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Sharma R, Nath PC, Lodh BK, Mukherjee J, Mahata N, Gopikrishna K, Tiwari ON, Bhunia B. Rapid and sensitive approaches for detecting food fraud: A review on prospects and challenges. Food Chem 2024; 454:139817. [PMID: 38805929 DOI: 10.1016/j.foodchem.2024.139817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 05/13/2024] [Accepted: 05/22/2024] [Indexed: 05/30/2024]
Abstract
Precise and reliable analytical techniques are required to guarantee food quality in light of the expanding concerns regarding food safety and quality. Because traditional procedures are expensive and time-consuming, quick food control techniques are required to ensure product quality. Various analytical techniques are used to identify and detect food fraud, including spectroscopy, chromatography, DNA barcoding, and inotrope ratio mass spectrometry (IRMS). Due to its quick findings, simplicity of use, high throughput, affordability, and non-destructive evaluations of numerous food matrices, NI spectroscopy and hyperspectral imaging are financially preferred in the food business. The applicability of this technology has increased with the development of chemometric techniques and near-infrared spectroscopy-based instruments. The current research also discusses the use of several multivariate analytical techniques in identifying food fraud, such as principal component analysis, partial least squares, cluster analysis, multivariate curve resolutions, and artificial intelligence.
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Affiliation(s)
- Ramesh Sharma
- Bioproducts Processing Research Laboratory (BPRL), Department of Bio Engineering, National Institute of Technology, Agartala 799046, India; Department of Food Technology, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu-641062, India.
| | - Pinku Chandra Nath
- Bioproducts Processing Research Laboratory (BPRL), Department of Bio Engineering, National Institute of Technology, Agartala 799046, India.
| | - Bibhab Kumar Lodh
- Department of Chemical Engineering, National Institute of Technology, Agartala-799046, India.
| | - Jayanti Mukherjee
- Department of Pharmaceutical Chemistry, CMR College of Pharmacy, Hyderabad- 501401, Telangana, India.
| | - Nibedita Mahata
- Department of Biotechnology, National Institute of Technology Durgapur, Durgapur-713209.
| | - Konga Gopikrishna
- SEED Division, Department of Science and Technology, New Delhi, 110016, India.
| | - Onkar Nath Tiwari
- Centre for Conservation and Utilisation of Blue Green Algae (CCUBGA), Division of Microbiology, ICAR-Indian Agricultural Research Institute (IARI), New Delhi, 110012, India.
| | - Biswanath Bhunia
- Bioproducts Processing Research Laboratory (BPRL), Department of Bio Engineering, National Institute of Technology, Agartala 799046, India.
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Wang Z, Yu Y, Liu J, Zhang Q, Guo X, Yang Y, Shi Y. Peanut origin traceability: A hybrid neural network combining an electronic nose system and a hyperspectral system. Food Chem 2024; 447:138915. [PMID: 38452539 DOI: 10.1016/j.foodchem.2024.138915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 02/26/2024] [Accepted: 02/29/2024] [Indexed: 03/09/2024]
Abstract
Peanuts, sourced from various regions, exhibit noticeable differences in quality owing to the impact of their natural environments. This study proposes a fast and nondestructive detection method to identify peanut quality by combining an electronic nose system with a hyperspectral system. First, the electronic nose and hyperspectral systems are used to gather gas and spectral information from peanuts. Second, a module for extracting gas and spectral information is designed, combining the lightweight multi-head transposed attention mechanism (LMTA) and convolutional computation. The fusion of gas and spectral information is achieved through matrix combination and lightweight convolution. A hybrid neural network, named UnitFormer, is designed based on the information extraction and fusion processes. UnitFormer demonstrates an accuracy of 99.06 %, a precision of 99.12 %, and a recall of 99.05 %. In conclusion, UnitFormer effectively distinguishes quality differences among peanuts from various regions, offering an effective technological solution for quality supervision in the food market.
