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Bai Z, Zhao Z, Wang S, Li H, Chen DDY. Ambient mass spectrometry imaging of food natural products by angled direct analysis in real time high-resolution mass spectrometry. Food Chem 2024; 454:139802. [PMID: 38797098 DOI: 10.1016/j.foodchem.2024.139802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 05/08/2024] [Accepted: 05/20/2024] [Indexed: 05/29/2024]
Abstract
Direct surface analysis in ambient conditions provides information on the position and chemical composition of an object at the time of investigation. An angled sampling probe is developed in this work for direct analysis in real time (DART) ionization high-resolution mass spectrometry. The DART ion source and the interface were modified for improved surface resolution, increased ion transfer efficiency, as well as enabling two-dimensional surface scanning. The angled probe DART-MS system was used for investigating a variety of food samples including fruit peels, ginseng root, plant leaves and sections of radish. Abundant signals and distinct chemical profiles are obtained in seconds, and spatial distribution of different molecules across the sample surfaces can be observed. In addition, the developed system can quickly identify the chemical changes when the surfaces were treated. The method is capable of directly evaluating food sample surfaces with different shapes, hardness, and conditions, without any sample pretreatments.
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Affiliation(s)
- Zhiru Bai
- Jiangsu Collaborative Innovation Center of Biomedical Functional Materials, Jiangsu Key Laboratory of Biomedical Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, China
| | - Zhengyan Zhao
- Jiangsu Collaborative Innovation Center of Biomedical Functional Materials, Jiangsu Key Laboratory of Biomedical Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, China
| | - Saiting Wang
- Jiangsu Collaborative Innovation Center of Biomedical Functional Materials, Jiangsu Key Laboratory of Biomedical Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, China
| | - Hongli Li
- Jiangsu Collaborative Innovation Center of Biomedical Functional Materials, Jiangsu Key Laboratory of Biomedical Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, China.
| | - David Da Yong Chen
- Jiangsu Collaborative Innovation Center of Biomedical Functional Materials, Jiangsu Key Laboratory of Biomedical Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, China; Department of Chemistry, University of British Columbia, Vancouver, BC V6T 1Z1, Canada
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Wang Y. Recent advances in the application of direct analysis in real time-mass spectrometry (DART-MS) in food analysis. Food Res Int 2024; 188:114488. [PMID: 38823841 DOI: 10.1016/j.foodres.2024.114488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/30/2024] [Accepted: 05/07/2024] [Indexed: 06/03/2024]
Abstract
Direct analysis in real time-mass spectrometry (DART-MS) has evolved as an effective analytical technique for the rapid and accurate analysis of food samples. The current advancements of DART-MS in food analysis are described in this paper. We discussed the DART principles, which include devices, ionization mechanisms, and parameter settings. Numerous applications of DART-MS in the fields of food and food products analysis published during 2018-2023 were reviewed, including contamination detection, food authentication and traceability, and specific analyte analysis in the food matrix. Furthermore, the challenges and limitations of DART-MS, such as matrix effect, isobaric component analysis, cost considerations and accessibility, and compound selectivity and identification, were discussed as well.
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Affiliation(s)
- Yang Wang
- Jilin Ginseng Academy, Changchun University of Chinese Medicine, Changchun 130117, China.
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Zacometti C, Sammarco G, Massaro A, Lefevre S, Frégière-Salomon A, Lafeuille JL, Candalino IF, Piro R, Tata A, Suman M. Authenticity assessment of ground black pepper by combining headspace gas-chromatography ion mobility spectrometry and machine learning. Food Res Int 2024; 179:114023. [PMID: 38342542 DOI: 10.1016/j.foodres.2024.114023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/08/2024] [Accepted: 01/12/2024] [Indexed: 02/13/2024]
Abstract
Currently, the authentication of ground black pepper is a major concern, creating a need for a rapid, highly sensitive and specific detection tool to prevent the introduction of adulterated batches into the food chain. To this aim, head space gas-chromatography ion mobility spectrometry (HS-GC-IMS), combined with machine learning, is tested in this initial, proof-of-concept study. A broad variety of authentic samples originating from eight countries and three continents were collected and spiked with a range of adulterants, both endogenous sub-products and an assortment of exogenous materials. The method is characterized by no sample preparation and requires 20 min for chromatographic separation and ion mobility data acquisition. After an explorative analysis of the data, those were submitted to two different machine learning algorithms (partial least squared discriminant analysis-PLS-DA and support vector machine-SVM). While the PLS-DA model did not provide fully satisfactory performances, the combination of HS-GC-IMS and SVM successfully classified the samples as authentic, exogenously-adulterated or endogenously-adulterated with an overall accuracy of 90 % and 96 % on withheld test set 1 and withheld test set 2, respectively (at a 95 % confidence level). Some limitations, expected to be mitigated by further research, were encountered in the correct classification of endogenously adulterated ground black pepper. Correct categorization of the ground black pepper samples was not adversely affected by the operator or the time span of data collection (the method development and model challenge were carried out by two operators over 6 months of the study, using ground black pepper harvested between 2015 and 2019). Therefore, HS-GC-IMS, coupled to an intelligent tool, is proposed to: (i) aid in industrial decision-making before utilization of a new batch of ground black pepper in the production chain; (ii) reduce the use of time-consuming conventional analyses and; (iii) increase the number of ground black pepper samples analyzed within an industrial quality control frame.
