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Journal articles on the topic 'Chemometrics'

To see the other types of publications on this topic, follow the link: Chemometrics.

Author: Grafiati

Published: 4 June 2021

Last updated: 31 July 2024

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1

Mocák, Ján. "Chemometrics in Medicine and Pharmacy." Nova Biotechnologica et Chimica 11, no. 1 (June 1, 2012): 11–26. http://dx.doi.org/10.2478/v10296-012-0002-3.

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Chemometrics in Medicine and PharmacyThis minireview summarizes the basic ways of application of chemometrics in medicine and pharmacy. It brings a collection of applications of chemometric used for the solution of diverse practical problems, e.g. exploitation of biologically active species, effective use of biomarkers, advancement of clinical diagnosis, monitoring of the patient's state and prediction of its perspectives, drug design or classification of toxic chemical substances. The aim of this contribution is a brief presentation of versatile potentialities of contemporary chemometrical techniques and relevant software. They are exemplified by typical cases from literature as well as by own research results of the Chemometrics group at Department of Chemistry, the University of Ss. Cyril & Methodius in Trnava.

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2

Pomerantsev, Alexey L., and Oxana Ye Rodionova. "Chemometric view on “comprehensive chemometrics”." Chemometrics and Intelligent Laboratory Systems 103, no. 1 (August 2010): 19–24. http://dx.doi.org/10.1016/j.chemolab.2010.05.001.

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3

Deming, S. N. "Chemometrics: an overview." Clinical Chemistry 32, no. 9 (September 1, 1986): 1702–6. http://dx.doi.org/10.1093/clinchem/32.9.1702.

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Abstract Chemometrics is broadly defined as the application of mathematical and statistical methods to chemistry. Because the mathematical and statistical aspects of chemistry require measured values, analytical chemists have been at the forefront of the "chemometric revolution." Using the analysis of variance as a paradigm, I present an overview of chemometrics as it is practiced today. Receiving special emphasis are: the design of experiments to acquire information from the relevant universe of possible measurements; the establishment of relationships among independent and dependent variables; the importance of minimizing purely experimental uncertainty; sequential simplex optimization; analysis of principal components; and cluster analysis.

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4

Tarapoulouzi, Maria, Monica Mironescu, Chryssoula Drouza, Ion Dan Mironescu, and Sofia Agriopoulou. "Insight into the Recent Application of Chemometrics in Quality Analysis and Characterization of Bee Honey during Processing and Storage." Foods 12, no. 3 (January 19, 2023): 473. http://dx.doi.org/10.3390/foods12030473.

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The application of chemometrics, a widely used science in food studies (and not only food studies) has begun to increase in importance with chemometrics being a very powerful tool in analyzing large numbers of results. In the case of honey, chemometrics is usually used for assessing honey authenticity and quality control, combined with well-established analytical methods. Research related to investigation of the quality changes in honey due to modifications after processing and storage is rare, with a visibly increasing tendency in the last decade (and concentrated on investigating novel methods to preserve the honey quality, such as ultrasound or high-pressure treatment). This review presents the evolution in the last few years in using chemometrics in analyzing honey quality during processing and storage. The advantages of using chemometrics in assessing honey quality during storage and processing are presented, together with the main characteristics of some well-known chemometric methods. Chemometrics prove to be a successful tool to differentiate honey samples based on changes of characteristics during storage and processing.

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5

Komsta, Łukasz. "Chemometrics in Fingerprinting by Means of Thin Layer Chromatography." Chromatography Research International 2012 (November 28, 2012): 1–5. http://dx.doi.org/10.1155/2012/893246.

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The paper is written as an introductory review, presenting summary of current knowledge about chemometric fingerprinting in the context of TLC, due to a rather small interest in the literature about joining TLC and chemometrics. The paper shortly covers the most important aspects of the chemometric fingerprinting in general, creating the TLC fingerprints, denoising, baseline removal, warping/registering, and chemometric processing itself. References being good candidates as a starting point are given for each topic and processing step.

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6

Karadžić Banjac, Milica, Strahinja Kovačević, and Sanja Podunavac-Kuzmanović. "Ongoing Multivariate Chemometric Approaches in Bioactive Compounds and Functional Properties of Foods—A Systematic Review." Processes 12, no. 3 (March 14, 2024): 583. http://dx.doi.org/10.3390/pr12030583.

