Partial Least-Squares (PLS) Correlation between Refined Product Yields and Physicochemical Properties with the1H Nuclear Magnetic Resonance (NMR) Spectra of Colombian Crude Oils

2007 ◽  
Vol 21 (3) ◽  
pp. 1674-1680 ◽  
Author(s):  
Molina ◽  
Uriel Navarro Uribe ◽  
Juan Murgich
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Physicochemical Properties ◽  
Least Squares ◽  
Partial Least Squares ◽  
Nmr Spectra ◽  
Crude Oils ◽  
Product Yields ◽  
Nuclear Magnetic

scholarly journals Indirect Nuclear Magnetic Resonance (NMR) Spectroscopic Determination of Acrylamide in Coffee Using Partial Least Squares (PLS) Regression

Beverages ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 31
Author(s):  
Vera Rief ◽  
Christina Felske ◽  
Andreas Scharinger ◽  
Katrin Krumbügel ◽  
Simone Stegmüller ◽  
...  
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Least Squares ◽  
Partial Least Squares ◽  
Nmr Spectra ◽  
Proton Nuclear Magnetic Resonance ◽  
International Agency ◽  
Pls Regression ◽  
Nmr Spectrum ◽  
Nuclear Magnetic

Acrylamide is probably carcinogenic to humans (International Agency for Research on Cancer, group 2A) with major occurrence in heated, mainly carbohydrate-rich foods. For roasted coffee, a European Union benchmark level of 400 µg/kg acrylamide is of importance. Regularly, the acrylamide contents are controlled using liquid chromatography combined with tandem mass spectrometry (LC–MS/MS). This reference method is reliable and precise but laborious because of the necessary sample clean-up procedure and instrument requirements. This research investigates the possibility of predicting the acrylamide content from proton nuclear magnetic resonance (NMR) spectra that are already recorded for other purposes of coffee control. In the NMR spectrum acrylamide is not directly quantifiable, so that the aim was to establish a correlation between the reference value and the corresponding NMR spectrum by means of a partial least squares (PLS) regression. Therefore, 40 commercially available coffee samples with already available LC–MS/MS data and NMR spectra were used as calibration data. To test the accuracy and robustness of the model and its limitations, 50 coffee samples with extreme roasting degrees and blends were additionally prepared as the test set. The PLS model shows an applicability for the varieties Coffea arabica and C. canephora, which were medium to very dark roasted using drum or infrared roasters. The root mean square error of prediction (RMSEP) is 79 µg/kg acrylamide (n = 32). The current PLS model is judged as suitable to predict the acrylamide values of commercially available coffee samples.

scholarly journals Indirect Nuclear Magnetic Resonance (NMR) Spectroscopic Determination of Acrylamide in Coffee Using Partial Least Squares (PLS) Regression

2021 ◽  
Author(s):  
Vera Rief ◽  
Christina Felske ◽  
Andreas Scharinger ◽  
Katrin Krumbügel ◽  
Simone Stegmüller ◽  
...  
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Least Squares ◽  
Partial Least Squares ◽  
Nmr Spectra ◽  
Proton Nuclear Magnetic Resonance ◽  
International Agency ◽  
Pls Regression ◽  
Nmr Spectrum ◽  
Nuclear Magnetic

Acrylamide is probably carcinogenic to humans (International Agency for Research on Cancer, group 2A) with major occurrence in heated, mainly carbohydrate-rich foods. For roasted coffee, a European Union benchmark level of 400 µg/kg acrylamide is of importance. Regularly, the acrylamide contents are controlled using liquid chromatography combined with tandem mass spectrometry (LC-MS/MS). This reference method is reliable and precise but laborious because of the necessary sample clean-up procedure and instrument requirements. This research investigates the possibility of predicting the acrylamide content from proton nuclear magnetic resonance (NMR) spectra that are already recorded for other purposes of coffee control. In the NMR spectrum acrylamide is not directly quantifiable, so that the aim was to establish a correlation between the reference value and the corresponding NMR spectrum by means of a partial least squares (PLS) regression. Therefore, 40 commercially available coffee samples with already available LC-MS/MS data and NMR spectra were used as calibration data. To test the accuracy and robustness of the model and its limitations, 50 coffee samples with extreme roasting degrees and blends were additionally prepared as test set. The PLS model shows an applicability for the varieties C. arabica and C. canephora, which were medium to very dark roasted using drum or infrared roasters. The root mean square error of prediction (RMSEP) is 79 µg/kg acrylamide (n=32). The PLS model is judged as suitable to predict the acrylamide values of commercially available coffee samples. On the other hand, very light roasts containing more than 1000 µg/kg acrylamide are currently not suitable for PLS prediction.

scholarly journals Peak Estimation of Univariate Spectraby the Best L1 Piecewise Monotonic Approximation Method

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Least Squares ◽  
Approximation Method ◽  
Nmr Spectra ◽  
Inorganic Compound ◽  
Piecewise Monotonic ◽  
Inherent Problem ◽  
New Algorithms ◽  
Nuclear Magnetic

We consider applications of the best L1 piecewise monotonic approximation method for the peak estimation of three sets of up to 2500 measurements of Raman, Infrared and Nuclear Magnetic Resonance (NMR)spectra. Peak estimation is an inherent problem of spectroscopy. The location of peaks and their intensities arethe signature of a sample of an organic or an inorganic compound. The diversity and the complexity of our measurements makes it a difficult test of the effectiveness of the method. We find that the method identifies efficientlypeaks and we compare to the results obtained by the analogous least squares calculations. These results havemany similarities and occasionally considerable differences due to both properties of the norms employed in theoptimization calculations and nature of the spectra. Our results may be helpful to subject analysts as part of theinformation on which decisions will be made for estimating peaks in sequences of spectra and to the developmentof new algorithms that are particularly suitable for peak estimation calculations.

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