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Cui Y, Lu W, Xue J, Ge L, Yin X, Jian S, Li H, Zhu B, Dai Z, Shen Q. Machine learning-guided REIMS pattern recognition of non-dairy cream, milk fat cream and whipping cream for fraudulence identification. Food Chem 2023; 429:136986. [PMID: 37516053 DOI: 10.1016/j.foodchem.2023.136986] [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: 11/04/2022] [Revised: 07/02/2023] [Accepted: 07/22/2023] [Indexed: 07/31/2023]
Abstract
The illegal adulteration of non-dairy cream in milk fat cream during the manufacturing process of baked goods has significantly hindered the robust growth of the dairy industry. In this study, a method based on rapid evaporative ionization mass spectrometry (REIMS) lipidomics pattern recognition integrated with machine learning algorithms was established. A total of 26 ions with importance were picked using multivariate statistical analysis as salient contributing features to distinguish between milk fat cream and non-dairy cream. Furthermore, employing discriminant analysis, decision trees, support vector machines, and neural network classifiers, machine learning models were utilized to classify non-dairy cream, milk fat cream, and minute quantities of non-dairy cream adulterated in milk fat cream. These approaches were enhanced through hyperparameter optimization and feature engineering, yielding accuracy rates at 98.4-99.6%. This artificial intelligent method of machine learning-guided REIMS pattern recognition can accurately identify adulteration of whipped cream and might help combat food fraud.
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Affiliation(s)
- Yiwei Cui
- Collaborative Innovation Center of Seafood Deep Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China; Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China
| | - Weibo Lu
- Collaborative Innovation Center of Seafood Deep Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China
| | - Jing Xue
- Collaborative Innovation Center of Seafood Deep Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China; Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China
| | - Lijun Ge
- Collaborative Innovation Center of Seafood Deep Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China
| | - Xuelian Yin
- Collaborative Innovation Center of Seafood Deep Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China
| | - Shikai Jian
- Collaborative Innovation Center of Seafood Deep Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China
| | - Haihong Li
- Hangzhou Linping District Maternal & Child Health Care Hospital, Hangzhou 311113, China
| | - Beiwei Zhu
- National Engineering Research Center of Seafood, Collaborative Innovation Center of Provincial and Ministerial Co-Construction for Seafood Deep Processing, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China
| | - Zhiyuan Dai
- Collaborative Innovation Center of Seafood Deep Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China; Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China.
| | - Qing Shen
- Department of Clinical Laboratory, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000, China; Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China.
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2
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Plamada D, Teleky BE, Nemes SA, Mitrea L, Szabo K, Călinoiu LF, Pascuta MS, Varvara RA, Ciont C, Martău GA, Simon E, Barta G, Dulf FV, Vodnar DC, Nitescu M. Plant-Based Dairy Alternatives-A Future Direction to the Milky Way. Foods 2023; 12:foods12091883. [PMID: 37174421 PMCID: PMC10178229 DOI: 10.3390/foods12091883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 04/27/2023] [Accepted: 05/01/2023] [Indexed: 05/15/2023] Open
Abstract
One significant food group that is part of our daily diet is the dairy group, and both research and industry are actively involved to meet the increasing requirement for plant-based dairy alternatives (PBDAs). The production tendency of PBDAs is growing with a predictable rate of over 18.5% in 2023 from 7.4% at the moment. A multitude of sources can be used for development such as cereals, pseudocereals, legumes, nuts, and seeds to obtain food products such as vegetal milk, cheese, cream, yogurt, butter, and different sweets, such as ice cream, which have nearly similar nutritional profiles to those of animal-origin products. Increased interest in PBDAs is manifested in groups with special dietary needs (e.g., lactose intolerant individuals, pregnant women, newborns, and the elderly) or with pathologies such as metabolic syndromes, dermatological diseases, and arthritis. In spite of the vast range of production perspectives, certain industrial challenges arise during development, such as processing and preservation technologies. This paper aims at providing an overview of the currently available PBDAs based on recent studies selected from the electronic databases PubMed, Web of Science Core Collection, and Scopus. We found 148 publications regarding PBDAs in correlation with their nutritional and technological aspects, together with the implications in terms of health. Therefore, this review focuses on the relationship between plant-based alternatives for dairy products and the human diet, from the raw material to the final products, including the industrial processes and health-related concerns.
