing the independent work stage).
 Thematic independent work (the ability to solve complex problems and the level of the student's mastery of the relevant competence are tested).
 
 The use of calculation problems on the example of various topics of practical classes with different types of control and the scheme for evaluating student activity are described. It is shown that the use of calculation problems in the educational process contributes to the improvement of students' perception of the educational material in the discipline, encourages them to perform independent work, and forms the competencies necessary for successful learning.
 The proposed system contributes to a higher quality study of the fundamental discipline by a future pharmacist, makes it possible to intensify the educational process, acquaints students with the theoretical foundations of problem-solving methods, develops the ability to solve problems in analytical chemistry of various types provided by the current curriculum.

Provision conditions for increasing the reliability of trace analysis of toxic metals

Introduction. Heavy metals are included in one of the significant groups of ecotoxicants. Determining heavy metals at low levels is one of the main problems in analytical chemistry, which depends on various factors. Ignoring the contribution of these factors to the total uncertainty can increase the probability of distortion of the results due to an erroneous decision on the compliance of the obtained data with a particular standard. The most significant influences include the purity of reagents, dishes, and air in laboratory rooms. Purpose of the work. Search for ways of reducing the listed influences provided that the expense of time, material and labour resources are minimized. Materials and methods. In the course of the work, many experiments were carried out, including the analysis of nitric acid for the content of metals before and after cleaning by distillation, the analysis of washes from new fluoroplastic laboratory glassware and glassware that had been cleaned by steaming, an assessment of the effect of air pollution in the laboratory room based on a study of calibration curves, which were constructed at the analysis of standard iron solutions prepared in the Clean Workplace and in a conventional fume hood. All measurements were performed by inductively coupled plasma mass spectrometry on Agilent 7800 ICP-MS mass spectrometer. Results. The efficiency of the proposed methods for eliminating interfering influences on the analysis has been experimentally proved. Conclusion. The technical and analytical problem, which consists of finding optimal conditions for preparation of reagents, glassware cleaning and decreasing the influence of laboratory air pollution, allows to increase reliability of the obtained results and prevent distortion of information about the observed degree of environmental pollution.

Self-Optimizing Support Vector Elastic Net.

Chemometrics is widely used to solve various quantitative and qualitative problems in analytical chemistry. A self-optimizing chemometrics method facilitates scientists to exploit the advantages of chemometrics. In this report, a parameter-free support vector elastic net that self-optimizes two key regularization constants, i.e., λ for L2 regularization and t for L1 regularization, is developed and referred to as self-optimizing support vector elastic net (SOSVEN). Response surface modeling (RSM) and bootstrapped Latin partitions (BLPs) are incorporated for the optimization. Responses at a set of design points over the ranges of the two factors are evaluated with an internal BLP validation using a calibration set. A 2-dimensional interpolation with a cubic spline fits a response surface to determine the best condition that gives the best-estimated response. The SOSVEN with RSM had comparable performances with the one tuned by grid search, while the RSM is more efficient. The developed SOSVEN was compared with two parameter-free chemometrics methods, super partial least-squares regression (sPLSR) and super support vector regression (sSVR) for calibration, and sPLS-discriminant analysis (sPLS-DA) and support vector classification (SVC) for classification. For calibration, the SOSVEN with RSM worked equivalently well or better than the other two self-optimizing methods for the evaluations using meat and hemp oil data sets. For classification, a reference wine data set and mass spectra of different marijuana extracts were used. The three classifiers had similar performances to identify the cultivars of wines with nearly 98% of accuracy. The SOSVEN significantly outperformed sPLS-DA and SVC to classify the mass spectra of marijuana extracts with an overall accuracy of 97%. These results demonstrated excellent abilities of SOSVEN for classification and calibration.

Reliable chiral recognition with an optoelectronic nose

Chiral discrimination is a key problem in analytical chemistry. It is generally performed using expensive instruments or highly-specific miniaturized sensors. An electronic nose is a bio-inspired instrument capable after training of discriminating a wide variety of analytes. However, generality is achieved at the cost of specificity which makes chiral recognition a challenging task for this kind of device. Recently, a peptide-based optoelectronic nose which can board up to hundreds of different sensing materials has shown promising results, especially in terms of specificity. In line with these results, we describe here its use for chiral recognition. This challenging task requires care, especially in terms of statistical reliability and experimental confounds. For these reasons, we set up an automatic gas sampling system and recorded data over two long sessions, taking care to exclude possible confounds. Two couples of chiral molecules, namely (R) and (S) Limonene and (R) and (S) Carvone, were tested and several statistical analyses indicate the almost perfect discrimination of their two enantiomers. A method to highlight discriminative sensing materials is also proposed and shows that successful discrimination is likely achieved using just a few peptides.

