Advanced mass spectrometry coupled with chemometric data analysis for innovative food authenticity measurement

Abstract

This thesis encompasses an extensive exploration and application of advanced mass spectrometry, chemometric techniques, and data fusion in food authenticity verification, pushing the boundaries of conventional analytical methodologies. By harnessing these cutting-edge techniques, I aimed to support the delivery of a robust framework to verify the authenticity of food sources, and simultaneously address pressing issues related to food fraud, adulteration, and mislabeling.

Firstly, the use of Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) and chemometric modeling for the identification and classification of biomarkers in five different sources of animal and plant milk were demonstrated. The results not only underscored the classification accuracy but also the efficiency and rapidity of DESI-MS, emphasizing its value for milk fraud control.

Secondly, a Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-ToF MS) combined with chemometric analysis approach were developed to distinguish between wild and farmed salmon. With the application of Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA), and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA), 100% accuracy in classifying and identifying salmon samples was achieved. These findings suggest that this analytical approach could serve as a robust method for detecting salmon fraud and provide insights into the potential application of MALDI-ToF technology in food authenticity verification.

Thirdly, a novel dual-platform mass spectrometry combining mid-level data fusion with multivariate analysis approach were introduced. Using dual-platform mass spectrometry data from Rapid Evaporative Ionization Mass Spectrometry (REIMS) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS), the geographical origin and production methods of salmon were successfully classified and determined with 100% accuracy. The findings led to the identification of robust lipid and elemental markers, which provide substantial evidence of the salmon origin and production method.

Then, the application of ICP-MS combined with chemometric analysis to determine the geographical origin of soya were explored. With a cross-validation classification accuracy of 99%, this approach could reliably distinguish soya samples from various producing countries. This advanced method serves as a reliable technique for identifying the geographical origin of soya and could serve as a potential tool in tackling Amazon deforestation soya.

This research underscores the value of applying advanced mass spectrometry techniques in combination with chemometric analysis to identify food authenticity. It has delivered promising solutions to address existing challenges in the food industry. The results shown from this research provide compelling evidence for the potential efficacy of these novel approaches in addressing and mitigating issues of food fraud within the food industry. Additionally, this work presents a foundation for future investigations into food sustainability and other related studies, further advancing the field of food authenticity analysis.

Date of Award	Dec 2023
Original language	English
Awarding Institution	Queen's University Belfast
Supervisor	Christopher Elliott (Supervisor) & Saskia van Ruth (Supervisor)