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Affiliation(s)
- Diana Plamada
- Life Science Institute, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 400372 Cluj-Napoca, Romania
- Faculty of Food Science and Technology, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Calea Mănăștur 3-5, 400372 Cluj-Napoca, Romania
| | - Bernadette-Emőke Teleky
- Life Science Institute, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 400372 Cluj-Napoca, Romania
| | - Silvia Amalia Nemes
- Life Science Institute, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 400372 Cluj-Napoca, Romania
- Faculty of Food Science and Technology, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Calea Mănăștur 3-5, 400372 Cluj-Napoca, Romania
| | - Laura Mitrea
- Faculty of Food Science and Technology, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Calea Mănăștur 3-5, 400372 Cluj-Napoca, Romania
| | - Katalin Szabo
- Faculty of Food Science and Technology, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Calea Mănăștur 3-5, 400372 Cluj-Napoca, Romania
| | - Lavinia-Florina Călinoiu
- Faculty of Food Science and Technology, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Calea Mănăștur 3-5, 400372 Cluj-Napoca, Romania
| | - Mihaela Stefana Pascuta
- Life Science Institute, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 400372 Cluj-Napoca, Romania
- Faculty of Food Science and Technology, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Calea Mănăștur 3-5, 400372 Cluj-Napoca, Romania
| | - Rodica-Anita Varvara
- Life Science Institute, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 400372 Cluj-Napoca, Romania
| | - Călina Ciont
- Life Science Institute, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 400372 Cluj-Napoca, Romania
| | - Gheorghe Adrian Martău
- Life Science Institute, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 400372 Cluj-Napoca, Romania
| | - Elemer Simon
- Life Science Institute, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 400372 Cluj-Napoca, Romania
- Faculty of Food Science and Technology, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Calea Mănăștur 3-5, 400372 Cluj-Napoca, Romania
| | - Gabriel Barta
- Life Science Institute, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 400372 Cluj-Napoca, Romania
- Faculty of Food Science and Technology, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Calea Mănăștur 3-5, 400372 Cluj-Napoca, Romania
| | - Francisc Vasile Dulf
- Faculty of Agriculture, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Calea Mănăștur 3-5, 400372 Cluj-Napoca, Romania
| | - Dan Cristian Vodnar
- Life Science Institute, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 400372 Cluj-Napoca, Romania
| | - Maria Nitescu
- Department of Preclinical-Complementary Sciences, University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
- National Institute for Infectious Diseases "Prof. Dr. Matei Bals", 021105 Bucharest, Romania
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3
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Shi J, Liang J, Pu J, Li Z, Zou X. Nondestructive detection of the bioactive components and nutritional value in restructured functional foods. Curr Opin Food Sci 2023. [DOI: 10.1016/j.cofs.2022.100986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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4
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Windarsih A, Arsanti Lestari L, Erwanto Y, Rosiana Putri A, Irnawati, Ahmad Fadzillah N, Rahmawati N, Rohman A. Application of Raman Spectroscopy and Chemometrics for Quality Controls of Fats and Oils: A Review. FOOD REVIEWS INTERNATIONAL 2021. [DOI: 10.1080/87559129.2021.2014860] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Anjar Windarsih
- Research Division for Natural Product Technology (BPTBA), National Research and Innovation Agency (BRIN), Yogyakarta, 55861, Indonesia
- Center of Excellence Institute for Halal Industry & Systems (IHIS), Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Lily Arsanti Lestari
- Center of Excellence Institute for Halal Industry & Systems (IHIS), Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Yuny Erwanto
- Center of Excellence Institute for Halal Industry & Systems (IHIS), Universitas Gadjah Mada, Yogyakarta, Indonesia
- Division of Animal Products Technology, Faculty of Animal Science, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Anggita Rosiana Putri
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Irnawati
- Study Program of Pharmacy, Faculty of Pharmacy, Halu Oleo University, Kendari, Indonesia
