scholarly journals Chemometrics in analytical chemistry

2021 ◽  
Vol 9 (2) ◽  
Author(s):  
Darinka Brodnjak Vončina
Keyword(s):  
Analytical Chemistry ◽  
Environmental Samples ◽  
Edible Oils ◽  
Principal Component ◽  
Chemometric Methods ◽  
Prevention Measures ◽  
Mutual Correlation ◽  
Seasonal Behavior ◽  
Linear Discriminant

Chemometrics is a scientific discipline closely connected with statistics and mathematics. It has an important role in analytical chemistry. Modern analytical methods provide opportunity to collect large amounts of data for various samples. For handling analytical results different chemometric methods are employed, such as basic statistical methods for the determination of mean and median values, standard deviations, minimal and maximal values of measured parameters and their mutual correlation coefficients, the principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA). The objectives of chemometrics in analytical chemistry are focused on characterization and chemometrical classification of different samples. The quality of environmental samples such as water, sediment, soil, air samples etc. can be determined according to measured physical and chemical parameters, which represent the individual samples. Chemometric methods give information regarding measured parameters about similarity between sampling locations, sources of pollution, seasonal behavior and time trends. Monitoring of general pollution of environmental samples and following measuring parameters which are above permitted level given by legislation can be used for searching of pollution source and for planning prevention measures from pollution. Food samples can also be characterized by chemometrical methods. Chemometrics can be used for fast and efficient determination of food sample categories, such as edible oils, wines, fruits and fruit juices etc. Classification can also be performed according to the origin, source or season. From all these facts it is evident that the aim of chemometrics in analytical chemistry is high and extensive.

scholarly journals Multivariate statistical methods for characterization of waste water quality

2021 ◽  
Vol 9 (3) ◽  
Author(s):  
Darja Kavšek ◽  
Darinka Brodnjak Vončina
Keyword(s):  
Water Quality ◽  
Statistical Methods ◽  
Oxygen Demand ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Waste Waters ◽  
Prevention Measures ◽  
Mutual Correlation ◽  
Seasonal Behavior ◽  
Linear Discriminant

The aim of this work is focused on water quality classification of the waste waters and evaluation of pollution by the monitoring measurements during period 2006-2008. Environmental monitoring was performed in the region of Trbovlje, Slovenia, with two sampling sites and 15 chemical and physicochemical water quality parameters (pH, temperature, suspended solids, settling matter, chemical oxygen demand, biochemical oxygen demand, AOX (adsorbable organic halogens), total phosphorus, ammonium, nitrite, sulphate, chloride, fluoride, sulphide and mineral oil content) monitored in monthly periods (total of 60 objects x 15 variables). For handling the results different chemometric methods were employed, such as basic statistical methods for the determination of mean and median values, standard deviations, minimal and maximal values of measured parameters and their mutual correlation coefficients, the principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA). Monitoring of general pollution of waste waters and following measuring parameters which are above permitted concentration level can be used for searching of pollution source and for planning prevention measures from pollution, as well. The study allows drawing new information from the data sets such as patterns of similarity between sampling locations, sources of pollution in the environment, seasonal behavior of chemical contents and time trends.

scholarly journals Full spectrum and genetic algorithm-selected spectrum-based chemometric methods for simultaneous determination of azilsartan medoxomil, chlorthalidone, and azilsartan: Development, validation, and application on commercial dosage form

2021 ◽  
Vol 19 (1) ◽  
pp. 205-213
Author(s):  
Hany W. Darwish ◽  
Abdulrahman A. Al Majed ◽  
Ibrahim A. Al-Suwaidan ◽  
Ibrahim A. Darwish ◽  
Ahmed H. Bakheit ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simultaneous Determination ◽  
Predictive Power ◽  
Principal Component Regression ◽  
Selection Procedure ◽  
Principal Component ◽  
Chemometric Methods ◽  
Full Spectrum ◽  
Azilsartan Medoxomil

