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Sim J, Dixit Y, Mcgoverin C, Oey I, Frew R, Reis MM, Kebede B. Support vector regression for prediction of stable isotopes and trace elements using hyperspectral imaging on coffee for origin verification. Food Res Int 2023; 174:113518. [PMID: 37986508 DOI: 10.1016/j.foodres.2023.113518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/24/2023] [Accepted: 09/26/2023] [Indexed: 11/22/2023]
Abstract
The potential of using rapid and non-destructive near-infrared - hyperspectral imaging (HSI-NIR) for the prediction of an integrated stable isotope and multi-element dataset was explored for the first time with the help of support vector regression. Speciality green coffee beans sourced from three continents, eight countries, and 22 regions were analysed using a push-broom HSI-NIR (700-1700 nm), together with five isotope ratios (δ13C, δ15N, δ18O, δ2H, and δ34S) and 41 trace elements. Support vector regression with the radial basis function kernel was conducted using X as the HSI-NIR data and Y as the geochemistry markers. Model performance was evaluated using root mean squared error, coefficient of determination, and mean absolute error. Three isotope ratios (δ18O, δ2H, and δ34S) and eight elements (Zn, Mn, Ni, Mo, Cs, Co, Cd, and La) had an R2predicted 0.70 - 0.99 across all origin scales (continent, country, region). All five isotope ratios were well predicted at the country and regional levels. The wavelength regions contributing the most towards each prediction model were highlighted, including a discussion of the correlations across all geochemical parameters. This study demonstrates the feasibility of using HSI-NIR as a rapid and non-destructive method to estimate traditional geochemistry parameters, some of which are origin-discriminating variables related to altitude, temperature, and rainfall differences across origins.
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Affiliation(s)
- Joy Sim
- Department of Food Science, University of Otago, PO BOX 56, Dunedin 9054, New Zealand.
| | - Yash Dixit
- AgResearch, Grasslands Research Centre, Palmerston North, New Zealand
| | - Cushla Mcgoverin
- Department of Physics, University of Auckland, Auckland 1010, New Zealand; The Dodd-Walls Centre for Photonic and Quantum Technologies, Auckland 1010, New Zealand
| | - Indrawati Oey
- Department of Food Science, University of Otago, PO BOX 56, Dunedin 9054, New Zealand; Riddet Institute, Palmerston North, New Zealand
| | | | - Marlon M Reis
- AgResearch, Grasslands Research Centre, Palmerston North, New Zealand
| | - Biniam Kebede
- Department of Food Science, University of Otago, PO BOX 56, Dunedin 9054, New Zealand.
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Chien HJ, Zheng YF, Wang WC, Kuo CY, Hsu YM, Lai CC. Determination of adulteration, geographical origins, and species of food by mass spectrometry. Mass Spectrom Rev 2023; 42:2273-2323. [PMID: 35652168 DOI: 10.1002/mas.21780] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 04/07/2022] [Accepted: 04/12/2022] [Indexed: 06/15/2023]
Abstract
Food adulteration, mislabeling, and fraud, are rising global issues. Therefore, a number of precise and reliable analytical instruments and approaches have been proposed to ensure the authenticity and accurate labeling of food and food products by confirming that the constituents of foodstuffs are of the kind and quality claimed by the seller and manufacturer. Traditional techniques (e.g., genomics-based methods) are still in use; however, emerging approaches like mass spectrometry (MS)-based technologies are being actively developed to supplement or supersede current methods for authentication of a variety of food commodities and products. This review provides a critical assessment of recent advances in food authentication, including MS-based metabolomics, proteomics and other approaches.