| | - Nurrulhidayah Ahmad Fadzillah
- International Institute for Halal Research and Training (INHART), International Islamic University Malaysia (IIUM), Malaysia
| | - Nuning Rahmawati
- Medicinal Plant and Traditional Medicine, Research and Development Centre, Karanganyar, Indonesia
| | - Abdul Rohman
- Center of Excellence Institute for Halal Industry & Systems (IHIS), Universitas Gadjah Mada, Yogyakarta, Indonesia
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta, Indonesia
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5
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Wang K, Li Z, Li J, Lin H. Raman spectroscopic techniques for nondestructive analysis of agri-foods: A state-of-the-art review. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.10.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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6
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Wang J, Chen Q, Belwal T, Lin X, Luo Z. Insights into chemometric algorithms for quality attributes and hazards detection in foodstuffs using Raman/surface enhanced Raman spectroscopy. Compr Rev Food Sci Food Saf 2021; 20:2476-2507. [DOI: 10.1111/1541-4337.12741] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 02/08/2021] [Accepted: 02/23/2021] [Indexed: 12/12/2022]
Affiliation(s)
- Jingjing Wang
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
| | - Quansheng Chen
- School of Food and Biological Engineering Jiangsu University Zhenjiang People's Republic of China
| | - Tarun Belwal
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
| | - Xingyu Lin
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
| | - Zisheng Luo
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
- Ningbo Research Institute Zhejiang University Ningbo People's Republic of China
- Fuli Institute of Food Science Hangzhou People's Republic of China
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7
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Cebi N, Arici M, Sagdic O. The famous Turkish rose essential oil: Characterization and authenticity monitoring by FTIR, Raman and GC-MS techniques combined with chemometrics. Food Chem 2021; 354:129495. [PMID: 33743448 DOI: 10.1016/j.foodchem.2021.129495] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 02/05/2021] [Accepted: 02/25/2021] [Indexed: 12/27/2022]
Abstract
There is a necessity for rapid, robust, easy, accurate and cost-effective methodologies for the quality control of essential oils from medicinal and aromatic plants. Rosa damascena essential oil is a high-value natural product with its unique quality properties and economic importance. This research evaluated the capability of Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy and gas chromatography-mass spectrometry (GC-MS) techniques combined with chemometrics for determination of the authenticity of R. damascena essential oil. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were successfully employed with 100% accuracy for discrimination of authentic R. damascena essential oil samples from fraudulent commercial samples. Consistent results were obtained by FTIR, Raman and GC-MS techniques. Two of twenty commercial samples were determined as authentic R. damascena essential oil samples using the three analytical techniques. Findings showed that FTIR and Raman spectroscopy combined with chemometrics could be used as reliable, robust, rapid, accurate and low-cost analytical techniques for quality evaluation of R. damascena essential oil.
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Affiliation(s)
- Nur Cebi
- Department of Food Engineering, Faculty of Chemical and Metallurgical Engineering, Yıldız Technical University, 34210 Istanbul, Turkey.
| | - Muhammet Arici
- Department of Food Engineering, Faculty of Chemical and Metallurgical Engineering, Yıldız Technical University, 34210 Istanbul, Turkey
| | - Osman Sagdic
- Department of Food Engineering, Faculty of Chemical and Metallurgical Engineering, Yıldız Technical University, 34210 Istanbul, Turkey
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8
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Hassoun A, Måge I, Schmidt WF, Temiz HT, Li L, Kim HY, Nilsen H, Biancolillo A, Aït-Kaddour A, Sikorski M, Sikorska E, Grassi S, Cozzolino D. Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years. Foods 2020; 9:E1069. [PMID: 32781687 PMCID: PMC7466239 DOI: 10.3390/foods9081069] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 07/29/2020] [Accepted: 08/01/2020] [Indexed: 12/27/2022] Open
Abstract
Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.
