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???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
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dc.contributor.advisor | 陳玟伶 | zh_TW |
dc.contributor.advisor | Wen-Ling Chen | en |
dc.contributor.author | 李易潔 | zh_TW |
dc.contributor.author | Yi-Chieh Lee | en |
dc.date.accessioned | 2023-10-03T17:44:13Z | - |
dc.date.available | 2023-11-10 | - |
dc.date.copyright | 2023-10-03 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-08-10 | - |
dc.identifier.citation | 1. Thomson, J.M., The grey mullets. Oceanography and marine biology. an annual review 1966. 4, 301-355.
2. Jixing, Z.; Guomin, W., The status quo and prospects of propagation and aquaculture of marine fish in Guangdong. Transactions of Oceanology and Limnology 2002. 4, 83-93. 3. 行政院農業委員會漁業署, 民國 109年 (2020)漁業統計年報 https://www.fa.gov.tw/redirect_file.php?theme=FS_AR&id=11414 (accessed 2020 Jun 28). 4. 周宜瑩, 台灣地區養殖及野生烏魚辨別技術研究. 食品科學系研究所, 2014. 5. 黃品皓, 烏魚子之原料物種鑑定及脂質氧化穩定性之探討. 食品科學系, 2015. 6. 邵廣昭, 漁業署漁業統計年報漁獲量趨勢圖 https://fishdb.sinica.edu.tw/chi/yearrpt2.php?sc=Mugil%20cephalus (accessed 2020 Dec 07). 7. 王佳惠; 李明安; 沈康寧, 烏魚動態解析及管理措施之研究. 台灣海洋大學環境生物與漁業科學系(所) 2013. 8. Chien, A.; Kirby, R.; Sheen, S.S., The relevance of mitochondrial lineages of Taiwanese cultured grey mullet, Mugil cephalus, to commercial products of Roe. Aquaculture Research 2016. 47 (8), 2455-2460. 9. Chien, A.; Kirby, R.; Sheen, S.-S., One cryptic species of grey mullet (Mugil cephalus mitotype: NWP3) from Taiwan’s waters is worth cultivating for large roes using aquaculture. Aquaculture Research 2018. 49 (10), 3477-3481. 10. Shen, K.N.; Jamandre, B.W.; Hsu, C.C.; Tzeng, W.N.; Durand, J.D., Plio-Pleistocene sea level and temperature fluctuations in the northwestern Pacific promoted speciation in the globally-distributed flathead mullet Mugil cephalus. Bmc Evolutionary Biology 2011. 11, 17. 11. Wang, C.H.; Hsu, C.C.; Chang, C.W.; You, C.F.; Tzeng, W.N., The Migratory Environmental History of Freshwater Resident Flathead Mullet Mugil cephalus L. in the Tanshui River, Northern Taiwan. Zoological Studies 2010. 49 (4), 504-514. 12. Shen, K.N.; Chang, C.W.; Durand, J.D., Spawning segregation and philopatry are major prezygotic barriers in sympatric cryptic Mugil cephalus species. Comptes Rendus Biologies 2015. 338 (12), 803-811. 13. Hung, C.M.; Shaw, D., The Impact of Upstream Catch and Global Warming on the Grey Mullet Fishery in Taiwan: A Non-cooperative Game Analysis. Marine Resource Economics 2006. 21 (3), 285-300. 14. Whitfield, A.K.; Panfili, J.; Durand, J.D., A global review of the cosmopolitan flathead mullet Mugil cephalus Linnaeus 1758 (Teleostei: Mugilidae), with emphasis on the biology, genetics, ecology and fisheries aspects of this apparent species complex. Reviews in Fish Biology and Fisheries 2012. 22 (3), 641-681. 15. Rasoarahona, J.R.E.; Barnathan, G.; Bianchini, J.P.; Gaydou, E.M., Influence of season on the lipid content and fatty acid profiles of three tilapia species (Oreochromis niloticus, O. macrochir and Tilapia rendalli) from Madagascar. Food Chemistry 2005. 91 (4), 683-694. 