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In this review, papers published in the chemometrics field were selected in order to gather information and conduct a systematic review regarding food science and technology; more precisely, regarding the domain of bioactive compounds and the functional properties of foods. More than 50 papers covering different food samples, experimental techniques and chemometric techniques were selected and presented, focusing on the chemometric methods used and their outcomes. This study is one way to approach an overview of the current publications related to this subject matter. The application of the multivariate chemometrics approach to the study of bioactive compounds and the functional properties of foods can open up even more in coming years, since it is fast-growing and highly competitive research area.

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7

EL-Gindy, Alaa, and Ghada M. Hadad. "Chemometrics in Pharmaceutical Analysis: An Introduction, Review, and Future Perspectives." Journal of AOAC INTERNATIONAL 95, no. 3 (May 1, 2012): 609–23. http://dx.doi.org/10.5740/jaoacint.sge_el-gindy.

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Abstract Chemometrics is the application of statistical and mathematical methods to analytical data to permit maximum collection and extraction of useful information. The utility of chemometric techniques as tools enabling multidimensional calibration of selected spectroscopic, electrochemical, and chromatographic methods is demonstrated. Application of this approach mainly for interpretation of UV-Vis and near-IR (NIR) spectra, as well as for data obtained by other instrumental methods, makes identification and quantitative analysis of active substances in complex mixtures possible, especially in the analysis of pharmaceutical preparations present in the market. Such analytical work is carried out by the use of advanced chemical instruments and data processing, which has led to a need for advanced methods to design experiments, calibrate instruments, and analyze the resulting data. The purpose of this review is to describe various chemometric methods in combination with UV-Vis spectrophotometry, NIR spectroscopy, fluorescence spectroscopy, electroanalysis, chromatographic separation, and flow-injection analysis for the analysis of drugs in pharmaceutical preparations. Theoretical and practical aspects are described with pharmaceutical examples of chemometric applications. This review will concentrate on gaining an understanding of how chemometrics can be useful in the modern analytical laboratory. A selection of the most challenging problems faced in pharmaceutical analysis is presented, the potential for chemometrics is considered, and some consequent implications for utilization are discussed. The reader can refer to the citations wherever appropriate.

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8

Zappi, Alessandro, Valentina Marassi, Stefano Giordani, Nicholas Kassouf, Barbara Roda, Andrea Zattoni, Pierluigi Reschiglian, and Dora Melucci. "Extracting Information and Enhancing the Quality of Separation Data: A Review on Chemometrics-Assisted Analysis of Volatile, Soluble and Colloidal Samples." Chemosensors 11, no. 1 (January 4, 2023): 45. http://dx.doi.org/10.3390/chemosensors11010045.

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Instrument automation, technological advancements and improved computational power made separation science an extremely data-rich approach, requiring the use of statistical and data analysis tools that are able to optimize processes and combine multiple outputs. The use of chemometrics is growing, greatly improving the ability to extract meaningful information. Separation–multidetection generates multidimensional data, whose elaboration should not be left to the discretion of the operator. However, some applications or techniques still suffer from the lack of method optimization through DoE and downstream multivariate analysis, limiting their potential. This review aims at summarizing how chemometrics can assist analytical chemists in terms of data elaboration and method design, focusing on what can be achieved by applying chemometric approaches to separation science. Recent applications of chemometrics in separation analyses, in particular in gas, liquid and size-exclusion chromatography, together with field flow fractionation, will be detailed to visualize the state of the art of separation chemometrics, encompassing volatile, soluble and solid (colloidal) analytes. The samples considered will range from food chemistry and environmental chemistry to bio/pharmaceutical science.

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9

Liu, Jingjing, Yifei Xu, Shikun Liu, Shixin Yu, Zhirun Yu, and Sze Shin Low. "Application and Progress of Chemometrics in Voltammetric Biosensing." Biosensors 12, no. 7 (July 7, 2022): 494. http://dx.doi.org/10.3390/bios12070494.

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The voltammetric electrochemical sensing method combined with biosensors and multi-sensor systems can quickly, accurately, and reliably analyze the concentration of the main analyte and the overall characteristics of complex samples. Simultaneously, the high-dimensional voltammogram contains the rich electrochemical features of the detected substances. Chemometric methods are important tools for mining valuable information from voltammetric data. Chemometrics can aid voltammetric biosensor calibration and multi-element detection in complex matrix conditions. This review introduces the voltammetric analysis techniques commonly used in the research of voltammetric biosensor and electronic tongues. Then, the research on optimizing voltammetric biosensor results using classical chemometrics is summarized. At the same time, the incorporation of machine learning and deep learning has brought new opportunities to further improve the detection performance of biosensors in complex samples. Finally, smartphones connected with miniaturized voltammetric biosensors and chemometric methods provide a high-quality portable analysis platform that shows great potential in point-of-care testing.

