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Hong Y, Birse N, Quinn B, Li Y, Jia W, McCarron P, Wu D, da Silva GR, Vanhaecke L, van Ruth S, Elliott CT. Data fusion and multivariate analysis for food authenticity analysis. Nat Commun 2023; 14:3309. [PMID: 37291121 PMCID: PMC10250487 DOI: 10.1038/s41467-023-38382-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 04/27/2023] [Indexed: 06/10/2023] Open
Abstract
A mid-level data fusion coupled with multivariate analysis approach is applied to dual-platform mass spectrometry data sets using Rapid Evaporative Ionization Mass Spectrometry and Inductively Coupled Plasma Mass Spectrometry to determine the correct classification of salmon origin and production methods. Salmon (n = 522) from five different regions and two production methods are used in the study. The method achieves a cross-validation classification accuracy of 100% and all test samples (n = 17) have their origins correctly determined, which is not possible with single-platform methods. Eighteen robust lipid markers and nine elemental markers are found, which provide robust evidence of the provenance of the salmon. Thus, we demonstrate that our mid-level data fusion - multivariate analysis strategy greatly improves the ability to correctly identify the geographical origin and production method of salmon, and this innovative approach can be applied to many other food authenticity applications.
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Affiliation(s)
- Yunhe Hong
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Nicholas Birse
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Brian Quinn
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Yicong Li
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Wenyang Jia
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Philip McCarron
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Di Wu
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Gonçalo Rosas da Silva
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Lynn Vanhaecke
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
- Laboratory of Integrative Metabolomics, Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Saskia van Ruth
- Food Quality and Design Group, Wageningen University and Research, Wageningen, The Netherlands
- School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland
| | - Christopher T Elliott
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom.
- School of Food Science and Technology, Faculty of Science and Technology, Thammasat University, 99 Mhu 18, Pahonyothin Road, Khong Luang, Pathum Thani, 12120, Thailand.
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Xia Z, Che X, Ye L, Zhao N, Guo D, Peng Y, Lin Y, Liu X. A Synergetic Strategy for Brand Characterization of Colla Corii Asini (Ejiao) by LIBS and NIR Combined with Partial Least Squares Discriminant Analysis. Molecules 2023; 28:molecules28041778. [PMID: 36838765 PMCID: PMC9965801 DOI: 10.3390/molecules28041778] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 02/02/2023] [Accepted: 02/07/2023] [Indexed: 02/16/2023] Open
Abstract
A synergetic strategy was proposed to address the critical issue in the brand characterization of Colla corii asini (Ejiao, CCA), a precious traditional Chinese medicine (TCM). In all brands of CCA, Dong'e Ejiao (DEEJ) is an intangible cultural heritage resource. Seventy-eight CCA samples (including forty DEEJ samples and thirty-eight samples from other different manufacturers) were detected by laser-induced breakdown spectroscopy (LIBS) and near-infrared spectroscopy (NIR). Partial least squares discriminant analysis (PLS-DA) models were built first considering individual techniques separately, and then fusing LIBS and NIR data at low-level. The statistical parameters including classification accuracy, sensitivity, and specificity were calculated to evaluate the PLS-DA model performance. The results demonstrated that two individual techniques show good classification performance, especially the NIR. The PLS-DA model with single NIR spectra pretreated by the multiplicative scatter correction (MSC) method was preferred as excellent discrimination. Though individual spectroscopic data obtained good classification performance. A data fusion strategy was also attempted to merge atomic and molecular information of CCA. Compared to a single data block, data fusion models with SNV and MSC pretreatment exhibited good predictive power with no misclassification. This study may provide a novel perspective to employ a comprehensive analytical approach to brand discrimination of CCA. The synergetic strategy based on LIBS together with NIR offers atomic and molecular information of CCA, which could be exemplary for future research on the rapid discrimination of TCM.
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Affiliation(s)
- Ziyi Xia
- College of Integrated Traditional Chinese and Western Medicine, Binzhou Medical University, Yantai 264003, China
| | - Xiaoqing Che
- Shandong Runzhong Pharmaceutical Co., Ltd., Yantai 256603, China
| | - Lei Ye
- College of Integrated Traditional Chinese and Western Medicine, Binzhou Medical University, Yantai 264003, China
| | - Na Zhao
- Key Laboratory of Xinjiang Phytomedicine Resources and Utilization in Ministry of Education, School of Pharmacy, Shihezi University, Shihezi 832002, China
| | - Dongxiao Guo
- Shandong Institute of Food and Drug Inspection, Jinan 250101, China
| | - Yanfang Peng
- Pharmacy Faculty, Hubei University of Chinese Medicine, Wuhan 430065, China
| | - Yongqiang Lin
- Shandong Institute of Food and Drug Inspection, Jinan 250101, China
- Correspondence: (Y.L.); (X.L.)
| | - Xiaona Liu
- College of Integrated Traditional Chinese and Western Medicine, Binzhou Medical University, Yantai 264003, China
- Correspondence: (Y.L.); (X.L.)
