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Xu X, Bi K, Wu G, Yang P, Li H, Jia W, Zhang C. The Effect of Enzymatic Hydrolysis and Maillard Reaction on the Flavor of Chicken Osteopontin. Foods 2024; 13:702. [PMID: 38472815 DOI: 10.3390/foods13050702] [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: 01/16/2024] [Revised: 02/16/2024] [Accepted: 02/21/2024] [Indexed: 03/14/2024] Open
Abstract
To reveal the changes in the flavor quality of chicken osteopontin (CO) before and after enzymatic hydrolysis and a thermal reaction, the present study was carried out to evaluate the volatile compounds and non-volatile compounds in CO. The results show that the chicken boneset enzymatic solution (CBES) presented a notably richer aroma after the enzymatic hydrolysis treatment. At the same time, the concentrations of the total free amino acids (FAAs) and 5'-nucleotides in the CBES increased dramatically. The ERP (enzymatic reaction paste) scores and the ORC (osteopontin reactive cream) scores were exceptionally high in terms of the umami and salty flavor profiles. As precursors, FAAs and 5'-nucleotides also boosted the Maillard reaction, leading to the generation of wide volatile compounds. Compared to CO, CBES, and ORC, the sensory evaluation showed that ERP scored the highest. In summary, the enzymatic hydrolysis treatment coupled with the Maillard reaction significantly enhanced the flavor profile of CO. These findings offer valuable insights into the high-value utilization of bone by-products, making a significant advancement in the field.
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Affiliation(s)
- Xiong Xu
- College of Food Science, Southwest University, Chongqing 400715, China
- Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Ke Bi
- Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Guangyu Wu
- Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Ping Yang
- Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Hongjun Li
- College of Food Science, Southwest University, Chongqing 400715, China
| | - Wei Jia
- Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Chunhui Zhang
- Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
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2
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Zhou C, Wang J, Xiang J, Fu Q, Sun X, Liu L, Ai L, Wang J. Rapid detection of duck ingredient in adulterated foods by isothermal recombinase polymerase amplification assays. FOOD CHEMISTRY. MOLECULAR SCIENCES 2023; 6:100162. [PMID: 36654874 PMCID: PMC9841362 DOI: 10.1016/j.fochms.2023.100162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 01/03/2023] [Accepted: 01/07/2023] [Indexed: 01/11/2023]
Abstract
Duck is often used in meat fraud as a substitute for more expensive meats. Rapid detection of duck ingredient in meat products is of great significance for combating meat fraud and safeguarding the interests of consumers. Therefore, we aim to develop duck-specific recombinase polymerase amplification (RPA)-based assays for the rapid detection of duck ingredient in animal-derived foods. Using Cytb gene as target, the real-time RPA and RPA combined with lateral flow strips (LFS RPA) were developed successfully for the rapid detection of ducks in 20 min at 39 °C and 40 °C, respectively. The assays did not show cross-reactions with 6 other livestock and poultry. The developed RPA assays could detect 10 pg duck genomic DNA per reaction and 0.1 % (w/w) duck ingredient in duck and mutton mixed powder within 30 min, including a rapid nucleic acid extraction. Furthermore, duck ingredient could be detected in 30 different actual foods including heat-processed meats and blood products. Therefore, duck-specific real-time RPA and LFS RPA assays were successfully developed with good specificity and sensitivity, which could enable rapid detection of duck ingredient in the field and provide technical support for combating the meat fraud.