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Affiliation(s)
- Abdo Hassoun
- Nofima AS, Norwegian Institute of Food, Fisheries, and Aquaculture Research, Muninbakken 9-13, 9291 Tromsø, Norway; (I.M.); (H.N.)
| | - Ingrid Måge
- Nofima AS, Norwegian Institute of Food, Fisheries, and Aquaculture Research, Muninbakken 9-13, 9291 Tromsø, Norway; (I.M.); (H.N.)
| | - Walter F. Schmidt
- United States Department of Agriculture, Agricultural Research Service, 10300 Baltimore Avenue, Beltsville, MD 20705-2325, USA;
| | - Havva Tümay Temiz
- Department of Food Engineering, Bingol University, 12000 Bingol, Turkey;
| | - Li Li
- Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China;
| | - Hae-Yeong Kim
- Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Korea;
| | - Heidi Nilsen
- Nofima AS, Norwegian Institute of Food, Fisheries, and Aquaculture Research, Muninbakken 9-13, 9291 Tromsø, Norway; (I.M.); (H.N.)
| | - Alessandra Biancolillo
- Department of Physical and Chemical Sciences, University of L’Aquila, 67100 Via Vetoio, Coppito, L’Aquila, Italy;
| | | | - Marek Sikorski
- Faculty of Chemistry, Adam Mickiewicz University in Poznan, Uniwersytetu Poznanskiego 8, 61-614 Poznan, Poland;
| | - Ewa Sikorska
- Institute of Quality Science, Poznań University of Economics and Business, al. Niepodległości 10, 61-875 Poznań, Poland;
| | - Silvia Grassi
- Department of Food, Environmental and Nutritional Sciences (DeFENS), Università degli Studi di Milano, via Celoria, 2, 20133 Milano, Italy;
| | - Daniel Cozzolino
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, 39 Kessels Rd, Coopers Plains, QLD 4108, Australia;
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9
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Genis DO, Sezer B, Durna S, Boyaci IH. Determination of milk fat authenticity in ultra-filtered white cheese by using Raman spectroscopy with multivariate data analysis. Food Chem 2020; 336:127699. [PMID: 32768905 DOI: 10.1016/j.foodchem.2020.127699] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 07/25/2020] [Accepted: 07/26/2020] [Indexed: 11/30/2022]
Abstract
Cheese is one of the most widely consumed food products in the world. However, the increasing demand for nutritionally enhanced or functional products by the cheese industry has created new approaches that partially or fully replace milk fat. With this, new methods of adulteration have also been noted, potentially leading to these fully/partially-replaced products being offered as cheese. In this study, Raman spectroscopy was used to determine origins of fats in margarine, corn, and palm oils present in white and ultra-filtered cheese samples. Raman spectra were evaluated with partial least square-discriminant (PLS-DA) and PLS to identify fat/oil origins and adulteration ratios. The coefficients of determination and limits of detection for margarine, and corn and palm oil adulteration were found to be 0.990, 0.993, 0.991 and 3.38%, 3.36% and 3.59%, respectively.
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Affiliation(s)
- Duygu Ozer Genis
- Department of Food Engineering, Hacettepe University, Beytepe 06800, Ankara, Turkey
| | - Banu Sezer
- Department of Food Engineering, Hacettepe University, Beytepe 06800, Ankara, Turkey; NANOSENS Industry and Trade Inc., Ankara University Technology Development Zone, 06830 Golbasi, Ankara, Turkey
| | - Sahin Durna
- Atatürk Foresty Farm, 06560 Yenimahalle, Ankara, Turkey
| | - Ismail Hakki Boyaci
- Department of Food Engineering, Hacettepe University, Beytepe 06800, Ankara, Turkey.
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Rohman A, Windarsih A. The Application of Molecular Spectroscopy in Combination with Chemometrics for Halal Authentication Analysis: A Review. Int J Mol Sci 2020; 21:E5155. [PMID: 32708254 PMCID: PMC7403989 DOI: 10.3390/ijms21145155] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 04/25/2020] [Accepted: 04/29/2020] [Indexed: 12/18/2022] Open
Abstract
Halal is an Arabic term used to describe any components allowed to be used in any products by Muslim communities. Halal food and halal pharmaceuticals are any food and pharmaceuticals which are safe and allowed to be consumed according to Islamic law (Shariah). Currently, in line with halal awareness, some Muslim countries such as Indonesia, Malaysia, and Middle East regions have developed some standards and regulations on halal products and halal certification. Among non-halal components, the presence of pig derivatives (lard, pork, and porcine gelatin) along with other non-halal meats (rat meat, wild boar meat, and dog meat) is typically found in food and pharmaceutical products. This review updates the recent application of molecular spectroscopy, including ultraviolet-visible, infrared, Raman, and nuclear magnetic resonance (NMR) spectroscopies, in combination with chemometrics of multivariate analysis, for analysis of non-halal components in food and pharmaceutical products. The combination of molecular spectroscopic-based techniques and chemometrics offers fast and reliable methods for screening the presence of non-halal components of pig derivatives and non-halal meats in food and pharmaceutical products.