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3
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Brar DS, Pant K, Krishna R, Kaur S, Rasane P, Nanda V, Saxena S, Gautam S. A comprehensive review on unethical honey: Validation by emerging techniques. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Wang X, Li Y, Chen L, Zhou J. Analytical Strategies for LC-MS-Based Untargeted and Targeted Metabolomics Approaches Reveal the Entomological Origins of Honey. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:1358-1366. [PMID: 35023735 DOI: 10.1021/acs.jafc.1c07153] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
A comprehensive liquid chromatography-mass spectrometry (LC-MS)-based metabolomics approach was developed to discriminate honey harvested from Apis mellifera ligustica Spinola (A. mellifera) and Apis cerana cerana Fabricius (A. cerana). Based on an untargeted strategy, ultrahigh-performance liquid chromatography electrospray ionization quadrupole orbitrap high-resolution mass spectrometry (UPLC Q-Orbitrap) was combined with chemometrics techniques to screen and identify tentative markers from A. mellifera and A. cerana honey. In targeted metabolomics analysis, a sensitive method of solid-phase extraction followed by ultrahigh-performance liquid chromatography coupled with triple quadrupole tandem mass spectrometry (UPLC-MS/MS) was established for quantifying three markers, and the results showed that 3-amino-2-naphthoic acid and methyl indole-3-acetate could be considered markers of A. cerana honey, as they were present in higher amounts in A. cerana honey than in A. mellifera honey, whereas kynurenic acid was determined to be a marker of A. mellifera honey. This work highlights critical information for the authentication of A. cerana and A. mellifera honey.
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Affiliation(s)
- Xinran Wang
- Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, P. R. China
| | - Yi Li
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, P. R. China
| | - Lanzhen Chen
- Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, P. R. China
- Laboratory of Risk Assessment for Quality and Safety of Bee Products, Ministry of Agriculture and Rural Affairs, Beijing 100093, P. R. China
- Key Laboratory of Bee Products for Quality and Safety Control, Ministry of Agriculture and Rural Affairs, Beijing 100093, P. R. China
| | - Jinhui Zhou
- Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, P. R. China
- Laboratory of Risk Assessment for Quality and Safety of Bee Products, Ministry of Agriculture and Rural Affairs, Beijing 100093, P. R. China
- Key Laboratory of Bee Products for Quality and Safety Control, Ministry of Agriculture and Rural Affairs, Beijing 100093, P. R. China
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Abraham EJ, Kellogg JJ. Chemometric-Guided Approaches for Profiling and Authenticating Botanical Materials. Front Nutr 2021; 8:780228. [PMID: 34901127 PMCID: PMC8663772 DOI: 10.3389/fnut.2021.780228] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 10/31/2021] [Indexed: 01/08/2023] Open
Abstract
Botanical supplements with broad traditional and medicinal uses represent an area of growing importance for American health management; 25% of U.S. adults use dietary supplements daily and collectively spent over $9. 5 billion in 2019 in herbal and botanical supplements alone. To understand how natural products benefit human health and determine potential safety concerns, careful in vitro, in vivo, and clinical studies are required. However, botanicals are innately complex systems, with complicated compositions that defy many standard analytical approaches and fluctuate based upon a plethora of factors, including genetics, growth conditions, and harvesting/processing procedures. Robust studies rely upon accurate identification of the plant material, and botanicals' increasing economic and health importance demand reproducible sourcing, as well as assessment of contamination or adulteration. These quality control needs for botanical products remain a significant problem plaguing researchers in academia as well as the supplement industry, thus posing a risk to consumers and possibly rendering clinical data irreproducible and/or irrelevant. Chemometric approaches that analyze the small molecule composition of materials provide a reliable and high-throughput avenue for botanical authentication. This review emphasizes the need for consistent material and provides insight into the roles of various modern chemometric analyses in evaluating and authenticating botanicals, focusing on advanced methodologies, including targeted and untargeted metabolite analysis, as well as the role of multivariate statistical modeling and machine learning in phytochemical characterization. Furthermore, we will discuss how chemometric approaches can be integrated with orthogonal techniques to provide a more robust approach to authentication, and provide directions for future research.
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Affiliation(s)
- Evelyn J Abraham
- Intercollege Graduate Degree Program in Plant Biology, The Pennsylvania State University (PSU), University Park, PA, United States
| | - Joshua J Kellogg
- Intercollege Graduate Degree Program in Plant Biology, The Pennsylvania State University (PSU), University Park, PA, United States.,Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA, United States
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Rivera-Pérez A, Romero-González R, Garrido Frenich A. Application of an innovative metabolomics approach to discriminate geographical origin and processing of black pepper by untargeted UHPLC-Q-Orbitrap-HRMS analysis and mid-level data fusion. Food Res Int 2021; 150:110722. [PMID: 34865751 DOI: 10.1016/j.foodres.2021.110722] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/10/2021] [Accepted: 09/22/2021] [Indexed: 12/16/2022]
Abstract
An untargeted metabolomics approach based on ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) fingerprinting was applied to investigate the metabolic differences of black pepper among three geographical origins (Sri Lanka, Vietnam, and Brazil) and two post-harvest processing (sterilized and non-sterilized spice). Principal component analysis (PCA) was employed to assess the overall clustering of samples, whereas supervised orthogonal partial least squares discriminant analysis (OPLS-DA) was effectively used for discrimination purposes. OPLS-DA models were fully validated (R2Y and Q2 values > 0.5) and the variable importance in projection (VIP) approach was employed to provide valuable data about differential metabolites with high discrimination potential (8 markers were putatively identified). For origin differentiation, three markers were highlighted with VIP values > 1.5 (i.e. reynosin, artabsinolide D, and tatridin B). Fatty acid derivates were the most frequent markers within the metabolites annotated for processing discrimination (e.g. 10,16-dihydroxyhexadecanoic acid and 9-hydroperoxy-10E-octadecenoic acid). Additionally, different combinations of mid-level data fusion of chromatographic-mass spectrometric techniques (UHPLC and gas chromatography coupled to HRMS) and proton nuclear magnetic resonance spectroscopy (1H NMR) were evaluated for the first time for geographical and processing discrimination of black pepper. The NMR-UHPLC-GC mid-level fused model was preferred among the tested fusion approaches since good sample clustering and no misclassification were achieved. Enhanced correct classification rate was achieved by mid-level data fusion compared with the findings obtained for one of the individual techniques (1H NMR fingerprinting) (from 92% to 100% of samples correctly classified). This study opens the path to new metabolomics approaches for black pepper authentication and quality control.
