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Gao R, Liu L, Monto AR, Su K, Zhang H, Shi T, Xiong Z, Xu G, Luo Y, Bao Y, Yuan L. Metabolomic profile of muscles from tilapia cultured in recirculating aquaculture systems and traditional aquaculture in ponds and protein stability during freeze-thaw cycles. Food Chem 2024; 451:139325. [PMID: 38657519 DOI: 10.1016/j.foodchem.2024.139325] [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: 02/02/2024] [Revised: 04/02/2024] [Accepted: 04/08/2024] [Indexed: 04/26/2024]
Abstract
Muscle protein stability during freeze-thaw (F-T) cycles was investigated with tilapia cultured in recirculating aquaculture systems (RAS) and traditional aquaculture in ponds (TAP). This study found that fatty acids (eg., palmitic acid) were enriched in TAP, while antioxidants (eg., glutathione) were enriched in RAS. Generally, proteins in the RAS group exhibited greater stability against denaturation during the F-T cycle, suggested by a less decrease in haem protein content (77% in RAS and 86% in TAP) and a less increase in surface hydrophobicity of sarcoplasmic protein (63% in RAS and 101% in TAP). There was no significant difference in oxidative stability of myofibrillar protein between the two groups. This study provides a theoretical guide for the quality control of tilapia cultured in RAS during frozen storage.
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Affiliation(s)
- Ruichang Gao
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Lu Liu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Abdul Razak Monto
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Kai Su
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Hao Zhang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Tong Shi
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Zhiyu Xiong
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Gangchun Xu
- Key Laboratory of Freshwater, Fisheries and Germplasm Resources Utilization, Freshwater Fisheries Research Centre of Chinese Academy of Fishery Sciences, Ministry of Agriculture and Rural Affairs, Wuxi, Jiangsu 214081, China
| | - Yongju Luo
- Guangxi Institute of Aquatic Sciences, Guangxi Key Laboratory of Aquatic Genetic Breeding and Healthy Farming, Nanning, Guangxi 530021, China
| | - Yulong Bao
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China.
| | - Li Yuan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China.
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2
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Haridas PC, Ravichandran R, Shaikh N, Kishore P, Kumar Panda S, Banerjee K, Sekhar Chatterjee N. Authentication of the species identity of squid rings using UHPLC-Q-Orbitrap MS/MS-based lipidome fingerprinting and chemoinformatics. Food Chem 2024; 442:138525. [PMID: 38271906 DOI: 10.1016/j.foodchem.2024.138525] [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: 08/19/2023] [Revised: 12/20/2023] [Accepted: 01/18/2024] [Indexed: 01/27/2024]
Abstract
Species mislabeling of commercial loliginidae squid can undermine important conservation efforts and prevent consumers from making informed decisions. A comprehensive lipidomic fingerprint of Uroteuthis singhalensis, Uroteuthis edulis, and Uroteuthis duvauceli rings was established using high-resolution mass spectrometry-based lipidomics and chemoinformatics analysis. The principal component analysis showed a clear separation of sample groups, with R2X and Q2 values of 0.97 and 0.85 for ESI+ and 0.96 and 0.86 for ESI-, indicating a good model fit. The optimized OPLS-DA and PLS-DA models could discriminate the species identity of validation samples with 100 % accuracy. A total of 67 and 90 lipid molecules were putatively identified as biomarkers in ESI+ and ESI-, respectively. Identified lipids, including PC(40:6), C14 sphingomyelin, PS(O-36:0), and PE(41:4), played an important role in species discrimination. For the first time, this study provides a detailed lipidomics profile of commercially important loliginidae squid and establishes a faster workflow for species authentication.
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Affiliation(s)
- Pranamya C Haridas
- National Reference Laboratory, ICAR-Central Institute of Fisheries Technology, Matsyapuri P.O., W. Island, Cochin 682029, India; Department of Chemical Oceanography, School of Marine Sciences, Cochin University of Science and Technology, Cochin 682016, India
| | - Rajesh Ravichandran
- National Reference Laboratory, ICAR-Central Institute of Fisheries Technology, Matsyapuri P.O., W. Island, Cochin 682029, India
| | - Nasiruddin Shaikh
- National Referral Laboratory, ICAR-National Research Centre for Grapes, Manjri Farm, Pune 412307, India
| | - Pankaj Kishore
- National Reference Laboratory, ICAR-Central Institute of Fisheries Technology, Matsyapuri P.O., W. Island, Cochin 682029, India
| | - Satyen Kumar Panda
- National Reference Laboratory, ICAR-Central Institute of Fisheries Technology, Matsyapuri P.O., W. Island, Cochin 682029, India; Food Safety and Standards Authority of India, FDA Bhawan, Kotla Road, New Delhi 110002, India
| | - Kaushik Banerjee
- National Referral Laboratory, ICAR-National Research Centre for Grapes, Manjri Farm, Pune 412307, India
| | - Niladri Sekhar Chatterjee
- National Reference Laboratory, ICAR-Central Institute of Fisheries Technology, Matsyapuri P.O., W. Island, Cochin 682029, India.
