scholarly journals Authentication of the Origin, Variety and Roasting Degree of Coffee Samples by Non-Targeted HPLC-UV Fingerprinting and Chemometrics. Application to the Detection and Quantitation of Adulterated Coffee Samples

Foods ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 378 ◽  
Author(s):  
Nerea Núñez ◽  
Xavi Collado ◽  
Clara Martínez ◽  
Javier Saurina ◽  
Oscar Núñez
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
High Performance ◽  
Partial Least Squares Regression ◽  
Prediction Errors ◽  
Least Squares Regression ◽  
Chromatographic Fingerprints ◽  
Sample Classification ◽  
Chemical Descriptors

In this work, non-targeted approaches relying on HPLC-UV chromatographic fingerprints were evaluated to address coffee characterization, classification, and authentication by chemometrics. In general, high-performance liquid chromatography with ultraviolet detection (HPLC-UV) fingerprints were good chemical descriptors for the classification of coffee samples by partial least squares regression-discriminant analysis (PLS-DA) according to their country of origin, even for nearby countries such as Vietnam and Cambodia. Good classification was also observed according to the coffee variety (Arabica vs. Robusta) and the coffee roasting degree. Sample classification rates higher than 89.3% and 91.7% were obtained in all the evaluated cases for the PLS-DA calibrations and predictions, respectively. Besides, the coffee adulteration studies carried out by partial least squares regression (PLSR), and based on coffees adulterated with other production regions or variety, demonstrated the good capability of the proposed methodology for the detection and quantitation of the adulterant levels down to 15%. Calibration, cross-validation, and prediction errors below 2.9%, 6.5%, and 8.9%, respectively, were obtained for most of the evaluated cases.

Rapid determination of biomass and polypropylene in three types of wood plastic composites (WPCs) using FTIR spectroscopy and partial least squares regression (PLSR)

Holzforschung ◽  
2015 ◽  
Vol 69 (4) ◽  
pp. 399-404 ◽  
Author(s):  
Gaiyun Li ◽  
Wanli Lao ◽  
Tefu Qin ◽  
Luohua Huang
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Rapid Determination ◽  
External Validation ◽  
Practical Importance ◽  
Prediction Errors ◽  
Least Squares Regression ◽  
Wood Plastic Composites ◽  
Plastic Composites

Abstract The biomass/plastic ratio in wood plastic composites (WPCs) has been evaluated because of the great practical importance of this topic. To this purpose, FTIR spectra of 59 polypropylene (PP)-based WPCs from three biomass species (Chinese fir, poplar, and bamboo) were recorded and the spectral dates were evaluated by means of the partial least squares regression (PLSR) approach aiming at the prediction of the biomass/PP ratio in the WPCs. The results of the full cross-validation of the data showed that first derivative spectra corrected by standard normal variate (SNV) yielded the optimal model for prediction of the WPC composition. For both biomass and PP prediction, the coefficients of determination (R2) of external validation were above 0.94. The standard errors of prediction (SEP) were between 1.38 and 1.39. And the ratios of performance to deviation (RPD) were about 4.20. The relative prediction errors in this context were lower than ±6%. FTIR combined with PLSR is a useful tool for a rapid and reliable estimation of the biomass and PP contents in different types of PP-based WPCs.

scholarly journals Authenticity Assessment and Fraud Quantitation of Coffee Adulterated with Chicory, Barley, and Flours by Untargeted HPLC-UV-FLD Fingerprinting and Chemometrics

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 840
Author(s):  
Nerea Núñez ◽  
Javier Saurina ◽  
Oscar Núñez
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
High Performance ◽  
Prediction Models ◽  
Extraction Procedure ◽  
Gradient Elution ◽  
Reversed Phase ◽  
Prediction Errors ◽  
Least Squares Regression ◽  
Authenticity Assessment

Coffee, one of the most popular drinks around the world, is also one of the beverages most susceptible of being adulterated. Untargeted high-performance liquid chromatography with ultraviolet and fluorescence detection (HPLC-UV-FLD) fingerprinting strategies in combination with chemometrics were employed for the authenticity assessment and fraud quantitation of adulterated coffees involving three different and common adulterants: chicory, barley, and flours. The methodologies were applied after a solid–liquid extraction procedure with a methanol:water 50:50 (v/v) solution as extracting solvent. Chromatographic fingerprints were obtained using a Kinetex® C18 reversed-phase column under gradient elution conditions using 0.1% formic acid aqueous solution and methanol as mobile phase components. The obtained coffee and adulterants extract HPLC-UV-FLD fingerprints were evaluated by partial least squares regression-discriminants analysis (PLS-DA) resulting to be excellent chemical descriptors for sample discrimination. One hundred percent classification rates for both PLS-DA calibration and prediction models were obtained. In addition, Arabica and Robusta coffee samples were adulterated with chicory, barley, and flours, and the obtained HPLC-UV-FLD fingerprints subjected to partial least squares (PLS) regression, demonstrating the feasibility of the proposed methodologies to assess coffee authenticity and to quantify adulteration levels (down to 15%), showing both calibration and prediction errors below 1.3% and 2.4%, respectively.