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Affiliation(s)
- Araceli Rivera-Pérez
- Research Group "Analytical Chemistry of Contaminants", Department of Chemistry and Physics, Research Centre for Mediterranean Intensive Agrosystems and Agrifood Biotechnology (CIAIMBITAL), Agrifood Campus of International Excellence (ceiA3), University of Almeria, E-04120 Almeria, Spain.
| | - Roberto Romero-González
- Research Group "Analytical Chemistry of Contaminants", Department of Chemistry and Physics, Research Centre for Mediterranean Intensive Agrosystems and Agrifood Biotechnology (CIAIMBITAL), Agrifood Campus of International Excellence (ceiA3), University of Almeria, E-04120 Almeria, Spain.
| | - Antonia Garrido Frenich
- Research Group "Analytical Chemistry of Contaminants", Department of Chemistry and Physics, Research Centre for Mediterranean Intensive Agrosystems and Agrifood Biotechnology (CIAIMBITAL), Agrifood Campus of International Excellence (ceiA3), University of Almeria, E-04120 Almeria, Spain.
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Liu Z, Yang S, Wang Y, Zhang J. Multi-platform integration based on NIR and UV-Vis spectroscopies for the geographical traceability of the fruits of Amomum tsao-ko. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 258:119872. [PMID: 33957443 DOI: 10.1016/j.saa.2021.119872] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/01/2021] [Accepted: 04/21/2021] [Indexed: 06/12/2023]
Abstract
Due to the world-wide concern relating to herb quality and safety, there is a momentum to authenticate the geographical origin of herb with multi-platform techniques. This study attempted to assess multi-platform information as a practical strategy for the geographical traceability of the fruits of Amomum tsao-ko. To this aim, one hundred and eighty dried fruits of A. tsao-ko from five geographical regions were analyzed by near infrared (NIR) and ultraviolet visible (UV-Vis) spectroscopy. On this basis, two variable dimension reduction strategies, including principal component analysis (PCA) and sequential and orthogonalized partial-least squares (SO-PLS), and two variables selection strategies, including variable importance in projection (VIP) and sequential and orthogonalized covariance selection (SO-CovSel), were performed to extract the feature information in the two blocks. Partial least squares discriminant analysis (PLS-DA) classification algorithm combined with fused matrices was used to identify the geographical origins. The results of PLS-DA models indicated that SO-PLS and SO-CovSel, taking advantage of the sequential modeling coupled to orthogonalization, could not only identify the common information presented in the two blocks but also provide more concise methods without any loss of classification ability, which could be employed in authenticating the geographical regions of the fruits of A. tsao-ko, effectively.
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Affiliation(s)
- Zhimin Liu
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China; School of Agriculture, Yunnan University, Kunming 650500, China
| | - Shaobing Yang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
| | - Jinyu Zhang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China; School of Agriculture, Yunnan University, Kunming 650500, China.
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Koulis GA, Tsagkaris AS, Aalizadeh R, Dasenaki ME, Panagopoulou EI, Drivelos S, Halagarda M, Georgiou CA, Proestos C, Thomaidis NS. Honey Phenolic Compound Profiling and Authenticity Assessment Using HRMS Targeted and Untargeted Metabolomics. Molecules 2021; 26:2769. [PMID: 34066694 PMCID: PMC8125859 DOI: 10.3390/molecules26092769] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 05/04/2021] [Accepted: 05/06/2021] [Indexed: 11/16/2022] Open
Abstract
Honey consumption is attributed to potentially advantageous effects on human health due to its antioxidant capacity as well as anti-inflammatory and antimicrobial activity, which are mainly related to phenolic compound content. Phenolic compounds are secondary metabolites of plants, and their content in honey is primarily affected by the botanical and geographical origin. In this study, a high-resolution mass spectrometry (HRMS) method was applied to determine the phenolic profile of various honey matrices and investigate authenticity markers. A fruitful sample set was collected, including honey from 10 different botanical sources (n = 51) originating from Greece and Poland. Generic liquid-liquid extraction using ethyl acetate as the extractant was used to apply targeted and non-targeted workflows simultaneously. The method was fully validated according to the Eurachem guidelines, and it demonstrated high accuracy, precision, and sensitivity resulting in the detection of 11 target analytes in the samples. Suspect screening identified 16 bioactive compounds in at least one sample, with abscisic acid isomers being the most abundant in arbutus honey. Importantly, 10 markers related to honey geographical origin were revealed through non-targeted screening and the application of advanced chemometric tools. In conclusion, authenticity markers and discrimination patterns were emerged using targeted and non-targeted workflows, indicating the impact of this study on food authenticity and metabolomic fields.