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Affiliation(s)
- Han-Ju Chien
- Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan
| | - Yi-Feng Zheng
- Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan
| | - Wei-Chen Wang
- Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan
| | - Cheng-Yu Kuo
- Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan
| | - Yu-Ming Hsu
- Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan
| | - Chien-Chen Lai
- Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan
- Graduate Institute of Chinese Medical Science, China Medical University, Taichung, Taiwan
- Advanced Plant Biotechnology Center, National Chung Hsing University, Taichung, Taiwan
- Ph.D. Program in Translational Medicine, National Chung Hsing University, Taichung, Taiwan
- Rong Hsing Research Center For Translational Medicine, National Chung Hsing University, Taichung, Taiwan
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Sim J, Mcgoverin C, Oey I, Frew R, Kebede B. Stable isotope and trace element analyses with non-linear machine-learning data analysis improved coffee origin classification and marker selection. J Sci Food Agric 2023; 103:4704-4718. [PMID: 36924039 DOI: 10.1002/jsfa.12546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/03/2023] [Accepted: 03/16/2023] [Indexed: 06/06/2023]
Abstract
BACKGROUND This study investigated the geographical origin classification of green coffee beans from continental to country and regional levels. An innovative approach combined stable isotope and trace element analyses with non-linear machine learning data analysis to improve coffee origin classification and marker selection. Specialty green coffee beans sourced from three continents, eight countries, and 22 regions were analyzed by measuring five isotope ratios (δ13 C, δ15 N, δ18 O, δ2 H, and δ34 S) and 41 trace elements. Partial least squares discriminant analysis (PLS-DA) was applied to the integrated dataset for origin classification. RESULTS Origins were predicted well at the country level and showed promise at the regional level, with discriminating marker selection at all levels. However, PLS-DA predicted origin poorly at the continental and Central American regional levels. Non-linear machine learning techniques improved predictions and enabled the identification of a higher number of origin markers, and those that were identified were more relevant. The best predictive accuracy was found using ensemble decision trees, random forest and extreme gradient boost, with accuracies of up to 0.94 and 0.89 for continental and Central American regional models, respectively. CONCLUSION The potential for advanced machine learning models to improve origin classification and the identification of relevant origin markers was demonstrated. The decision-tree-based models were superior with their embedded variable identification features and visual interpretation. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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Affiliation(s)
- Joy Sim
- Department of Food Science, University of Otago, Dunedin, New Zealand
| | - Cushla Mcgoverin
- Department of Physics, University of Auckland, Auckland, New Zealand
- The Dodd-Walls Centre for Photonic and Quantum Technologies, Dunedin, New Zealand
| | - Indrawati Oey
- Department of Food Science, University of Otago, Dunedin, New Zealand
- The Riddet Institute, Palmerston North, New Zealand
| | | | - Biniam Kebede
- Department of Food Science, University of Otago, Dunedin, New Zealand
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Sim J, McGoverin C, Oey I, Frew R, Kebede B. Near-infrared reflectance spectroscopy accurately predicted isotope and elemental compositions for origin traceability of coffee. Food Chem 2023; 427:136695. [PMID: 37385064 DOI: 10.1016/j.foodchem.2023.136695] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 06/12/2023] [Accepted: 06/19/2023] [Indexed: 07/01/2023]
Abstract
Stable isotope ratios and trace elements are well-established tools that act as signatures of the product's environmental conditions and agricultural processes; but they involve time, money, and environmentally destructive chemicals. In this study, we tested for the first time the potential of near-infrared reflectance spectroscopy (NIR) to estimate/predict isotope and elemental compositions for the origin verification of coffee. Green coffee samples from two continents, 4 countries, and 10 regions were analysed for five isotope ratios (δ13C, δ15N, δ18O, δ2H, and δ34S) and 41 trace elements. NIR (1100-2400 nm) calibrations were developed using pre-processing with extended multiplicative scatter correction (EMSC) and mean centering and partial-least squares regression (PLS-R). Five elements (Mn, Mo, Rb, B, La) and three isotope ratios (δ13C, δ18O, δ2H) were moderately to well predicted by NIR (R2: 0.69 to 0.93). NIR indirectly measured these parameters by association with organic compounds in coffee. These parameters were related to altitude, temperature and rainfall differences across countries and regions and were previously found to be origin discriminators for coffee.
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Affiliation(s)
- Joy Sim
- Department of Food Science, University of Otago, PO BOX 56, Dunedin 9054, New Zealand.
| | - Cushla McGoverin
- Department of Physics, University of Auckland, Auckland 1010, New Zealand; The Dodd-Walls Centre for Photonic and Quantum Technologies, Auckland 1010, New Zealand
| | - Indrawati Oey
- Department of Food Science, University of Otago, PO BOX 56, Dunedin 9054, New Zealand; Riddet Institute, Palmerston North, New Zealand
| | | | - Biniam Kebede
- Department of Food Science, University of Otago, PO BOX 56, Dunedin 9054, New Zealand.