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Affiliation(s)
- Zi Wang
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Team, Northeast Electric Power University, Jilin 132012, China.
| | - Yang Yu
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Team, Northeast Electric Power University, Jilin 132012, China; Advanced Sensor Research Institution, Northeast Electric Power University, Jilin 132012, China.
| | - Junqi Liu
- School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Team, Northeast Electric Power University, Jilin 132012, China.
| | - Qinglun Zhang
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.
| | - Xiaoqin Guo
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Team, Northeast Electric Power University, Jilin 132012, China.
| | - Yixin Yang
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Team, Northeast Electric Power University, Jilin 132012, China.
| | - Yan Shi
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Team, Northeast Electric Power University, Jilin 132012, China; Advanced Sensor Research Institution, Northeast Electric Power University, Jilin 132012, China.
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Vinothkanna A, Dar OI, Liu Z, Jia AQ. Advanced detection tools in food fraud: A systematic review for holistic and rational detection method based on research and patents. Food Chem 2024; 446:138893. [PMID: 38432137 DOI: 10.1016/j.foodchem.2024.138893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 02/15/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024]
Abstract
Modern food chain supply management necessitates the dire need for mitigating food fraud and adulterations. This holistic review addresses different advanced detection technologies coupled with chemometrics to identify various types of adulterated foods. The data on research, patent and systematic review analyses (2018-2023) revealed both destructive and non-destructive methods to demarcate a rational approach for food fraud detection in various countries. These intricate hygiene standards and AI-based technology are also summarized for further prospective research. Chemometrics or AI-based techniques for extensive food fraud detection are demanded. A systematic assessment reveals that various methods to detect food fraud involving multiple substances need to be simple, expeditious, precise, cost-effective, eco-friendly and non-intrusive. The scrutiny resulted in 39 relevant experimental data sets answering key questions. However, additional research is necessitated for an affirmative conclusion in food fraud detection system with modern AI and machine learning approaches.
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Affiliation(s)
- Annadurai Vinothkanna
- School of Life and Health Sciences, Hainan University, Haikou 570228, China; Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou 570311, China.
| | - Owias Iqbal Dar
- School of Chemistry and Chemical Engineering, Hainan University, Haikou 570228, China
| | - Zhu Liu
- School of Life and Health Sciences, Hainan University, Haikou 570228, China.
| | - Ai-Qun Jia
- Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou 570311, China.
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Vega-Castellote M, Sánchez MT, Torres-Rodríguez I, Entrenas JA, Pérez-Marín D. NIR Sensing Technologies for the Detection of Fraud in Nuts and Nut Products: A Review. Foods 2024; 13:1612. [PMID: 38890841 PMCID: PMC11172355 DOI: 10.3390/foods13111612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 05/18/2024] [Accepted: 05/20/2024] [Indexed: 06/20/2024] Open
Abstract
Food fraud is a major threat to the integrity of the nut supply chain. Strategies using a wide range of analytical techniques have been developed over the past few years to detect fraud and to assure the quality, safety, and authenticity of nut products. However, most of these techniques present the limitations of being slow and destructive and entailing a high cost per analysis. Nevertheless, near-infrared (NIR) spectroscopy and NIR imaging techniques represent a suitable non-destructive alternative to prevent fraud in the nut industry with the advantages of a high throughput and low cost per analysis. This review collects and includes all major findings of all of the published studies focused on the application of NIR spectroscopy and NIR imaging technologies to detect fraud in the nut supply chain from 2018 onwards. The results suggest that NIR spectroscopy and NIR imaging are suitable technologies to detect the main types of fraud in nuts.
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Affiliation(s)
- Miguel Vega-Castellote
- Department of Bromatology and Food Technology, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain;
| | - María-Teresa Sánchez
- Department of Bromatology and Food Technology, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain;
| | - Irina Torres-Rodríguez
- Department of Animal Production, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain; (I.T.-R.); (J.-A.E.)
| | - José-Antonio Entrenas
- Department of Animal Production, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain; (I.T.-R.); (J.-A.E.)
| | - Dolores Pérez-Marín
- Department of Animal Production, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain; (I.T.-R.); (J.-A.E.)