16. Ozogul, Y.; Ozogul, F., Fatty acid profiles of commercially important fish species from the Mediterranean, Aegean and Black Seas. Food Chemistry 2007. 100 (4), 1634-1638. 17. Albertsen, C.M.; Hussy, K.; Serre, S.H.; Hemmer-Hansen, J.; Thomsen, T.B., Estimating migration patterns of fish from otolith chemical composition time series. Canadian Journal of Fisheries and Aquatic Sciences 2021. 78 (10), 1512-1523. 18. Ara, I.; Ayubi, M.M.; Huque, R.; Khatun, M.A.; Islam, M.; Hossain, M.A., Morphometric, meristic and proximate composition between freshwater and marine hilsa fish. International Journal of Fisheries and Aquatic Studies 2019. 7 (6), 125-129. 19. Tarricone, S.; Jambrenghi, A.C.; Cagnetta, P.; Ragni, M., Wild and Farmed Sea Bass (Dicentrarchus Labrax): Comparison of Biometry Traits, Chemical and Fatty Acid Composition of Fillets. Fishes 2022. 7 (1), 45. 20. Tabezar, N.; Sadeghi, P.; Fariman, G.A., Monsoon Effect on Heavy Metal and Chemical Composition in Parastromateus niger of the Oman Sea: Health Risk Assessment of Fish Consumption. Biological Trace Element Research 2023. 201 (8), 4093-4102. 21. 國立臺灣海洋大學, 臺灣良好農業規範:養殖魚類. 2020. 22. Chakraborty, P.; Islam, M.R.; Hossain, M.A.; Fatema, U.K.; Shaha, D.C.; Sarker, M.S.A.; Akter, T., Earthworm meal (Perionyx excavatus) as an alternative protein source to fish meal in feed for juvenile butter catfish (Ompok pabda). Aquaculture International 2021. 29 (5), 2119-2129. 23. Kousoulaki, K.; Sveen, L.; Noren, F.; Espmark, A., Atlantic Salmon (Salmo salar) Performance Fed Low Trophic Ingredients in a Fish Meal and Fish Oil Free Diet. Frontiers in Physiology 2022. 13, 884740. 24. Santos, M.H.S., Biogenic amines: Their importance in foods. International Journal of Food Microbiology 1996. 29 (2-3), 213-231. 25. 林岱緯; 林品華; 羅良慧; 林明宜; 王怡惠; 羅濟威; 莊裕澤, 2019我國百大社會課題調查研究-重要度與政策回應期待解析. 2020. 26. Chen, P.Y.; Ho, C.W.; Chen, A.C.; Huang, C.Y.; Liu, T.Y.; Liang, K.H., Investigating seafood substitution problems and consequences in Taiwan using molecular barcoding and deep microbiome profiling. Scientific Reports 2020. 10 (1), 21997. 27. Chang, C.H.; Lin, H.Y.; Ren, Q.; Lin, Y.S.; Shao, K.T., DNA barcode identification of fish products in Taiwan: Government-commissioned authentication cases. Food Control 2016. 66, 38-43. 28. Fox, M.; Mitchell, M.; Dean, M.; Elliott, C.; Campbell, K., The seafood supply chain from a fraudulent perspective. Food Security 2018. 10 (4), 939-963. 29. Chen, H.-L.; Chang, N.-N.; Hsiao, W.V.; Chen, W.-J.; Wang, C.-H.; Shiao, J.-C., Using molecular phylogenetic and stable isotopic analysis to identify species, geographical origin and production method of mullet roes. Food Control 2022. 141, 109206. 30. Kuo, J.H.; Tsuei, H.W.; Jia, Z.L.; Lin, C.Y.; Chang, Y.H.; Chen, B.L.; Kuan, J.; Lin, H.Y.; Chiueh, L.C.; Shih, D.Y.C.; Cheng, H.F., Identification of Ingredient in Mullet Roe Products by the Real-Time PCR Method. Food Analytical Methods 2018. 11 (4), 992-1000. 31. Caredda, M.; Addis, M.; Pes, M.; Fois, N.; Sanna, G.; Piredda, G.; Sanna, G., Physico-chemical, colorimetric, rheological parameters and chemometric discrimination of the origin of Mugil cephalus' roes during the manufacturing process of Bottarga. Food Research International 2018. 