Classes Of Analytes Research Articles

Adulteration of Gabus (Channa striata) fish oil with corn oil and palm oil: the use of FTIR spectra and chemometrics

The adulteration practice in fats and oils industry can be in the form of addition or substitution high quality oils such as Gabus Fish oil (GFO) with lower price oils. This research highlighted the application of FTIR spectroscopy combined with multivariate calibrations and discriminant analysis (DA) for quantitative analysis and classification of oil adulterants of palm oil (PO) and corn oil (CO) in GFO. The methods involved preparation of training/calibration and validation samples, scanning of samples using FTIR spectrophotometer using attenuated total reflectance, development of calibration and validation models for quantitative analysis of oil adulterants assisted with multivariate calibrations and classification between genuine GFO and GFO adulterated with PO and CO using discriminant analysis. The quantitative analysis of PO in ternary mixtures with CO as an adulterant in GFO was carried out using first derivative spectra at wavenumbers of 3200-600 cm-1 assisted with partial least square (PLS), while quantitative analysis of CO in ternary mixture with PO and GFO was performed using first derivative spectra at wavenumbers region of 3200-2700 cm-1 assisted with principle component regression (PCR). The R2 values for the correlation between actual and predicted values of PO and CO in ternary mixtures either in calibration or prediction samples were of > 0.97 with low errors. In addition, DA using the same wavenumbers region as used in the quantitative analysis could classify or discriminate genuine GFO and GFO mixed/adulterated with PO and CO with an accuracy level of 100%. FTIR spectroscopy using suitable wavenumbers region combined with PLS, PCR and DA could be proposed as analytical tools for quantification and classification of oil adulterants in GFO.

A docosyl-terminated polyamine amphiphile-bonded stationary phase for multimodal separations in liquid chromatography

A convenient synthetic approach to a linear alkyl-polyamine amphiphilic chromatographic selector was proposed. Successive immobilization of the amphiphile onto silica gel afforded a multimodal stationary phase for high-performance liquid chromatography (HPLC). The as-prepared silica material was studied comparatively with a conventional octadecyl (C18) and an amide-embedded C18 stationary phase. The new uniform docosyl-triamine tandem was featured by an enhanced shape selectivity towards geometric isomers, and a low silanol activity towards alkaline solutes. The presence of multiple amino groups rendered the new adsorbent operable in different modes, such as hydrophilic interaction and ion-exchange modes. The satisfactory performance of the said stationary phase in separating different classes of analytes, including polycyclic aromatic hydrocarbons, flavonoids, tricyclic antidepressants, calcium channel blockers, aromatic acids, inorganic anions, nucleosides and estrogens, revealed its great potential and high adaptability for multipurpose LC utility.

DART-MS Spectral Similarity of Infrared Thermally Desorbed Solid Particulate and Solution Cast Propellant Samples.

Security and forensic applications employ test and reference materials to develop, calibrate, and validate analytical instrumentation such as mass spectrometry for the trace detection and chemical analysis of target analytes. An emerging class of target analytes includes homemade fuel oxidizer explosives based on pyrotechnics, propellants, and powder mixtures. Test materials for these compounds must appropriately and accurately embody the physical and chemical nature of the threat. Precision liquid deposition methods have long been employed for creation of trace level test materials. Mass spectral similarity and chemical signature differences between solid particulate and solution cast (i.e., liquid deposited) propellant samples were investigated by infrared thermal desorption direct analysis in real time mass spectrometry (IRTD-DART-MS). Differences in the mass spectra and ion distributions of solid and liquid deposited black powders and black powder substitutes were observed. These differences were attributed to chemical processes (e.g., degradation) and physical differences in the crystal formation, spatial distribution, morphology, and size. The production and deposition of test and reference materials remain critical to developing new technologies and detecting evolving threats.

Examining the Function of Meromorphic with Using the Linear Convolution Operator

In this study, it is mentioned that meromorphic functions are univalent functions that are analytical everywhere. Complex analytical transformations were investigated by mentioning the necessary form for f (z) to have meromorphic function. It is a function that satisfies the condition   0 hz  . For analytic functions of f and g in the D unit disk, ()fz shows the meromorphic function class with P and subclasses of the P meromorphic analytical function class using the subordination principle between functions with the help of Hadamard product and linear operators. In this way proves is provided.