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Affiliation(s)
- Georgios A. Koulis
- Analytical Chemistry Laboratory, Chemistry Department, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771 Athens, Greece; (G.A.K.); (A.S.T.); (R.A.); (E.I.P.); (N.S.T.)
- Food Chemistry Laboratory, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771 Athens, Greece;
| | - Aristeidis S. Tsagkaris
- Analytical Chemistry Laboratory, Chemistry Department, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771 Athens, Greece; (G.A.K.); (A.S.T.); (R.A.); (E.I.P.); (N.S.T.)
- Department of Food Analysis and Nutrition, Faculty of Food and Biochemical Technology, University of Chemistry and Technology Prague, Technická 5, Prague 6—Dejvice, 16628 Prague, Czech Republic
| | - Reza Aalizadeh
- Analytical Chemistry Laboratory, Chemistry Department, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771 Athens, Greece; (G.A.K.); (A.S.T.); (R.A.); (E.I.P.); (N.S.T.)
| | - Marilena E. Dasenaki
- Analytical Chemistry Laboratory, Chemistry Department, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771 Athens, Greece; (G.A.K.); (A.S.T.); (R.A.); (E.I.P.); (N.S.T.)
- Food Chemistry Laboratory, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771 Athens, Greece;
| | - Eleni I. Panagopoulou
- Analytical Chemistry Laboratory, Chemistry Department, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771 Athens, Greece; (G.A.K.); (A.S.T.); (R.A.); (E.I.P.); (N.S.T.)
| | - Spyros Drivelos
- Chemistry Laboratory, Department of Food Science and Human Nutrition, Agricultural University of Athens, 75 Iera Odos, 11855 Athens, Greece; (S.D.); (C.A.G.)
| | - Michał Halagarda
- Department of Food Product Quality, Cracow University of Economics, ul. Sienkiewicza 5, 30033 Krakow, Poland;
| | - Constantinos A. Georgiou
- Chemistry Laboratory, Department of Food Science and Human Nutrition, Agricultural University of Athens, 75 Iera Odos, 11855 Athens, Greece; (S.D.); (C.A.G.)
| | - Charalampos Proestos
- Food Chemistry Laboratory, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771 Athens, Greece;
| | - Nikolaos S. Thomaidis
- Analytical Chemistry Laboratory, Chemistry Department, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771 Athens, Greece; (G.A.K.); (A.S.T.); (R.A.); (E.I.P.); (N.S.T.)
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Data processing strategies for non-targeted analysis of foods using liquid chromatography/high-resolution mass spectrometry. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116188] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Schmidt C, Eichelberger K, Rohm H. New Zealand mānuka honey - A review on specific properties and possibilities to distinguish mānuka from kānuka honey. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2020.110311] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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Zhang J, Chen H, Fan C, Gao S, Zhang Z, Bo L. Classification of the botanical and geographical origins of Chinese honey based on 1H NMR profile with chemometrics. Food Res Int 2020; 137:109714. [PMID: 33233286 DOI: 10.1016/j.foodres.2020.109714] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 09/07/2020] [Accepted: 09/07/2020] [Indexed: 12/21/2022]
Abstract
In this paper, we report a newly developed non-target 1H NMR detection associated with chemometrics method to classify the botanical and geographical origins of the monofloral Chinese honey. 1H NMR tests of 218 monofloral honey samples of 8 classes (Acacia, Jujube, Linden, Longan, Orange, Rape, Sunflower, Vitex) collected in 2017-2019 across China were conducted under the optimal sample preparation conditions and NMR acquisition parameters. The whole profiles of NMR spectra instead of individual or partial signals from specific components were processed and extracted, then fed to SIMCA-P to classify the botanical and geographical origins through non-target statistical analysis. For the botanical origins, most of them could be classified clearly according to Principal Component Analysis (PCA) with both R2 and Q2 close to 1. Orthogonal Partial Least Squares Discrimination Analysis (OPLS-DA) model could classify the honey floral types successfully with R2Y and Q2 greater than 0.85. It is found that the integral bin for data extraction has no obvious influence on the classification. For the geographical origins, the classification at different geographical levels (providence and town) could be successfully distinguished by OPLS-DA model. The promising preliminary results with the geographical classification at 40 km level unambiguously demonstrate the application of this NMR-based multi-species non-targeted method for the honey authenticity. Successful result is obtained on a pilot prediction of the geographical classification. Comparing with the methods based on other techniques, the advantages of this reported one are less sample amount needed, simple preparation, short test time, and non-targeted multi-species detection.