108, 128-135. 32. Thomatou, A.A.; Psarra, E.; Mazarakioti, E.C.; Katerinopoulou, K.; Tsirogiannis, G.; Zotos, A.; Kontogeorgos, A.; Patakas, A.; Ladavos, A., Stable Isotope Analysis for the Discrimination of the Geographical Origin of Greek Bottarga 'Avgotaracho Messolongiou': A Preliminary Research. Foods 2022. 11 (19), 2960. 33. Costa, R.; Albergamo, A.; Piparo, M.; Zaccone, G.; Capillo, G.; Manganaro, A.; Dugo, P.; Mondello, L., Multidimensional gas chromatographic techniques applied to the analysis of lipids from wild-caught and farmed marine species. European Journal of Lipid Science and Technology 2017. 119 (2), 1600043. 34. Esteki, M.; Simal-Gandara, J.; Shahsavari, Z.; Zandbaaf, S.; Dashtaki, E.; Heyden, Y.V., A review on the application of chromatographic methods, coupled to chemometrics, for food authentication. Food Control 2018. 93, 165-182. 35. Lim, D.K.; Mo, C.; Lee, J.H.; Long, N.P.; Dong, Z.; Li, J.; Lim, J.; Kwon, S.W., The integration of multi-platform MS-based metabolomics and multivariate analysis for the geographical origin discrimination of Oryza sativa L. Journal of Food and Drug Analysis 2018. 26 (2), 769-777. 36. Creydt, M.; Fischer, M., Food authentication in real life: How to link nontargeted approaches with routine analytics? Electrophoresis 2020. 41 (20), 1665-1679. 37. Lacalle-Bergeron, L.; Izquierdo-Sandoval, D.; Sancho, J.V.; Lopez, F.J.; Hernandez, F.; Portoles, T., Chromatography hyphenated to high resolution mass spectrometry in untargeted metabolomics for investigation of food (bio)markers. Trac-Trends in Analytical Chemistry 2021. 135, 116161. 38. Turnipseed, S.B.; Lohne, J.J.; Boison, J.O., Review: Application of High Resolution Mass Spectrometry to Monitor Veterinary Drug Residues in Aquacultured Products. Journal of Aoac International 2015. 98 (3), 550-558. 39. Pan, Y.; Gu, H.W.; Lv, Y.; Yin, X.L.; Chen, Y.; Long, W.J.; Fu, H.Y.; She, Y.B., Untargeted metabolomic analysis of Chinese red wines for geographical origin traceability by UPLC-QTOF-MS coupled with chemometrics. Food Chemistry 2022. 394, 133473. 40. Worley, B.; Powers, R., Multivariate analysis in metabolomics. Current metabolomics 2013. 1 (1), 92-107. 41. Buve, C.; Saeys, W.; Rasmussen, M.A.; Neckebroeck, B.; Hendrickx, M.; Grauwet, T.; Van Loey, A., Application of multivariate data analysis for food quality investigations: An example-based review. Food Research International 2022. 151, 110878. 42. Veerasamy, R.; Rajak, H.; Jain, A.; Sivadasan, S.; Varghese, C.P.; Agrawal, R.K., Validation of QSAR models-strategies and importance. Int. J. Drug Des. Discov 2011. 3, 511-519. 43. de Andrade, B.M.; de Gois, J.S.; Xavier, V.L.; Luna, A.S., Comparison of the performance of multiclass classifiers in chemical data: Addressing the problem of overfitting with the permutation test. Chemometrics and Intelligent Laboratory Systems 2020. 201, 104013. 44. Nyblom, J., Permutation tests in linear regression. Modern Nonparametric, Robust and Multivariate Methods: Festschrift in Honour of Hannu Oja 2015, 69-90. 