Rapid MALDI-MS Assays for Drug Quantification in Biological Matrices: Lessons Learned, New Developments, and Future Perspectives.

Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has rarely been used in the field of therapeutic drug monitoring, partly because of the complexity of the ionization processes between the compounds to be quantified and the many MALDI matrices available. The development of a viable MALDI-MS method that meets regulatory guidelines for bioanalytical method validation requires prior knowledge of the suitability of (i) the MALDI matrix with the analyte class and properties for ionization, (ii) the crystallization properties of the MALDI matrix with automation features, and (iii) the MS instrumentation used to achieve sensitive and specific measurements in order to determine low pharmacological drug concentrations in biological matrices. In the present hybrid article/white paper, we review the developments required for the establishment of MALDI-MS assays for the quantification of drugs in tissues and plasma, illustrated with concrete results for the different steps. We summarize the necessary parameters that need to be controlled for the successful development of fully validated MALDI-MS methods according to regulatory authorities, as well as currently unsolved problems and promising ways to address them. Finally, we propose an expert opinion on future perspectives and needs in order to establish MALDI-MS as a universal method for therapeutic drug monitoring.

Preliminary Study on the Performance of Two Chemistry Classes Subjected to Self-recorded Videos and Traditional Face-to-face Learning

Nowadays, most of the educators in higher institutions adopt videos in their subjects as a powerful teaching tool. The educators integrate video into the teaching of analytical, instrumental and organic laboratory classes aside from other teaching tools such as case study and flipped classroom. The adoption of video as a teaching tool is inspired by their inability to apply it to the real-life owing to the ambiguity on the chemistry concepts among the undergraduate students. Also, it creates an active learning environment among the undergraduate students. Therefore, a preliminary study is conducted to compare the effect of self-recorded chemistry educational videos and the traditional face-to-face learning. The comparison is investigated on the performance of two classes of Chemistry students at INTI International University, by measuring the percent of correctness. In conclusion, based on the preliminary study, the use of self-recorded videos by the instructor is evidently enhance the performance of the class of students. Therefore, further research will be conducted to validate the hypothesis.

Time series motifs discovery under DTW allows more robust discovery of conserved structure

In recent years, time series motif discovery has emerged as perhaps the most important primitive for many analytical tasks, including clustering, classification, rule discovery, segmentation, and summarization. In parallel, it has long been known that Dynamic Time Warping (DTW) is superior to other similarity measures such as Euclidean Distance under most settings. However, due to the computational complexity of both DTW and motif discovery, virtually no research efforts have been directed at combining these two ideas. The current best mechanisms to address their lethargy appear to be mutually incompatible. In this work, we present the first efficient, scalable and exact method to find time series motifs under DTW. Our method automatically performs the best trade-off of time-to-compute versus tightness-of-lower-bounds for a novel hierarchy of lower bounds that we introduce. As we shall show through extensive experiments, our algorithm prunes up to 99.99% of the DTW computations under realistic settings and is up to three to four orders of magnitude faster than the brute force search, and two orders of magnitude faster than the only other competitor algorithm. This allows us to discover DTW motifs in massive datasets for the first time. As we will show, in many domains, DTW-based motifs represent semantically meaningful conserved behavior that would escape our attention using all existing Euclidean distance-based methods.

Local Analytic Classification for Quasi-linear Implicit Differential Systems at Transversal Singular Points

The paper is devoted to the systems of equations $A(x) \dot x = v(x)$ in real finite-dimensional phase space. The elements of the matrix A are real analytic functions, as well as the components of the vector function v. Generically, the matrix A degenerates on a certain hypersurface ${\Gamma } = \{ \det A(x)= 0\}$ . Points of Γ are called singular (or impasse) points of the system. The local analytic classification for such systems at their transversal singular points is presented. Transversal singular points are defined by the conditions that Γ is regular and v is transversal to Im A.

