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Hong Y, Birse N, Quinn B, Li Y, Jia W, van Ruth S, Elliott CT. MALDI-ToF MS and chemometric analysis as a tool for identifying wild and farmed salmon. Food Chem 2024; 432:137279. [PMID: 37657341 DOI: 10.1016/j.foodchem.2023.137279] [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: 05/09/2023] [Revised: 08/10/2023] [Accepted: 08/23/2023] [Indexed: 09/03/2023]
Abstract
In this study, the difference between wild and farmed salmon production was successfully profiled and differentiated by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-ToF MS) combined with chemometric analysis. The established method based on multivariate analysis mainly involved principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least squares-discriminant analysis (OPLS-DA) as the screening and verifying tools to provide insights into the distinctive features found in wild and farmed salmon products, respectively. The discrimination between farmed and wild salmon was accomplished with 100% classification accuracy using chemometric models, 100% identification accuracy was also achieved in distinguishing wild Salmo salar and Oncorhynchus nerka samples. The results of the present work suggest that the proposed method could serve as a reference for detecting salmon fraud relating to wild or farmed production and expand the application of MALDI-ToF technology further into 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, 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, 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, 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, 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, United Kingdom
| | - Saskia van Ruth
- Food Quality and Design Group, Wageningen University and Research, Wageningen, 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, 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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2
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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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3
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He Y, Zeng W, Zhao Y, Zhu X, Wan H, Zhang M, Li Z. Rapid detection of adulteration of goat milk and goat infant formulas using near-infrared spectroscopy fingerprints. Int Dairy J 2022. [DOI: 10.1016/j.idairyj.2022.105536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Cui Y, Ge L, Lu W, Wang S, Li Y, Wang H, Huang M, Xie H, Liao J, Tao Y, Luo P, Ding YY, Shen Q. Real-Time Profiling and Distinction of Lipids from Different Mammalian Milks Using Rapid Evaporative Ionization Mass Spectrometry Combined with Chemometric Analysis. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:7786-7795. [PMID: 35696488 DOI: 10.1021/acs.jafc.2c01447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The price of mammalian milk from different animal species varies greatly due to differences in their yield and nutritional value. Therefore, the authenticity of dairy products has become a hotspot issue in the market due to the replacement or partial admixture of high-cost milk with its low-cost analog. Herein, four common commercial varieties of milk, including goat milk, buffalo milk, Holstein cow milk, and Jersey cow milk, were successfully profiled and differentiated from each other by rapid evaporative ionization mass spectrometry (REIMS) combined with chemometric analysis. This method was developed as a real-time lipid fingerprinting technique. Moreover, the established chemometric algorithms based on multivariate statistical methods mainly involved principal component analysis, orthogonal partial least squares-discriminant analysis, and linear discriminant analysis as the screening and verifying tools to provide insights into the distinctive molecules constituting the four varieties of milk. The ions with m/z 229.1800, 243.1976, 257.2112, 285.2443, 299.2596, 313.2746, 341.3057, 355.2863, 383.3174, 411.3488, 439.3822, 551.5051, 577.5200, 628.5547, 656.5884, 661.5455, 682.6015, and 684.6146 were selected as potential classified markers. The results of the present work suggest that the proposed method could serve as a reference for recognizing dairy fraudulence related to animal species and expand the application field of REIMS technology.
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Affiliation(s)
- Yiwei Cui
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
| | - Lijun Ge
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
| | - Weibo Lu
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
| | - Shitong Wang
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
| | - Yunyan Li
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
| | - Haifeng Wang
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
| | - Min Huang
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
| | - Hujun Xie
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
| | - Jie Liao
- Zhejiang Huacai Testing Technology Co., Ltd., Shaoxing, Zhejiang 311800, China
| | - Ye Tao
- Hangzhou Linping District Maternal & Child Health Care Hospital, Hangzhou, Zhejiang 311113, China
| | - Pei Luo
- State Key Laboratories for Quality Research in Chinese Medicines, Faculty of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Yin-Yi Ding
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
| | - Qing Shen
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
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Kelis Cardoso VG, Sabin GP, Hantao LW. Rapid evaporative ionization mass spectrometry (REIMS) combined with chemometrics for real-time beer analysis. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:1540-1546. [PMID: 35302124 DOI: 10.1039/d2ay00063f] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The beer industry plays an important role in the economy since this is the third most consumed beverage worldwide. Efficient analytical methods must be developed to ensure the quality of the product. Rapid evaporative ionization mass spectrometry (REIMS) can provide molecular-level information, while enabling fast analysis. REIMS requires minimal sample preparation and it is ideal for the analysis of homogeneous liquid samples, such as beers, within only five seconds. In this article, 32 different beers were analyzed by REIMS in positive and negative ionization modes using a hybrid quadrupole time-of-flight mass spectrometer. The positive and negative MS spectrum blocks were augmented for data fusion. A predictive model by partial least squares discriminant analysis (PLS-DA) was used to discriminate the samples (i) by their brands and (ii) by the beer type (Premium and Standard American lagers). The results showed that REIMS provided a rich fingerprint of beers, which was successfully used to discriminate the brands and types with 96.9% and 97.9% accuracy, respectively. We believe that this proof-of-concept has great potential to be applied on a larger scale for industrial purposes due to its high-throughput.
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Affiliation(s)
| | - Guilherme Post Sabin
- Institute of Chemistry, University of Campinas, 270 Monteiro Lobato, Campinas, São Paulo, 13083-862, Brazil.
- OpenScience, Office 916, 233 Conceição Street, Campinas, São Paulo, 13010-050, Brazil
| | - Leandro Wang Hantao
- Institute of Chemistry, University of Campinas, 270 Monteiro Lobato, Campinas, São Paulo, 13083-862, Brazil.
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