1
|
Shen Z, Wang H, Liang J, Zhao Q, Lu W, Cui Y, Wang P, Shen Q, Chen J. An in situ and real-time analytical method for detection of freeze-thew cycles in tuna via IKnife rapid evaporative ionization mass spectrometry. Food Chem X 2024; 23:101705. [PMID: 39229614 PMCID: PMC11369502 DOI: 10.1016/j.fochx.2024.101705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 06/22/2024] [Accepted: 07/25/2024] [Indexed: 09/05/2024] Open
Abstract
Freezing is one of the most commonly used preservation methods for Bluefin tuna (Thunnus orientalis). However, repeated freezing and thawing would inevitably occur due to the temperature fluctuation, leading to the microstructure damage, lipid oxidation and protein integrity decline of tuna muscle without notable visual appearance change. In this study, we used a rapid evaporative ionization mass spectrometry (REIMS) technique for the real-time determination of the extent of repeated freezing and thawing cycles in tuna fillets. We found significant variance in the relative abundance of fatty acids between bluefin tuna and its fresh counterpart following freeze-thaw cycles. Meanwhile, the difference is statistically significant (p < 0.05). The quality of tuna remains largely unaffected by a single freeze-thaw cycle but significantly deteriorates after freeze-thaw cycles (freeze-thaw count ≥2), and the relative fatty acid content of the ionized aerosol analysis in the REIMS system positively correlated with the number of freeze-thaw cycles. Notably, palmitic acid (C 16:0, m/z 255.23), oleic acid (C 18:1, m/z 281.24), and docosahexaenoic acid (C 22:6, m/z 327.23) displayed the most pronounced changes within the spectrum of fatty acid groups.
Collapse
Affiliation(s)
- Zhifeng Shen
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, China
| | - Honghai Wang
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, China
| | - Jingjing Liang
- Zhejiang Provincial Institute for Food and Drug Control, Hangzhou 310052, China
- Key Laboratory of Quality and Safety of Functional Food for State Market Regulation
| | - Qiaoling Zhao
- Zhoushan Institute of Food & Drug Control, Zhoushan 316000, 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, China
| | - 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, China
| | - Pingya Wang
- Zhoushan Institute of Food & Drug Control, Zhoushan 316000, 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, China
| | - Jian Chen
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, China
| |
Collapse
|
2
|
Xue W, Li F, Li X, Liu Y. A Support Vector Machine-Assisted Metabolomics Approach for Non-Targeted Screening of Multi-Class Pesticides and Veterinary Drugs in Maize. Molecules 2024; 29:3026. [PMID: 38998975 PMCID: PMC11243018 DOI: 10.3390/molecules29133026] [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: 05/08/2024] [Revised: 06/15/2024] [Accepted: 06/19/2024] [Indexed: 07/14/2024] Open
Abstract
The contamination risks of plant-derived foods due to the co-existence of pesticides and veterinary drugs (P&VDs) have not been fully understood. With an increasing number of unexpected P&VDs illegally added to foods, it is essential to develop a non-targeted screening method for P&VDs for their comprehensive risk assessment. In this study, a modified support vector machine (SVM)-assisted metabolomics approach by screening eligible variables to represent marker compounds of 124 multi-class P&VDs in maize was developed based on the results of high-performance liquid chromatography-tandem mass spectrometry. Principal component analysis and orthogonal partial least squares discriminant analysis indicate the existence of variables with obvious inter-group differences, which were further investigated by S-plot plots, permutation tests, and variable importance in projection to obtain eligible variables. Meanwhile, SVM recursive feature elimination under the radial basis function was employed to obtain the weight-squared values of all the variables ranging from large to small for the screening of eligible variables as well. Pairwise t-tests and fold changes of concentration were further employed to confirm these eligible variables to represent marker compounds. The results indicate that 120 out of 124 P&VDs can be identified by the SVM-assisted metabolomics method, while only 109 P&VDs can be found by the metabolomics method alone, implying that SVM can promote the screening accuracy of the metabolomics method. In addition, the method's practicability was validated by the real contaminated maize samples, which provide a bright application prospect in non-targeted screening of contaminants. The limits of detection for 120 P&VDs in maize samples were calculated to be 0.3~1.5 µg/kg.