45. Cuadros-Rodriguez, L.; Ruiz-Samblas, C.; Valverde-Som, L.; Perez-Castano, E.; Gonzalez-Casado, A., Chromatographic fingerprinting: An innovative approach for food 'identitation' and food authentication - A tutorial. Analytica Chimica Acta 2016. 909, 9-23. 46. Li, Q.; Song, J., Analysis of widely targeted metabolites of the euhalophyte Suaeda salsa under saline conditions provides new insights into salt tolerance and nutritional value in halophytic species. Bmc Plant Biology 2019. 19 (1), 388. 47. Campmajo, G.; Saurina, J.; Nunez, O., Liquid chromatography coupled to high-resolution mass spectrometry for nut classification and marker identification. Food Control 2023. 152, 109834. 48. Li, Z.H.; Zhao, C.; Dong, L.; Huan, Y.; Yoshimoto, M.; Zhu, Y.Q.; Tada, I.; Wang, X.H.; Zhao, S.; Zhang, F.J.; Li, L.; Arita, M., Comprehensive Metabolomic Comparison of Five Cereal Vinegars Using Non-Targeted and Chemical Isotope Labeling LC-MS Analysis. Metabolites 2022. 12 (5), 427. 49. Casal, S.; Mendes, E.; Alves, M.R.; Alves, R.C.; Beatriz, M.; Oliveira, P.P.; Ferreira, M.A., Free and conjugated biogenic amines in green and roasted coffee beans. Journal of Agricultural and Food Chemistry 2004. 52 (20), 6188-6192. 50. Chinnici, F.; Duran-Guerrero, E.; Riponi, C., Discrimination of some European vinegars with protected denomination of origin as a function of their amino acid and biogenic amine content. Journal of the Science of Food and Agriculture 2016. 96 (11), 3762-3771. 51. Zaman, M.Z.; Abu Bakar, F.; Selamat, J.; Bakar, J., Occurrence of Biogenic Amines and Amines Degrading Bacteria in Fish Sauce. Czech Journal of Food Sciences 2010. 28 (5), 440-449. 52. Aflaki, F.; Ghoulipour, V.; Saemian, N.; Shiebani, S.; Salahinejad, M., Chemometrics Approaches to Monitoring of Biogenic Amines Changes in Three Fish Species. Journal of Aquatic Food Product Technology 2017. 26 (1), 43-53. 53. Tiris, G.; Yanikoglu, R.S.; Ceylan, B.; Egeli, D.; Tekkeli, E.K.; Onal, A., A review of the currently developed analytical methods for the determination of biogenic amines in food products. Food Chemistry 2023. 398, 133919. 54. Yang, Y.X.; Mu, C.L.; Zhang, J.F.; Zhu, W.Y., Determination of Biogenic Amines in Digesta by High Performance Liquid Chromatography with Precolumn Dansylation. Analytical Letters 2014. 47 (8), 1290-1298. 55. Zhong, J.J.; Ye, X.Q.; Fang, Z.X.; Xie, G.F.; Liao, N.B.; Shu, J.; Liu, D.H., Determination of biogenic amines in semi-dry and semi-sweet Chinese rice wines from the Shaoxing region. Food Control 2012. 28 (1), 151-156. 56. Papageorgiou, M.; Lambropoulou, D.; Morrison, C.; Modzinska, E.; Namiesnik, J.; Plotka-Wasylka, J., Literature update of analytical methods for biogenic amines determination in food and beverages. Trac-Trends in Analytical Chemistry 2018. 98, 128-142. 57. Mung, D.; Li, L., Development of Chemical Isotope Labeling LC-MS for Milk Metabolomics: Comprehensive and Quantitative Profiling of the Amine/Phenol Submetabolome. Analytical Chemistry 2017. 89 (8), 4435-4443. 