Spatial Mapping of Diphtheria Vulnerability Level in East Java, Indonesia, using Analytical Hierarchy Process – Natural Break Classification

Diphtheria is a serious infectious disease induced by the Corynebacterium Diphtheriae bacteria and often causes outbreaks (extraordinary events) in various regions. Based on data from the Ministry of Health, East Java is the biggest benefactor to diphtheria cases in Indonesia. Diphtheria cases in East Java tend to increase, especially in 2018 there were 753 diphtheria cases in 38 districts. Efforts made to prevent, treat, and control diphtheria outbreaks by the government are to analyze the level of susceptibility to diphtheria. This paper proposes a new approach to analyze the level of diphtheria susceptibility using the analytical hierarchy process (AHP) and natural breaks classification in East Java Province, Indonesia. AHP method is used to obtain diphtheria susceptibility values based on seven criteria, such as the number of sufferers, number of deaths, DPT 1, DPT 2, DPT 3 immunization, population density, and humidity. Natural break classification is used to classify the vulnerability values from AHP into three levels of vulnerability, consisting of low, medium, and high. The results of the grouping are displayed in the form of spatial mapping in the form of a web-based geographic information system (web-GIS) on the determination of the diphtheria vulnerability level using the AHP-natural breaks classification. The GVF evaluation for 2016, 2017, and 2018 are respectively 0.66, 0.67, and 0.65 (more than 0.5), which means that the proposed method achieved accurate and significant classification. Spatial-temporal analysis for 2013-2018 also achieves accurate and significant with a GVF value of 0.77. The spatial-temporal analysis can predict the high potential for vulnerability.

Realization of analytic moduli for parabolic Dulac germs

AbstractIn a previous paper [P. Mardešić and M. Resman. Analytic moduli for parabolic Dulac germs. Russian Math. Surveys, to appear, 2021, arXiv:1910.06129v2.] we determined analytic invariants, that is, moduli of analytic classification, for parabolic generalized Dulac germs. This class contains parabolic Dulac (almost regular) germs, which appear as first-return maps of hyperbolic polycycles. Here we solve the problem of realization of these moduli.

Persistence time of solutions of the three-dimensional Navier-Stokes equations in Sobolev-Gevrey classes

In this paper, we study existence times of strong solutions of the three-dimensional Navier-Stokes equations in time-varying analytic Gevrey classes based on Sobolev spaces Hs,s>12. This complements the seminal work of Foias and Temam (1989) [26] on H1 based Gevrey classes, thus enabling us to improve estimates of the analyticity radius of solutions for certain classes of initial data. The main thrust of the paper consists in showing that the existence times in the much stronger Gevrey norms (i.e. the norms defining the analytic Gevrey classes which quantify the radius of real-analyticity of solutions) match the best known persistence times in Sobolev classes. Additionally, as in the case of persistence times in the corresponding Sobolev classes, our existence times in Gevrey norms are optimal for 12<s<52.

Application of gas chromatography separation based on metal-organic framework material as stationary phase

金属有机骨架材料(MOFs)是一类由有机配体和金属离子(或金属簇)自组装形成的新型多功能材料。MOFs具有孔隙度高、比表面积大、孔径可调、化学和热稳定性高等特点,被广泛应用于吸附、分离、催化等多个领域。近年来,MOFs作为新型气相色谱固定相用于分离异构体受到了广泛关注。与传统无机多孔材料相比,MOFs在结构和功能上展现出高度的可调性,通过合理地选择配体和金属中心,可以设计合成具有不同孔道大小和孔道环境的MOFs,从而分别从热力学和动力学角度优化色谱分离效果,有效提高分离选择性。该文结合MOFs的结构,讨论了MOFs气相色谱固定相分离不同类型分析物的分离机理。分离机理主要包括MOFs孔道的分子筛效应或形状选择性,MOFs不饱和的金属位点与分析物中不同的官能团之间产生的相互作用,分析物与MOFs孔道之间产生的不同范德华力、π-π相互作用和氢键相互作用。此外,MOFs的手性分离可能主要依赖于外消旋体与手性MOFs中手性活性位点之间的相互作用。该文也对不同分析目标物进行了归类,综述了多种MOFs气相色谱固定相对烷烃、二甲苯异构体和乙基甲苯、外消旋体、含氧有机物、环境有机污染物的气相色谱分离效果。最后,该文还对MOFs在该领域的应用进行了总结与展望,旨在为MOFs气相色谱高效分离的研究提供参考。

Machine learning and chemometrics for electrochemical sensors: moving forward to the future of analytical chemistry.