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Ding B, Tao Y, Xie J, Zeng G, Huang H. Traceability Evaluation of Wild and Cultivated Cordyceps sinensis by Elemental Analysis and GasBench II Coupled to Stable Isotope Ratio Mass Spectrometry. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02433-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Gajek M, Pawlaczyk A, Maćkiewicz E, Albińska J, Wysocki P, Jóźwik K, Szynkowska-Jóźwik MI. Assessment of the Authenticity of Whisky Samples Based on the Multi-Elemental and Multivariate Analysis. Foods 2022; 11:foods11182810. [PMID: 36140938 PMCID: PMC9498178 DOI: 10.3390/foods11182810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 08/25/2022] [Accepted: 08/29/2022] [Indexed: 11/16/2022] Open
Abstract
Two hundred and five samples of whisky, including 170 authentic and 35 fake products, were analyzed in terms of their elemental profiles in order to distinguish them according to the parameter of their authenticity. The study of 31 elements (Ag, Al, B, Ba, Be, Bi, Cd, Co, Cr, Cu, Li, Mn, Mo, Ni, Pb, Sb, Sn, Sr, Te, Tl, U, V, Ca, Fe, K, Mg, P, S, Ti and Zn) was performed using the Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and Cold Vapor-Atomic Absorption (CVAAS) techniques. Additionally, the pH values of all samples were determined by pH-meter, and their isotopic ratios of 88Sr/86Sr, 84Sr/86Sr, 87Sr/86Sr and 63Cu/65Cu were assessed, based on the number of counts by ICP-MS. As a result of conducted research, elements, such as Mn, K, P and S, were identified as markers of whisky adulteration related to the age of alcohol. The concentrations of manganese, potassium and phosphorus were significantly lower in the fake samples (which were not aged, or the aging period was much shorter than legally required), compared to the original samples (in all cases subjected to the aging process). The observed differences were related to the migration of these elements from wooden barrels to the alcohol contained in them. On the other hand, the sulfur concentration in the processed samples was much higher in the counterfeit samples than in the authentic ones. The total sulfur content, such as that of alkyl sulfides, decreases in alcohol with aging in the barrels. Furthermore, counterfeit samples can be of variable origin and composition, so they cannot be characterized as one group with identical or comparable features. Repeatedly, the element of randomness dominates in the production of these kinds of alcohols. However, as indicated in this work, the extensive elemental analysis supported by statistical tools can be helpful, especially in the context of detecting age-related adulteration of whisky. The results presented in this paper are the final part of a comprehensive study on the influence of selected factors on the elemental composition of whisky.
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Affiliation(s)
- Magdalena Gajek
- Faculty of Chemistry, Institute of General and Ecological Chemistry, Lodz University of Technology, Zeromskiego 116, 90-924 Lodz, Poland
- Correspondence: ; Tel.: +48-42-631-30-95
| | - Aleksandra Pawlaczyk
- Faculty of Chemistry, Institute of General and Ecological Chemistry, Lodz University of Technology, Zeromskiego 116, 90-924 Lodz, Poland
| | - Elżbieta Maćkiewicz
- Faculty of Chemistry, Institute of General and Ecological Chemistry, Lodz University of Technology, Zeromskiego 116, 90-924 Lodz, Poland
| | - Jadwiga Albińska
- Faculty of Chemistry, Institute of General and Ecological Chemistry, Lodz University of Technology, Zeromskiego 116, 90-924 Lodz, Poland
| | - Piotr Wysocki
- Faculty of Chemistry, Institute of General and Ecological Chemistry, Lodz University of Technology, Zeromskiego 116, 90-924 Lodz, Poland
| | - Krzysztof Jóźwik
- Faculty of Mechanical Engineering, Institute of Turbomachinery, Lodz University of Technology, Wolczanska 219/223, 90-924 Lodz, Poland
| | - Małgorzata Iwona Szynkowska-Jóźwik
- Faculty of Chemistry, Institute of General and Ecological Chemistry, Lodz University of Technology, Zeromskiego 116, 90-924 Lodz, Poland
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Shuai M, Yang Y, Bai F, Cao L, Hou R, Peng C, Cai H. Geographical origin of American ginseng (Panax quinquefolius L.) based on chemical composition combined with chemometric. J Chromatogr A 2022; 1676:463284. [DOI: 10.1016/j.chroma.2022.463284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 06/20/2022] [Accepted: 06/24/2022] [Indexed: 11/16/2022]
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Wang Y, Kang L, Zhao Y, Xiong F, Yuan Y, Nie J, Huang L, Yang J. Stable isotope and multi-element profiling of Cassiae Semen tea combined with chemometrics for geographical discrimination. J Food Compost Anal 2022; 107:104359. [DOI: 10.1016/j.jfca.2021.104359] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Ren YF, Feng C, Ye ZH, Zhu HY, Hou RY, Granato D, Cai HM, Peng CY. Keemun black tea: Tracing its narrow-geographic origins using comprehensive elemental fingerprinting and chemometrics. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108614] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Saadat S, Pandya H, Dey A, Rawtani D. Food forensics: techniques for authenticity determination of food products. Forensic Sci Int 2022. [DOI: 10.1016/j.forsciint.2022.111243] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 12/21/2022]