Self-Optimized One-Class Classification Using Sum of Ranking Differences Combined with a Receiver Operator Characteristic Curve.

A significant and common problem in analytical chemistry is determining if a sample belongs to a specific class, e.g., establishing if a food product is genuine or counterfeit or a tissue sample is benign or malignant. This problem is termed one-class classification (class modeling). Problematic with class modeling is determining which one-class classifier to use followed by the challenge of optimizing the chosen classifier (identifying the best tuning parameter value(s)). With spectroscopic data, two other conundrums arise: which data preprocessing method(s) and spectral region(s) to use. Presented in this paper is a hybrid fusion process that can combine nonoptimized classifiers across multiple instruments, preprocessing methods, and measurements. Instead of optimizing classifiers, a window of tuning parameters is used for each classifier. The flexible fusion method of sum of ranking differences (SRD) is applied to combine all assessment values. Defining the best SRD ranking value (threshold) for determining class membership is the one tuning parameter value needed. However, this SRD ranking value is automatically optimized by using a receiver operator characteristic (ROC) curve. The approach is demonstrated on two analytical data sets. The first is a beer authentication sample set measured on five instruments: near-infrared, mid infrared (MIR), ultraviolet, visible, and thermogravimetric. Three different fusion protocols of all five instruments are demonstrated. The second data set is MIR spectra of strawberry puree with two categories: strawberry puree and nonstrawberry puree. Fusing nonoptimized classifiers provides reliable classifications relative to accuracy, sensitivity, and specificity.

Confirmation of organic compounds hydrates formation under the reversed-phase HPLC conditions

The confirmation of the correspondence of the detected forms of analytes to their molecular structures seems to be one of the principal problems of analytical chemistry. The previous analysis of the series of drugs by reversed-phase HPLC allowed revealing that the retention regularities of the complex polyfunctional organic compounds containing sulfonamide groups -SO2-N< differ from those of other compounds. It was explained by reversible formation of their hydrate forms. To verify this proposition, three model N-substituted arylsulfon­ami­des were synthesized, namely N,N-diethyl- (I), N-allyl- (II), and N-phenyl (III). The consideration of the dependencies of these analytes net retention times (tR) in reverse-phase HPLC on the content of orga­nic solvent (C, methanol) in an eluent did not permit us to reveal any regularities or anomalies due to the non-linearity of such depen­den­cies. However, to achieve this, the recurrent appro­ximation was re­com­mended, tR(C + DC) = atR(C ) + b (*), where DC = const – con­stant varia­tions of concentration of methanol (5% in our case), а and b – coefficients calculated by Least Squares Method. If the chemical origin of the analytes at the outlet of the chromatographic column remained the same within concentration range Сmin < С < Сmax, dependencies (*) were linear with correlation coefficients R > 0.999. Yet, if additional chemical transformations of analytes took place, na­me­ly rever­sible formation of hydrates (or variations of their composition) due to the presence of water in an eluent, it led to the deviations of recurrent dependencies (*) from linea­rity. Three sulfonamides under characterization corresponded to different kinds of such deviations: non-linearity within the whole range Сmin < С < Сmax (amide I), presence of two linear dependencies instead of one (amide II), and linearity within the whole range Сmin < С < Сmax (amide III). First two cases corres­ponded to the interconver­si­on of anhydrous forms and hydrates, or (less probable) different hydrates, while the latter meant the existence of analyte in the single form (pro­ba­b­ly hydrate). Thus, the ana­ly­sis of mo­del compounds confirmed that the presence of the polar functionality -SO2-N< was just the reason of hydrate formation in water soluti­ons.

