Determination of Variance of Secondary Metabolites in Lettuces Grown Under Different Light Sources by Flow Injection Mass Spectrometric (FIMS) Fingerprinting and ANOVA–PCA

Mass Spectrometry-Based Nontargeted and Targeted Analytical Approaches in Fingerprinting and Metabolomics of Food and Agricultural Research

Mass spectrometry (MS)-based techniques have been extensively applied in food and agricultural research. This review aims to address the advances and applications of MS-based analytical strategies in nontargeted and targeted analysis and summarizes the recent publications of MS-based techniques, including flow injection MS fingerprinting, chromatography-tandem MS metabolomics, direct analysis using ambient mass spectrometry, as well as development in MS data deconvolution software packages and databases for metabolomic studies. Various nontargeted and targeted approaches are employed in marker compounds identification, material adulteration detection, and the analysis of specific classes of secondary metabolites. In the newly emerged applications, the recent advances in computer tools for the fast deconvolution of MS data in targeted secondary metabolite analysis are highlighted.

FIA-HRMS fingerprinting subjected to chemometrics as a valuable tool to address food classification and authentication: Application to red wine, paprika, and vegetable oil samples

The rise of food fraud practices, affecting a wide variety of goods and their specific characteristics (e.g., quality or geographical origin), demands rapid high-throughput analytical approaches to ensure consumers protection. In this context, this study assesses flow injection analysis coupled to high-resolution mass spectrometry (FIA-HRMS), using a fingerprinting approach and combined with chemometrics, to address four food authentication issues: (i) the geographical origin of three Spanish red wines, (ii) the geographical origin of three European paprikas, (iii) the distinction of olive oil from other vegetable oils and (iv) the assessment of its quality category. In each case, negative and positive ionisation FIA-HRMS fingerprints, and two different data fusion strategies, were evaluated. After external validation, excellent classification accuracies were reached. Moreover, high-resolution mass spectrometry (HRMS) allowed sample matrices characterisation by the putative identification of the most common ions.

Chemical analysis and classification of black pepper (Piper nigrum L.) based on their country of origin using mass spectrometric methods and chemometrics

The current study applied gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and thermal desorption direct analysis in real-time mass spectrometry (TD-DART-MS) methods to the analysis of black pepper (Piper nigrum L.) samples from different countries. The black pepper powder samples were analyzed directly by TD-DART-MS without any extraction, but for GC-MS and LC-MS methods, a methanol extraction procedure was employed before the analysis. Various compounds, such as piperamides and terpenes, were detected. Partial least squares-discriminant analysis (PLS-DA) was used to classify black pepper samples based on their origins. Total ion mass spectrum (TMS) data profiles from GC-MS, LC-MS, and TD-DART-MS methods were constructed and evaluated for the performance of classification. A cubic-root data transformation was tested in the data preprocessing and found to be effective for improving the classification rates. The average classification rates of PLS-DA models with GC-MS-cubic-root-TMS, LC-MS-cubic-root-TMS, and DART-MS-cubic-root-TMS data representations were 94.1 ± 0.6%, 87.7 ± 0.6%, and 97.0 ± 0.3% respectively, for 100-time bootstrapped-Latin-partition cross-validation. This study presents for the first time the analysis of plant-based food materials by using TD-DART-MS, and it has been demonstrated as a simple and high-throughput method for classification studies.