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Cohen CG, Mazer BD, Jean-Claude BJ. Molecular Profiling of Peanut under Raw, Roasting, and Autoclaving Conditions Using High-Resolution Magic Angle Spinning and Solution 1H NMR Spectroscopy. Molecules 2023; 29:162. [PMID: 38202743 PMCID: PMC10780471 DOI: 10.3390/molecules29010162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
Higher rates of peanut allergy have been observed in countries that commonly roast peanuts prior to consumption. Despite the importance of understanding the role of thermal processing in allergy and on peanut composition, studies toward generating signatures that identify molecular contents following processing are scant. Here, we identified spectral signatures to track changes and differences in the molecular composition of peanuts under raw, roasted, and high-pressure and high-temperature autoclaved conditions. We analyzed both the solid flesh of the seed and solutions derived from soaking peanuts using High-Resolution Magic Angle Spinning (HR-MAS) and solution 1H Nuclear Magnetic Resonance (NMR) spectroscopy, respectively. The NMR spectra of intact peanuts revealed triglycerides as the dominant species, assigned on the basis of multiplets at 4.1 and 4.3 ppm, and corresponding defatted flours revealed the presence of sugars. Sucrose assigned based on a doublet at 5.4 ppm (anomeric proton), and triglycerides were the most abundant small molecules observed, with little variation between conditions. Soaked peanut solutions were devoid of lipids, and their resulting spectra matched the profiles of defatted peanuts. Spectral signatures resulting from autoclaving differed strikingly between those from raw and roasted peanuts, with considerable line-broadening in regions corresponding to proteins and amino-acid side chains, from 0.5 to 2.0 ppm and 6.5 to 8.5 ppm. Taken together, by using complementary NMR methods to obtain a fingerprint of the molecular components in peanuts, we demonstrated that autoclaving led to a distinct composition, likely resulting from the hydrolytic cleavage of proteins, the most important molecule of the allergic reaction.
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Affiliation(s)
- Casey G. Cohen
- The Research Institute of the McGill University Health Centre, Department of Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC H4A 3J1, Canada
| | - Bruce D. Mazer
- The Research Institute of the McGill University Health Centre, Department of Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC H4A 3J1, Canada
- Department of Pediatrics, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC H4A 3J1, Canada
| | - Bertrand J. Jean-Claude
- The Research Institute of the McGill University Health Centre, Department of Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC H4A 3J1, Canada
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Aslam R, Sharma SR, Kaur J, Panayampadan AS, Dar OI. A systematic account of food adulteration and recent trends in the non-destructive analysis of food fraud detection. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01846-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
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Zhu C, Deng J, Jiang H. Parameter Optimization of Support Vector Machine to Improve the Predictive Performance for Determination of Aflatoxin B 1 in Peanuts by Olfactory Visualization Technique. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27196730. [PMID: 36235267 PMCID: PMC9573054 DOI: 10.3390/molecules27196730] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/07/2022] [Accepted: 10/08/2022] [Indexed: 11/04/2022]
Abstract
This study proposes a novel method for detection of aflatoxin B1 (AFB1) in peanuts using olfactory visualization technique. First, 12 kinds of chemical dyes were selected to prepare a colorimetric sensor to assemble olfactory visualization system, which was used to collect the odor characteristic information of peanut samples. Then, genetic algorithm (GA) with back propagation neural network (BPNN) as the regressor was used to optimize the color component of the preprocessed sensor feature image. Support vector regression (SVR) quantitative analysis model was constructed by using the optimized combination of characteristic color components to achieve determination of the AFB1 in peanuts. In this process, the optimization performance of grid search (GS) algorithm and sparrow search algorithm (SSA) on SVR parameter was compared. Compared with GS-SVR model, the model performance of SSA-SVR was better. The results showed that the SSA-SVR model with the combination of seven characteristic color components obtained the best prediction effect. Its correlation coefficients of prediction (RP) reached 0.91. The root mean square error of prediction (RMSEP) was 5.7 μg·kg-1, and ratio performance deviation (RPD) value was 2.4. The results indicate that it is reliable to use the colorimetric sensor array with strong specificity for the determination of the AFB1 in peanuts. In addition, it is necessary to properly optimize the parameters of the prediction model, which can obviously improve the generalization performance of the multivariable model.
