A fast and effective approach for the discrimination of garlic origin using wooden-tip electrospray ionization mass spectrometry and multivariate classification

Abstract

This paper presents the combination of wooden-tip electrospray ionization mass spectrometry (WTESI-MS) and multivariate pattern recognition methods (principal component analysis, PCA and partial least squares discriminant analysis, PLS-DA) for the rapid and reliable discrimination, via chemical fingerprints, of garlic origin. A total of 312 garlic samples grown in different countries (Brazil, China, Argentina, Spain, and Chile) were studied. The methodology was based on a direct sampling approach, which relies on loading the sample by penetrating the garlic cloves with a pre-wetted wooden tip, followed by direct prompt analysis by WTESI-MS. Thus, no sample preparation is needed, which prevents the degradation of important metabolites and increases the analytical throughput. Parameters that affects the WTESI were optimized and the best performance in terms of signal stability and intensity was achieved using the positive ion mode. Most of the ions in WTESI mass spectra were assigned to amino acids, sugars, organosulfur compounds, and lipids. The discriminative model showed good performance (accuracy rates between 81.9% and 98.6%) and enabled identifying diagnostic ions for garlic samples from different origins. The differentiation and classification of garlic origin is of major importance as this food flavoring product is widely consumed, with worldwide trade representing billions of dollars every year, and is very often the subject of fraud.

Keywords: Ambient mass spectrometry; Chemometrics; Food authentication; Garlic chemical fingerprints; PLS-DA; WTESI-MS.