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Affiliation(s)
- Jialin Zhang
- Agro-product Safety Research Center, Chinese Academy of Inspection and Quarantine, China
| | - Hui Chen
- Agro-product Safety Research Center, Chinese Academy of Inspection and Quarantine, China
| | - Chunlin Fan
- Agro-product Safety Research Center, Chinese Academy of Inspection and Quarantine, China
| | - Shuai Gao
- Agro-product Safety Research Center, Chinese Academy of Inspection and Quarantine, China
| | - Zijuan Zhang
- Agro-product Safety Research Center, Chinese Academy of Inspection and Quarantine, China.
| | - Lin Bo
- Agro-product Safety Research Center, Chinese Academy of Inspection and Quarantine, China
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Letertre MPM, Dervilly G, Giraudeau P. Combined Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry Approaches for Metabolomics. Anal Chem 2020; 93:500-518. [PMID: 33155816 DOI: 10.1021/acs.analchem.0c04371] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Fakhlaei R, Selamat J, Khatib A, Razis AFA, Sukor R, Ahmad S, Babadi AA. The Toxic Impact of Honey Adulteration: A Review. Foods 2020; 9:E1538. [PMID: 33114468 PMCID: PMC7692231 DOI: 10.3390/foods9111538] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/10/2020] [Accepted: 09/11/2020] [Indexed: 12/11/2022] Open
Abstract
Honey is characterized as a natural and raw foodstuff that can be consumed not only as a sweetener but also as medicine due to its therapeutic impact on human health. It is prone to adulterants caused by humans that manipulate the quality of honey. Although honey consumption has remarkably increased in the last few years all around the world, the safety of honey is not assessed and monitored regularly. Since the number of consumers of honey adulteration have increased in recent years, their trust and interest in this valuable product has decreased. Honey adulterants are any substances that are added to the pure honey. In this regard, this paper provides a comprehensive and critical review of the different types of adulteration, common sugar adulterants and detection methods, and draws a clear perspective toward the impact of honey adulteration on human health. Adulteration increases the consumer's blood sugar, which can cause diabetes, abdominal weight gain, and obesity, raise the level of blood lipids and can cause high blood pressure. The most common organ affected by honey adulterants is the liver followed by the kidney, heart, and brain, as shown in several in vivo research designs.
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Affiliation(s)
- Rafieh Fakhlaei
- Food Safety and Food Integrity (FOSFI), Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia;
| | - Jinap Selamat
- Food Safety and Food Integrity (FOSFI), Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia;
- Department of Food Science, Faculty of Food Science and Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; (A.F.A.R.); (R.S.)
| | - Alfi Khatib
- Pharmacognosy Research Group, Department of Pharmaceutical Chemistry, Kulliyyah of Pharmacy, International Islamic University Malaysia, Kuantan 25200, Pahang Darul Makmur, Malaysia;
- Faculty of Pharmacy, Airlangga University, Surabaya 60155, Indonesia
| | - Ahmad Faizal Abdull Razis
- Department of Food Science, Faculty of Food Science and Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; (A.F.A.R.); (R.S.)
- Natural Medicines and Products Research Laboratory, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
| | - Rashidah Sukor
- Department of Food Science, Faculty of Food Science and Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; (A.F.A.R.); (R.S.)
| | - Syahida Ahmad
- Department of Biochemistry, Faculty of Biotechnology & Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia;
| | - Arman Amani Babadi
- School of Energy and Power Engineering, Jiangsu University, Zhenjiang 212013, China;
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Choudhury R, Beezley J, Davis B, Tomeck J, Gratzl S, Golzarri-Arroyo L, Wan J, Raftery D, Baumes J, O'Connell TM. Viime: Visualization and Integration of Metabolomics Experiments. JOURNAL OF OPEN SOURCE SOFTWARE 2020; 5:2410. [PMID: 33768193 PMCID: PMC7990241 DOI: 10.21105/joss.02410] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Metabolomics involves the comprehensive measurement of metabolites from a biological system. The resulting metabolite profiles are influenced by genetics, lifestyle, biological stresses, disease, diet and the environment and therefore provides a more holistic biological readout of the pathological condition of the organism (Beger et al., 2016; Wishart, 2016). The challenge for metabolomics is that no single analytical platform can provide a truly comprehensive coverage of the metabolome. The most commonly used platforms are based on mass-spectrometry (MS) and nuclear magnetic resonance (NMR). Investigators are increasingly using both methods to increase the metabolite coverage. The challenge for this type of multi-platform approach is that the data structure may be very different in these two platforms. For example, NMR data may be reported as a list of spectral features, e.g., bins or peaks with arbitrary intensity units or more directly with named metabolites reported in concentration units ranging from micromolar to millimolar. Some MS approaches can also provide data in the form of identified metabolite concentrations, but given the superior sensitivity of MS, the concentrations can be several orders of magnitude lower than for NMR. Other MS approaches yield data in the form of arbitrary response units where the dynamic range can be more than 6 orders of magnitude. Importantly, the variability and reproducibility of the data may differ across platforms. Given the diversity of data structures (i.e., magnitude and dynamic range) integrating the data from multiple platforms can be challenging. This often leads investigators to analyze the datasets separately, which prevents the observation of potentially interesting relationships and correlations between metabolites detected on different platforms. Viime (VIsualization and Integration of Metabolomics Experiments) is an open-source, web-based application designed to integrate metabolomics data from multiple platforms. The workflow of Viime for data integration and visualization is shown in Figure 1.