Abstract Five various chemometric methods were established for the simultaneous determination of azilsartan medoxomil (AZM) and chlorthalidone in the presence of azilsartan which is the core impurity of AZM. The full spectrum-based chemometric techniques, namely partial least squares (PLS), principal component regression, and artificial neural networks (ANN), were among the applied methods. Besides, the ANN and PLS were the other two methods that were extended by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. The models were developed by applying a multilevel multifactor experimental design. The predictive power of the suggested models was evaluated through a validation set containing nine mixtures with different ratios of the three analytes. For the analysis of Edarbyclor® tablets, all the proposed procedures were applied and the best results were achieved in the case of ANN, GA-ANN, and GA-PLS methods. The findings of the three methods were revealed as the quantitative tool for the analysis of the three components without any intrusion from the co-formulated excipient and without prior separation procedures. Moreover, the GA impact on strengthening the predictive power of ANN- and PLS-based models was also highlighted.

scholarly journals The Determination of Parkinson’s Drugs in Human Urine by Applying Chemometric Methods

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Guzide Pekcan Ertokus
Keyword(s):  
Urine Sample ◽  
Human Urine ◽  
Principal Component Regression ◽  
Principal Component ◽  
Healthy Person ◽  
Drug Analysis ◽  
Uv Spectrophotometry ◽  
Least Squares Regression ◽  
Chemometric Methods

The spectrophotometric-chemometric analysis of levodopa and carbidopa that are used for Parkinson’s disease was analyzed without any prior reservation. Parkinson’s drugs in the urine sample of a healthy person (never used drugs in his life) were analyzed at the same time spectrophotometrically. The chemometric methods used were partial least squares regression (PLS) and principal component regression (PCR). PLS and PCR were successfully applied as chemometric determination of levodopa and carbidopa in human urine samples. A concentration set including binary mixtures of levodopa and carbidopa in 15 different combinations was randomly prepared in acetate buffer (pH 3.5).). UV spectrophotometry is a relatively inexpensive, reliable, and less time-consuming method. Minitab program was used for absorbance and concentration values. The normalization values for each active substance were good (r2>0.9997). Additionally, experimental data were validated statistically. The results of the analyses of the results revealed high recoveries and low standard deviations. Hence, the results encouraged us to apply the method to drug analysis. The proposed methods are highly sensitive and precise, and therefore they were implemented for the determination of the active substances in the urine sample of a healthy person in triumph.

scholarly journals Determination of HPLC-UV Fingerprints of Spanish Paprika (Capsicum annuum L.) for Its Classification by Linear Discriminant Analysis

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4479 ◽  
Author(s):  
Xavier Cetó ◽  
Núria Serrano ◽  
Miriam Aragó ◽  
Alejandro Gámez ◽  
Miquel Esteban ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Reversed Phase ◽  
Classification Rate ◽  
Phenolic Profile ◽  
Linear Discriminant ◽  
Sample Extraction

The development of a simple HPLC-UV method towards the evaluation of Spanish paprika’s phenolic profile and their discrimination based on the former is reported herein. The approach is based on C18 reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identified in paprika. Paprika samples were subjected to a sample extraction stage by sonication and centrifugation; extracting procedure and conditions were optimized to maximize the generation of enough discriminant fingerprints. Finally, chromatograms were baseline corrected, compressed employing fast Fourier transform (FFT), and then analyzed by means of principal component analysis (PCA) and LDA to carry out the classification of paprika samples. Under the developed procedure, a total of 96 paprika samples were analyzed, achieving a classification rate of 100% for the test subset (n = 25).

Chemometric Approaches In Analysis Of Vegetable Oils As A Way To Improve An Efficiency Of Forensic Examination

2019 ◽  
Vol 14 (4) ◽  
pp. 192-199
Author(s):  
V. Rudniev ◽  
E. Simakova-Yefremian ◽  
V. Khosha ◽  
V. Ostropilets
Keyword(s):  
Vegetable Oils ◽  
Methyl Esters ◽  
Principal Component ◽  
Detailed Examination ◽  
Chemometric Methods ◽  
Split Ratio ◽  
Forensic Examination ◽  
Hidden Correlations ◽  
Identification Research