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Affiliation(s)
- Abdul Rohman
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
- Institute of Halal Industry and Systems (IHIS), Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
| | - Anjar Windarsih
- Research Division for Natural Product Technology (BPTBA), Indonesian Institute of Sciences (LIPI), Yogyakarta 55861, Indonesia
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11
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de Lima TK, Musso M, Bertoldo Menezes D. Using Raman spectroscopy and an exponential equation approach to detect adulteration of olive oil with rapeseed and corn oil. Food Chem 2020; 333:127454. [PMID: 32679414 DOI: 10.1016/j.foodchem.2020.127454] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 06/23/2020] [Accepted: 06/28/2020] [Indexed: 10/23/2022]
Abstract
This study presents a method to determine adulteration of olive oil (obtained from Olea europea, i.e. olives) with rapeseed oil (obtained from Brassica napus) or with corn oil (also named maize oil, obtained from Zea mays, i.e. maize) using Raman spectroscopy and a mathematical method based on exponential equation fit. The samples were prepared by mixing olive oil with volume fractions (0-100%) of rapeseed or corn oil. The oils were differentiated spectroscopically using intensity ratio for specific Raman peaks; Raman spectroscopy is able to detect changes within a liquid molecular environment without the need for sample treatment. It was possible to determine rapeseed or corn oil volume fractions added into the olive oil using the method proposed. Thus, the potential of Raman spectroscopy as a technique for determining adulteration of olive oil was corroborated clearly, opening the potential to investigate adulteration of other liquid foods, without any need for sample preparation.
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Affiliation(s)
- Thaís Karine de Lima
- Federal Institute of Triângulo Mineiro, 38400-970, mailbox: 1020, Uberlândia, Minas Gerais, Brazil.
| | - M Musso
- Department of Chemistry and Physics of Materials, University of Salzburg, Jakob-Haringer-Strasse 2a, 5020 Salzburg, Austria.
| | - D Bertoldo Menezes
- Federal Institute of Triângulo Mineiro, 38400-970, mailbox: 1020, Uberlândia, Minas Gerais, Brazil; Department of Chemistry and Physics of Materials, University of Salzburg, Jakob-Haringer-Strasse 2a, 5020 Salzburg, Austria.
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12
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Sádecká J, Jakubíková M. Varietal classification of white wines by fluorescence spectroscopy. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2020; 57:2545-2553. [PMID: 32549605 PMCID: PMC7271340 DOI: 10.1007/s13197-020-04291-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 07/30/2019] [Accepted: 02/03/2020] [Indexed: 10/25/2022]
Abstract
The Slovak Tokaj region is one of the producers of high-quality white wine having protected designations of origin. The main grape varieties of this region are Furmint, Lipovina and Muscat blanc, which have specific sensory characteristics. This research work presents a strategy for the classification of three mentioned varieties of white wines using fluorescence spectroscopy with chemometrics. Emission and synchronous fluorescence spectra were obtained for bulk as well as diluted samples, principal component analysis (PCA) was applied for exploratory analysis and the scores of the selected PCs were used in linear discriminant analysis (LDA). For undiluted samples, the best PCA-LDA models based on either emission spectra excited at 370 nm or synchronous fluorescence spectra obtained at wavelength difference of 40 and 100 nm provided total correct classifications of 100, 100 and 93% for the calibration, validation and prediction steps, respectively. For diluted samples, the best PCA-LDA models (excitation at 280 nm; wavelength difference of 40 nm) again provided total correct classifications as mentioned above.