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Affiliation(s)
- Cang Zhou
- School of Public Health, Hebei Medical University, Shijiazhuang 050017, China,Food Microbiology and Animal Quarantine Laboratory, Technology Center of Shijiazhuang Customs, Shijiazhuang 050051, China,Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, China
| | - Jinfeng Wang
- Food Microbiology and Animal Quarantine Laboratory, Technology Center of Shijiazhuang Customs, Shijiazhuang 050051, China
| | - Jialin Xiang
- Food Microbiology and Animal Quarantine Laboratory, Technology Center of Shijiazhuang Customs, Shijiazhuang 050051, China
| | - Qi Fu
- Food Microbiology and Animal Quarantine Laboratory, Technology Center of Shijiazhuang Customs, Shijiazhuang 050051, China
| | - Xiaoxia Sun
- Food Microbiology and Animal Quarantine Laboratory, Technology Center of Shijiazhuang Customs, Shijiazhuang 050051, China
| | - Libing Liu
- Food Microbiology and Animal Quarantine Laboratory, Technology Center of Shijiazhuang Customs, Shijiazhuang 050051, China
| | - Lianfeng Ai
- School of Public Health, Hebei Medical University, Shijiazhuang 050017, China,Food Microbiology and Animal Quarantine Laboratory, Technology Center of Shijiazhuang Customs, Shijiazhuang 050051, China,Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, China
| | - Jianchang Wang
- School of Public Health, Hebei Medical University, Shijiazhuang 050017, China,Food Microbiology and Animal Quarantine Laboratory, Technology Center of Shijiazhuang Customs, Shijiazhuang 050051, China,Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, China,Corresponding author at: School of Public Health, Hebei Medical University, Shijiazhuang 050017, China.
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3
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Wang Z, Da W, Negi CS, Ghimire PL, Wangdi K, Yadav PK, Pubu Z, Lama L, Yarpel K, Maunsell SC, Liu Y, Kunte K, Bawa KS, Yang D, Pierce NE. Profiling, monitoring and conserving caterpillar fungus in the Himalayan region using anchored hybrid enrichment markers. Proc Biol Sci 2022; 289:20212650. [PMID: 35473372 PMCID: PMC9043734 DOI: 10.1098/rspb.2021.2650] [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] [Indexed: 11/16/2022] Open
Abstract
The collection of caterpillar fungus accounts for 50–70% of the household income of thousands of Himalayan communities and has an estimated market value of $5–11 billion across Asia. However, Himalayan collectors are at multiple economic disadvantages compared with collectors on the Tibetan Plateau because their product is not legally recognized. Using a customized hybrid-enrichment probe set and market-grade caterpillar fungus (with samples up to 30 years old) from 94 production zones across Asia, we uncovered clear geography-based signatures of historical dispersal and significant isolation-by-distance among caterpillar fungus hosts. This high-throughput approach can readily distinguish samples from major production zones with definitive geographical resolution, especially for samples from the Himalayan region that form monophyletic clades in our analysis. Based on these results, we propose a two-step procedure to help local communities authenticate their produce and improve this multi-national trade-route without creating opportunities for illegal exports and other forms of economic exploitation. We argue that policymakers and conservation practitioners must encourage the fair trade of caterpillar fungus in addition to sustainable harvesting to support a trans-boundary conservation effort that is much needed for this natural commodity in the Himalayan region.
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Affiliation(s)
- Zhengyang Wang
- Department of Organismic and Evolutionary Biology and Museum of Comparative Zoology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, USA
| | - Wa Da
- Tibetan Plateau Institute of Biology, Tibet Autonomous Region, Lhasa 850001, People's Republic of China
| | - Chandra Singh Negi
- Department of Zoology, M B Government Postgraduate College, Haldwani (Nainital) 263139, Uttarakhand, India
| | - Puspa Lal Ghimire
- Asia Network for Sustainable Agriculture and Bioresources (ANSAB), Baneshwor, Kathmandu, Nepal
| | - Karma Wangdi
- Ugyen Wangchuck Institute for Conservation and Environmental Research, Lamai Goempa, Bumthang, Jakar 32001, Bhutan
| | - Pramod K Yadav
- Department of Parks, Recreation, and Tourism Management, Clemson University, Clemson, SC 29634-0735, USA
| | - Zhuoma Pubu
- Tibetan Plateau Institute of Biology, Tibet Autonomous Region, Lhasa 850001, People's Republic of China
| | - Laiku Lama
- Himalayan Herbs Traders, Baluwatar-4 Bagta Marga 161, Kathmandu, Nepal
| | | | - Sarah C Maunsell
