scholarly journals GC-MS-based metabolomics for the detection of adulteration in oregano samples

2021 ◽  
pp. 89-89
Author(s):  
Stefan Ivanovic ◽  
Manuela Mandrone ◽  
Katarina Simic ◽  
Mirjana Ristic ◽  
Marina Todosijevic ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Regression Models ◽  
Quinic Acid ◽  
Mixing Ratio ◽  
Origanum Vulgare ◽  
Olive Leaves ◽  
Cotinus Coggygria ◽  
Culinary Herb ◽  
Concentration Levels

Oregano is one of the most used culinary herb and it is often adulterated with cheaper plants. In this study, GC-MS is used for identification and quantification of metabolites from 104 samples of oregano (Origanum vulgare and O. onites) adulterated with olive (Olea europaea), venetian sumac (Cotinus coggygria), and myrtle (Myrtus communis) leaves, at five different concentration levels. The metabolomics profiles obtained after two-step derivatization involving methoxyamination and silanization were subjected to multivariate data analysis to reveal markers of adulteration and to build the regression models on the basis of the oregano-to-adulterants mixing ratio. Orthogonal Partial Least Squares enabled detection of oregano adulterations with olive, Venetian sumac, and myrtle leaves. Sorbitol levels distinguished oregano samples adulterated with olive leaves, while shikimic and quinic acids were recognized as discrimination factor for adulteration of oregano with venetian sumac. Fructose and quinic acid levels correlated with oregano adulteration with myrtle. Orthogonal Partial Least Squares Discriminant Analysis enabled discrimination of O. vulgare and O. onites samples, where catechollactate was found to be discriminating metabolite.

A Calibration Tutorial for Spectral Data. Part 1: Data Pretreatment and Principal Component Regression Using Matlab

1996 ◽  
Vol 4 (1) ◽  
pp. 225-242 ◽  
Author(s):  
Paul Geladi ◽  
Harald Martens
Keyword(s):  
Least Squares ◽  
Spectral Data ◽  
Partial Least Squares ◽  
Regression Models ◽  
Principal Component Regression ◽  
Principal Component ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Statistical Parameters ◽  
Calibration Samples

Regression and calibration play an important role in analytical chemistry. All analytical instrumentation is dependent on a calibration that uses some regression model for a set of calibration samples. The ordinary least squares (OLS) method of building a multivariate linear regression (MLR) model has strict limitations. Therefore, biased or regularised regression models have been introduced. Some selected ones are ridge regression (RR), principal component regression (PCR) and partial least squares regression (PLS or PLSR). Also, artificial neural networks (ANN) based on back-propagation can be used as regression models. In order to understand regression models more is needed than just a set of statistical parameters. A deeper understanding of the underlying chemistry and physics is always equally important. For spectral data this means that a basic understanding of spectra and their errors is useful and that spectral representation should be included in judging the usefulness of the data treatment. A “constructed” spectrometric example is introduced. It consists of real spectrometric measurements in the range 408–1176 nm for 26 calibration samples and 10 test samples. The main response variable is litmus concentration, but other constituents such as bromocresolgreen and ZnO are added as interferents and also the pH is changed. The example is introduced as a tutorial. All calculations are shown in detail in Matlab. This makes it easy for the reader to follow and understand the calculations. It also makes the calculations completely traceable. The raw data are available as a file. In Part 1, the emphasis is on pretreatment of the data and on visualisation in different stages of the calculations. Part 1 ends with principal component regression calculations. Partial least squares calculations and some ANN results are presented in Part 2.

Prediction of Long-Residue Properties of Potential Blends from Mathematically Mixed Infrared Spectra of Pure Crude Oils by Partial Least-Squares Regression Models

2009 ◽  
Vol 23 (4) ◽  
pp. 2164-2168 ◽  
Author(s):  
Peter de Peinder ◽  
Tom Visser ◽  
Derek D. Petrauskas ◽  
Fabien Salvatori ◽  
Fouad Soulimani ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Infrared Spectra ◽  
Regression Models ◽  
Partial Least Squares Regression ◽  
Crude Oils ◽  
Least Squares Regression

Optimization Study of Stepwise Regression and Partial Least Squares Regression Models for Dam Security Monitoring

2014 ◽  
Vol 578-579 ◽  
pp. 1101-1107 ◽  
Author(s):  
Wei Ling Hu ◽  
Nian Wu Deng ◽  
Qiu Shi Liu
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Regression Models ◽  
Stepwise Regression ◽  
Partial Least Squares Regression ◽  
Monitoring Data ◽  
Least Squares Regression ◽  
Security Monitoring ◽  
Optimization Study ◽  
Optimized Model

Both Stepwise Regression (SR) and Partial Least Squares Regression (PLSR) can be applied in data analysis of dam security monitoring, and achieve in fitting and forecasting. However, SR and PLSR models still can be optimized. A variety of programs are studied and compared based on actual dam security monitoring data. The results show that the optimized-model is better in fitting and forecasting the monitoring data.

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