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Benedetto A, Robotti E, Belay MH, Ghignone A, Fabbris A, Goggi E, Cerruti S, Manfredi M, Barberis E, Peletto S, Arillo A, Giaccio N, Masini MA, Brandi J, Cecconi D, Marengo E, Brizio P. Multi-Omics Approaches for Freshness Estimation and Detection of Illicit Conservation Treatments in Sea Bass ( Dicentrarchus Labrax): Data Fusion Applications. Int J Mol Sci 2024; 25:1509. [PMID: 38338789 PMCID: PMC10855268 DOI: 10.3390/ijms25031509] [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: 12/30/2023] [Revised: 01/18/2024] [Accepted: 01/20/2024] [Indexed: 02/12/2024] Open
Abstract
Fish freshness consists of complex endogenous and exogenous processes; therefore, the use of a few parameters to unravel illicit practices could be insufficient. Moreover, the development of strategies for the identification of such practices based on additives known to prevent and/or delay fish spoilage is still limited. The paper deals with the identification of the effect played by a Cafodos solution on the conservation state of sea bass at both short-term (3 h) and long-term (24 h). Controls and treated samples were characterized by a multi-omic approach involving proteomics, lipidomics, metabolomics, and metagenomics. Different parts of the fish samples were studied (muscle, skin, eye, and gills) and sampled through a non-invasive procedure based on EVA strips functionalized by ionic exchange resins. Data fusion methods were then applied to build models able to discriminate between controls and treated samples and identify the possible markers of the applied treatment. The approach was effective in the identification of the effect played by Cafodos that proved to be different in the short- and long-term and complex, involving proteins, lipids, and small molecules to a different extent.
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Affiliation(s)
- Alessandro Benedetto
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d’Aosta, Via Bologna 148, 10154 Torino, Italy; (A.B.); (S.P.); (A.A.); (N.G.); (P.B.)
| | - Elisa Robotti
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Masho Hilawie Belay
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
- Department of Chemistry, Mekelle University, Mekelle P.O. Box 231, Ethiopia
| | - Arianna Ghignone
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Alessia Fabbris
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Eleonora Goggi
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Simone Cerruti
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Marcello Manfredi
- Department of Translational Medicine, University of Piemonte Orientale, Via Solaroli 17, 28100 Novara, Italy;
| | - Elettra Barberis
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Simone Peletto
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d’Aosta, Via Bologna 148, 10154 Torino, Italy; (A.B.); (S.P.); (A.A.); (N.G.); (P.B.)
| | - Alessandra Arillo
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d’Aosta, Via Bologna 148, 10154 Torino, Italy; (A.B.); (S.P.); (A.A.); (N.G.); (P.B.)
| | - Nunzia Giaccio
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d’Aosta, Via Bologna 148, 10154 Torino, Italy; (A.B.); (S.P.); (A.A.); (N.G.); (P.B.)
| | - Maria Angela Masini
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Jessica Brandi
- Department of Biotechnology, University of Verona, Strada le Grazie 15, 37134 Verona, Italy; (J.B.); (D.C.)
| | - Daniela Cecconi
- Department of Biotechnology, University of Verona, Strada le Grazie 15, 37134 Verona, Italy; (J.B.); (D.C.)
| | - Emilio Marengo
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Paola Brizio
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d’Aosta, Via Bologna 148, 10154 Torino, Italy; (A.B.); (S.P.); (A.A.); (N.G.); (P.B.)