- Department of Organismic and Evolutionary Biology and Museum of Comparative Zoology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, USA
| | - Yong Liu
- Institute of Plant Protection, Sichuan Academy of Agricultural Sciences, Chengdu 610066, People's Republic of China
| | - Krushnamegh Kunte
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bengaluru 560065, India
| | - Kamaljit S Bawa
- University of Massachusetts, Boston, MA 02125, USA.,Ashoka Trust for Research in Ecology and the Environment, Bangalore 560024, India
| | - Darong Yang
- Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, Yunnan 666303, People's Republic of China
| | - Naomi E Pierce
- Department of Organismic and Evolutionary Biology and Museum of Comparative Zoology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, USA
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4
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Sangaré M, Karoui R. Evaluation and monitoring of the quality of sausages by different analytical techniques over the last five years. Crit Rev Food Sci Nutr 2022; 63:8136-8160. [PMID: 35333686 DOI: 10.1080/10408398.2022.2053059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Sausages are among the most vulnerable and perishable products, although those products are an important source of essential nutrients for human organisms. The evaluation of the quality of sausages becomes more and more required by consumers, producers, and authorities to thwarter falsification. Numerous analytical techniques including chemical, sensory, chromatography, and so on, are employed for the determination of the quality and authenticity of sausages. These methods are expensive and time consuming, and are often sensitive to significant sources of variation. Therefore, rapid analytical techniques such as fluorescence spectroscopy, near infrared (NIR), mid infrared (MIR), nuclear magnetic resonance (NMR), among others were considered helpful tools in this domain. This review will identify current gaps related to different analytical techniques in assessing and monitoring the quality of sausages and discuss the drawbacks of existing analytical methods regarding the quality and authenticity of sausages from 2015 up to now.
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Affiliation(s)
- Moriken Sangaré
- Univ. Artois, Univ. Lille, Univ. Littoral Côte d'Opale, Univ. Picardie Jules Verne, Univ. de Liège, INRAE, BioEcoAgro, Lens, France
- Institut Supérieur des Sciences et Médecine Vétérinaire de Dalaba, Département de Technologie et Contrôle des Produits Alimentaires, DTCPA, ISSMV/Dalaba, Guinée
- Univ. Gamal Abdel Nasser de Conakry, Guinée, Uganc, Guinée
| | - Romdhane Karoui
- Univ. Artois, Univ. Lille, Univ. Littoral Côte d'Opale, Univ. Picardie Jules Verne, Univ. de Liège, INRAE, BioEcoAgro, Lens, France
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5
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Zhang M, Li Y, Zhang Y, Kang C, Zhao W, Ren N, Guo W, Wang S. Rapid LC-MS/MS method for the detection of seven animal species in meat products. Food Chem 2022; 371:131075. [PMID: 34543926 DOI: 10.1016/j.foodchem.2021.131075] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 08/07/2021] [Accepted: 09/05/2021] [Indexed: 12/20/2022]
Abstract
The adulteration of meat products has been reported worldwide, and detection of specific peptides through mass spectrometry (MS) is a reliable method for meat species identification. However, the practical application of this method is limited by complicated steps and long reaction time of the traditional sample preparation. Therefore, this paper introduced a convenient and time-saving sample preparation by optimizing the steps of reduction, alkylation, digestion, and purification. With the rapid sample preparation, 35 species-specific peptides for seven species (pig, cattle, sheep, deer, chicken, duck, and turkey) were screened using high-resolution MS, and a rapid LC-MS/MS method was established. The method only takes 3 h from sample receipt to results. The meat species of 20 processed meat products were detected, and three samples were found potentially adulterated. The method is proved to have high sensitivity, specificity, practicability with respect to rapid identification of meat species in meat products.
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Affiliation(s)
| | - Yingying Li
- China Meat Research Center, 100068 Beijing, China
| | | | - Chaodi Kang
- China Meat Research Center, 100068 Beijing, China
| | - Wentao Zhao
- China Meat Research Center, 100068 Beijing, China
| | - Nan Ren
- China Meat Research Center, 100068 Beijing, China
| | - Wenping Guo
- China Meat Research Center, 100068 Beijing, China
| | - Shouwei Wang
- China Meat Research Center, 100068 Beijing, China.