Collapse
Affiliation(s)
- Weifeng Xue
- Technology Centre of Dalian Customs, Dalian 116000, China
| | - Fang Li
- Technology Centre of Dalian Customs, Dalian 116000, China
| | - Xuemei Li
- Technology Centre of Dalian Customs, Dalian 116000, China
| | - Ying Liu
- Technology Centre of Dalian Customs, Dalian 116000, China
| |
Collapse
|
3
|
Cafarella C, Mangraviti D, Rigano F, Dugo P, Mondello L. Rapid evaporative ionization mass spectrometry: A survey through 15 years of applications. J Sep Sci 2024; 47:e2400155. [PMID: 38772742 DOI: 10.1002/jssc.202400155] [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: 02/28/2024] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/23/2024]
Abstract
Rapid evaporative ionization mass spectrometry (REIMS) is a relatively recent MS technique explored in many application fields, demonstrating high versatility in the detection of a wide range of chemicals, from small molecules (phenols, amino acids, di- and tripeptides, organic acids, and sugars) to larger biomolecules, that is, phospholipids and triacylglycerols. Different sampling devices were used depending on the analyzed matrix (liquid or solid), resulting in distinct performances in terms of automation, reproducibility, and sensitivity. The absence of laborious and time-consuming sample preparation procedures and chromatographic separations was highlighted as a major advantage compared to chromatographic methods. REIMS was successfully used to achieve a comprehensive sample profiling according to a metabolomics untargeted analysis. Moreover, when a multitude of samples were available, the combination with chemometrics allowed rapid sample differentiation and the identification of discriminant features. The present review aims to provide a survey of literature reports based on the use of such analytical technology, highlighting its mode of operation in different application areas, ranging from clinical research, mostly focused on cancer diagnosis for the accurate identification of tumor margins, to the agri-food sector aiming at the safeguard of food quality and security.
Collapse
Affiliation(s)
- Cinzia Cafarella
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
| | - Domenica Mangraviti
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
| | - Francesca Rigano
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
| | - Paola Dugo
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Chromaleont s.r.l., former Veterinary School, University of Messina, Messina, Italy
| | - Luigi Mondello
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Chromaleont s.r.l., former Veterinary School, University of Messina, Messina, Italy
| |
Collapse
|
4
|
Zhang Z, Li Y, Zhao S, Qie M, Bai L, Gao Z, Liang K, Zhao Y. Rapid analysis technologies with chemometrics for food authenticity field: A review. Curr Res Food Sci 2024; 8:100676. [PMID: 38303999 PMCID: PMC10830540 DOI: 10.1016/j.crfs.2024.100676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 12/15/2023] [Accepted: 01/07/2024] [Indexed: 02/03/2024] Open
Abstract
In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.
Collapse
Affiliation(s)
- Zixuan Zhang
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yalan Li
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Shanshan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Mengjie Qie
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lu Bai
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Zhiwei Gao
- Hangzhou Nutritome Biotech Co., Ltd., Hangzhou, China
| | - Kehong Liang
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| |
Collapse
|
5
|
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.
Collapse
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.
| |
Collapse
|
6
|
Rankin‐Turner S, Sears P, Heaney LM. Applications of ambient ionization mass spectrometry in 2022: An annual review. ANALYTICAL SCIENCE ADVANCES 2023; 4:133-153. [PMID: 38716065 PMCID: PMC10989672 DOI: 10.1002/ansa.202300004] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 04/16/2023] [Accepted: 04/17/2023] [Indexed: 06/28/2024]
Abstract
The development of ambient ionization mass spectrometry (AIMS) has transformed analytical science, providing the means of performing rapid analysis of samples in their native state, both in and out of the laboratory. The capacity to eliminate sample preparation and pre-MS separation techniques, leading to true real-time analysis, has led to AIMS naturally gaining a broad interest across the scientific community. Since the introduction of the first AIMS techniques in the mid-2000s, the field has exploded with dozens of novel ion sources, an array of intriguing applications, and an evident growing interest across diverse areas of study. As the field continues to surge forward each year, ambient ionization techniques are increasingly becoming commonplace in laboratories around the world. This annual review provides an overview of AIMS techniques and applications throughout 2022, with a specific focus on some of the major fields of research, including forensic science, disease diagnostics, pharmaceuticals and food sciences. New techniques and methods are introduced, demonstrating the unwavering drive of the analytical community to further advance this exciting field and push the boundaries of what analytical chemistry can achieve.
Collapse
Affiliation(s)
- Stephanie Rankin‐Turner
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public HealthJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Patrick Sears
- School of Chemistry and Chemical EngineeringUniversity of SurreyGuildfordUK
| | - Liam M Heaney
- School of Sport, Exercise and Health SciencesLoughborough UniversityLoughboroughUK
| |
Collapse
|