Comparative analysis of targeted metabolomics: Dominance-based rough set approach versus orthogonal partial least square-discriminant analysis

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

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Identification and Quantification of Metamizole in Traditional Herbal Medicines using Spectroscopy FTIR-ATR combined with Chemometrics

Research Journal of Pharmacy and Technology ◽

10.52711/0974-360x.2021.00766 ◽

2021 ◽

pp. 4413-4419

Author(s):

Dharmastuti Cahya Fatmarahmi ◽

Ratna Asmah Susidarti ◽

Respati Tri Swasono ◽

Abdul Rohman

Keyword(s):

Discriminant Analysis ◽

Herbal Medicine ◽

Ftir Spectroscopy ◽

Herbal Medicines ◽

Principal Component ◽

Attenuated Total Reflection ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Precision Data

The study aims to develop an effective, efficient, and reliable method using Fourier Transform Infrared (FTIR) spectroscopy with Attenuated Total Reflection (ATR) combined with chemometric for identifying the synthetic drug in Indonesian herbal medicine known as Jamu. Jamu powders, Metamizole, and the binary mixture of Jamu and Metamizole were measured using FTIR-ATR at the mid-infrared region (4000-650 cm-1). The obtained spectra profiles were further analyzed by Principal Component Analysis, Partial Least Square Regression, Principal Component Regression, and Discriminant Analysis. Jamu Pegel Linu (JPL), Jamu Encok (JE), Jamu Sakit Pinggang (JSP), Metamizole (M), and adulterated Jamu by Metamizole were discriminated well on PCA score plot. PLSR and PCR showed the accuracy and precision data to quantify JPL, JE, and JSP, and each adulterated by M with R2 value > 0,995 and low value of RMSEC and RMSEP. Discriminant Analysis (DA) was successfully grouping Jamu and Metamizole without any misclassification. A combination of FTIR spectroscopy and chemometrics offered useful tools for detecting Metamizole in traditional herbal medicine.

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Authentication of geographical origin of palm oil by chromatographic fingerprinting of triacylglycerols and partial least square-discriminant analysis

Talanta ◽

10.1016/j.talanta.2013.07.054 ◽

2013 ◽

Vol 116 ◽

pp. 788-793 ◽

Cited By ~ 27

Author(s):

Cristina Ruiz-Samblás ◽

Cristina Arrebola-Pascual ◽

Alba Tres ◽

Saskia van Ruth ◽

Luis Cuadros-Rodríguez

Keyword(s):

Discriminant Analysis ◽

Palm Oil ◽

Geographical Origin ◽

Partial Least Square ◽

Least Square ◽

Chromatographic Fingerprinting

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Near-Infrared Spectroscopy for Discrimination of Charcoal from Eucalyptus and Native Cerrado Species—Contribution to a Database for Forestry Supervision

Forest Science ◽

10.1093/forsci/fxab015 ◽

2021 ◽

Author(s):

Silvana Nisgoski ◽

Thaís A P Gonçalves ◽

Júlia Sonsin-Oliveira ◽

Adriano W Ballarin ◽

Graciela I B Muñiz

Keyword(s):

Infrared Spectroscopy ◽

Discriminant Analysis ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

New Technologies ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Eucalyptus Species ◽

Linear Discriminant

Abstract The illegal charcoal trade is an internationally well-known forest crime. In Brazil, government agents try to control it using the document of forest origin (DOF). To confirm a load’s legality, the agents must compare it with the declared content of the DOF. However, to identify charcoal is difficult even for specialists in wood anatomy. Hence, new technologies would facilitate the agents’ work. Near-infrared spectroscopy (NIR) provides a rapid and precise response to differentiate carbonized species. Considering the rich Brazilian flora, NIR studies are still underdeveloped. Our work aimed to differentiate charcoals of seven eucalypts and 10 Cerrado species based on NIR analysis and to add information to a charcoal database. Data were collected with a spectrophotometer in reflectance mode. Partial least square regression with discriminant analysis (PLS-DA) and a linear discriminant analysis (LDA) was applied to confirm the performance and potential of NIR spectra to distinguish native Cerrado species from eucalyptus species. Wavenumbers from 4,000 to 6,000 cm−1 and transversal surface presented the best results. NIR had the potential to distinguish eucalypt charcoals from Cerrado species and in comparison to reference samples. NIR is a potential tool for forestry supervision to guarantee the sustainability of the charcoal supply in Brazil and countries with similar conditions. Study Implications It is a challenge to protect the Cerrado biome against deforestation for charcoal production. The application of new technologies such as near-infrared spectroscopy (NIR) for charcoal identification might improve the work of government agents. In this article, we studied the spectra of Cerrado and eucalypt species. Our results present good separation between the analyzed groups. The main goal is to develop a reliable NIR database that would be useful in the practical work of agents. The database will be available for all control agencies, and future training will be done for a rapid initial evaluation in the field.