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Affiliation(s)
- Jana Sádecká
- Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak University of Technology, Radlinského 9, 812 37 Bratislava, Slovak Republic
| | - Michaela Jakubíková
- Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak University of Technology, Radlinského 9, 812 37 Bratislava, Slovak Republic
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13
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Raman Spectral Analysis for Quality Determination of Grignard Reagent. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10103545] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Grignard reagent is one of the most popular materials in chemical and pharmaceutical reaction processes, and requires high quality with minimal adulteration. In this study, Raman spectroscopic technique was investigated for the rapid determination of toluene content, which is one of the common adulterants in Grignard reagent. Raman spectroscopy is the most suitable spectroscopic method to mitigate moisture and CO2 interference in the molecules of Grignard reagent. Raman spectra for the mixtures of toluene and Grignard reagent with different concentrations were analyzed with a partial least square regression (PLSR) method. The combination of spectral wavebands in the prediction model was optimized with a variables selection method of variable importance in projection (VIP). The results obtained from the VIP-based PLSR model showed the reliable performance of Raman spectroscopy for predicting the toluene concentration present in Grignard reagent with a correlation coefficient value of 0.97 and a standard error of prediction (SEP) of 0.71%. The results showed that Raman spectroscopy combined with multivariate analysis could be an effective analytical tool for rapid determination of the quality of Grignard reagent.
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Classification of Milk Samples Using CART. FOOD ANAL METHOD 2020. [DOI: 10.1007/s12161-019-01493-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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15
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Zhang ZY. The statistical fusion identification of dairy products based on extracted Raman spectroscopy. RSC Adv 2020; 10:29682-29687. [PMID: 35518240 PMCID: PMC9056169 DOI: 10.1039/d0ra06318e] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 07/28/2020] [Indexed: 11/21/2022] Open
Abstract
At present, practical and rapid identification techniques for dairy products are still scarce. Taking different brands of pasteurized milk as an example, they are all milky white in appearance, and their Raman spectra are very similar, so it is not feasible to identify them directly using the naked eye. In the current work, a clear feature extraction and fusion strategy based on a combination of Raman spectroscopy and a support vector machine (SVM) algorithm was demonstrated. The results showed a 58% average recognition accuracy rate for dairy products as based on the original Raman full spectral data and up to nearly 70% based on a single spectral interval. Data normalization processing effectively improved the recognition accuracy rate. The average recognition accuracy rate of dairy products reached 91% based on the normalized Raman full spectral data or nearly 85% based on a normalized single spectral interval. The fusion of multispectral feature regions yielded high accuracy and operation efficiency. After screening and optimizing based on SVM algorithm, the best spectral feature intervals were determined to be 335–354 cm−1, 435–454 cm−1, 485–540 cm−1, 820–915 cm−1, 1155–1185 cm−1, 1300–1414 cm−1, and 1415–1520 cm−1 under the experimental conditions, and the average identification accuracy rate here reached 93%. The developed scheme has the advantages of clear feature extraction and fusion, and short identification time, and it provides a technical reference for food quality control. At present, practical and rapid identification techniques for dairy products are still scarce.![]()
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Affiliation(s)
- Zheng-Yong Zhang
- State Key Laboratory of Dairy Biotechnology
- Shanghai Engineering Research Center of Dairy Biotechnology
- Dairy Research Institute
- Bright Dairy & Food Co., Ltd
- Shanghai 200436
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16
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Iordache SM, Gatin E, Iordache AM, Luculescu C. Evaluation of the quality of local butters: A new approach based on Raman spectroscopy and supported by the classical pycnometer method. FOOD SCI TECHNOL INT 2019; 26:113-122. [DOI: 10.1177/1082013219871188] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In this study, the quality of the local Romanian butters was investigated using the classical pycnometer and optic microscopy methods, combined with Raman spectroscopy. We used a pool of 10 samples with different characteristics, and analyzed them by the three aforementioned methods. Pycnometric measurements showed a direct correlation between the fat content and the density values of the samples. Raman spectroscopy validated the results from the pycnometric measurements and the optical microscopy and indicated several other properties, such as protein content, hydration, saturation level of the polycarbonate chains, as well as the total cis isomer content and the type of arrangement preferred by the aliphatic chains (polymorphic transition). The methods employed in the present study have a strong potential to become analytical tools for the food industry and food safety agencies in order to assess the quality of butters and margarines, in a fast and cost-effective manner.