Electrochemical sensors and biosensors have been successfully used in a wide range of applications, but systematic optimization and nonlinear relationships have been compromised for electrode fabrication and data analysis. Machine learning and experimental designs are chemometric tools that have been proved to be useful in method development and data analysis. This minireview summarizes recent applications of machine learning and experimental designs in electroanalytical chemistry. First, experimental designs, e.g., full factorial, central composite, and Box-Behnken are discussed as systematic approaches to optimize electrode fabrication to consider the effects from individual variables and their interactions. Then, the principles of machine learning algorithms, including linear and logistic regressions, neural network, and support vector machine, are introduced. These machine learning models have been implemented to extract complex relationships between chemical structures and their electrochemical properties and to analyze complicated electrochemical data to improve calibration and analyte classification, such as in electronic tongues. Lastly, the future of machine learning and experimental designs in electrochemical sensors is outlined. These chemometric strategies will accelerate the development and enhance the performance of electrochemical devices for point-of-care diagnostics and commercialization.

О формальной нормальной форме ростков полугиперболических отображений на плоскости

There are consider the problem of constructing an analytical classification holomorphic resonance maps germs of Siegel-type in dimension 2. Namely, semi-hyperbolic maps of general form: such maps have one parabolic multiplier (equal to one), and the other hyperbolic (not equal in modulus to zero or one). In this paper, the first stage of constructing an analytical classification by the method of functional invariants is carried out: a theorem on the reducibility of a germ to its formal normal form by "semiformal" changes of coordinates is proved. The one-time shift along the saddle node vector field (the formal normal form in the problem of the analytical classification of saddle-node vector fields on a plane) is chosen as the formal normal form.

On coefficient inequalities of certain subclasses of bi-univalent functions involving the Sălăgean operator

In the present investigation, with motivation from the pioneering work of Srivastava et al. [28], which in recent years actually revived the study of analytic and bi-univalent functions, we introduce the subclasses T*?(n,?) and T?(n,?) of analytic and bi-univalent function class ? defined in the open unit disk U = {z ? C : |z| &lt; 1g and involving the S?l?gean derivative operator Dn. Moreover, we derive estimates on the initial coefficients |a2| and |a3| for functions in these subclasses and pointed out connections with some earlier known results.

TRNA Modifications as a Readout of S and Fe-S Metabolism.

Iron-Sulfur (Fe-S) clusters function as core prosthetic groups known to modulate the activity of metalloenzymes, act as trafficking vehicles for biological iron and sulfur, and participate in several intersecting metabolic pathways. The formation of these clusters is initiated by a class of enzymes called cysteine desulfurases, whose primary function is to shuttle sulfur from the amino acid L-cysteine to a variety of sulfur transfer proteins involved in Fe-S cluster synthesis as well as in the synthesis of other thiocofactors. Thus, sulfur and Fe-S cluster metabolism are connected through shared enzyme intermediates, and defects in their associated pathways cause a myriad of pleiotropic phenotypes, which are difficult to dissect. Post-transcriptionally modified transfer RNA (tRNA) represents a large class of analytes whose synthesis often requires the coordinated participation of sulfur transfer and Fe-S enzymes. Therefore, these molecules can be used as biologically relevant readouts for cellular Fe and S status. Methods employing LC-MS technology provide a valuable experimental tool to determine the relative levels of tRNA modification in biological samples and, consequently, to assess genetic, nutritional, and environmental factors modulating reactions dependent on Fe-S clusters. Herein, we describe a robust method for extracting RNA and analytically evaluating the degree of Fe-S-dependent and -independent tRNA modifications via an LC-MS platform.