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Asefa BG, Sun C, Van Beers R, Saeys W, Ruyters S. A feasibility study on nondestructive classification of frozen Atlantic salmon (Salmo salar) fillets based on temperature history at the logistics using NIR spectroscopy. J Food Sci 2022; 87:2847-2857. [PMID: 35638339 DOI: 10.1111/1750-3841.16195] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 04/20/2022] [Accepted: 04/25/2022] [Indexed: 11/28/2022]
Abstract
Temperature fluctuation commonly occurs in the cold chain leading to complete or partial thawing and refreezing of frozen products resulting in a multifrozen product. Such oscillation of temperature could cause significant quality reduction compared to single frozen products. This study was designed to differentiate frozen Atlantic salmon fillets based on the level of temperature fluctuation. Near-infrared spectroscopy (NIRS) coupled with chemometrics was used to classify the frozen fillets stored at no fluctuation (NF), low fluctuation (LF), high fluctuation (HF), and very high fluctuation (VF) temperature. Using spectral profiles obtained at both frozen and thawed states, fillets were classified based on the level of temperature fluctuation by partial least squares discriminant analysis (PLS-DA). The thawed samples showed better classification accuracy (71%) than frozen samples (66%) in a four-class model. Considering the small variation within the first two (NF, LF) and the last two (HF, VF) groups, a two-class classification model was developed using thawed samples, and the obtained model correctly classified the two groups ([NF, LF] and [HF, VF]) with 100 % classification accuracy. Protein- and water-related changes were found important to distinguish the fillets. Based on these findings, the four-class prediction model is found insufficient to be used for nondestructive determination of temperature history of frozen fillets. However, the two-class prediction model with further external validation can be applied to determine the level of temperature fluctuation particularly using fillets scanned at thawed state. PRACTICAL APPLICATION: NIR spectroscopy can be used to evaluate the degree of temperature fluctuation and thus related quality loss throughout the logistics of frozen Atlantic salmon fillets. Researchers, food control authorities, and the retail industry could be the primary beneficiaries of this research output.
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Affiliation(s)
- Bezuayehu Gutema Asefa
- Food Science and Nutrition Research Department, National Fishery and Aquatic Life Research Center (NFALRC), Ethiopian Institute of Agricultural Research (EIAR), Sebeta, Ethiopia.,Department of Biosystems (BIOSYST), Division of Mechatronics, Biostatistics and Sensors (MeBioS), University of Leuven (KU Leuven), Leuven, Belgium
| | - Chanjun Sun
- Department of Biosystems (BIOSYST), Division of Mechatronics, Biostatistics and Sensors (MeBioS), University of Leuven (KU Leuven), Leuven, Belgium
| | - Robbe Van Beers
- Department of Biosystems (BIOSYST), Division of Mechatronics, Biostatistics and Sensors (MeBioS), University of Leuven (KU Leuven), Leuven, Belgium
| | - Wouter Saeys
- Department of Biosystems (BIOSYST), Division of Mechatronics, Biostatistics and Sensors (MeBioS), University of Leuven (KU Leuven), Leuven, Belgium
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5
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Zhong P, Wei X, Li X, Wei X, Wu S, Huang W, Koidis A, Xu Z, Lei H. Untargeted metabolomics by liquid chromatography‐mass spectrometry for food authentication: A review. Compr Rev Food Sci Food Saf 2022; 21:2455-2488. [DOI: 10.1111/1541-4337.12938] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 02/20/2022] [Accepted: 02/21/2022] [Indexed: 12/17/2022]
Affiliation(s)
- Peng Zhong
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Xiaoqun Wei
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Xiangmei Li
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Xiaoyi Wei
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Shaozong Wu
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Weijuan Huang
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Anastasios Koidis
- Institute for Global Food Security Queen's University Belfast Belfast UK
| | - Zhenlin Xu
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Hongtao Lei
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
- Guangdong Laboratory for Lingnan Modern Agriculture South China Agricultural University Guangzhou 510642 China
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6
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Seafood Processing, Preservation, and Analytical Techniques in the Age of Industry 4.0. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031703] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Fish and other seafood products are essential dietary components that are highly appreciated and consumed worldwide. However, the high perishability of these products has driven the development of a wide range of processing, preservation, and analytical techniques. This development has been accelerated in recent years with the advent of the fourth industrial revolution (Industry 4.0) technologies, digitally transforming almost every industry, including the food and seafood industry. The purpose of this review paper is to provide an updated overview of recent thermal and nonthermal processing and preservation technologies, as well as advanced analytical techniques used in the seafood industry. A special focus will be given to the role of different Industry 4.0 technologies to achieve smart seafood manufacturing, with high automation and digitalization. The literature discussed in this work showed that emerging technologies (e.g., ohmic heating, pulsed electric field, high pressure processing, nanotechnology, advanced mass spectrometry and spectroscopic techniques, and hyperspectral imaging sensors) are key elements in industrial revolutions not only in the seafood industry but also in all food industry sectors. More research is still needed to explore how to harness the Industry 4.0 innovations in order to achieve a green transition toward more profitable and sustainable food production systems.
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