Recent Advances in Atmospheric Ionization Mass Spectrometry: Developments and Applications

When did you start the research about mass spectrometry?We paid attention to mass spectrometry and then designed a new method, called intermittent electrospray for mass spectrometry, dating back to 18 years ago. It has the property of high sensitivity and advantages for protein analysis.Why do you get into mass spectrometry? Could you please share some experiences with our readers?For passion and responsibility. No matter what happens, just always keep thinking.What is the most important personality for scientific research?Forward‐looking vision, keen insight, divergent thinking and down‐to‐earth work.What is the most favorite chemistry developed in your research group?We have been working on detecting active transient intermediates and organic reaction mechanisms. It is a big challenge for us initially because of weak foundation, but every time we put all efforts to find problem and solve it.How do you deal with the relationship with your students?Accept and make suggestions like friends.What are your research interests focus on recently?Recently, our group have focused on the development of novel and efficient mass spectrometry technologies like atmospheric ionization mass spectrometry to settle some unsolved problems in analytical chemistry, organic chemistry and biological researches.

What Color Is Your Method? Adaptation of the RGB Additive Color Model to Analytical Method Evaluation.

Evaluation of an analytical method is a fundamental problem in analytical chemistry, and it is never straightforward. In this article, we show a perspective for facing this issue using an original tool. We propose a model that allows one to evaluate any analytical method/procedure in a global manner. It refers to the RGB additive color model and uses three primary colors to represent three main attributes of the evaluated method: analytical performance - Red, compliance with the "green" chemistry principles - Green, and productivity/practical effectiveness - Blue. A final color of the method results from the additive synthesis of the primary colors. To simplify classifications, we propose the set of nine final colors of the method (white, magenta, cyan, yellow, red, green, blue, colorless/gray, and black). The model provides also a quantitative parameter, named the "method brilliance", which integrates all primary colors and treats them with varying importance, adjusted to the evaluation context and subjective user preferences. The evaluation is performed using standard Excel worksheets interpretable "at-a-glance" and adjustable to the particular method specifications. We discuss the opportunities offered by this model, potential obstacles, and related countermeasures, as well as future perspectives for its utilization. The paper shows also examples of using the model for the evaluation of real methods. We believe that the model can be applied not only in analytical science but also in other chemical subdisciplines.

Межфазное распределение сорбиновой кислоты при экстракции сополимером N-винилформамида с 1-винил-3,5-диметилпиразолом

С целью изучения экстракции сорбиновой кислоты из объектов со сложной матрицей изучено межфазное распределение сорбиновой кислоты в системе сополимер N-винилформамид – 1-винил-3,5-диметилпиразол – хлорид аммония. Рассчитаны коэффициенты распределения и степень извлечения консерванта при различных соотношениях фаз и концентрации раствора сополимера. Установлено влияние рН на экстракцию сорбиновой кислоты, предложен механизм ее взаимодействия с экстрагентом. Определение сорбиновой кислоты в водно-солевой фазе осуществлено методом капиллярного электрофореза в выбранных условиях.
 
 
 
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 Korenman Ya. I. Koeffitsienty raspredeleniya organicheskix soedineny. Spravochnik. [Distribution Coefficients of Organic Compounds. Handbook]. Voronezh, Voronezh State Un-t Publ., 1992, 336 p. (in Russ.)
 Mokshina Ya., Bykovskiy D. V., Shatalov G. V., Pakhomova O. A. J. of Analyt. Chem., 2016, vol. 71, no. 2, pp. 201-204. https://doi.org/10.1134/s1061934816020106 
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Consensus Classification Using Non-Optimized Classifiers.

Classifying samples into categories is a common problem in analytical chemistry and other fields. Classification is usually based on only one method, but numerous classifiers are available with some being complex, such as neural networks, and others are simple, such as k nearest neighbors. Regardless, most classification schemes require optimization of one or more tuning parameters for best classification accuracy, sensitivity, and specificity. A process not requiring exact selection of tuning parameter values would be useful. To improve classification, several ensemble approaches have been used in past work to combine classification results from multiple optimized single classifiers. The collection of classifications for a particular sample are then combined by a fusion process such as majority vote to form the final classification. Presented in this Article is a method to classify a sample by combining multiple classification methods without specifically classifying the sample by each method, that is, the classification methods are not optimized. The approach is demonstrated on three analytical data sets. The first is a beer authentication set with samples measured on five instruments, allowing fusion of multiple instruments by three ways. The second data set is composed of textile samples from three classes based on Raman spectra. This data set is used to demonstrate the ability to classify simultaneously with different data preprocessing strategies, thereby reducing the need to determine the ideal preprocessing method, a common prerequisite for accurate classification. The third data set contains three wine cultivars for three classes measured at 13 unique chemical and physical variables. In all cases, fusion of nonoptimized classifiers improves classification. Also presented are atypical uses of Procrustes analysis and extended inverted signal correction (EISC) for distinguishing sample similarities to respective classes.