58. Zhou, R.; Tseng, C.L.; Huan, T.; Li, L., IsoMS: Automated Processing of LC-MS Data Generated by a Chemical Isotope Labeling Metabolomics Platform. Analytical Chemistry 2014. 86 (10), 4675-4679. 59. Shen, W.F.; Han, W.; Li, Y.N.; Meng, Z.Q.; Cai, L.M.; Li, L., Development of chemical isotope labeling liquid chromatography mass spectrometry for silkworm hemolymph metabolomics. Analytica Chimica Acta 2016. 942, 1-11. 60. Huan, T.; Li, L., Counting Missing Values in a Metabolite-Intensity Data Set for Measuring the Analytical Performance of a Metabolomics Platform. Analytical Chemistry 2015. 87 (2), 1306-1313. 61. Dieterle, F.; Ross, A.; Schlotterbeck, G.; Senn, H., Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in H-1 NMR metabonomics. Analytical Chemistry 2006. 78 (13), 4281-4290. 62. Lin, C.Y.; Chen, W.L.; Chen, T.Z.; Lee, S.H.; Liang, H.J.; Chou, C.C.K.; Tang, C.H.; Cheng, T.J., Lipid changes in extrapulmonary organs and serum of rats after chronic exposure to ambient fine particulate matter. Science of the Total Environment 2021. 784, 147018. 63. Farres, M.; Platikanov, S.; Tsakovski, S.; Tauler, R., Comparison of the variable importance in projection (VIP) and of the selectivity ratio (SR) methods for variable selection and interpretation. Journal of Chemometrics 2015. 29 (10), 528-536. 64. Xia, Z.M.; Li, M.; Tian, Y.; Li, Y.Z.; Li, B.; Zhang, G.J.; Lv, J.P.; Fu, Q.Y.; Zhou, H.M.; Dong, J.X., Lipidomics of Serum and Hippocampus Reveal the Protective Effects of Fermented Soybean Lipid on Rats of Microwave-Induced Cognitive Damage. Acs Chemical Neuroscience 2021. 12 (12), 2122-2132. 65. Krejcova, A.; Pouzar, M.; Cernohorsky, T.; Peskova, K., The cryogenic grinding as the important homogenization step in analysis of inconsistent food samples. Food Chemistry 2008. 109 (4), 848-854. 66. He, J.; Sirendalai; Chen, Q.; Yi, L.; Ming, L.; Ji, R., Proteomics and microstructure profiling of Bactrian camel milk protein after homogenization. Lwt-Food Science and Technology 2021. 152, 112287. 67. Peters, V.C.T.; Dunkel, A.; Frank, O.; McCormack, B.; Dowd, E.; Didzbalis, J.; Dawid, C.; Hofmann, T., A high throughput toolbox for comprehensive flavor compound mapping in mint. Food Chemistry 2021. 365, 130522. 68. Chambers, E.; Wagrowski-Diehl, D.M.; Lu, Z.L.; Mazzeo, J.R., Systematic and comprehensive strategy for reducing matrix effects in LC/MS/MS analyses. Journal of Chromatography B-Analytical Technologies in the Biomedical and Life Sciences 2007. 852 (1-2), 22-34. 69. Zhang, Y.J.; Zhang, Y.; Zhou, Y.; Li, G.H.; Yang, W.Z.; Feng, X.S., A review of pretreatment and analytical methods of biogenic amines in food and biological samples since 2010. Journal of Chromatography A 2019. 1605, 360361. 70. Czajkowska-Myslek, A.; Leszczynska, J., Liquid Chromatography-Single-Quadrupole Mass Spectrometry as a Responsive Tool for Determination of Biogenic Amines in Ready-to-Eat Baby Foods. Chromatographia 2018. 81 (6), 901-910. 71. Abe, H.; Ohmama, S., Effect of Starvation and Sea-Water Acclimation on the Concentration of Free L-Histidine and Related Dipeptides in the Muscle of Eel, Rainbow-Trout and Japanese Dace. Comparative Biochemistry and Physiology B-Biochemistry & Molecular Biology 1987. 