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10

Maritha, Vevi, Putri Widyanti Harlina, Ida Musfiroh, Amirah Mohd Gazzali, and Muchtaridi Muchtaridi. "The Application of Chemometrics in Metabolomic and Lipidomic Analysis Data Presentation for Halal Authentication of Meat Products." Molecules 27, no. 21 (November 4, 2022): 7571. http://dx.doi.org/10.3390/molecules27217571.

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The halal status of meat products is an important factor being considered by many parties, especially Muslims. Analytical methods that have good specificity for the authentication of halal meat products are important as quality assurance to consumers. Metabolomic and lipidomic are two useful strategies in distinguishing halal and non-halal meat. Metabolomic and lipidomic analysis produce a large amount of data, thus chemometrics are needed to interpret and simplify the analytical data to ease understanding. This review explored the published literature indexed in PubMed, Scopus, and Google Scholar on the application of chemometrics as a tool in handling the large amount of data generated from metabolomic and lipidomic studies specifically in the halal authentication of meat products. The type of chemometric methods used is described and the efficiency of time in distinguishing the halal and non-halal meat products using chemometrics methods such as PCA, HCA, PLS-DA, and OPLS-DA is discussed.

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11

Ouyang, Tinghui, Chongwu Wang, Zhangjun Yu, Robert Stach, Boris Mizaikoff, Bo Liedberg, Guang-Bin Huang, and Qi-Jie Wang. "Quantitative Analysis of Gas Phase IR Spectra Based on Extreme Learning Machine Regression Model." Sensors 19, no. 24 (December 14, 2019): 5535. http://dx.doi.org/10.3390/s19245535.

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Advanced chemometric analysis is required for rapid and reliable determination of physical and/or chemical components in complex gas mixtures. Based on infrared (IR) spectroscopic/sensing techniques, we propose an advanced regression model based on the extreme learning machine (ELM) algorithm for quantitative chemometric analysis. The proposed model makes two contributions to the field of advanced chemometrics. First, an ELM-based autoencoder (AE) was developed for reducing the dimensionality of spectral signals and learning important features for regression. Second, the fast regression ability of ELM architecture was directly used for constructing the regression model. In this contribution, nitrogen oxide mixtures (i.e., N2O/NO2/NO) found in vehicle exhaust were selected as a relevant example of a real-world gas mixture. Both simulated data and experimental data acquired using Fourier transform infrared spectroscopy (FTIR) were analyzed by the proposed chemometrics model. By comparing the numerical results with those obtained using conventional principle components regression (PCR) and partial least square regression (PLSR) models, the proposed model was verified to offer superior robustness and performance in quantitative IR spectral analysis.

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12

Sajdak, Marcin. "Application of chemometrics to identifying solid fuels and their origin." Open Chemistry 11, no. 2 (February 1, 2013): 151–59. http://dx.doi.org/10.2478/s11532-012-0145-8.

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AbstractThe aim of this work was to implement a chemometric analysis to detect the relationships between the analysed variables in samples of solid fuels. Efforts are being made to apply chemometrics methods in environmental issues by developing methods for the rapid assessment of solid fuels and their compliance with the required emission characteristics regulations. In the present investigation, two clustering techniques—hierarchical clustering analysis (HCA) and principal components analysis (PCA)—are used to obtain the linkage between solid fuel properties and the type of sample. These analyses allowed us to detect the relationships between the studied parameters of the investigated solid fuels. Furthermore, the usefulness of chemometrics methods for identification of the origin of biofuels is shown. These methods will enable control of the degree of contamination.

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13

Santos, Mônica Cardoso, Paloma Andrade Martins Nascimento, Wesley Nascimento Guedes, Edenir Rodrigues Pereira-Filho, Érica Regina Filletti, and Fabíola Manhas Verbi Pereira. "Chemometrics in analytical chemistry – an overview of applications from 2014 to 2018." Eclética Química Journal 44, no. 2 (April 25, 2019): 11. http://dx.doi.org/10.26850/1678-4618eqj.v44.2.2019.p11-25.