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6
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Chaora NS, Khanyile KS, Magwedere K, Pierneef R, Tabit FT, Muchadeyi FC. A 16S Next Generation Sequencing Based Molecular and Bioinformatics Pipeline to Identify Processed Meat Products Contamination and Mislabelling. Animals (Basel) 2022; 12:ani12040416. [PMID: 35203124 PMCID: PMC8868451 DOI: 10.3390/ani12040416] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 09/28/2021] [Accepted: 10/07/2021] [Indexed: 12/03/2022] Open
Abstract
Simple Summary Meat adulteration and fraud encompasses the deliberate fraudulent addition or substitution of proteins of animal or plant origin in edible products primarily for economic gain. The mitochondrial 16S ribosomal (rRNA) gene was used to identify species that are present in pure and processed meat samples. The meat samples were sequenced using an Illumina sequencing platform, and bioinformatics analysis was carried out for species identification. The results indicated that pork was the major contaminant in most of the meat samples. The bioinformatics pipeline demonstrated its specificity through identification of species specific and quantification of the contamination levels across all samples. Food business operators and regulatory sectors can validate this method for food fraud checks and manage any form of mislabeling in the animal or plant protein food ecosystem. Abstract Processed meat is a target in meat adulteration for economic gain. This study demonstrates a molecular and bioinformatics diagnostic pipeline, utilizing the mitochondrial 16S ribosomal RNA (rRNA) gene, to determine processed meat product mislabeling through Next-Generation Sequencing. Nine pure meat samples were collected and artificially mixed at different ratios to verify the specificity and sensitivity of the pipeline. Processed meat products (n = 155), namely, minced meat, biltong, burger patties, and sausages, were collected across South Africa. Sequencing was performed using the Illumina MiSeq sequencing platform. Each sample had paired-end reads with a length of ±300 bp. Quality control and filtering was performed using BBDuk (version 37.90a). Each sample had an average of 134,000 reads aligned to the mitochondrial genomes using BBMap v37.90. All species in the artificial DNA mixtures were detected. Processed meat samples had reads that mapped to the Bos (90% and above) genus, with traces of reads mapping to Sus and Ovis (2–5%) genus. Sausage samples showed the highest level of contamination with 46% of the samples having mixtures of beef, pork, or mutton in one sample. This method can be used to authenticate meat products, investigate, and manage any form of mislabeling.
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Affiliation(s)
- Nyaradzo Stella Chaora
- Department of Life and Consumer Sciences, College of Agriculture and Environmental Sciences, University of South Africa, Rooderpoort 1709, South Africa; (N.S.C.); (F.T.T.)
- Biotechnology Platform, Agricultural Research Council, Private Bag X 05, Onderstepoort, Pretoria 0110, South Africa; (K.S.K.); (R.P.)
| | - Khulekani Sedwell Khanyile
- Biotechnology Platform, Agricultural Research Council, Private Bag X 05, Onderstepoort, Pretoria 0110, South Africa; (K.S.K.); (R.P.)
| | - Kudakwashe Magwedere
- Directorate of Veterinary Public Health, Department of Agriculture, Land Reform and Rural Development, Pretoria 0001, South Africa;
| | - Rian Pierneef
- Biotechnology Platform, Agricultural Research Council, Private Bag X 05, Onderstepoort, Pretoria 0110, South Africa; (K.S.K.); (R.P.)
| | - Frederick Tawi Tabit
- Department of Life and Consumer Sciences, College of Agriculture and Environmental Sciences, University of South Africa, Rooderpoort 1709, South Africa; (N.S.C.); (F.T.T.)
| | - Farai Catherine Muchadeyi
- Biotechnology Platform, Agricultural Research Council, Private Bag X 05, Onderstepoort, Pretoria 0110, South Africa; (K.S.K.); (R.P.)
- Correspondence:
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7
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Han F, Huang X, Aheto JH, Zhang X, Rashed MMA. Fusion of a low-cost electronic nose and Fourier transform near-infrared spectroscopy for qualitative and quantitative detection of beef adulterated with duck. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:417-426. [PMID: 35014996 DOI: 10.1039/d1ay01949j] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
A low-cost electronic nose (E-nose) based on colorimetric sensors fused with Fourier transform-near-infrared (FT-NIR) spectroscopy was proposed as a rapid and convenient technique for detecting beef adulterated with duck. The total volatile basic nitrogen, protein, fat, total sugar and ash contents were measured to investigate the differences of basic properties between raw beef and duck; GC-MS was employed to analyze the difference of the volatile organic compounds emitted from these two types of meat. For variable selection and spectra denoising, the simple T-test (p < 0.05) separately intergraded with first derivative, second derivative, centralization, standard normal variate transform, and multivariate scattering correction were performed and the results compared. Extreme learning machine models were built to identify the adulterated beef and predict the adulteration levels. Results showed that for recognizing the independent samples of raw beef, beef-duck mixtures, and raw duck, FT-NIR offered a 100% identification rate, which was superior to the E-nose (83.33%) created herein. In terms of predicting adulteration levels, the root means square error (RMSE) and the correlation coefficient (r) for independent meat samples using FT-NIR were 0.511% and 0.913, respectively. At the same time, for E-nose, these two indicators were 1.28% and 0.841, respectively. When the E-nose and FT-NIR data were fused, the RMSE decreased to 0.166%, and the r improved to 0.972. All the results indicated that fusion of the low-cost E-nose and FT-NIR could be employed for rapid and convenient testing of beef adulterated with duck.