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Gao F, Hao X, Zeng G, Guan L, Wu H, Zhang L, Wei R, Wang H, Li H. Identification of the geographical origin of Ecolly (Vitis vinifera L.) grapes and wines from different Chinese regions by ICP-MS coupled with chemometrics. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2021.104248] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Ghiasi S, Parastar H. Chemometrics-assisted isotope ratio fingerprinting based on gas chromatography/combustion/isotope ratio mass spectrometry for saffron authentication. J Chromatogr A 2021; 1657:462587. [PMID: 34628349 DOI: 10.1016/j.chroma.2021.462587] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 09/26/2021] [Accepted: 09/27/2021] [Indexed: 11/24/2022]
Abstract
In the present contribution, the capability of isotopic ratio mass spectrometry (IRMS) for saffron authentication and detection of four common plant-derived adulterants (marigold flower, safflower, rubia, and saffron style) was investigated. For this purpose, 62 authentic saffron samples were analyzed by elemental analyzer-IRMS (EA-IRMS) and gas chromatography-combustion-IRMS (GC-C-IRMS). In this regard, EA-IRMS and GC-C-IRMS isotope fingerprints of carbon-13 and nitrogen-15 isotopes of saffron components were provided and then analyzed by chemometric methods. Principal component analysis (PCA) showed two different behaviors regarding two main regions. Then, a representative saffron sample was provided to study adulteration. On this matter, binary mixtures of saffron and adulterants were prepared at five different weight percentages (5%, 10%, 15%, 25%, and 35%) and analyzed by EA-IRMS and GC-C-IRMS. Data-driven soft independent modeling of class analogy (DD-SIMCA) was used to model authentic saffron samples and find a boundary between authentic and adulterated samples with a sensitivity of 100% by GC-C-IRMS. After that, discriminant models of linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and partial least squares-discriminant analysis (PLS-DA) were tested to find the best discrimination line and also detection of the lowest level of adulterants. Among different models, the QDA model outperformed other methods and showed the ability to predict adulterants at 5% w/w level with 100% accuracy and precision. Finally, the developed QDA model was successfully used to discriminate a set of mixed samples of saffron and four adulterants as well as some commercial samples.
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Affiliation(s)
- SeyedAli Ghiasi
- Department of Chemistry, Sharif University of Technology, Tehran, Iran
| | - Hadi Parastar
- Department of Chemistry, Sharif University of Technology, Tehran, Iran.
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Pironti C, Ricciardi M, Motta O, Camin F, Bontempo L, Proto A. Application of 13C Quantitative NMR Spectroscopy to Isotopic Analyses for Vanillin Authentication Source. Foods 2021; 10:foods10112635. [PMID: 34828916 PMCID: PMC8625575 DOI: 10.3390/foods10112635] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 10/27/2021] [Accepted: 10/28/2021] [Indexed: 11/30/2022] Open
Abstract
The carbon stable isotope ratio (δ13C) is a valuable chemical parameter in the investigation of the geographic origin, quality, and authenticity of foods. The aim of this study is the evaluation of the feasibility of 13C-NMR (Nuclear Magnetic Resonance) spectroscopy to determine the carbon stable isotope ratio, at natural abundance, of small organic molecules, such as vanillin, without the use of IRMS (Isotope Ratio Mass Spectrometry). The determination of vanillin origin is an active task of research, and differentiating between its natural and artificial forms is important to guarantee the quality of food products. To reach our goal, nine vanillin samples were analyzed using both 13C quantitative NMR spectroscopy (under optimized experimental conditions) and IRMS, and the obtained δ13C values were compared using statistical analysis (linear regression, Bland–Altman plot, and ANOVA (analysis of variance)). The results of our study show that 13C-NMR spectroscopy can be used as a valuable alternative methodology to determine the bulk carbon isotope ratio and to identify the origin of vanillin. This makes it attractive for the analysis in the same experiment of site-specific and total isotope effects for testing authenticity, quality, and typicality of food samples. Moreover, the improvement of NMR spectroscopy makes it possible to avoid the influence of additives on carbon stable isotope ratio analysis and to clearly identify fraud and falsification in commercial samples.