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A compilation of papers published between 2014 and 2018 was evaluated. Many papers related to multivariate calibration and classification have been reported, as well as, design of experiments applications and artificial intelligence methods. Some applications were highlighted, as medical and pharmaceutical, food analysis, fuels, biological and forensic for the chemometric techniques on this review. Most studies are related to developing methods for practical solutions in industry or routine analysis. A promising scenario is shown considering the number of published papers: a total of 832 for this period using the keywords, multivariate classification, multivariate calibration, analysis, chemometrics, prediction, analytical chemistry, artificial neural networks (ANN), design of experiments (DoE) and factorial design. An useful overview for Analytical Chemistry researchers´ combined with Chemometrics is presented in this review.

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14

Pereira, Fabiola. "Chemometrics reveals not-so-obvious analytical information." Brazilian Journal of Analytical Chemistry 9, no. 37 (October 5, 2022): 11–13. http://dx.doi.org/10.30744/brjac.2179-3425.letter-fabiolaverbi.n37.

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The application of chemometric tools in analytical chemistry or other areas of chemistry has become essential. This is mainly due to the large amount and nature of the generated data1,2 and the need to extract useful information from these and optimize steps throughout a process. It allows the quick decision-making visualization of interactions among variables, such as synergism or antagonism between parameters, during the development of a method,3 as shown in Figure 1. Classical chemometric techniques have been disseminated and can be divided according to the study approach, among which exploratory data analysis stands out. Principal component analysis (PCA) is one of the most accessible and well-established ways to perform an initial exploration and extract relevant information from a given dataset and has been used quite successfully in various spectroscopic techniques.4 Principal component analysis consists of projecting the data in a smaller dimension, enabling the detection of anomalous samples (outliers), the selection of essential variables in a given system, and unsupervised classification.1,2,4 Another branch of chemometrics involves the design of experiments (DoE). The primary purpose of the factorial design is to study the influence or effect of a given variable and its interactions in a specific system.5-9 Multivariate calibration is another aspect of chemometrics, where several variables are used to calibrate one (or more) property or the concentration of a given chemical analyte.10,11 Since the first publications of chemometric tools, numerous variations of these techniques, proposals for data fusion strategies, and applications using hyphenated instrumental techniques have been proposed.12-14 Industrial quality control and development (R&D) laboratories require an approach addressing adequate quality by design (QbD). The QbD strategies consider four steps that include an analytical target profile (ATP), a risk assessment, a design space (DS), and control strategy and validation based on figures of merit, for instance.9 Principal component analysis is the most widely multivariate technique used for data analysis. Jolliffe wrote a review reporting his wonderful experience with PCA in the last 50 years.15 Indeed, PCA is an invaluable method for data, and I agree with it. PCA is the algorithm of choice for numerous chemometric techniques.16 Other computational languages, such as Python, are currently experiencing a rise in popularity in the field of chemistry. The R language has also become more popular than it was ten years ago. The scripts, functions, or codes are easily written with fewer lines and specific commands that minimize steps and help speed up calculations. The dissemination of free software has also become popular, and the sharing of codes through publications, social media, communities, or websites has become relatively easy. From my point of view, chemometrics is no longer faced as a giant monster or a way to become scientific papers fancier without helpful content. Chemometrics extract information that is not easy to visualize at first through univariate evaluations or using simple plots. Nowadays, thousands of instrumental data can furnish important chemical information, and we must use them for significant proposals. Indeed, QbD is proof of that, as Industry 4.0 is a reality.

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15

Hibbert, David B. "Vocabulary of concepts and terms in chemometrics (IUPAC Recommendations 2016)." Pure and Applied Chemistry 88, no. 4 (April 1, 2016): 407–43. http://dx.doi.org/10.1515/pac-2015-0605.

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AbstractRecommendations are given concerning the terminology relating to chemometrics. Building on ISO definitions of terms for basic concepts in statistics the vocabulary is concerned with mainstream chemometric methods. Where methods are used widely in science, definitions are given that are most useful to chemical applications. Vocabularies are given for general data processing, experimental design, classification, calibration and general multivariate methods.

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16

Tarapoulouzi, Maria, Sofia Agriopoulou, Anastasios Koidis, Charalampos Proestos, Hesham Ali El Enshasy, and Theodoros Varzakas. "Recent Advances in Analytical Methods for the Detection of Olive Oil Oxidation Status during Storage along with Chemometrics, Authenticity and Fraud Studies." Biomolecules 12, no. 9 (August 25, 2022): 1180. http://dx.doi.org/10.3390/biom12091180.