88 (2), 507-511. 72. Hegab, S.A.; Hanke, W., The significance of the amino acids during osmotic adjustment in teleost fish—II. Changes in the stenohaline cyprinus carpio. Comparative Biochemistry and Physiology Part A: Physiology 1983. 74 (3), 537-543. 73. Schymanski, E.L.; Jeon, J.; Gulde, R.; Fenner, K.; Ruff, M.; Singer, H.P.; Hollender, J., Identifying Small Molecules via High Resolution Mass Spectrometry: Communicating Confidence. Environmental Science & Technology 2014. 48 (4), 2097-2098. 74. Huan, T.; Li, L., Quantitative Metabolome Analysis Based on Chromatographic Peak Reconstruction in Chemical Isotope Labeling Liquid Chromatography Mass Spectrometry. Analytical Chemistry 2015. 87 (14), 7011-7016. 75. Zhao, S.; Li, H.; Han, W.; Chan, W.; Li, L., Metabolomic Coverage of Chemical-Group-Submetabolome Analysis: Group Classification and Four-Channel Chemical Isotope Labeling LC-MS. Analytical Chemistry 2019. 91 (18), 12108-12115. 76. Sanchez-Parra, M.; Lopez, A.; Ordonez-Diaz, J.L.; Rodriguez-Solana, R.; Montenegro-Gomez, J.C.; Perez-Aparicio, J.; Moreno-Rojas, J.M., Evaluation of Biogenic Amine and Free Fatty Acid Profiles During the Manufacturing Process of Traditional Dry-Cured Tuna. Food and Bioprocess Technology 2023. 77. Tao, Z.H.; Sato, M.; Zhang, H.M.; Yamaguchi, T.; Nakano, T., A survey of histamine content in seafood sold in markets of nine countries. Food Control 2011. 22 (3-4), 430-432. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90813 | - |
dc.description.abstract | 烏魚子是臺灣高價水產加工品,其價格與原物料烏魚卵之產地來源、生產方式來源與物種來源有關。本研究目的是以化學同位素標誌結合高解析質譜法分析烏魚子之胺指紋(amine fingerprints),以及利用胺指紋鑑別不同來源之烏魚卵。
本研究蒐集70個不同來源的烏魚卵樣本,分成:不同產地(臺灣50個與進口20個)、生產方式(野生35個與養殖15個)及物種(NWP1種26個與NWP2種24個)。樣本(200 mg)經蛋白質沉澱與液-液萃取後,以12C2-丹磺醯氯(dansyl chloride, DnsCl)進行個別樣本胺類輕標誌衍生化;另以13C2-丹磺醯氯(13C2-DnsCl)進行混合樣本重標誌胺類衍生化。衍生後的個別樣本與混合樣本以等體積均勻混合後,以極致液相層析-四極桿飛行時間質譜儀在資料相依擷取模式下收集所有小分子化合物(m/z 50-1200)高解析質譜資訊。正離子波峰經提取後,挑選同時標誌了12C2/13C2之潛在胺類化合物,並以12C2/13C2波峰面積比值為相對濃度。來源鑑別是以胺類化合物的相對濃度作為變量,建立偏最小平方判別分析模型。候選指標化合物的篩選條件是variable importance in the projection(VIP)值 > 1以及Wilcoxon rank sum test檢定結果有顯著差異(p < 0.05)。最後透過搜尋資料庫、比對滯留時間、分子離子與碎片離子,進行指標胺的鑑定。 本研究成功應用化學同位素標誌法結合非目標胺分析法建立烏魚卵胺指紋。前處理方法最適化之結果顯示,相較於僅使用蛋白質沉澱,加上液-液萃取使前處理效率提升了1.6倍。以酸性甲醇進行蛋白質沉澱,再利用氯仿及超純水進行液-液萃取,使胺類化合物的前處理效率高於80%、基質干擾低於50%,確保良好的生物胺分析回收率。以501個正離子衍生化分子建立的偏最小平方判別分析模型能穩健鑑別不同產地、生產方式及物種的烏魚卵(|" R2Y - Q2 " | < 0.3,Q2 > 0.5,permutation test Q2斜率 > 0),顯示不同來源組別的烏魚卵之胺類化合物組成有顯著差異。本研究共篩選118個具有來源特徵之候選指標化合物(產地:61個;生產方式:52個;物種:31個),並發現組胺酸在進口烏魚卵中之相對濃度高於臺灣烏魚卵(1.39至8.31倍),可能與烏魚的生長水體鹽度有關。本研究未發現與人體健康危害的胺類化合物隨烏魚卵的來源有所差異。 本研究完成烏魚卵胺指紋分析,且證實此胺指紋具有鑑別來源之應用價值。本研究所發現的候選指標胺可供後續研究進行進一步鑑定,以做為未來烏魚子溯源檢驗及品質精進之參考。 | zh_TW |
dc.description.abstract | Wuyutsu (烏魚子) is a high-value processed aquatic product in Taiwan, and its price depends on the mullet roe origins, including geographic origins, production methods, and species. This study aimed to apply chemical isotope labeling (CIL) combined with high-resolution mass spectrometry to untargeted amine fingerprinting in mullet roe origins. The discrimination of mullet roe origins was performed using the amine fingerprints.