The approach to forensic examination performance through accelerated classification and identification research of vegetable oils is demonstrated. It includes derivatization of the original objects, analysis of obtained methyl esters mixture using GC-MS technique and applying of chemometric tools for gathering preliminary data. Subsequent processing of obtained chromatograms using principal component analysis for grouping of objects simplifies further detailed examination. An analysis of hidden correlations between variables and influence of the initial data on the first to third major components formation is provided. Using values of content of only 5 most widespread fat acids leads to satisfied visual pattern for prior recognition of oil samples. Applying of various split ratios is recommended at different stages of gas-chromatographic analysis. Split ratio 1 : 50 is recommended for gathering of data treated by chemometric methods and 1 : 2 is useful for determination of minor components presence as specific features.

scholarly journals Identification of Copper in Stems and Roots of Jatropha curcas L. by Hyperspectral Imaging

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 823
Author(s):  
Juan Francisco García-Martín ◽  
Amanda Teixeira Badaró ◽  
Douglas Fernandes Barbin ◽  
Paloma Álvarez-Mateos
Keyword(s):  
Jatropha Curcas ◽  
Near Infrared ◽  
Prediction Models ◽  
Plant Stress ◽  
Principal Component ◽  
Linear Discriminant ◽  
Jatropha Curcas L ◽  
Potential Applications

The in situ determination of metals in plants used for phytoremediation is still a challenge that must be overcome to control the plant stress over time due to metals uptake as well as to quantify the concentration of these metals in the biomass for further potential applications. In this exploratory study, we acquired hyperspectral images in the visible/near infrared regions of dried and ground stems and roots of Jatropha curcas L. to which different amounts of copper (Cu) were added. The spectral information was extracted from the images to build classification models based on the concentration of Cu. Optimum wavelengths were selected from the peaks and valleys showed in the loadings plots resulting from principal component analysis, thus reducing the number of spectral variables. Linear discriminant analysis was subsequently performed using these optimum wavelengths. It was possible to differentiate samples without addition of copper from samples with low (0.5–1% wt.) and high (5% wt.) amounts of copper (83.93% accuracy, >0.70 sensitivity and specificity). This technique could be used after enhancing prediction models with a higher amount of samples and after determining the potential interference of other compounds present in plants.

The Role of Multivariant Analysis on the Interpretation of FTIR and Raman Spectra

2020 ◽  
pp. 104-129 ◽  
Author(s):  
Sefa Celik ◽  
Ali Tugrul Albayrak ◽  
Sevim Akyuz ◽  
Aysen E. Ozel
Keyword(s):  
Biological Systems ◽  
Principal Component ◽  
Biological Macromolecules ◽  
Spectroscopic Techniques ◽  
Linear Discriminant ◽  
Wide Range ◽  
Vibrational Bands ◽  
Multivariant Analysis

FTIR and Raman spectroscopy are complementary spectroscopic techniques that play an important role in the analysis of molecular structure and the determination of characteristic vibrational bands. Vibrational spectroscopy has a wide range of applications including mainly in physics and biology. Its applications have gained tremendous speed in the field of biological macromolecules and biological systems, such as tissue, blood, and cells. However, the vibrational spectra obtained from the biological systems contain a large number of data and information that make the interpretation difficult. To facilitate the analysis, multivariant analysis comprising the reduction of the dimension of spectrum data and classification of them by eliminating redundancy data, which are obtained from the spectra and does not have any role, becomes critical. In this chapter, the applications of Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and their combination PCA-LDA, which are widely used among multivariant techniques on biological systems will be disclosed.

scholarly journals Quality Control of Mutton by Using Volatile Compound Fingerprinting Techniques and Chemometric Methods

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Ping Zhan ◽  
Honglei Tian ◽  
Baoguo Sun ◽  
Yuyu Zhang ◽  
Haitao Chen
Keyword(s):  
Gas Chromatography ◽  
Quality Control ◽  
Principal Component ◽  
Chemical Markers ◽  
Chemometric Methods ◽  
Chromatographic Fingerprint ◽  
Gas Chromatography Olfactometry ◽  
Gas Chromatography Mass Spectroscopy ◽  
Chromatographic Fingerprinting