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Olive oil is considered to be a food of utmost importance, especially in the Mediterranean countries. The quality of olive oil must remain stable regarding authenticity and storage. This review paper emphasizes the detection of olive oil oxidation status or rancidity, the analytical techniques that are usually used, as well as the application and significance of chemometrics in the research of olive oil. The first part presents the effect of the oxidation of olive oil during storage. Then, lipid stability measurements are described in parallel with instrumentation and different analytical techniques that are used for this particular purpose. The next part presents some research publications that combine chemometrics and the study of lipid changes due to storage published in 2005–2021. Parameters such as exposure to light, air and various temperatures as well as different packaging materials were investigated to test olive oil stability during storage. The benefits of each chemometric method are provided as well as the overall significance of combining analytical techniques and chemometrics. Furthermore, the last part reflects on fraud in olive oil, and the most popular analytical techniques in the authenticity field are stated to highlight the importance of the authenticity of olive oil.

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17

Dumancas, Gerard G., Ghalib Bello, Jeff Hughes, Renita Murimi, Lakshmi Viswanath, Casey O. Orndorff, Glenda Fe G. Dumancas, Jacy O'Dell, Prakash Ghimire, and Catherine Setijadi. "Chemometrics." International Journal of Fog Computing 2, no. 1 (January 2019): 1–42. http://dx.doi.org/10.4018/ijfc.2019010101.

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The accumulation of data from various instrumental analytical instruments has paved a way for the application of chemometrics. Challenges, however, exist in processing, analyzing, visualizing, and storing these data. Chemometrics is a relatively young area of analytical chemistry that involves the use of statistics and computer applications in chemistry. This article will discuss various computational and storage tools of big data analytics within the context of analytical chemistry with examples, applications, and usage details in relation to fog computing. The future of fog computing in chemometrics will also be discussed. The article will dedicate particular emphasis to preprocessing techniques, statistical and machine learning methodology for data mining and analysis, tools for big data visualization, and state-of-the-art applications for data storage using fog computing.

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18

Lavine, Barry, and Jerry Workman. "Chemometrics." Analytical Chemistry 82, no. 12 (June 15, 2010): 4699–711. http://dx.doi.org/10.1021/ac101202z.

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19

Lavine, Barry, and Jerome J. Workman. "Chemometrics." Analytical Chemistry 76, no. 12 (June 2004): 3365–72. http://dx.doi.org/10.1021/ac040053p.

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20

Brown, Steven D. "Chemometrics." Analytical Chemistry 62, no. 12 (June 15, 1990): 84–101. http://dx.doi.org/10.1021/ac00211a008.

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21

Brown, Steven D., Todd Q. Barker, Robert J. Larivee, Stephen L. Monfre, and Harlan R. Wilk. "Chemometrics." Analytical Chemistry 60, no. 12 (June 15, 1988): 252–73. http://dx.doi.org/10.1021/ac00163a018.

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22

Lavine, Barry K. "Chemometrics." Analytical Chemistry 72, no. 12 (June 2000): 91–98. http://dx.doi.org/10.1021/a1000016x.

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23

Brown, Steven D., Stephen T. Sum, Frederic Despagne, and Barry K. Lavine. "Chemometrics." Analytical Chemistry 68, no. 12 (January 1996): 21–62. http://dx.doi.org/10.1021/a1960005x.

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24

Lavine, Barry K. "Chemometrics." Analytical Chemistry 70, no. 12 (June 1998): 209–28. http://dx.doi.org/10.1021/a19800085.

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25

Lavine, Barry, and Jerry Workman. "Chemometrics." Analytical Chemistry 78, no. 12 (June 2006): 4137–45. http://dx.doi.org/10.1021/ac060717q.

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26

Lavine, Barry K., and Jerome Workman. "Chemometrics." Analytical Chemistry 85, no. 2 (December 3, 2012): 705–14. http://dx.doi.org/10.1021/ac303193j.

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27

Brown, Steven D., Robert S. Bear, and Thomas B. Blank. "Chemometrics." Analytical Chemistry 64, no. 12 (June 15, 1992): 22–49. http://dx.doi.org/10.1021/ac00036a002.

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28

MacFie, H. J. H. "Chemometrics." Analytica Chimica Acta 186 (1986): 345. http://dx.doi.org/10.1016/s0003-2670(00)81817-6.

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29

Lavine, Barry K., and Jerome Workman. "Chemometrics." Analytical Chemistry 74, no. 12 (June 2002): 2763–70. http://dx.doi.org/10.1021/ac020224v.

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30

Brown, Steven D., Thomas B. Blank, Stephen T. Sum, and Lois G. Weyer. "Chemometrics." Analytical Chemistry 66, no. 12 (June 1994): 315–59. http://dx.doi.org/10.1021/ac00084a014.

