Classification of Three Green Tea Varieties by Micellar Electrokinetic Electrophoresis-Laser Induced Fluorescence and Pattern Recognition Methods

2012 ◽  
Vol 554-556 ◽  
pp. 1289-1292
Author(s):  
Neng Sheng Ye ◽  
Ya Li Xie ◽  
Chang Liu ◽  
Jian Li
Keyword(s):  
Cluster Analysis ◽  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Laser Induced Fluorescence ◽  
Partial Least Square ◽  
Least Square ◽  
Sodium Borate ◽  
Laser Induced Fluorescence Detection ◽  
Pattern Recognition Methods

A micellar electrokinetic electrophoresis-laser induced fluorescence detection (MEKC-LIF) method was applied for the profiling of native fluorescence in three different kinds of Chinese green teas. The running buffer was 100 mM sodium borate (pH9.8) containing 20 mM SDS, and tea infusion was injected at the pressure of 0.5 psi for 5 seconds. The applied voltage was set at 25 kV, and the excited wavelength was 488 nm, and the detected wavelength was 520 nm. Ten tea samples were collected and analyzed by the developed method. Eighteen common peaks in the MEKC-LIF profiling of green teas were selected and these samples were classified into 3 groups by cluster analysis and partial least square discriminant analysis (PLS-DA). Our primary results indicated that the developed method could be applied for the classification of three different Chinese green teas

Classification of extra virgin olive oil and verification of adulteration using digital images and discriminant analysis

2015 ◽  
Vol 7 (20) ◽  
pp. 8839-8846 ◽  
Author(s):  
Karla Danielle Tavares de Melo Milanez ◽  
Márcio José Coelho Pontes
Keyword(s):  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Soybean Oil ◽  
Olive Oil ◽  
Digital Images ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Pattern Recognition Methods

This work proposes a new methodology based on digital images and supervised pattern recognition methods for the classification of extra virgin olive oil (EVOO) samples with respect to brand (A, B and C) and verification of adulteration with soybean oil.

Soft classification of single samples based on multi-analyte spectra

2019 ◽  
Vol 34 (12) ◽  
pp. 2370-2377 ◽  
Author(s):  
Nai-Ho Cheung
Keyword(s):  
Discriminant Analysis ◽  
Fluorescence Spectrum ◽  
Partial Least Square ◽  
Least Square ◽  
Membership Probability ◽  
Soft Classification

Plume fluorescence spectrum of an unknown can be mapped to a point (black) in class space of partial-least-square discriminant analysis. Its proximity to known classes (red, green and blue) gives its membership probability. The accuracy and confidence of prediction is excellent.

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.

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