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Affiliation(s)
- Fangkai Han
- School of Biological and Food Engineering, Suzhou University, Bianhe Middle Road 49, Suzhou 234000, Anhui, P. R. China.
| | - Xingyi Huang
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, Jiangsu, P. R. China
| | - Joshua H Aheto
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, Jiangsu, P. R. China
| | - Xiaorui Zhang
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, Jiangsu, P. R. China
| | - Marwan M A Rashed
- School of Biological and Food Engineering, Suzhou University, Bianhe Middle Road 49, Suzhou 234000, Anhui, P. R. China.
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8
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Afifa khatun M, Hossain A, Hossain MS, Kamruzzaman Munshi M, Huque R. Detection of species adulteration in meat products and Mozzarella-type cheeses using duplex PCR of mitochondrial cyt b gene: A food safety concern in Bangladesh. FOOD CHEMISTRY: MOLECULAR SCIENCES 2021; 2:100017. [PMID: 35415622 PMCID: PMC8991966 DOI: 10.1016/j.fochms.2021.100017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 03/08/2021] [Accepted: 03/12/2021] [Indexed: 11/25/2022]
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9
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Li YC, Liu SY, Meng FB, Liu DY, Zhang Y, Wang W, Zhang JM. Comparative review and the recent progress in detection technologies of meat product adulteration. Compr Rev Food Sci Food Saf 2020; 19:2256-2296. [PMID: 33337107 DOI: 10.1111/1541-4337.12579] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 05/06/2020] [Accepted: 05/06/2020] [Indexed: 12/11/2022]
Abstract
Meat adulteration, mainly for the purpose of economic pursuit, is widespread and leads to serious public health risks, religious violations, and moral loss. Rapid, effective, accurate, and reliable detection technologies are keys to effectively supervising meat adulteration. Considering the importance and rapid advances in meat adulteration detection technologies, a comprehensive review to summarize the recent progress in this area and to suggest directions for future progress is beneficial. In this review, destructive meat adulteration technologies based on DNA, protein, and metabolite analyses and nondestructive technologies based on spectroscopy were comparatively analyzed. The advantages and disadvantages, application situations of these technologies were discussed. In the future, determining suitable indicators or markers is particularly important for destructive methods. To improve sensitivity and save time, new interdisciplinary technologies, such as biochips and biosensors, are promising for application in the future. For nondestructive techniques, convenient and effective chemometric models are crucial, and the development of portable devices based on these technologies for onsite monitoring is a future trend. Moreover, omics technologies, especially proteomics, are important methods in laboratory detection because they enable multispecies detection and unknown target screening by using mass spectrometry databases.