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Affiliation(s)
- Concetta Pironti
- Department of Medicine and Surgery, University of Salerno, via S. Allende, 84081 Baronissi, SA, Italy; (C.P.); (M.R.)
| | - Maria Ricciardi
- Department of Medicine and Surgery, University of Salerno, via S. Allende, 84081 Baronissi, SA, Italy; (C.P.); (M.R.)
| | - Oriana Motta
- Department of Medicine and Surgery, University of Salerno, via S. Allende, 84081 Baronissi, SA, Italy; (C.P.); (M.R.)
- Correspondence: ; Tel.: +39-089963083
| | - Federica Camin
- Fondazione Edmund Mach, Research and Innovation Center, Food Quality and Nutrition Department, 38010 San Michele all’Adige, TN, Italy; (F.C.); (L.B.)
- Centre Agriculture Food Environment C3A, University of Trento, 38010 San Michele all’Adige, TN, Italy
- International Atomic Energy Agency, IAEA, International Centre, P.O. Box 100, A-1400 Vienna, Austria
| | - Luana Bontempo
- Fondazione Edmund Mach, Research and Innovation Center, Food Quality and Nutrition Department, 38010 San Michele all’Adige, TN, Italy; (F.C.); (L.B.)
| | - Antonio Proto
- Department of Chemistry and Biology, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, SA, Italy;
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Issaad FZ, Bouhedjar K, Ikhlef A, Lachlah H, Smain DH, Boutaghane K, Bensouici C. Multivariate analysis of physico-chemical, bioactive, microbial and spectral data characterisation of Algerian honey. Food Measure 2021; 15:3634-3648. [DOI: 10.1007/s11694-021-00946-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Núñez N, Pons J, Saurina J, Núñez O. Non-targeted high-performance liquid chromatography with ultraviolet and fluorescence detection fingerprinting for the classification, authentication, and fraud quantitation of instant coffee and chicory by multivariate chemometric methods. Lebensm Wiss Technol 2021; 147:111646. [DOI: 10.1016/j.lwt.2021.111646] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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Artavia G, Cortés-Herrera C, Granados-Chinchilla F. Selected Instrumental Techniques Applied in Food and Feed: Quality, Safety and Adulteration Analysis. Foods 2021; 10:1081. [PMID: 34068197 PMCID: PMC8152966 DOI: 10.3390/foods10051081] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 03/13/2021] [Accepted: 03/19/2021] [Indexed: 12/28/2022] Open
Abstract
This review presents an overall glance at selected instrumental analytical techniques and methods used in food analysis, focusing on their primary food science research applications. The methods described represent approaches that have already been developed or are currently being implemented in our laboratories. Some techniques are widespread and well known and hence we will focus only in very specific examples, whilst the relatively less common techniques applied in food science are covered in a wider fashion. We made a particular emphasis on the works published on this topic in the last five years. When appropriate, we referred the reader to specialized reports highlighting each technique's principle and focused on said technologies' applications in the food analysis field. Each example forwarded will consider the advantages and limitations of the application. Certain study cases will typify that several of the techniques mentioned are used simultaneously to resolve an issue, support novel data, or gather further information from the food sample.