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Local Linear Logistic Discriminant Analysis with Partial Least Square Components

Advanced Data Mining and Applications - Lecture Notes in Computer Science ◽

10.1007/11811305_64 ◽

2006 ◽

pp. 574-581 ◽

Cited By ~ 2

Author(s):

Jangsun Baek ◽

Young Sook Son

Keyword(s):

Discriminant Analysis ◽

Partial Least Square ◽

Least Square ◽

Local Linear

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Determination of the viability of retinispora (Hinoki cypress) seeds using shortwave infrared hyperspectral imaging spectroscopy

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033519898890 ◽

2020 ◽

Vol 28 (2) ◽

pp. 70-80 ◽

Cited By ~ 1

Author(s):

Perez Mukasa ◽

Collins Wakholi ◽

Akbar Faqeerzada Mohammad ◽

Eunsoo Park ◽

Jayoung Lee ◽

...

Keyword(s):

Discriminant Analysis ◽

Hyperspectral Imaging ◽

Imaging Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Successive Projections Algorithm ◽

Hinoki Cypress ◽

Sorting System ◽

Shortwave Infrared ◽

Successive Projections

The combination of hyperspectral imaging with multivariate data analysis methods has recently been applied to develop a nondestructive technique, required to determine the seed viability of artificially aged vegetable and cereal seeds. In this study, the potential of shortwave infrared hyperspectral imaging to determine the viability of naturally aged seeds was investigated and thereafter a model for online seed sorting system was developed. The hyperspectral images of 400 Hinoki cypress tree seeds were acquired, and germination tests were conducted for viability confirmation, which indicated 31.5% of the viable seeds. Partial least square discriminant analysis models with 179 variables in the wavelength region of 1000–1800 nm were developed with a maximum model accuracy of 98.4% and 93.8% in both the calibration and validation sets, respectively. The partial least square discriminant analysis beta coefficient revealed the key wavelengths to differentiate viable from nonviable seeds, determined based on the differences in the chemical compositions of the seeds, including their lipid and fatty acid contents, which may control the germination ability of the seeds. The most effective wavelengths were selected using two model-based variable selection methods (i.e., the variable importance of projection (15 variables) and the successive projections algorithm (8 variables)) to develop the model. The successive projections algorithm wavelength selection method was considered to develop a viability model, and its application to the raw data resulted in a prediction accuracy of 94.7% in the calibration set and 92.2% in the validation set. These results demonstrate the potential of shortwave infrared hyperspectral imaging spectroscopy as a powerful nondestructive method to determine the viability of Hinoki cypress seeds. This method could be applied to develop an online seed sorting system for seed companies and nurseries.

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Characterization of Biodiesel by Infrared Spectroscopy with Partial Least Square Discriminant Analysis

Analytical Letters ◽

10.1080/00032719.2016.1267186 ◽

2017 ◽

Vol 50 (13) ◽

pp. 2117-2128 ◽

Cited By ~ 2

Author(s):

Ademar Domingos Viagem Máquina ◽

Letícia Maria de Souza ◽

Lucas Caixeta Gontijo ◽

Douglas Queiroz Santos ◽

Waldomiro Borges Neto

Keyword(s):

Infrared Spectroscopy ◽

Discriminant Analysis ◽

Partial Least Square ◽

Least Square

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Changes in Amino Acid and Acylcarnitine Plasma Profiles for Distinguishing Patients with Multiple Sclerosis from Healthy Controls

Multiple Sclerosis International ◽

10.1155/2020/9010937 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Marat F. Kasakin ◽

Artem D. Rogachev ◽

Elena V. Predtechenskaya ◽

Vladimir J. Zaigraev ◽

Vladimir V. Koval ◽

...

Keyword(s):

Multiple Sclerosis ◽

Amino Acid ◽

Discriminant Analysis ◽

Clinical Decision ◽

Partial Least Square ◽

Least Square ◽

Control Group ◽

Healthy Controls ◽

Linear Discriminant ◽

Tandem Mass Spectrometric

McDonald criteria and magnetic resonance imaging (MRI) are used for the diagnosis of multiple sclerosis (MS); nevertheless, it takes a considerable amount of time to make a clinical decision. Amino acid and fatty acid metabolic pathways are disturbed in MS, and this information could be useful for diagnosis. The aim of our study was to find changes in amino acid and acylcarnitine plasma profiles for distinguishing patients with multiple sclerosis from healthy controls. We have applied a targeted metabolomics approach based on tandem mass-spectrometric analysis of amino acids and acylcarnitines in dried plasma spots followed by multivariate statistical analysis for discovery of differences between MS (n=16) and control (n=12) groups. It was found that partial least square discriminant analysis yielded better group classification as compared to principal component linear discriminant analysis and the random forest algorithm. All the three models detected noticeable changes in the amino acid and acylcarnitine profiles in the MS group relative to the control group. Our results hold promise for further development of the clinical decision support system.

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