A method for chromatographic fingerprinting of flavor was established for the quality control of mutton. Twenty-five mutton samples that were chosen from twelve batches were investigated by gas chromatography-mass spectroscopy (GC-MS) and gas chromatography-olfactometry (GC-O). Spectral correlative chromatograms combined with GC-O assessment were employed, and 32 common odor-active compounds that characterize mutton flavor fingerprint were obtained. Based on the flavor chromatographic fingerprint data, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were designed and employed as chromatographic fingerprint methods. Defined categories were perfectly discriminated after PLS-DA was conducted on the fused matrix, demonstrating a 100% accurate classification. Fourteen constituents were further screened with PLS-DA to be the main chemical markers, and they were used to develop similar approaches for the determination of mutton quality and traceability. The flavor fingerprint of mutton established using SPME-GC-MS/O coupled with PLS-DA is appropriate for differentiating and identifying samples, and the procedure would be used in quality control.

scholarly journals Nondestructive Detection of Blackheart in Potato by Visible/Near Infrared Transmittance Spectroscopy

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Zhu Zhou ◽  
Songwei Zeng ◽  
Xiaoyu Li ◽  
Jian Zheng
Keyword(s):  
Near Infrared ◽  
Discrimination Performance ◽  
Principal Component ◽  
Spectroscopic Technique ◽  
Chemometric Methods ◽  
Classification Rate ◽  
Linear Discriminant ◽  
Infrared Transmittance ◽  
Validation Set ◽  
Weight Correction

The possibility of using visible/near infrared (Vis/NIR) transmission spectroscopic technique in the 513–850 nm region coupled with partial least squares-linear discriminant analysis (PLS-LDA) and other chemometric methods to classify potatoes with blackheart was investigated. The discrimination performance of different morphological correction methods, including weight correction, height correction, and volume correction, was compared. The results showed that height corrected transmittance has the best performance, with both calibration and validation sets having a success rate of 97.11%. Out of 1800 wavelengths, only six wavelengths (711, 817, 741, 839, 678, and 698 nm) were selected as the optimum wavelengths for the discrimination of blackheart tubers based on principal component analysis (PCA). The data analysis showed that the overall classification rate by PLS-LDA method decreased from 97.11% to 96.82% in calibration set and from 97.11% to 96.53% in validation set, which was acceptable. The importance of these conclusions may be helpful to transfer Vis/NIR transmission technology from laboratory to industrial application in nondestructive, real-time, or portable measurement of potatoes quality.

APPLICATION OF CHEMOMETRIC METHODS FOR DETERMINATION OF CLOTRIMAZOLE AND BECLOMETHASONE DIPROPIONATE IN PHARMACEUTICAL DOSAGE FORM

INDIAN DRUGS ◽  
2020 ◽  
Vol 57 (02) ◽  
pp. 45-50
Author(s):  
Umang Shah ◽  
Bhumika Desai ◽  
Vyomesh Nandrubarkar
Keyword(s):  
Beclomethasone Dipropionate ◽  
Principal Component Regression ◽  
Principal Component ◽  
Quality Parameters ◽  
Uv Spectra ◽  
Chemical Information ◽  
Chemometric Methods ◽  
Validation Data ◽  
Pharmaceutical Dosage Form

Chemometry is the use of mathematical and statistical methods to improve the understanding of chemical information and to correlate quality parameters or physical properties to analytical instrument data. In the present work, two chemometric methods, named as principal component regression (PCR) and (PLS) based on the use of spectrophotometric data, were developed for simultaneous determination of clotrimazole (CLO) and beclomethasone dipropionate (BE C) in bulk and cream form. The absorbance of zero order UV spectra of CLO and BE C in the range of 80-400 μg/mL and 2-10 μg/mL, respectively were recorded in the wavelength range 230-272 nm at 3 nm wavelength intervals. Twenty-five (25) mixed solutions were prepared for the chemometric calibration as training set and sixteen varied solutions were prepared as a validation set. The suitability of the models was decided based on the RMSECV, RMSEP and PRESS values of calibration and validation data. The % recovery study of both the methods was compared, and it was found near each other. The assay of CLO and BE C for both the methods was found to be in the range of 99.78 to 101.20%. Hence, the proposed methods can be used for simultaneous analysis of the mixture of the drugs, without chemical pre-treatment, with good speed of analysis.

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