A total of 70 mullet roe samples from varied sources, including the geographic origins (Taiwan: n = 50, import: n = 20), production methods (wild: n = 35, culture: n = 15), and species (NWP1: n = 26, NWP2: n = 24) were collected. The samples (200 mg) were processed through protein precipitation and liquid-liquid extraction. Each individual sample was derivatized using 12C2-dansyl chloride (DnsCl) as a light-labeled reagent, while a pooled sample was derivatized using 13C2-DnsCl as a heavy-labeled reagent. Each labeled individual sample was mixed with the labeled pooled sample in equal volumes. Ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry was employed to acquire high-resolution mass spectrometry information of small-molecule compounds (m/z 50-1200) in data-dependent acquisition mode. Molecular features in positive mode were extracted followed by peak pair picking for the amines labeled with 12C2-DnsCl and 13C2-DnsCl. The relative concentration of an amine was the peak area ratio of its 12C2- to 13C2-labeled species. The relative concentrations of all amines were employed in discrimination modeling using partial least squares-discriminant analysis. Candidate markers were selected based on the variable importance in the projection (VIP > 1) and Wilcoxon rank sum test (p < 0.05). Candidate amine markers were identified by searching in databases and comparing the retention time, molecular ions, and fragment ions. This study successfully applied CIL combined with untargeted amine analysis to the amine fingerprints in mullet roe. The results of sample preparation method optimization revealed that including liquid-liquid extraction enhanced the sample preparation efficiency by 1.6 times compared to solely conducting protein precipitation. Adequate amine recoveries were achieved by performing protein using acidic methanol and subsequent liquid-liquid extraction by adding chloroform and water, which resulted in high sample preparation efficiencies (> 80%) and low matrix interference (< 50%). The partial least squares-discriminant analysis models based on 501 derivatized positive ions robustly discriminated mullet roe from different geographic origins, production methods, and species (|" R2Y - Q2 " | < 0.3, Q2 > 0.5 and permutation test Q2 slope > 0), indicating that the amine composition in mullet roe differed by origins. A total of 118 candidate markers (61 for geographic origins, 52 for production methods, and 31 for species) were selected. Histidine was found to be more abundant (1.39 to 8.31 times) in the imported than in Taiwan mullet roe. None of the amines relevant to adverse health effects were found to be different between origins. This study completed mullet roe amine fingerprinting and demonstrated that the amine fingerprint could be applied to origin discrimination. The candidate amine markers found in this study could be identified by further studies and applied to Wuyutsu traceability inspection and quality improvement in the future. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-10-03T17:44:13Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-10-03T17:44:13Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 口試委員審定書 i
誌謝 ii 中文摘要 iii Abstract v Content viii Contact of Figure xi Contact of Table xiv 1 Introduction 1 1.1 Background of mullet roe 1 1.2 Characters of mullet roe 3 1.3 Supply chain integrity and identification methods 4 1.4 Untargeted analysis in food 6 1.5 Analytical challenges for determination of amines 8 1.6 Chemical isotope labeling 9 1.7 Aim of this study 10 2 Hypothesis, objectives, and frame 11 2.1 Hypothesis 11 2.2 Objectives 11 2.3 Frame 12 3 Materials and methods 14 3.1 Chemicals and reagents 14 3.2 Samples 14 3.3 Sample preparation 15 3.4 LC-HRMS analysis 16 3.5 Amine feature picking 17 3.6 Origin discrimination modeling 18 3.7 Candidate marker selection and identification 19 3.8 Optimization of sample preparation in mullet roe 20 3.8.1 Homogenization 20 3.8.2 Protein precipitation 20 3.8.3 Evaluation of sample preparation steps 21 4 Results and discussion 23 4.1 Sample homogenization 23 4.2 Protein preparation conditions 24 4.3 Improved efficiency with liquid-liquid extraction 25 4.4 Improved amine selectivity and sensitivity with dansylation 27 4.5 12C2/13C2 dansylated features 28 4.6 Amine fingerprints in mullet roe 29 4.6.1 Amine fingerprints of wild, culture and imported mullet roe 29 4.6.2 Amine fingerprints differed by geographic origins 30 4.6.3 Amine fingerprints differed by production methods 32 4.6.4 Amine