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Affiliation(s)
- Carmela Zacometti
- Istituto Zooprofilattico Sperimentale delle Venezie, Laboratory of Experimental Chemistry, Vicenza, Italy
| | - Giuseppe Sammarco
- Advanced Laboratory Research, Barilla G. e R. Fratelli S.p.A., Via Mantova, 166, 43122 Parma, Italy
| | - Andrea Massaro
- Istituto Zooprofilattico Sperimentale delle Venezie, Laboratory of Experimental Chemistry, Vicenza, Italy
| | - Stephane Lefevre
- Food Integrity Laboratory, Global Quality and Food Safety Center of Excellence, McCormick & Co., Inc., 999 avenue des Marchés, 84200 Carpentras, France
| | - Aline Frégière-Salomon
- Food Integrity Laboratory, Global Quality and Food Safety Center of Excellence, McCormick & Co., Inc., 999 avenue des Marchés, 84200 Carpentras, France
| | - Jean-Louis Lafeuille
- Global Quality and Food Safety Center of Excellence, McCormick & Co., Inc., 999 avenue des Marchés, 84200 Carpentras, France
| | - Ingrid Fiordaliso Candalino
- Global Quality and Food Safety Center of Excellence, McCormick & Co., Inc., Viale Iotti Nilde, 50038 San Piero (FI), Italy
| | - Roberto Piro
- Istituto Zooprofilattico Sperimentale delle Venezie, Laboratory of Experimental Chemistry, Vicenza, Italy
| | - Alessandra Tata
- Istituto Zooprofilattico Sperimentale delle Venezie, Laboratory of Experimental Chemistry, Vicenza, Italy
| | - Michele Suman
- Advanced Laboratory Research, Barilla G. e R. Fratelli S.p.A., Via Mantova, 166, 43122 Parma, Italy; Catholic University Sacred Heart, Department for Sustainable Food Process, Piacenza, Italy.
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Rocamora-Rivera B, Arroyo-Manzanares N, Viñas P. Detection of Adulterated Oregano Samples Using Untargeted Headspace-Gas Chromatography-Ion Mobility Spectrometry Analysis. Foods 2024; 13:516. [PMID: 38397493 PMCID: PMC10888469 DOI: 10.3390/foods13040516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Oregano is often adulterated for economic reasons. This fraud mainly consists of adding other species with lower commercial value, such as olive leaves. To ensure the authenticity of oregano, an analytical method based on the analysis of the volatile organic compound (VOC) profile obtained by headspace gas chromatography coupled to ion mobility spectrometry (HS-GC-IMS) was developed and validated. Samples of ecological Mediterranean oregano adulterated with different percentages of two types of olive leaves (cornicabra and manzanilla) were studied using a non-targeted analysis. Moreover, a total of 30 VOCs were identified in the analyzed samples, and 24 compounds could be quantified using calibration curves based on Boltzmann's equation. A chemometric model based on orthogonal partial least squares discriminant analysis (OPLS-DA) was used to detect the adulterated oregano samples, obtaining a 100% validation success rate, and partial least squares (PLS) analysis was used to quantify the percentage of adulterant. Finally, the proposed methodology was applied to 15 commercial oregano samples, resulting in two of them being classified as adulterated with 31 and 43% of olive leaves, respectively.
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Affiliation(s)
| | - Natalia Arroyo-Manzanares
- Department of Analytical Chemistry, Faculty of Chemistry, University of Murcia, 30100 Murcia, Spain; (B.R.-R.); (P.V.)
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