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Affiliation(s)
- Graciela Artavia
- Centro Nacional de Ciencia y Tecnología de Alimentos, Sede Rodrigo Facio, Universidad de Costa Rica, San José 11501-2060, Costa Rica;
| | - Carolina Cortés-Herrera
- Centro Nacional de Ciencia y Tecnología de Alimentos, Sede Rodrigo Facio, Universidad de Costa Rica, San José 11501-2060, Costa Rica;
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da Silva Rosa Bonadiman B, Weis GCC, da Rosa JR, Assmann CE, de Oliveira Alves A, Longhi P, Bagatini MD. Effects of caffeic acid on oxidative balance and cancer. Cancer 2021. [DOI: 10.1016/b978-0-12-819547-5.00026-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Jiang H, Zhang M, Wang D, Yu F, Zhang N, Song C, Granato D. Analytical strategy coupled to chemometrics to differentiate Camellia sinensis tea types based on phenolic composition, alkaloids, and amino acids. J Food Sci 2020; 85:3253-3263. [PMID: 32856300 DOI: 10.1111/1750-3841.15390] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 06/11/2020] [Accepted: 07/06/2020] [Indexed: 11/26/2022]
Abstract
Catechins, amino acids, and alkaloids are primary chemical components of tea and play a crucial role in determining tea quality. Their composition and content largely vary among different types of tea. In this study, a convenient chemical classification method was developed for six Camellia sinensis tea types (white, green, oolong, black, dark, and yellow) based on the quantification of their major components. Twenty-one free amino acids, 6 catechins, 2 alkaloids, and gallic acid in 24 teas were quantified using ultra-high-performance liquid chromatography (UHPLC). The total catechin contents in these tea samples ranged from 10.96 to 95.67 mg/g, while total free amino acid content ranged from 2.63 to 25.89 mg/g. Theanine (Thea) was the most abundant amino acid in all tea varieties. Catechin and amino acid levels in tea were markedly reduced upon fermentation of tea. Furthermore, high-temperature processing (roasting) during tea production induced degradation and epimerization of catechins, yielding epimerized catechins, simple catechins, and gallic acid. Principal component analysis revealed that major ester-catechins (EGCG and ECG), major amino acids (Thea), and major alkaloids (caffeine) are potential factors for distinguishing different types of tea. Linear discriminant analysis showed that 100% of teas were correctly classified in which (+)-catechin, ECG, EGC, gallic acid, GABA, cysteine, lysine, and threonine were the most discriminating compounds. This study shows that quantification of the major tea components combined with chemometric analysis, can serve as a simple, convenient, and reliable approach for classifying tea according to fermentation level. PRACTICAL APPLICATION: Different Camellia sinensis tea types can be produced worldwide but it is still challenging to know which chemical markers can be used to trace their production. in this paper we used a targeted methodology to classify six tea types (white, green, oolong, black, dark, and yellow) based on phenolic composition, alkaloids, and amino acids. The main chemical markers responsible for the discrimination were pinpointed with the use of chemometric tools.
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Affiliation(s)
- Hao Jiang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, 130 West Changjiang Road, Hefei, 230036, China.,School of Tea and Food Science and Technology, Anhui Agricultural University, 130 West Changjiang Road, Hefei, 230036, China
| | - Mengting Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, 130 West Changjiang Road, Hefei, 230036, China.,School of Tea and Food Science and Technology, Anhui Agricultural University, 130 West Changjiang Road, Hefei, 230036, China
| | - Dongxu Wang
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang, 212003, China
| | - Feng Yu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, 130 West Changjiang Road, Hefei, 230036, China.,School of Tea and Food Science and Technology, Anhui Agricultural University, 130 West Changjiang Road, Hefei, 230036, China
| | - Na Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, 130 West Changjiang Road, Hefei, 230036, China.,School of Tea and Food Science and Technology, Anhui Agricultural University, 130 West Changjiang Road, Hefei, 230036, China
| | - Chuankui Song
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, 130 West Changjiang Road, Hefei, 230036, China.,School of Tea and Food Science and Technology, Anhui Agricultural University, 130 West Changjiang Road, Hefei, 230036, China
| | - Daniel Granato
- Food Processing and Quality, Natural Resources Institute Finland, Tietotie 2, Espoo, 02150, Finland
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Monteiro PI, Santos JS, Rodionova OY, Pomerantsev A, Chaves ES, Rosso ND, Granato D. Chemometric Authentication of Brazilian Coffees Based on Chemical Profiling. J Food Sci 2019; 84:3099-3108. [PMID: 31645089 DOI: 10.1111/1750-3841.14815] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 08/14/2019] [Accepted: 08/16/2019] [Indexed: 11/28/2022]