Revisions to Penicillium ser. Corymbifera: agents responsible for blue mould storage rot of various flower and vegetable bulbs

2005 ◽  
Vol 83 (11) ◽  
pp. 1422-1433 ◽  
Author(s):  
D.P. Overy ◽  
J.G. Valdez ◽  
J.C. Frisvad
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Secondary Metabolite ◽  
Partial Least Squares Regression ◽  
Phenotypic Expression ◽  
Chemical Diversity ◽  
Data Matrix ◽  
Phenotypic Traits ◽  
Least Squares Regression

Fifteen strains representing each Penicillium ser. Corymbifera taxa were compared using phenotypic and chemotaxonomic characters by cluster analysis and discriminant partial least squares regression. Variability in phenotypic expression of species strains resulted in a more fragmented classification compared with secondary metabolite expression. Although the observed phenotypic expression varied for strains cultured upon the same media, it was possible to classify strains into species groupings based only upon a few distinctive phenotypic traits. Data analysis of secondary metabolite profiles generated from HPLC-diode array dectection analysis gave reliable strain classification when more than one media type was employed. Depending on the species, Czapek yeast autolysate agar typically yielded the greatest chemical diversity; however, several metabolites (terrestric acid, corymbiferone, the corymbiferan lactones, and daldinin D) were only produced when strains were grown on either yeast extract sucrose or oatmeal agar. For the classification of strains based on a binary data matrix, application of the Yule coefficient gave the best clustering. Several secondary metabolites, of importance for the classification of ser. Corymbifera strains, were identified by discriminant-partial least squares regression analysis. A diagnostic key based on phenotypic, chemotaxonomic, and pathogenic traits is provided as an aid for species identification.

QSAR MODEL OF THE QUORUM-QUENCHING N-ACYL-HOMOSERINE LACTONE LACTONASE ACTIVITY

2008 ◽  
Vol 16 (02) ◽  
pp. 279-293 ◽  
Author(s):  
CHANIN NANTASENAMAT ◽  
THEERAPHON PIACHAM ◽  
TANAWUT TANTIMONGCOLWAT ◽  
THANAKORN NAENNA ◽  
CHARTCHALERM ISARANKURA-NA-AYUDHYA ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Quantum Chemical ◽  
Partial Least Squares Regression ◽  
Homoserine Lactone ◽  
Acyl Homoserine Lactone ◽  
Least Squares Regression ◽  
Quantum Chemical Descriptors ◽  
Data Set ◽  
Chemical Descriptors

A quantitative structure-activity relationship (QSAR) study was performed to model the lactonolysis activity of N-acyl-homoserine lactone lactonase. A data set comprising of 20 homoserine lactones and related compounds was taken from the work of Wang et al. Quantum chemical descriptors were calculated using the semiempirical AM1 method. Partial least squares regression was utilized to construct a predictive model. This computational approach reliably reproduced the lactonolysis activity with high accuracy as illustrated by the correlation coefficient in excess of 0.9. It is demonstrated that the combined use of quantum chemical descriptors with partial least squares regression are suitable for modeling the AHL lactonolysis activity.

Use of the Partial Least Squares Method with Acoustic Vibration Spectra as a New Grading Technique for Structural Timber

Holzforschung ◽  
2003 ◽  
Vol 57 (6) ◽  
pp. 644-652 ◽  
Author(s):  
L. Brancheriau ◽  
H. Baillères
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
High Performance ◽  
Partial Least Squares Regression ◽  
Least Squares Method ◽  
Principal Component Regression ◽  
Principal Component ◽  
Acoustic Vibration ◽  
Modulus Of Rupture ◽  
Least Squares Regression

Summary This study develops a high performance grading process based on the analysis of acoustic vibrations in the audible frequency range. The unique feature of the method is that the spectrum is directly applied to obtain predictive variables for estimating the modulus of elasticity and modulus of rupture. A partial least squares regression was used. This powerful method represents a compromise between principal component regression and multi-linear regression. Partial least squares regression screens for factors which account for the variance in the predictor variables and achieves the best correlation between factors and predicted variable. The method is based on projections, similar to principle components regression, whereby a set of correlated variables is compressed into a smaller set of uncorrelated factors.

scholarly journals Identification of phenolic and alcoholic compounds in wine spirits and their classification by use of multivariate analysis

2019 ◽  
Vol 84 (7) ◽  
pp. 663-677 ◽  
Author(s):  
Karla Hanousek-Cica ◽  
Martina Pezer ◽  
Jasna Mrvcic ◽  
Damir Stanzer ◽  
Jasna Cacic ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Higher Alcohols ◽  
Least Squares Regression ◽  
Phenol Composition ◽  
Ageing Period

During the ageing period wine spirits are changing their color, chemical composition and sensory characteristics. These changes should be simply monitored. The aim of this study was to develop partial least squares regression (PLS) models for higher alcohols and phenols in wine spirits as well as to show the feasibility of the NIR spectroscopy combined with chemometric tools to distinguish wine spirits and brandies with different ageing degree. To get the reference values, the usual methods for the analysis of spirits drinks were used. Ethanol, esters, acids, methanol and higher alcohols were studied. Wine spirits and brandies phenol composition was determined by liquid chromatography. Principal component analysis (PCA) was used to classify the wine spirits and brandies according to their phenolic and higher alcohols composition. Moreover, the Partial least squares regression (PLS regression) was used to calibrate and predict expected contents of higher alcohols and phenols in the wine spirits. Success of the classification of samples by ageing based on individual alcohols was 93.8 %, while success of the classification based on individual phenols raised to 100 %. This efficiency of the prediction was evaluated by use of linear discriminator analysis (LDA).

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