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Affiliation(s)
- Stefan-Marian Iordache
- Optospintronics Department, National Institute for Research and Development in Optoelectronics—INOE 2000, Magurele, Romania
- 3Nano-SAE Research Centre, University of Bucharest, Magurele, Romania
| | - Eduard Gatin
- Faculty of Medicine, University of Medicine “Carol Davila”, Bucharest, Romania
- Materials Department, Faculty of Physics, University of Bucharest, Bucharest, Romania
| | - Ana-Maria Iordache
- Optospintronics Department, National Institute for Research and Development in Optoelectronics—INOE 2000, Magurele, Romania
- 3Nano-SAE Research Centre, University of Bucharest, Magurele, Romania
| | - Catalin Luculescu
- National Institute for Laser, Plasma and Radiation Physics, CETAL, Magurele, Romania
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17
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Smithson SC, Fakayode BD, Henderson S, Nguyen J, Fakayode SO. Detection, Purity Analysis, and Quality Assurance of Adulterated Peanut (Arachis Hypogaea) Oils. Foods 2018; 7:E122. [PMID: 30065168 PMCID: PMC6112014 DOI: 10.3390/foods7080122] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Revised: 07/25/2018] [Accepted: 07/27/2018] [Indexed: 11/25/2022] Open
Abstract
The intake of adulterated and unhealthy oils and trans-fats in the human diet has had negative health repercussions, including cardiovascular disease, causing millions of deaths annually. Sadly, a significant percentage of all consumable products including edible oils are neither screened nor monitored for quality control for various reasons. The prospective intake of adulterated oils and the associated health impacts on consumers is a significant public health safety concern, necessitating the need for quality assurance checks of edible oils. This study reports a simple, fast, sensitive, accurate, and low-cost chemometric approach to the purity analysis of highly refined peanut oils (HRPO) that were adulterated either with vegetable oil (VO), canola oil (CO), or almond oil (AO) for food quality assurance purposes. The Fourier transform infrared spectra of the pure oils and adulterated HRPO samples were measured and subjected to a partial-least-square (PLS) regression analysis. The obtained PLS regression figures-of-merit were incredible, with remarkable linearity (R² = 0.994191 or better). The results of the score plots of the PLS regressions illustrate pattern recognition of the adulterated HRPO samples. Importantly, the PLS regressions accurately determined percent compositions of adulterated HRPOs, with an overall root-mean-square-relative-percent-error of 5.53% and a limit-of-detection as low as 0.02% (wt/wt). The developed PLS regressions continued to predict the compositions of newly prepared adulterated HRPOs over a period of two months, with incredible accuracy without the need for re-calibration. The accuracy, sensitivity, and robustness of the protocol make it desirable and potentially adoptable by health departments and local enforcement agencies for fast screening and quality assurance of consumable products.
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Affiliation(s)
- Shayla C Smithson
- Department of Physical Sciences, University of Arkansas Fort Smith, 5210 Grand Avenue, P.O. Box 3649, Fort Smith, AR 72913-3649, USA.
| | - Boluwatife D Fakayode
- Department of Physical Sciences, University of Arkansas Fort Smith, 5210 Grand Avenue, P.O. Box 3649, Fort Smith, AR 72913-3649, USA.
| | - Siera Henderson
- Department of Physical Sciences, University of Arkansas Fort Smith, 5210 Grand Avenue, P.O. Box 3649, Fort Smith, AR 72913-3649, USA.
| | - John Nguyen
- Department of Physical Sciences, University of Arkansas Fort Smith, 5210 Grand Avenue, P.O. Box 3649, Fort Smith, AR 72913-3649, USA.
| | - Sayo O Fakayode
- Department of Physical Sciences, University of Arkansas Fort Smith, 5210 Grand Avenue, P.O. Box 3649, Fort Smith, AR 72913-3649, USA.