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31

Ramos, L. Scott, Kenneth R. Beebe, W. Patrick Carey, Eugenio Sanchez, Brice C. Erickson, Bruce E. Wilson, Lawrence E. Wangen, and Bruce R. Kowalski. "Chemometrics." Analytical Chemistry 58, no. 5 (April 1986): 294–315. http://dx.doi.org/10.1021/ac00296a020.

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32

Leardi, Riccardo. "Chemometrics." TrAC Trends in Analytical Chemistry 11, no. 2 (February 1992): VII. http://dx.doi.org/10.1016/0165-9936(92)80076-i.

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33

DeTar, DeLosF. "Chemometrics." Computers & Chemistry 11, no. 1 (January 1987): 83. http://dx.doi.org/10.1016/0097-8485(87)80011-6.

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Lavine, Barry, and Jerome Workman. "Chemometrics." Analytical Chemistry 80, no. 12 (June 2008): 4519–31. http://dx.doi.org/10.1021/ac800728t.

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35

Aishima, Tetsuo. "Chemometrics." IEEJ Transactions on Sensors and Micromachines 117, no. 6 (1997): 290–93. http://dx.doi.org/10.1541/ieejsmas.117.290.

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Tatsubayashi, Kenichi, and Naoko Endo. "Chemometrics." IEEJ Transactions on Sensors and Micromachines 117, no. 6 (1997): 294–97. http://dx.doi.org/10.1541/ieejsmas.117.294.

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Ide, Junichi. "Chemometrics." IEEJ Transactions on Sensors and Micromachines 117, no. 6 (1997): 298–301. http://dx.doi.org/10.1541/ieejsmas.117.298.

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38

Sasaki, Ken, and Takashi Hamaoka. "Chemometrics." IEEJ Transactions on Sensors and Micromachines 117, no. 6 (1997): 302–5. http://dx.doi.org/10.1541/ieejsmas.117.302.

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39

AISHIMA, Tetsuo. "Chemometrics." Kagaku To Seibutsu 29, no. 7 (1991): 424–32. http://dx.doi.org/10.1271/kagakutoseibutsu1962.29.424.

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40

Kartini, Kartini, Yunita A. Andriani, Widho Priambodo, Nikmatul I. E. Jayani, and Mochammad A. Hadiyat. "Validating and developing TLC-based fingerprinting for Curcuma longa L." Journal of Pharmacy & Pharmacognosy Research 9, no. 5 (September 1, 2021): 704–15. http://dx.doi.org/10.56499/jppres21.1062_9.5.704.

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Context: Curcuma longa (turmeric) is extensively cultivated as spices and herbal medicines in tropical and sub-tropical countries. Geographical origin is known to significantly determine the quality of the herbs used and, consequently, the safety and efficacy of their products. Aims: To validate and develop TLC-fingerprint combined with chemometrics to differentiate C. longa collected from various origins. Methods: Thin Layer Chromatography (TLC) was employed together with chemometric methods, i.e., Principal Component Analysis (PCA) and Cluster Analysis (CA), to evaluate the quality of C. longa rhizomes collected from nine origins in Indonesia. Results: Chloroform, dichloromethane, and ethanol (64:64:1) were a suitable mobile phase for C. longa. The method used met the requirements for a stable and precise TLC system. As analyzed by the chemometric techniques, the TLC-fingerprints could discriminate C. longa from various origins. The PCA score plot of the first two principal components (PCs) and CA clearly distinguished two clusters of simples. Conclusions: When combined with PCA and CA, TLC-fingerprinting can discern the rhizomes of C. longa sourcedfrom various locations. TLC-fingerprints that are analyzed with chemometrics can be used as an alternative marker-oriented method for evaluating the quality of C. longa.

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41

Boyd, J. C. "Perspectives on the use of chemometrics in laboratory medicine." Clinical Chemistry 32, no. 9 (September 1, 1986): 1726–33. http://dx.doi.org/10.1093/clinchem/32.9.1726.

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Abstract Today's automated laboratory instruments are capable of generating prodigious volumes of high-quality measurements. Increasingly, the powerful mathematical and statistical methods of chemometrics are being called upon to help reduce these measurements to useful information. Chemometric methods have been important in automating various data-intensive functions of the clinical laboratory, including analysis of cellular images, identification of bacteria and fungi on the basis of their metabolic and chemical properties, and identification of drugs and toxic substances from their mass spectra. These methods also appear promising in aiding both the selection and interpretation of laboratory tests for diagnosis, monitoring, and prognosis. In spite of the demonstrated potential of these methods, significant problems remain to be solved in the areas of measurement standardization, data-base collection, and user familiarity with these approaches before chemometric methods can be used most fully by the clinical laboratory.

