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

Vera Rief,Andreas Scharinger,Christina Felske,Katrin Krumbügel,Elke Richling,Carmen M Breitling-Utzmann,Dirk W Lachenmeier,Stephan G Walch,Simone Stegmüller

doi:10.3390/beverages7020031

Abstract

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.

Highlights

IntroductionIn 2000, the research group led by Tareke et al was able to observe a correlation between fried food and increased acrylamide exposure [3] and subsequently showed in 2002 that heated carbohydrate-rich foods have high acrylamide contents [4]
The aim of this research was to use a partial least squares (PLS) regression model that allows a possible correlation between reference results from the LC–MS/MS method and associated nuclear magnetic resonance spectra (NMR) spectra
The y-data must be at least mean centered to be suitable for use in the PLS models [10]

Summary

Introduction

In 2000, the research group led by Tareke et al was able to observe a correlation between fried food and increased acrylamide exposure [3] and subsequently showed in 2002 that heated carbohydrate-rich foods have high acrylamide contents [4]. For the determination of acrylamide content, the coupling of liquid chromatography with tandem mass spectrometry (LC–MS/MS) is considered as the standard method [9]. This approach is time-consuming due to the necessary sample preparation and measurement procedure [4]. The aim of this research was to use a partial least squares (PLS) regression model that allows a possible correlation between reference results from the LC–MS/MS method and associated NMR spectra. In order to test the predictive accuracy and limitations of the model, 50 coffee samples are roasted, some of which correspond to commercially available coffees and some of which have extreme roasts and blends of varieties

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Conclusion