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18
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Yazgan Karacaglar NN, Bulat T, Boyaci IH, Topcu A. Raman spectroscopy coupled with chemometric methods for the discrimination of foreign fats and oils in cream and yogurt. J Food Drug Anal 2018; 27:101-110. [PMID: 30648563 PMCID: PMC9298642 DOI: 10.1016/j.jfda.2018.06.008] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Revised: 05/25/2018] [Accepted: 06/13/2018] [Indexed: 01/19/2023] Open
Abstract
The adulteration of milk fat in dairy products with cheaper non-milk based fats or oils is frequently encountered in the dairy industry. In this study, Raman spectroscopy with chemometric was used for the discrimination of foreign fats and oils in milk cream and yogurt. Firstly, binary mixtures of cream and oils (corn and sunflower oil), and vegetable fat blends which are potentially or currently used by the dairy industry were prepared. All fat or oil samples and their binary mixtures were examined by using Raman spectroscopy. Then, fat content of skim milk was adjusted to 3% (w/w) by the milk fat, external oils or fats, and binary mixtures, and was used in yogurt production. The lipid fraction of yogurt was extracted and characterized by Raman spectroscopy. The spectral data were then pre-processed and principal component analysis (PCA) was performed. Raman spectral data showed successful discrimination for about the source of the fats or oils. Temperature effect was also studied at six different temperatures (25, 30, 40, 50, 60 and 70 °C) in order to obtain the best spectral information. Raman spectra collected at higher temperatures were more intense. Obtained results showed that the performance of Raman spectroscopy with PCA was very promising and can be expected to provide a simple and quick way for the discrimination of foreign fats and oils in both milk cream and yogurt. Fermentation and yogurt processing affected clustering of fat samples by PCA, probably depending on some lipolysis or production of new products that can affect the Raman scattering. However, those changes did not affect differentiation of samples by Raman spectroscopy.
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Affiliation(s)
| | - Tugba Bulat
- Department of Food Engineering, Faculty of Engineering, Hacettepe University, Beytepe, 06800, Ankara, Turkey
| | - Ismail Hakki Boyaci
- Department of Food Engineering, Faculty of Engineering, Hacettepe University, Beytepe, 06800, Ankara, Turkey
| | - Ali Topcu
- Department of Food Engineering, Faculty of Engineering, Hacettepe University, Beytepe, 06800, Ankara, Turkey.
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19
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Valdés A, Beltrán A, Mellinas C, Jiménez A, Garrigós MC. Analytical methods combined with multivariate analysis for authentication of animal and vegetable food products with high fat content. Trends Food Sci Technol 2018. [DOI: 10.1016/j.tifs.2018.05.014] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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20
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Lee JY, Park JH, Mun H, Shim WB, Lim SH, Kim MG. Quantitative analysis of lard in animal fat mixture using visible Raman spectroscopy. Food Chem 2018; 254:109-114. [PMID: 29548429 DOI: 10.1016/j.foodchem.2018.01.185] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Revised: 01/29/2018] [Accepted: 01/30/2018] [Indexed: 01/25/2023]
Abstract
Food adulteration is a serious issue that requires verification and strict management due to healthcare, morality, and social value problems. In the context of fat, food manufacturers blend lard with vegetable oils or animal fats for convenience and gaining economic benefits. Thus, we herein report the classification of 4 animal fats, e.g., beef tallow, pork lard, chicken fat, duck oil, using Raman spectroscopy combined with simple calculation of intensity ratios of Raman signal at vibrational modes corresponding to unsaturated fatty acids and total fatty acids. Various calculated values of the species were compared to find a feature that is able to classify each fats using Raman peak ratio. As a result, we suggested "Oil gauge (OG)" as a standard feature for classification of the fats in Raman analysis field. Furthermore, a quantification of the lard in other fat was accomplished with good linear correlation using the OG values.
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Affiliation(s)
- Ju-Yong Lee
- Department of Chemistry, School of Physics and Chemistry, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea.
| | - Jin-Ho Park
- Advanced Photonics Research Institute, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea.
| | - Hyoyoung Mun
- Department of Chemistry, School of Physics and Chemistry, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea.
| | - Won-Bo Shim
- Department of Food Science and Technology, Gyeongsang National University, Jinju 52727, Republic of Korea.
| | - Sang-Hyun Lim
- Department of Chemistry, School of Physics and Chemistry, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea.
| | - Min-Gon Kim
- Department of Chemistry, School of Physics and Chemistry, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea.
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Wang X, Esquerre C, Downey G, Henihan L, O’Callaghan D, O’Donnell C. Feasibility of Discriminating Dried Dairy Ingredients and Preheat Treatments Using Mid-Infrared and Raman Spectroscopy. FOOD ANAL METHOD 2017. [DOI: 10.1007/s12161-017-1114-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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