Exploring the potential of high-resolution LC-MS in combination with ion mobility separation and surrogate minimal depth for enhanced almond origin authentication

• Published in: Talanta (Oxford) 2024-05, Vol.271, p.125598-125598, Article 125598
• Main Authors: Lösel, Henri, Arndt, Maike, Wenck, Soeren, Hansen, Lasse, Oberpottkamp, Marie, Seifert, Stephan, Fischer, Markus
• Format: Article
• Language: English
• Subjects: Almonds; Food authentication; Ion mobility; Machine learning; Mass spectrometry; SMD
• ISSN: 0039-9140; 1873-3573
• DOI: 10.1016/j.talanta.2023.125598

Almonds (Prunus dulcisMill.) are consumed worldwide and their geographical origin plays a crucial role in determining their market value. In the present study, a total of 250 almond reference samples from six countries (Australia, Spain, Iran, Italy, Morocco, and the USA) were non-polar extracted and analyzed by UPLC-ESI-IM-qToF-MS. Four harvest periods, more than 30 different varieties, including both sweet and bitter almonds, were considered in the method development. Principal component analysis showed that there are three groups of samples with similarities: Australia/USA, Spain/Italy and Iran/Morocco. For origin determination, a random forest achieved an accuracy of 88.8 %. Misclassifications occurred mainly between almonds from the USA and Australia, due to similar varieties and similar external influences such as climate conditions. Metabolites relevant for classification were selected using Surrogate Minimal Depth, with triacylglycerides containing oxidized, odd chained or short chained fatty acids and some phospholipids proven to be the most suitable marker substances. Our results show that focusing on the identified lipids (e. g., using a QqQ-MS instrument) is a promising approach to transfer the origin determination of almonds to routine analysis. [Display omitted] •Origin determination of almonds by lipidomics analysis via UPLC-ESI-IM-qToF-MS.•250 almond samples from six different countries were used for analysis.•Random forest analysis achieved an accuracy of 88.8 %.•Metabolites were selected by Surrogate Minimal Depth and identified as triacylglycerols.