Multivariate Calibration Methods Applied to Quantitative FT-IR Analyses

The multivariate calibration methods—principal component regression (PCR) and partial least squares (PLSs)—were employed for the prediction of total phenol contents of four Prunella species. High performance liquid chromatography (HPLC) and spectrophotometric approaches were used to determine the total phenol content of the Prunella samples. Several preprocessing techniques such as smoothing, normalization, and column centering were employed to extract the chemically relevant information from the data after alignment with correlation optimized warping (COW). The importance of the preprocessing was investigated by calculating the root mean square error (RMSE) for the calibration set of the total phenol content of Prunella samples. The models developed based on the preprocessed data were able to predict the total phenol content with a precision comparable to that of the reference of the Folin-Ciocalteu method. PLS model seems preferable, because of its predictive and describing abilities and good interpretability of the contribution of compounds to the total phenol content. Multivariate calibration methods were constructed to model the total phenol content of the Prunella samples from the HPLC profiles and indicate peaks responsible for the total phenol content successfully.

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Classification and Authentication of Paprika by UHPLC-HRMS Fingerprinting and Multivariate Calibration Methods (PCA and PLS-DA)

Foods ◽

10.3390/foods9040486 ◽

2020 ◽

Vol 9 (4) ◽

pp. 486 ◽

Cited By ~ 2

Author(s):

Sergio Barbosa ◽

Javier Saurina ◽

Lluís Puignou ◽

Oscar Núñez

Keyword(s):

Multivariate Calibration ◽

Principal Component ◽

Small Region ◽

Reversed Phase ◽

Negative Ion ◽

Mass Analyzer ◽

Least Squares Regression ◽

Calibration Methods ◽

Chemical Descriptors ◽

Negative Ion Mode

In this study, the feasibility of non-targeted UHPLC-HRMS fingerprints as chemical descriptors to address the classification and authentication of paprika samples was evaluated. Non-targeted UHPLC-HRMS fingerprints were obtained after a simple sample extraction method and C18 reversed-phase separation. Fingerprinting data based on signal intensities as a function of m/z values and retention times were registered in negative ion mode using a q-Orbitrap high-resolution mass analyzer, and the obtained non-targeted UHPLC-HRMS fingerprints subjected to unsupervised principal component analysis (PCA) and supervised partial least squares regression-discriminant analysis (PLS-DA) to study sample discrimination and classification. A total of 105 paprika samples produced in three different regions, La Vera PDO and Murcia PDO, in Spain, and the Czech Republic, and all of them composed of samples of at least two different taste varieties, were analyzed. Non-targeted UHPLC-HRMS fingerprints demonstrated to be excellent sample chemical descriptors to achieve the authentication of paprika production regions with 100% sample classification rates by PLS-DA. Besides, the obtained fingerprints were also able to perfectly discriminate among the different paprika taste varieties in all the studied cases, even in the case of the different La Vera PDO paprika tastes (sweet, bittersweet, and spicy) which are produced in a very small region.

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An electronic tongue for simultaneous determination of Ca2+, Mg2+, K+ and NH4+ in water samples by multivariate calibration methods

Talanta ◽

10.1016/j.talanta.2020.121110 ◽

2020 ◽

Vol 217 ◽

pp. 121110

Author(s):

Havva Bekiroğlu Ataş ◽

Adnan Kenar ◽

Mustafa Taştekin

Keyword(s):

Simultaneous Determination ◽

Water Samples ◽

Multivariate Calibration ◽

Electronic Tongue ◽

Calibration Methods

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Optimization of an automated FI-FT-IR procedure for the determination ofo-xylene, toluene and ethyl benzene inn-hexane

Journal of Automated Methods and Management in Chemistry ◽

10.1155/s1463924699000139 ◽

1999 ◽

Vol 21 (4) ◽

pp. 113-119 ◽

Cited By ~ 2

Author(s):

Ian Wells ◽

Paul J. Worsfold

Keyword(s):

Multivariate Calibration ◽

Sample Volume ◽

Standard Errors ◽

Ethyl Benzene ◽

Ir Detector ◽

Flow Cells ◽

Figures Of Merit ◽

Ft Ir ◽

Relative Root

The development and optimization of an automated flow injection (FI) manifold coupled with a Fourier transform infrared (FT-IR) detector for the determination of toluene, ethyl benzene ando-oxylene in ann-hexane matrix is described. FT-IR parameters optimized were resolution and number of co-added scans; FI parameters optimized were type of pump tubing, carrier flow rate and sample volume. ATR and transmission flow cells were compared for the determination ofo-xylene, the ATR cell was easier to use and gave better figures of merit, except for sensitivity, for which the transmission cell was twice as good. Multivariate calibration routines were applied to the FI-FT-IR data and the PLS1 algorithm gave relative root mean standard errors of crossvalidation (RRMSECVs) < 7% for all three analytes using mean-centred data and the first derivative foro-xylene.

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