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Affiliation(s)
- Chengyun Zhu
- School of Physics and Electronic Engineering, Yancheng Teachers University, Yancheng 224007, China
- Jiangsu Intelligent Optoelectronic Devices and Measurement and Control Engineering Research Center, Yancheng 224007, China
| | - Jihong Deng
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
- Correspondence:
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An overview on different detection methods of saffron (Crocus sativus L.) adulterants. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01586-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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Bachmann R, Horns AL, Paasch N, Schrieck R, Weidner M, Fransson I, Schrör JP. Minor metabolites as chemical marker for the differentiation of cane, beet and coconut blossom sugar. From profiling towards identification of adulterations. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.108832] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Soon JM. Food fraud countermeasures and consumers: A future agenda. FUTURE FOODS 2022. [DOI: 10.1016/b978-0-323-91001-9.00027-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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Strecker C, Ara V. Detecting Admixture to Mango Purée of the Alphonso Cultivar (Mangifera indica L. cv. Alphonso) by 1H-NMR Spectroscopy. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-021-02116-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
AbstractFood authenticity is becoming increasingly important but challenges existing analytical methods. In this study, we analyze the mango cultivar Alphonso with regard to authenticity using 1H-NMR spectroscopy. This cultivar has been termed “the king of mangoes” due to its unique flavor. Regarding its metabolites however, little is known about unique constellations that allow for differentiation of the Alphonso cultivar. We find that the Alphonso cultivar is distinguished by high levels of niacin, trigonelline, and histidine but features relatively low levels of alanine. Furthermore, we develop a model based on the local outlier factor algorithm that effectively detects admixture of non-Alphonso cultivars to Alphonso purée. This task is highly challenging because we identified no metabolites that are unique or uniquely absent in the Alphonso cultivar compared to other mango cultivars analyzed in this study. Our model shows promising results on a test set: Admixtures consisting of 35% non-Alphonso and 65% Alphonso mango purée were uncovered with a sensitivity of 88%. At the same time, our model verified Alphonso samples with a good specificity of 86%.
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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] [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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Watermann S, Schmitt C, Schneider T, Hackl T. Comparison of Regular, Pure Shift, and Fast 2D NMR Experiments for Determination of the Geographical Origin of Walnuts. Metabolites 2021; 11:metabo11010039. [PMID: 33429871 PMCID: PMC7827277 DOI: 10.3390/metabo11010039] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/05/2021] [Accepted: 01/06/2021] [Indexed: 11/16/2022] Open
Abstract
1H NMR spectroscopy, in combination with chemometric methods, was used to analyze the methanol/acetonitrile (1:1) extract of walnut (Juglans Regia L.) regarding the geographical origin of 128 authentic samples from different countries (France, Germany, China) and harvest years (2016–2019). Due to the large number of different metabolites within the acetonitrile/methanol extract, the one-dimensional (1D) 1H NOESY (nuclear Overhauser effect spectroscopy) spectra suffer from strongly overlapping signals. The identification of specific metabolites and statistical analysis are complicated. The use of pure shift 1H NMR spectra such as PSYCHE (pure shift yielded by chirp excitation) or two-dimensional ASAP-HSQC (acceleration by sharing adjacent polarization-heteronuclear single quantum correlation) spectra for multivariate analysis to determine the geographical origin of foods may be a promising method. Different types of NMR spectra (1D 1H NOESY, PSYCHE, and ASAP-HSQC) were acquired for each of the 128 walnut samples and the results of the statistical analysis were compared. A support vector machine classifier was applied for differentiation of samples from Germany/China, France/Germany, and France/China. The models obtained by conduction of a repeated nested cross-validation showed accuracies from 58.9% (±1.3%) to 95.9% (±0.8%). The potential of the 1H-13C HSQC as a 2D NMR experiment for metabolomics studies was shown.
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Affiliation(s)
- Stephanie Watermann
- Institute of Organic Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146 Hamburg, Germany; (S.W.); (C.S.); (T.S.)
| | - Caroline Schmitt
- Institute of Organic Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146 Hamburg, Germany; (S.W.); (C.S.); (T.S.)
| | - Tobias Schneider
- Institute of Organic Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146 Hamburg, Germany; (S.W.); (C.S.); (T.S.)
| | - Thomas Hackl
- Institute of Organic Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146 Hamburg, Germany; (S.W.); (C.S.); (T.S.)
- Hamburg School of Food Science—Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany
- Correspondence: ; Tel.: +49-40-42838-2804
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