Mass and stiffness spectrometry of nanoparticles and whole intact bacteria by multimode nanomechanical resonators.

The identification of species is a fundamental problem in analytical chemistry and biology. Mass spectrometers identify species by their molecular mass with extremely high sensitivity (<10−24 g). However, its application is usually limited to light analytes (<10−19 g). Here we demonstrate that by using nanomechanical resonators, heavier analytes can be identified by their mass and stiffness. The method is demonstrated with spherical gold nanoparticles and whole intact E. coli bacteria delivered by electrospray ionization to microcantilever resonators placed in low vacuum at 0.1 torr. We develop a theoretical procedure for obtaining the mass, position and stiffness of the analytes arriving the resonator from the adsorption-induced eigenfrequency jumps. These results demonstrate the enormous potential of this technology for identification of large biological complexes near their native conformation, a goal that is beyond the capabilities of conventional mass spectrometers.

Chemometric Classification Techniques as a Tool for Solving Problems in Analytical Chemistry

Supervised pattern recognition (classification) techniques, i.e., the family of chemometric methods whose aim is the prediction of a qualitative response on a set of samples, represent a very important assortment of tools for solving problems in several areas of applied analytical chemistry. This paper describes the theory behind the chemometric classification techniques most frequently used in analytical chemistry together with some examples of their application to real-world problems.

Self organising maps for visualising and modelling

The paper describes the motivation of SOMs (Self Organising Maps) and how they are generally more accessible due to the wider available modern, more powerful, cost-effective computers. Their advantages compared to Principal Components Analysis and Partial Least Squares are discussed. These allow application to non-linear data, are not so dependent on least squares solutions, normality of errors and less influenced by outliers. In addition there are a wide variety of intuitive methods for visualisation that allow full use of the map space. Modern problems in analytical chemistry include applications to cultural heritage studies, environmental, metabolomic and biological problems result in complex datasets. Methods for visualising maps are described including best matching units, hit histograms, unified distance matrices and component planes. Supervised SOMs for classification including multifactor data and variable selection are discussed as is their use in Quality Control. The paper is illustrated using four case studies, namely the Near Infrared of food, the thermal analysis of polymers, metabolomic analysis of saliva using NMR, and on-line HPLC for pharmaceutical process monitoring.

Evaluation of target factor analysis and net analyte signal as processes for classification purposes with application to benchmark data sets and extra virgin olive oil adulterant identification

Classifying samples into known categories is a common problem in analytical chemistry and other fields. For example, with spectroscopic data, samples are measured and the corresponding spectra are compared with existing spectral data sets of known classification (library sets) to determine the appropriate classification. Presented in this paper is a study of the simple and well known data analysis processes target factor analysis (TFA) and net analyte signal (NAS). Although TFA and NAS were originally derived for different purposes in analytical chemistry, they are based on the same calculation. The library set with the smallest TFA residual (smallest NAS) for a test sample spectrum can be used for classification purposes. Alternatively and equivalently, this paper uses the smallest angle (poorest selectivity in NAS terminology) between a new sample spectrum vector and the space spanned by each library loading vector basis set. The angle classification is compared with classifications by the Mahalanobis distance and k‐nearest neighbors. The measures are evaluated with three spectroscopic data sets consisting of benchmark identification of plastic type (Raman) and gasoil plant source (ultraviolet) and a new extra virgin olive oil adulterant identification (fluorescence) data set. A fourth data set is the benchmark archeological data set. The Mahalanobis distance and k‐nearest neighbors generally classify with 2%–40% and 0%–20% decreases in correct classifications, respectively, compared with TFA (NAS). Results from this study indicate that the simple TFA and NAS processes are useful underutilized classification and library searching tools. Copyright © 2012 John Wiley &amp; Sons, Ltd.