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Affiliation(s)
| | | | | | | | | | - Lilian Golzarri-Arroyo
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health
| | - Jun Wan
- Department of Medical and Molecular Genetics, Indiana University School of Medicine
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine
- Department of BioHealth Informatics, Indiana University School of Informatics and Computing
| | - Daniel Raftery
- Department of Anesthesiology and Pain Medicine, University of Washington
| | | | - Thomas M O'Connell
- Department of Otolaryngology-Head and Neck Surgery, Indiana University School of Medicine
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16
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Adamchuk L, Sukhenko V, Akulonok O, Bilotserkivets T, Vyshniak V, Lisohurska D, Lisohurska O, Slobodyanyuk N, Shanina O, Galyasnyj I. Methods for determining the botanical origin of honey. POTRAVINARSTVO 2020. [DOI: 10.5219/1386] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The demand for monofloral, original, and special (functional) kinds of honey, or those with geographical indication, is forecast. At the same time, there is a need to improve the methods for determining the botanical and geographical origin of honey. The purpose of the research was to select and apply a variety of techniques for identifying the botanical origin of honey for its correspondence to acacia species. Samples of honey from the Kyiv, Odesa, and Dnipro regions extracted in the spring and summer period were used in the research. Organoleptic, physicochemical, NMR spectrometry, and advanced melissopalynology methods were applied. The tests were carried out at the laboratories of the Department of Certification and Standardization of Agricultural Products, NULES, Ukraine; the Ukrainian Laboratory of Quality and Safety of Agricultural Products; and the Bruker BioSpin GmbH company (Germany). According to the research results, the requirements for acacia honey were met by the organoleptic method for samples B1 and B2; by the physicochemical method for A0 and A2; by NMR spectroscopy for not a single sample, all being assessed as polyfloral; and by pollen analysis for B1 and B2. The conducted studies confirm the need for a comprehensive approach to the identification of the botanical origin of honey for its conformity to acacia species. There is a need to review the physicochemical indicators for the compliance of honey with the acacia species obtained in Ukraine. After all, even the modern NMR spectrometry technique indicated that the specially fabricated sample that did not contain acacia pollen grains was acacia honey. Identification of the botanical origin of monofloral honey, in particular acacia, should be carried out in the following sequence: pollen analysis (by dominant pollen grains), safety (presence of antibiotics, pesticides), physicochemical parameters according to international requirements, organoleptic parameters.
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17
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Gamela RR, Costa VC, Sperança MA, Pereira-Filho ER. Laser-induced breakdown spectroscopy (LIBS) and wavelength dispersive X-ray fluorescence (WDXRF) data fusion to predict the concentration of K, Mg and P in bean seed samples. Food Res Int 2020; 132:109037. [DOI: 10.1016/j.foodres.2020.109037] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 01/23/2020] [Accepted: 01/25/2020] [Indexed: 12/23/2022]
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18
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Wang CY, Tang L, Li L, Zhou Q, Li YJ, Li J, Wang YZ. Geographic Authentication of Eucommia ulmoides Leaves Using Multivariate Analysis and Preliminary Study on the Compositional Response to Environment. FRONTIERS IN PLANT SCIENCE 2020; 11:79. [PMID: 32140161 PMCID: PMC7042207 DOI: 10.3389/fpls.2020.00079] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 01/21/2020] [Indexed: 05/03/2023]
Abstract
To explore the influences of different cultivated areas on the chemical profiles of Eucommia ulmoides leaves (EUL) and rapidly authenticate its geographical origins, 187 samples from 13 provinces in China were systematically investigated using three data fusion strategies (low, mid, and high level) combined with two discrimination model algorithms (partial least squares discrimination analysis; random forest, RF). RF models constructed by high-level data fusion with different modes of different spectral data (Fourier transform near-infrared spectrum and attenuated total reflection Fourier transform mid-infrared spectrum) were most suitable for identifying EULs from different geographical origins. The accuracy rates of calibration and validation set were 92.86% and 93.44%, respectively. In addition, climate parameters were systematically investigated the cluster difference in our study. Some interesting and novel information could be found from the clustering tree diagram of hierarchical cluster analysis. The Xinjiang Autonomous Region (Region 5) located in the high latitude area was the only region in the middle temperate zone of all sample collection areas in which the samples belonged to an individual class no matter their distance in the tree diagram. The samples were from a relatively high elevation in the Shennongjia Forest District in Hubei Province (>1200 m), which is the main difference from the samples from Xiangyang City (78 m). Thus, the sample clusters from region 9 are different from the sample clusters from other regions. The results would provide a reference for further research to those samples from the special cluster.