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42

Aruga, Roberto. "Proton dissociation of aqueous organic acids studied by multivariate chemometrics." Canadian Journal of Chemistry 73, no. 12 (December 1, 1995): 2170–77. http://dx.doi.org/10.1139/v95-269.

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Thermodynamic data of proton dissociation of 75 organic acids belonging to four classes (protonated amines, aliphatic carboxylic acids, benzoic acids, phenols) have been processed by multivariate chemometric techniques. The variables consist of conventional thermodynamic quantities (Gibbs function, enthalpy, entropy) and of partial components of these quantities (internal and external, electrostatic and nonelectrostatic components). The above data refer to the aqueous medium, at 25 °C and I = 0 mol dm−3. The Gibbs function of deprotonation in the gas phase and the Hammett σ constant have also been considered. Multivariate techniques include Principal Component Analysis, Factor Analysis, and feature selection. Factor Analysis and related concepts have proved to be useful in defining the causes of differences in acid strengths and their respective importance. Keywords: acid dissociation data, chemometrics of; chemometrics of acid dissociation data; factor analysis of acid dissociation data; principal component analysis of acid dissociation data; thermodynamics of acid dissociation.

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Whitfield, Matthew B., and Mari S. Chinn. "Near infrared spectroscopic data handling and chemometric analysis with the R statistical programming language: A practical tutorial." Journal of Near Infrared Spectroscopy 25, no. 6 (November 14, 2017): 363–80. http://dx.doi.org/10.1177/0967033517740768.

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Near infrared spectroscopy is widely used for compositional analysis of bulk materials because it is inexpensive, fast, and non-destructive. However, the chemometric techniques required to produce near infrared calibrations are varied and complex. While there are a number of commercial applications capable of implementing these techniques, there has also been a recent proliferation of R packages for chemometrics. The R programming language has greater capabilities for data processing, automation of multiple analyses, and user development of new techniques than many of the closed-source, graphical user interface-based commercial chemometrics applications do. The R project is thus a powerful, open-source option for generating and testing near infrared calibrations, albeit with a longer learning curve than many of the commercial chemometric applications. The calibration techniques available in R have been widely demonstrated in both the primary literature and introductory texts, but less so the steps between the acquisition of the data and the calibration. This tutorial seeks to bridge that gap by demonstrating a practical approach to data transfer and handling, using R and several packages available on the Comprehensive R Archive Network ( https://cran.r-project.org/ ), and then illustrates the use of the resulting data framework in the generation of near infrared calibrations.

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Sun, Yan, Wensheng Cai, and Xueguang Shao. "Chemometrics: An Excavator in Temperature-Dependent Near-Infrared Spectroscopy." Molecules 27, no. 2 (January 11, 2022): 452. http://dx.doi.org/10.3390/molecules27020452.

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Temperature-dependent near-infrared (NIR) spectroscopy has been developed and taken as a powerful technique for analyzing the structure of water and the interactions in aqueous systems. Due to the overlapping of the peaks in NIR spectra, it is difficult to obtain the spectral features showing the structures and interactions. Chemometrics, therefore, is adopted to improve the spectral resolution and extract spectral information from the temperature-dependent NIR spectra for structural and quantitative analysis. In this review, works on chemometric studies for analyzing temperature-dependent NIR spectra were summarized. The temperature-induced spectral features of water structures can be extracted from the spectra with the help of chemometrics. Using the spectral variation of water with the temperature, the structural changes of small molecules, proteins, thermo-responsive polymers, and their interactions with water in aqueous solutions can be demonstrated. Furthermore, quantitative models between the spectra and the temperature or concentration can be established using the spectral variations of water and applied to determine the compositions in aqueous mixtures.

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Geladi, Paul. "Chemometrics in spectroscopy. Part 1. Classical chemometrics." Spectrochimica Acta Part B: Atomic Spectroscopy 58, no. 5 (May 2003): 767–82. http://dx.doi.org/10.1016/s0584-8547(03)00037-5.

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Grootveld, Martin, Benita C. Percival, and Jie Zhang. "Extensive Chemometric Investigations of Distinctive Patterns and Levels of Biogenic Amines in Fermented Foods: Human Health Implications." Foods 9, no. 12 (December 5, 2020): 1807. http://dx.doi.org/10.3390/foods9121807.