A two-stage evolutionary algorithm based on sensitivity and accuracy for multi-class problems

The machine learning community has traditionally used correct classification rates or accuracy (C) values to measure classifier performance and has generally avoided presenting classification levels of each class in the results, especially for problems with more than two classes. C values alone are insufficient because they cannot capture the myriad of contributing factors that differentiate the performance of two different classifiers. Receiver Operating Characteristic (ROC) analysis is an alternative to solve these difficulties, but it can only be used for two-class problems. For this reason, this paper proposes a new approach for analysing classifiers based on two measures: C and sensitivity (S) (i.e., the minimum of accuracies obtained for each class). These measures are optimised through a two-stage evolutionary process. It was conducted by applying two sequential fitness functions in the evolutionary process, including entropy (E) for the first stage and a new fitness function, area (A), for the second stage. By using these fitness functions, the C level was optimised in the first stage, and the S value of the classifier was generally improved without significantly reducing C in the second stage. This two-stage approach improved S values in the generalisation set (whereas an evolutionary algorithm (EA) based only on the S measure obtains worse S levels) and obtained both high C values and good classification levels for each class. The methodology was applied to solve 16 benchmark classification problems and two complex real-world problems in analytical chemistry and predictive microbiology. It obtained promising results when compared to other competitive multi-class classification algorithms and a multi-objective alternative based on E and S.

Russian publications (1991–2010) devoted to ion mobility spectrometry

The physicochemical principles of ion mobility spectrometry are described; Russian publications issued between 1991 and 2010 and devoted to the development of the fundamentals and technical implementation of this method, its use for physicochemical research, and solving problems of analytical chemistry, safety, medicine, and ecology, are reviewed.

A comparison of several trilinear second-order calibration algorithms

A comprehensive and systematic strategy for evaluating the performances of several trilinear second-order calibration algorithms is presented in this paper, in particular with a view of practical applications. Several trilinear second-order calibration methods such as PARAFAC, ATLD, SWATLD and APTLD, which have the “second-order advantage” and are gaining widespread acceptance in the field of chemometrics, were compared. Based on different input parameters including noise level, initial value, number of estimated components and collinearity in simulated and real data, the performances of these methods were evaluated in terms of predicting ability, consistency of resolved and real profiles, fitness obtained by selected components and speed of convergence. The obtained results give a reevaluation of the position and role of these trilinear second-order calibration methods in chemometrics and provide a guidance in practical applications for solving complicated quantitative analysis problems in analytical chemistry. It is useful and helpful to choose, for example, which algorithm would be more suitable for predicting the concentration of the analyte(s) of interest even in the presence of unknown interferents in complex systems.

Identification of Fatty Acids, Phospholipids, and Their Oxidation Products Using Matrix-Assisted Laser Desorption Ionization Mass Spectrometry and Electrospray Ionization Mass Spectrometry

Electrospray ionization mass spectrometry (ESI-MS) and matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) have found increasing application in the analysis of biological samples. Using these techniques to solve problems in analytical chemistry should be an essential component of the training of undergraduate chemists. We present an experiment that teaches students how to prepare and analyze samples both qualitatively and quantitatively using modern mass spectrometric techniques. Students use ESI-MS and MALDI-MS to analyze phospholipids and mixtures of fatty acids, including those from soybean extracts. Changes in the mixtures upon ozonolysis are also investigated. This laboratory is suitable for upper-level university students enrolled in analytical chemistry or instrumental analysis classes. This experiment combines analytical chemistry with organic chemistry and biochemistry, a situation that students will encounter in their future careers as chemists.

Simultaneous Spectrophotometric Determination of Three Tolualdehyde Isomers by Artificial Neural Networks and Its Comparison with Partial Least Squares

AbstractThe simultaneous determination of tolualdehyde isomer mixtures by using a spectrophotometric method is a difficult problem in analytical chemistry, due to spectral interference. By multivariate calibration methods, such as partial least squares (PLS) and artificial neural network (ANN), it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. The ANN calibration model of simultaneous determination of tolualdehyde isomers was based on absorption spectra in the 230–304 nm range for 48 different mixtures of tolualdehyde isomers, and the concentration ranges for o‐tolualdehyde, m‐tolualdehyde and p‐tolualdehyde were 6.0–15.0, 7.0–16.0 and 8.0–19.0 µg·mL−1, respectively. Other 7 samples were applied to testing the predictive ability of the calibration model. The effect of the three isomers with different concentration ratios in their mixtures on the accuracy of determination results was discussed and the appropriate range of concentration ratio was found. The proposed method was applied to simultaneous determination of the three isomers in synthetic samples and the recoveries were between 84.00% and 109.60%. The results of determination were compared with the PLS model without significant difference at 0.05 level for o‐tolualdehyde and m‐tolualdehyde by paired t‐test. But a better performance for p‐tolualdehyde in their mixtures could be obtained by the ANN calibration model.