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Affiliation(s)
- Yun-Cheng Li
- College of Pharmacy and Biological Engineering, Chengdu University, Chengdu, China.,Key Laboratory of Meat Processing of Sichuan Province, Chengdu University, Chengdu, China
| | - Shu-Yan Liu
- College of Pharmacy and Biological Engineering, Chengdu University, Chengdu, China
| | - Fan-Bing Meng
- College of Pharmacy and Biological Engineering, Chengdu University, Chengdu, China.,Key Laboratory of Meat Processing of Sichuan Province, Chengdu University, Chengdu, China
| | - Da-Yu Liu
- College of Pharmacy and Biological Engineering, Chengdu University, Chengdu, China.,Key Laboratory of Meat Processing of Sichuan Province, Chengdu University, Chengdu, China
| | - Yin Zhang
- College of Pharmacy and Biological Engineering, Chengdu University, Chengdu, China.,Key Laboratory of Meat Processing of Sichuan Province, Chengdu University, Chengdu, China
| | - Wei Wang
- Key Laboratory of Meat Processing of Sichuan Province, Chengdu University, Chengdu, China
| | - Jia-Min Zhang
- Key Laboratory of Meat Processing of Sichuan Province, Chengdu University, Chengdu, China
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10
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Zia Q, Alawami M, Mokhtar NFK, Nhari RMHR, Hanish I. Current analytical methods for porcine identification in meat and meat products. Food Chem 2020; 324:126664. [PMID: 32380410 DOI: 10.1016/j.foodchem.2020.126664] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 03/20/2020] [Accepted: 03/20/2020] [Indexed: 12/21/2022]
Abstract
Authentication of meat products is critical in the food industry. Meat adulteration may lead to religious apprehensions, financial gain and food-toxicities such as meat allergies. Thus, empirical validation of the quality and constituents of meat is paramount. Various analytical methods often based on protein or DNA measurements are utilized to identify meat species. Protein-based methods, including electrophoretic and immunological techniques, are at times unsuitable for discriminating closely related species. Most of these methods have been replaced by more accurate and sensitive detection methods, such as DNA-based techniques. Emerging technologies like DNA barcoding and mass spectrometry are still in their infancy when it comes to their utilization in meat detection. Gold nanobiosensors have shown some promise in this regard. However, its applicability in small scale industries is distant. This article comprehensively reviews the recent developments in the field of analytical methods used for porcine identification.
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Affiliation(s)
- Qamar Zia
- A New Mind, Ash Shati, Al Qatif 32617-3732, Saudi Arabia.
| | - Mohammad Alawami
- A New Mind, Ash Shati, Al Qatif 32617-3732, Saudi Arabia; Depaartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, United Kingdom
| | | | | | - Irwan Hanish
- Halal Product Research Institute, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia; Department of Microbiology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia
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11
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Han F, Huang X, H. Aheto J, Zhang D, Feng F. Detection of Beef Adulterated with Pork Using a Low-Cost Electronic Nose Based on Colorimetric Sensors. Foods 2020; 9:foods9020193. [PMID: 32075051 PMCID: PMC7073938 DOI: 10.3390/foods9020193] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 02/05/2020] [Accepted: 02/10/2020] [Indexed: 12/22/2022] Open
Abstract
The present study was aimed at developing a low-cost but rapid technique for qualitative and quantitative detection of beef adulterated with pork. An electronic nose based on colorimetric sensors was proposed. The fresh beef rib steaks and streaky pork were purchased and used from the local agricultural market in Suzhou, China. The minced beef was mixed with pork ranging at levels from 0%~100% by weight at increments of 20%. Protein, fat, and ash content were measured for validation of the differences between the pure beef and pork used in basic chemical compositions. Fisher linear discriminant analysis (Fisher LDA) and extreme learning machine (ELM) were utilized comparatively for identification of the ground pure beef, beef–pork mixtures, and pure pork. Back propagation-artificial neural network (BP-ANN) models were built for prediction of the adulteration levels. Results revealed that the ELM model built was superior to the Fisher LDA model with higher identification rates of 91.27% and 87.5% in the training and prediction sets respectively. Regarding the adulteration level prediction, the correlation coefficient and the root mean square error were 0.85 and 0.147 respectively in the prediction set of the BP-ANN model built. This suggests, from all the results, that the low-cost electronic nose based on colorimetric sensors coupled with chemometrics has a great potential in rapid detection of beef adulterated with pork.
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Affiliation(s)
- Fangkai Han
- School of Biological and Food Engineering, Suzhou University, Bianhe Middle Road 49, Suzhou 234000, China (D.Z.)
| | - Xingyi Huang
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, China;
- Correspondence:
| | - Joshua H. Aheto
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, China;
| | - Dongjing Zhang
- School of Biological and Food Engineering, Suzhou University, Bianhe Middle Road 49, Suzhou 234000, China (D.Z.)
| | - Fan Feng
- School of Biological and Food Engineering, Suzhou University, Bianhe Middle Road 49, Suzhou 234000, China (D.Z.)
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