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Affiliation(s)
- Chao-Yong Wang
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- College of Biological Resources and Environmental Sciences, Jishou University, Jishou, China
| | - Li Tang
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- College of A & F Science and Technology, Hunan Applied Technology University, Changde, China
| | - Li Li
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- College of Biological Resources and Environmental Sciences, Jishou University, Jishou, China
| | - Qiang Zhou
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- College of Biological Resources and Environmental Sciences, Jishou University, Jishou, China
| | - You-Ji Li
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- College of Chemistry and Chemical Engineering, Jishou University, Jishou, China
| | - Jing Li
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- College of Biological Resources and Environmental Sciences, Jishou University, Jishou, China
- *Correspondence: Jing Li, ; Yuan-Zhong Wang,
| | - Yuan-Zhong Wang
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming, China
- *Correspondence: Jing Li, ; Yuan-Zhong Wang,
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19
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Bodor Z, Benedek C, Kaszab T, Zaukuu JLZ, Kertész I, Kovacs Z. Classical and correlative analytical methods for origin identification of Hungarian honeys. ACTA ALIMENTARIA 2019. [DOI: 10.1556/066.2019.48.4.9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Zs. Bodor
- Department of Physics and Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Somlói út 14–16. Hungary
- Department of Dietetics and Nutrition, Faculty of Health Sciences, Semmelweis University, H-1088 Budapest, Vas utca 17. Hungary
| | - Cs. Benedek
- Department of Dietetics and Nutrition, Faculty of Health Sciences, Semmelweis University, H-1088 Budapest, Vas utca 17. Hungary
| | - T. Kaszab
- Department of Physics and Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Somlói út 14–16. Hungary
| | - J.-L. Zinia Zaukuu
- Department of Physics and Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Somlói út 14–16. Hungary
| | - I. Kertész
- Department of Physics and Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Somlói út 14–16. Hungary
| | - Z. Kovacs
- Department of Physics and Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Somlói út 14–16. Hungary
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21
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Wang X, Yang S, He J, Chen L, Zhang J, Jin Y, Zhou J, Zhang Y. A green triple-locked strategy based on volatile-compound imaging, chemometrics, and markers to discriminate winter honey and sapium honey using headspace gas chromatography-ion mobility spectrometry. Food Res Int 2019; 119:960-967. [PMID: 30884736 DOI: 10.1016/j.foodres.2019.01.004] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 01/04/2019] [Accepted: 01/04/2019] [Indexed: 01/18/2023]
Abstract
A simple and environmentally approach using untargeted imaging of volatile substances combined with chemometrics and markers response was proposed for discriminating different species of honey with headspace gas-chromatography-ion-mobility (HS-GC-IMS). The 3D HS-GC-IMS imaging and their response differences enabled the clear discrimination between winter honey and sapium honey. Principal component analysis (PCA) and partial least-squares discrimination analysis (PLS-DA) were employed to discriminate different honey. Markers of two kinds of honey were identified and confirmed with a user-built imaging database combined with multivariate analysis. Benzaldehyde dimer and phenylacetaldehyde dimer were found to be reliable markers of winter honey, and phenylethyl acetate dimer was of sapium honey. Adulteration identification of the honey samples with different adulteration ratios were subjected to this triple-locked strategy analysis. The results demonstrate that HS-GC-IMS imaging coupled with chemometrics and marker identification is a useful triple-locked strategy to discriminate honey from different floral origins and adulterated honey.
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Affiliation(s)
- Xinran Wang
- Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, PR China
| | - Shupeng Yang
- Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, PR China; Laboratory of Risk Assessment for Quality and Safety of Bee Products, Ministry of Agriculture, Beijing 100093, PR China; Key Laboratory of Bee Products for Quality and Safety Control, Ministry of Agriculture, Beijing 100093, PR China; Bee Product Quality Supervision and Testing Centre, Ministry of Agriculture, Beijing 100093, PR China
| | - Jinna He
- Shandong Hanon Scientific Instruments Co., Ltd, Jinan 250101, PR China
| | - Lanzhen Chen
- Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, PR China; Laboratory of Risk Assessment for Quality and Safety of Bee Products, Ministry of Agriculture, Beijing 100093, PR China; Key Laboratory of Bee Products for Quality and Safety Control, Ministry of Agriculture, Beijing 100093, PR China; Bee Product Quality Supervision and Testing Centre, Ministry of Agriculture, Beijing 100093, PR China
| | - Jinzhen Zhang
- Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, PR China; Laboratory of Risk Assessment for Quality and Safety of Bee Products, Ministry of Agriculture, Beijing 100093, PR China; Key Laboratory of Bee Products for Quality and Safety Control, Ministry of Agriculture, Beijing 100093, PR China; Bee Product Quality Supervision and Testing Centre, Ministry of Agriculture, Beijing 100093, PR China
| | - Yue Jin
- Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, PR China; Laboratory of Risk Assessment for Quality and Safety of Bee Products, Ministry of Agriculture, Beijing 100093, PR China; Key Laboratory of Bee Products for Quality and Safety Control, Ministry of Agriculture, Beijing 100093, PR China; Bee Product Quality Supervision and Testing Centre, Ministry of Agriculture, Beijing 100093, PR China
| | - Jinhui Zhou
- Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, PR China; Laboratory of Risk Assessment for Quality and Safety of Bee Products, Ministry of Agriculture, Beijing 100093, PR China; Key Laboratory of Bee Products for Quality and Safety Control, Ministry of Agriculture, Beijing 100093, PR China; Bee Product Quality Supervision and Testing Centre, Ministry of Agriculture, Beijing 100093, PR China.
| | - Yongxin Zhang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, PR China.