Abstract
In this work, different chemometric tools were compared to classify n = 26 conventional (CONV) and n = 19 organic (ORG) coffees from the main Brazilian producing regions based on the chemical composition, physicochemical properties, and antioxidant activity. Principal component analysis separated ORG and CONV coffees but the distinction among the producing regions of Brazilian coffee was not possible. Partial least squares discriminant analysis classified all ORG and CONV coffees in the external validation. Similarly, linear discriminant analysis was able to discriminate 100% and 81% of ORG and CONV coffees in the external validation, respectively, in which total phenolic content (TPC), ferric reducing antioxidant activity, and caffeic acid were the main discriminant variables. Overall 100% of samples from Paraná, Minas Gerais, and blended samples were correctly classified, where TPC, flavonoids, inhibition of lipid peroxidation, caffeic acid, pH, and soluble solids were the main discriminant variables. Support vector machines classified 95% ORG and 88% CONV, 100% Coffea arabica, and 88% and 78% coffees produced in São Paulo and Minas Gerais. k-Nearest neighbors was effective in distinguishing 100% CONV, 89% ORG, 100% coffees from São Paulo, and 100% C. arabica coffees. Overall, HPLC data and simple physicochemical parameters allied to chemometrics were effective in authenticating the cultivation system and the botanical origin of Brazilian coffees. PRACTICAL APPLICATION: Coffee adulteration is a serious problem in the food chain as some fraudsters replace coffee powder by other cheaper products. In the case of organic coffee, this scenario is even worse as still there is not a universal method to differentiate conventionally grown coffee from its organic counterpart. In addition, Brazilian coffee is produced in different regions and the commercial value varies. Therefore, we analyzed some physicochemical, chemical, and antioxidant properties of Brazilian coffees from distinct origins and classified the samples using chemometrics. Our approach seems to be interesting for quality control purposes.
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Affiliation(s)
- Pablo Inocêncio Monteiro
- Graduation Program in Food Science and Technology, State Univ. of Ponta Grossa, Ponta Grossa, Paraná, 84030-900, Brazil
| | - Jânio Sousa Santos
- Graduation Program in Food Science and Technology, State Univ. of Ponta Grossa, Ponta Grossa, Paraná, 84030-900, Brazil
| | - Oxana Ye Rodionova
- Semenov Inst. of Chemical Physics, Russian Academy of Sciences, Moscow, 119991, Russia.,Branch of Inst. of Natural and Technical Systems, Russian Academy of Sciences, Sochi, 354024, Russia
| | - Alexey Pomerantsev
- Semenov Inst. of Chemical Physics, Russian Academy of Sciences, Moscow, 119991, Russia.,Branch of Inst. of Natural and Technical Systems, Russian Academy of Sciences, Sochi, 354024, Russia
| | - Eduardo Sidinei Chaves
- Dept. of Chemistry, Federal Univ. of Santa Catarina, Florianópolis, Santa Catarina, 88040-900, Brazil
| | - Neiva Deliberali Rosso
- Graduation Program in Food Science and Technology, State Univ. of Ponta Grossa, Ponta Grossa, Paraná, 84030-900, Brazil
| | - Daniel Granato
- Graduation Program in Food Science and Technology, State Univ. of Ponta Grossa, Ponta Grossa, Paraná, 84030-900, Brazil.,Author Granato is also with Food Processing and Quality, Innovative Food System, Production Systems Unit-Natural Resources Inst. Finland (Luke), Espoo, FI-02150, Finland
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Gazeli O, Bellou E, Stefas D, Couris S. Laser-based classification of olive oils assisted by machine learning. Food Chem 2020; 302:125329. [PMID: 31404874 DOI: 10.1016/j.foodchem.2019.125329] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 07/18/2019] [Accepted: 08/04/2019] [Indexed: 12/25/2022]
Abstract
Olive oil is an essential diet component in all Mediterranean countries having a considerable impact on the local economies, which are producing almost 90% of the world production. Therefore, the quality assessment of olive oil in terms of its acidity and its authentication in terms of PDO (Protected Designation of Origin) and PGI (Protected Geographical Indications) characterizations are nowadays necessary and of great importance for the market of olive oil and the related economic activities. In the present work, Laser Induced Breakdown Spectroscopy (LIBS) is used assisted by machine learning algorithms for retrieving of the information contained in the LIBS spectra to provide a simple, reliable, and ultrafast methodology for olive oils classification in terms of the degree of acidity and geographical origin. The combination of LIBS technique with machine learning statistical analysis approaches constitute a very powerful tool for the fast, in-situ and remote quality control of olive oil.
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