On the analytic classification of irreducible plane curve singularities

On the analytic classification of irreducible plane curve singularities

Development of an advanced strategy on the assay method transfer

Aim . The paper intends to frame and pilot the optimised science-based principles of the assay transfer. Materials and methods . The research was performed on desloratadine film-coated tablets, using an analytical balance Mettler Toledo XP 205DR and Class A volumetric glassware. Absorbance readings were measured on a UV-Vis spectrophotometer Lambda 25. Results and discussion . The concept of method transfer that complements the conventional approach to validation with the lifecycle initiative and the metrological base of the State Pharmacopoeia of Ukraine was substantiated, following which the transfer of the spectrophotometric procedure for desloratadine assay was conducted. For the batch intended for the transfer, the budget of analytical and technological variability was balanced. The deviation of a single assay result from the grand mean was used as the criterion for accuracy in the transfer. The requirement for the one-sided confidence interval for assay result runs not to exceed the target uncertainty of the procedure was used as the criterion for precision. The control strategy requirements for variability sources and the analytical target profile requirements for precision and accuracy were met in the receiving unit. Conclusion . The paper discusses the premise and advocates an alternative approach to the method transfer. Precision is proposed not to study during the transfer (in the short-term experiment) but assess from the stability data (in the long-term experiment). Compliance with the normal analytical practice (the maximum permissible variability attributed to analysts and analytical instruments) allows narrowing down the transfer design to the confirmation in the minimal experiment that the amplitude of variability sources lies within the predefined range

2-Cyano-3-(2-thienyl)acrylic Acid as a New MALDI Matrix for the Analysis of a Broad Spectrum of Analytes.

A low-cost synthetic 2-cyano-3-(2-thienyl)acrylic acid (CTA) is developed as a new MALDI matrix for the analysis of various classes of compounds such as lipids (e.g., fatty acids), peptides, proteins, saccharides, natural products (i.e., iridoids), PEGs, and organometallics in the positive-ion mode. The difficulty in the analysis of high molecular mass PEGs was overcome by using CTA as matrix even at low concentrations. Both high molecular mass proteins and peptides were successfully analyzed using CTA. The mass spectra of all of the studied analytes with CTA showed high signal-to-noise (S/N) ratios and spectral resolutions when compared to other conventional matrices such as SA, DHB, DT, and HCCA. However, in the case of peptide analysis with CTA, the resulting mass spectra are found to be similar to that of the well-established HCCA matrix. On the basis of the physicochemical properties of the analytes, the CTA works as a proton/cation or electron-transfer matrix. It proves that the CTA can be used as a common matrix for the analysis of majority classes of analytes instead of using a specific matrix for the particular class of analytes. Further, the CTA provides an advantage in the analysis of unknown samples as it rules out ambiguity in the selection of particular matrix and it may also offer a complete profile of the tissue surface in the MALDI-imaging experiments.

Identifying Binary Mixtures of Volatile Organic Compounds With Isomeric Components Using a Single Thermal Shock-Induced Generic SnO2 Gas Sensor

Identifying the components and their relative proportions in the binary mixtures of the structurally similar Volatile Organic Compounds (VOCs) are required for many applications. In this work, the binary vapor mixtures of VOCs with isomeric components at variable combined concentrations ranging from 200 to 2500 ppm are discriminated using a single generic tin oxide gas sensor. Sharp fluctuations in the pellet temperature bring in more valuable analyte nature-related information into the sensor's response patterns, which can be extracted and used for discriminating among complex mixtures. The classic staircase voltage waveform fails to produce the abrupt variations in the pellet temperature facilitated via the thermal shock induction method applied for the virtual array formation out of a single tin oxide chemiresistor. Linear discriminant analysis is applied after preprocessing and feature selection of the sensor response patterns. Two simple classifiers operating based on the Euclidean distance in the feature space are utilized for analyte classification. The recognitions of binary vapor mixtures of 1-propanol, 2-propanol, 1-butanol, and 2-butanol are demonstrated as examples. A total of 546 individual experiments are carried out to confirm the accuracy of the performance; all the binary test mixtures are identified correctly; with 2.3% of error in compositions determination. The experiments are satisfactorily repeated in the form of independent work sessions within a 1-month time interval to represent the repeatability of the response patterns. The thermal shock induction appears as a replacement for the sensor arrays which suffer from the multi-dimensional drift of the array components.