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22
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Hatzakis E. Nuclear Magnetic Resonance (NMR) Spectroscopy in Food Science: A Comprehensive Review. Compr Rev Food Sci Food Saf 2018; 18:189-220. [PMID: 33337022 DOI: 10.1111/1541-4337.12408] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 09/28/2018] [Accepted: 10/18/2018] [Indexed: 12/15/2022]
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is a robust method, which can rapidly analyze mixtures at the molecular level without requiring separation and/or purification steps, making it ideal for applications in food science. Despite its increasing popularity among food scientists, NMR is still an underutilized methodology in this area, mainly due to its high cost, relatively low sensitivity, and the lack of NMR expertise by many food scientists. The aim of this review is to help bridge the knowledge gap that may exist when attempting to apply NMR methodologies to the field of food science. We begin by covering the basic principles required to apply NMR to the study of foods and nutrients. A description of the discipline of chemometrics is provided, as the combination of NMR with multivariate statistical analysis is a powerful approach for addressing modern challenges in food science. Furthermore, a comprehensive overview of recent and key applications in the areas of compositional analysis, food authentication, quality control, and human nutrition is provided. In addition to standard NMR techniques, more sophisticated NMR applications are also presented, although limitations, gaps, and potentials are discussed. We hope this review will help scientists gain some of the knowledge required to apply the powerful methodology of NMR to the rich and diverse field of food science.
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Affiliation(s)
- Emmanuel Hatzakis
- Dept. of Food Science and Technology, The Ohio State Univ., Parker Building, 2015 Fyffe Rd., Columbus, OH, U.S.A.,Foods for Health Discovery Theme, The Ohio State Univ., Parker Building, 2015 Fyffe Rd., Columbus, OH, U.S.A
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23
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The scientific challenges in moving from targeted to non-targeted mass spectrometric methods for food fraud analysis: A proposed validation workflow to bring about a harmonized approach. Trends Food Sci Technol 2018. [DOI: 10.1016/j.tifs.2018.08.007] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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24
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Ballabio D, Robotti E, Grisoni F, Quasso F, Bobba M, Vercelli S, Gosetti F, Calabrese G, Sangiorgi E, Orlandi M, Marengo E. Chemical profiling and multivariate data fusion methods for the identification of the botanical origin of honey. Food Chem 2018; 266:79-89. [PMID: 30381229 DOI: 10.1016/j.foodchem.2018.05.084] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Revised: 05/17/2018] [Accepted: 05/18/2018] [Indexed: 02/08/2023]
Abstract
The characterization of 72 Italian honey samples from 8 botanical varieties was carried out by a comprehensive approach exploiting data fusion of IR, NIR and Raman spectroscopies, Proton Transfer Reaction - Time of Flight - Mass Spectrometry (PTR-MS) and electronic nose. High-, mid- and low-level data fusion approaches were tested to verify if the combination of several analytical sources can improve the classification ability of honeys from different botanical origins. Classification was performed on the fused data by Partial Least Squares - Discriminant Analysis; a strict validation protocol was used to estimate the predictive performances of the models. The best results were obtained with high-level data fusion combining Raman and NIR spectroscopy and PTR-MS, with classification performances better than those obtained on single analytical sources (accuracy of 99% and 100% on test and training samples respectively). The combination of just three analytical sources assures a limited time of analysis.
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Affiliation(s)
- Davide Ballabio
- Department of Earth and Environmental Sciences, University of Milano Bicocca, P.zza della Scienza, 1, 20126 Milano, Italy
| | - Elisa Robotti
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy.
| | - Francesca Grisoni
- Department of Earth and Environmental Sciences, University of Milano Bicocca, P.zza della Scienza, 1, 20126 Milano, Italy
| | - Fabio Quasso
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy
| | - Marco Bobba
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna, Via Bianchi 9, 25124 Brescia, Italy
| | - Serena Vercelli
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy
| | - Fabio Gosetti
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy
| | - Giorgio Calabrese
- Department of Pharmaceutical and Toxicological Chemistry, University of Napoli Federico II, Via Montesano 49, 80131 Naples, Italy
| | - Emanuele Sangiorgi
- Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna, Via Bianchi 9, 25124 Brescia, Italy
| | - Marco Orlandi
- Department of Earth and Environmental Sciences, University of Milano Bicocca, P.zza della Scienza, 1, 20126 Milano, Italy
| | - Emilio Marengo
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy
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26
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FT-MIR and NIR spectral data fusion: a synergetic strategy for the geographical traceability of Panax notoginseng. Anal Bioanal Chem 2017; 410:91-103. [DOI: 10.1007/s00216-017-0692-0] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Revised: 10/01/2017] [Accepted: 10/05/2017] [Indexed: 12/24/2022]
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28
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Application of analytical methods in authentication and adulteration of honey. Food Chem 2017; 217:687-698. [DOI: 10.1016/j.foodchem.2016.09.001] [Citation(s) in RCA: 147] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Revised: 08/31/2016] [Accepted: 09/01/2016] [Indexed: 01/22/2023]
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