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Although biogenic amines (BAs) present in fermented foods exert important health-promoting and physiological function support roles, their excessive ingestion can give rise to deleterious toxicological effects. Therefore, here we have screened the BA contents and supporting food quality indices of a series of fermented food products using a multianalyte-chemometrics strategy. A liquid chromatographic triple quadrupole mass spectrometric (LC-MS/MS) technique was utilized for the simultaneous multicomponent analysis of 8 different BAs, and titratable acidity, pH, total lipid content, and thiobarbituric acid-reactive substances (TBARS) values were also determined. Rigorous univariate and multivariate (MV) chemometric data analysis strategies were employed to evaluate results acquired. Almost all foods analyzed had individual and total BA contents that were within recommended limits. The chemometrics methods applied were useful for recognizing characteristic patterns of BA analytes and food quality measures between some fermented food classes, and for assessing their inter-relationships and potential metabolic sources. MV analysis of constant sum-normalized BA profile data demonstrated characteristic signatures for cheese (cadaverine only), fermented cod liver oil (2-phenylethylamine, tyramine, and tryptamine), and wine/vinegar products (putrescine, spermidine, and spermine). In conclusion, this LC-MS/MS-linked chemometrics approach was valuable for (1) contrasting and distinguishing BA catabolite signatures between differing fermented foods, and (2) exploring and evaluating the health benefits and/or possible adverse public health risks of such products.

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Deconinck, Eric, Celine Duchateau, Margot Balcaen, Lies Gremeaux, and Patricia Courselle. "Chemometrics and infrared spectroscopy – A winning team for the analysis of illicit drug products." Reviews in Analytical Chemistry 41, no. 1 (January 1, 2022): 228–55. http://dx.doi.org/10.1515/revac-2022-0046.

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Abstract Spectroscopic techniques such as infrared spectroscopy and Raman spectroscopy are used for a long time in the context of the analysis of illicit drugs, and their use is increasing due to the development of more performant portable devices and easy application in the context of harm reduction through drug checking or onsite forensic analysis. Although these instruments are routinely used with a spectral library, the importance of chemometric techniques to extract relevant information and give a full characterisation of samples, especially in the context of adulteration, is increasing. This review gives an overview of the applications described in the context of the analysis of illicit drug products exploiting the advantages of the combination of spectroscopy with chemometrics. Next to an overview of the literature, the review also tries to emphasize the shortcomings of the presented research papers and to give an incentive to what is needed to include chemometrics as a part of the daily routine of drug checking services and mobile forensic applications.

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Shah, M. A., H. U. Patel, and H. A. Raj. "DEVELOPMENT AND VALIDATION OF A CHEMOMETRICS ASSISTED SPECTROSCOPIC METHOD FOR THE SIMULTANEOUS ESTIMATION OF GALLIC ACID, ELLAGIC ACID AND CURCUMIN IN POLYHERBAL ANTIDIABETIC FORMULATIONS." INDIAN DRUGS 56, no. 06 (June 28, 2019): 67–73. http://dx.doi.org/10.53879/id.56.06.11591.

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Two chemometric methods, Inverse Least Square (ILS) and Classical Least Square (CLS), were applied for the simultaneous estimation of gallic acid, ellagic acid and curcumin in polyherbal antidiabetic formulation. Twenty mixed solutions were prepared for the chemometric calibration as training set and 10 mixed solutions were prepared as validation set. The absorbance data matrix was obtained by measuring the absorbance at 20 different wavelengths, from 241 to 279 nm with the interval of 2 nm (Δλ= 2 nm). The developed calibrations were successfully tested for three antidiabetic polyherbal formulations for their gallic acid, ellagic acid and curcumin contents. Developed methods were validated and root mean square error of precision (RMSEP) was determined. Both chemometric methods in this study can be satisfactorily used for the quantitative analysis in polyherbal dosage forms. The chemometric calculations were performed by using the chemometrics toolbox with MATLAB R2015a software.

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Ziegel, Eric R., D. Massart, R. Brereton, R. Dessy, P. Hopke, C. Spiegelman, and W. Wegscheider. "Chemometrics Tutorials." Technometrics 34, no. 2 (May 1992): 247. http://dx.doi.org/10.2307/1269275.

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Tunnell, David A., I. A. Cowe, J. W. McNicol, and D. C. Cuthbertson. "Chemometrics papers." Analytical Proceedings 27, no. 3 (1990): 59. http://dx.doi.org/10.1039/ap9902700059.

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