scholarly journals Discrimination of the Indonesian Roasted Arabica Coffees using 1H NMR-based Metabolomics

2020 ◽  
pp. 479-488
Author(s):  
Nizar Happyana ◽  
Elvira Hermawati ◽  
Yana Maolana Syah ◽  
Euis Holisotan Hakim Hakim
Keyword(s):  
Discriminant Analysis ◽  
1H Nmr ◽  
Nmr Spectra ◽  
Scientific Information ◽  
Partial Least Square ◽  
Least Square ◽  
Latent Structure ◽  
Chlorogenic Acids ◽  
Coffee Lipids ◽  
Important Marker

In this report, the roasted Arabica coffees obtained from 4 Indonesian regions were analyzed with 1H NMR based-metabolomics. In total, 23 compounds were detected in the coffee 1H NMR spectra. Orthogonal projection to latent structure-discriminant analysis (OPLSDA) model successfully classified metabolites of the coffees based on their origins. S-plots of two-classes partial least square discriminant analysis (PLSDA) models successfully identified discriminant metabolites for every coffee. Chlorogenic acids, trigonelline, arabinoses were found as the discriminant compounds for Preanger-Java coffee. Lipids, acetic acid and lactic acid were discovered as the characteristic metabolites for Gayo-Sumatra coffee. γ-quinide was found as the most important marker for Bajawa-Flores coffee. Meanwhile, Toraja-Sulawesi coffee were characterized with a balance chemical composition indicating its well-balanced taste. The findings revealed the diversity of Indonesian Arabica coffees and shed more light on scientific information of Indonesian coffees.

scholarly journals AUTHENTICATION OF TURMERIC USING PROTON-NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY AND MULTIVARIATE ANALYSIS

2018 ◽  
Vol 10 (6) ◽  
pp. 174
Author(s):  
Anjar Windarsih ◽  
Abdul Rohman ◽  
Respati Tri Swasono
Keyword(s):  
Multivariate Analysis ◽  
Discriminant Analysis ◽  
Nmr Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
1H Nmr ◽  
1H Nmr Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Mean Square

Objective: The objective of this study was to apply 1H-NMR spectroscopy-based metabolite fingerprinting in combination with multivariate analysis for authentication of turmeric (Curcuma longa) from C. heyneana and C. manga.Methods: Partial least square-discriminant analysis (PLS-DA) and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) were used for differentiation of authentic and adulterated C. longa with C. manga and C. heyneana. The variables used were peaks with certain chemical shifts at optimized 1H-NMR spectra of authentic and adulterated C. longa.Results: All of the authentic C. longa samples were clearly separated from the adulterated ones. The multivariate calibration of partial least square (PLS) was successfully applied to predict of adulterants in C. longa. The lower RMSEC (root mean square error of calibration) values, 0.94% for adulterated C. longa with C. heyneana and 1.37% for adulterated C. longa with C. manga, and the lower RMSEP (root mean square error of prediction) values, 0.83% for adulterated C. longa with C. heyneana and 1.34% for adulterated C. longa with C. manga indicated the good of accuracy and precision of the calibration models.Conclusion: The combination of 1H-NMR spectroscopy and chemometrics of multivariate analysis PLS-DA, OPLS-DA, and PLS proves an adequate technique for authentication of turmeric.

scholarly journals 1H nuclear magnetic resonance-based metabolite profiling of guava leaf extract: an attempt to develop a prototype for standardization of plant extracts

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Manasi S. Gholkar ◽  
Jia V. Li ◽  
Poonam G. Daswani ◽  
P. Tetali ◽  
Tannaz J. Birdi
Keyword(s):  
Ferulic Acid ◽  
Gallic Acid ◽  
Plant Extracts ◽  
Nmr Spectra ◽  
Leaf Extract ◽  
Partial Least Square ◽  
Least Square ◽  
E Coli ◽  
H Nmr ◽  
Guava Leaf

Abstract Background Herbal medicines are fast gaining popularity. However, their acceptability by modern practitioners is low which is often due to lack of standardization. Several approaches towards standardization of herbals have been employed. The current study attempted to recognize key peaks from 1H NMR spectra which together would comprise of a spectral fingerprint relating to efficacy of Psidium guajava (guava) leaf extract as an antidiarrhoeal when a number of unidentified active principles are involved. Methods Ninety samples of guava leaves were collected from three locations over three seasons. Hydroalcoholic (water and ethanol, 50:50) extracts of these samples were prepared and their 1H NMR spectra were acquired. Spectra were also obtained for quercetin, ferulic acid and gallic acid as standards. Eight bioassays reflecting different stages of diarrhoeal pathogenesis were undertaken and based on pre-decided cut-offs, the extracts were classified as ‘good’ or ‘poor’ extracts. The bioactivity data was then correlated with the 1H NMR profiles using Regression or Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA). Results OPLS-DA showed seasonal and regional segregation of extracts. Significant models were established for seven bioassays, namely those for anti-bacterial activity against Shigella flexneri and Vibrio cholerae, adherence of E. coli, invasion of E. coli and S. flexneri and production and binding of toxin produced by V. cholerae. It was observed that none of the extracts were good or bad across all the bioassays. The spectral analysis showed multiple peaks correlating with a particular activity. Based on NMR and LC-MS/MS, it was noted that the extracts contained quercetin, ferulic acid and gallic acid. However, they did not correlate with the peaks that segregated extracts with good and poor activity. Conclusions The current study identified key peaks in 1H NMR spectra contributing to the anti-diarrhoeal activity of guava leaf extracts. The approach of using spectral fingerprinting employed in the present study can thus be used as a prototype towards standardization of plant extracts with respect to efficacy.

scholarly journals A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

2021 ◽  
pp. 096703352098731
Author(s):  
Adenilton C da Silva ◽  
Lívia PD Ribeiro ◽  
Ruth MB Vidal ◽  
Wladiana O Matos ◽  
Gisele S Lopes
Keyword(s):  
Quality Control ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Linear Discriminant ◽  
Hand Sanitizer

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.

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

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.

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

Talanta ◽  
2013 ◽  
Vol 116 ◽  
pp. 788-793 ◽  
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

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

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.

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

2020 ◽  
Vol 28 (2) ◽  
pp. 70-80 ◽  
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.

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

2017 ◽  
Vol 50 (13) ◽  
pp. 2117-2128 ◽  
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

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

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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