fingerprints differed by species 33 4.7 Candidate markers selection and identification 34 5 Conclusion 37 6 References 38 7 Appendix 46 Figure 1. Mullet roe before (top) and after (bottom) processing 60 Figure 2. The annual yield of mullet and mullet roe in the past five years 61 Figure 3. The shape of mullet roe after two homogenization methods 62 Figure 4. Heatmap analysis of dansylated amines after protein precipitation of 100 mg mullet roe (n = 1) with different volumes and types of solvents 63 Figure 5. Heatmap analysis of dansylated amines after protein precipitation of 50 mg mullet roe (n = 3) with different volumes of methanol 64 Figure 6. Average extraction efficiency (A) and average matrix effect (B) of neutral methanol and acidic methanol 65 Figure 7. Chromatograms of sample with dansylation and without dansylation 66 Figure 8. Chromatograms of light labeled and heavy labeled glycine 67 Figure 9. Spectrum of glycine derivatized with light label and heavy label of dansylate chloride 68 Figure 10. Screening out 501 peak pairs of dansylated features labeled with 12C2-DnsCl and 13C2-DnsCl in mullet roe samples with LabelPick 69 Figure 11. The MS2 spectrum of the aspartic acid, lysine, histidine, phenylalanine and arginine in the dansylated sample and the dansylated amino acid standards 70 Figure 12. Two-dimensional score plot of PLS-DA of amine fingerprints among import, wild, culture mullet roe 71 Figure 13. The premutation test of the PLS-DA prediction model of amine fingerprints among import, wild, culture mullet roe 72 Figure 14. Two-dimensional score plot of PLS-DA of amine fingerprints between Taiwan and import mullet roe 73 Figure 15. The premutation test of the PLS-DA prediction model of amine fingerprints between Taiwan and import mullet roe 74 Figure 16. Two-dimensional score plot of PLS-DA model for the amine fingerprints between mullet roe from five geographic origins 75 Figure 17. The premutation test of PLS-DA prediction model for the amine fingerprints between mullet roe from five geographic origins 76 Figure 18. Two-dimensional score plot of PLS-DA model for the amine fingerprints between wild and culture mullet roe 77 Figure 19. The premutation test of the PLS-DA prediction model for the amine fingerprints between wild and culture mullet roe 78 Figure 20. Two-dimensional score plot of PLS-DA model for the amine fingerprints between NWP1 and NWP2 mullet roe 79 Figure 21. The premutation test of PLS-DA prediction model for the amine fingerprints between NWP1 and NWP2 mullet roe 80 Figure 22. The overall distribution of candidate markers in three classifications 81 Figure 23. The chromatogram of dansylated sample and amino acid mixtures, molecular ion in MS1 spectra and fragment ions in the MS2 spectra 82 Figure 24. The relative concentration of histidine in the import and Taiwan mullet roe 83 Table 1. List of mullet roe information of origin discrimination 46 Table 2. Parameter settings for high-resolution mass spectrometry 49 Table 3. Known amine list in TargetLynx 50 Table 4. List of ratios with an m/z difference of 2.0067 (partial only) 51 Table 5. Average sample preparation efficiencies (%) of isotope-labeled dansylated amines tested for extraction solvents 52 Table 6. The candidate markers in mullet roe of geographic origins 53 Table 7. The candidate markers in mullet roe of production methods 55 Table 8. The candidate markers in mullet roe of species 57 Table 9. The potential candidate markers match accurate mass in in-house database 59 | - |
dc.language.iso | en | - |
dc.title | 以非目標胺類指紋鑑別臺灣烏魚卵:化學同位素標誌法之應用 | zh_TW |
dc.title | Identification of Taiwan mullet roe origins using untargeted amine fingerprinting: Application of chemical isotope labeling | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.coadvisor | 陳冠元 | zh_TW |
dc.contributor.coadvisor | Guan-Yuan Chen | en |
dc.contributor.oralexamcommittee | 王佳惠;凌永健 | zh_TW |
dc.contributor.oralexamcommittee | Chia-Hui Wang;Yong-Chien Ling | en |
dc.subject.keyword | 烏魚卵,來源鑑別,胺類指紋,非目標分析,高解析質譜法,化學同位素標誌法, | zh_TW |
dc.subject.keyword | mullet roe,origin discrimination,amine fingerprints,untargeted analysis,high-resolution mass spectrometry,chemical isotope labeling, | en |
dc.relation.page | 83 | - |
dc.identifier.doi | 10.6342/NTU202303281 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2023-08-10 | - |
dc.contributor.author-college | 公共衛生學院 | - |
dc.contributor.author-dept | 食品安全與健康研究所 | - |
Appears in Collections: | 食品安全與健康研究所 |
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