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  1. NTU Theses and Dissertations Repository
  2. 生物資源暨農學院
  3. 生物機電工程學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/27369
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor蕭介宗(Jai-Tsung Shaw)
dc.contributor.authorChien-Yuan Kungen
dc.contributor.author龔建源zh_TW
dc.date.accessioned2021-06-12T18:02:42Z-
dc.date.available2008-02-01
dc.date.copyright2008-02-01
dc.date.issued2008
dc.date.submitted2008-01-23
dc.identifier.citation1.FOSS。1998。Laboratory products and applications。FOSS NIRSystems Company 泰國曼谷WINISI訓練講義。Maryland, USA:FOSS NIRSystems Company。
2.林亮全、劉登城、郭秀蘭、陳明造、邱湧忠。1987。屠體處理作業對大里肌肉品質之影響。中國畜牧學會會誌 16(3-4):167-178。
3.林慶文。2002。食品加工學。四版,11-69。台北:華香園出版社。
4.徐永衡。2003。利用淨最小平方迴歸、主成份迴歸與類神經網路分析近紅外線光譜資料。碩士論文。台中:國立中興大學農藝學系。
5.陳石松。1995。大里肌肉中血質鐵定量法及其應用於死大里肌肉鑑別之研究。碩士論文。台北:國立台灣大學食品科學研究所。
6.陳世銘、張文宏、謝廣文。1998。果汁糖度檢測模式之研究。農業機械學刊7(3):41-60。
7.張鴻文、蕭介宗、李盛銘、洪梅珠。2000。以近紅外線及影像技術鑑別水稻品種。農業機械學刊9(4):1-16。
8.黃竣吉、陳世銘、楊翕雯、陳加增。2004。近紅外光技術應用於穿山甲中藥成份之檢測。農業機械學刊 13(3):37-52。
9.楊正護。1988。食肉化學之最新發展。初版,175-207。台北:藝軒圖書出版社。
10.蕭介宗、周震煌、劉昌群。2005 a。以機器視覺與可見光/近紅外光應用於畜肉之分類。出自“農牧漁業生產及加工作業之品質偵測技術國際研討會”,II1-II9。台北:財團法人農業機械化研究發展中心。
11.蕭介宗。2005 b。國產冷藏雞腿與進口冷凍再解凍雞腿之鑑別。農委會計劃編號.94農科-12.1.4-牧-U1研究報告,國立台灣大學生物產業機電工程學系。
12.蕭介宗、駱秋英、陳力騏、劉昌群、洪淑玲、蘇淑禎。2004。大里肌肉鮮度指標與近紅外線光譜相關性之探討。農業機械學刊13(4):27-36。
13.劉昌群、蕭介宗、彭敬益、洪梅珠、沈明來。2005。近紅外線光譜的波長選擇對水稻品種鑑別的影響。農業機械學刊14(2):27-38。
14.羅蘇秦、張世英。1999。近紅外光譜儀器之分析技術及其應用。科儀新知20(5):13-30。
15.Brereton, R. G. 2000. Introduction to multivariate calibration in analytical chemistry. Analyst 125: 2125-2154.
16.Cozzolino, D. and I. Murray. 2004. Identification of animal meat muscles by visible and near infrared reflectance spectroscopy. Lebensmittel-Wissenschaft und-Technologie 37: 447-452.
17.Ding, H., R. J. Xu and D. K. Chan. 1999. Identification of broiler chicken meat using a visible/near-infrared spectroscopic technique. Journal of the Science of Food and Agriculture 79: 1382-1388.
18.Downey, G. and D. Beauchêne. 1997. Authentication of fresh vs. frozen-then-thawed beef by near infrared reflectance spectroscopy of dried drip juice. Lebensmittel-Wissenschaft und-Technologie 30(7): 721-726.
19.Gonzalez, R. C. and R. E. Woods. 2002. Digital Image Processing. 2nd ed., 282-348. New Jersey: Prentice Hall.
20.Heidi, N. and M. Esaiassen. 2005. Predictingsensory score of cod (Gadus morhua) from visible spectroscopy. Lebensmittel-Wissenschaft und-Technologie 38: 95-99.
21.Helland, I. S. 1990. Partial least squares regression and statistical models. Scand. J. Statist. 17: 97-114.
22.Irudayaraj J. and S. Sivakesava. 2001. Detection of adulteration in honey by discriminant analysis using FTIR Spectroscopy. Transactions of the ASAE 44(3): 643-650.
23.Johnson R. A. and D. W. Wichern. 2002. Applied Multivariate Statistical Analysis. 5th ed., 272-353. New Jersey: Prentice Hall.
24.Lindbloom, B. J. 2001. Useful Color Equations. Available at: http://brucelindbloom.com/. Accessed 10 December 2005.
25.Lu, J., J. Tan, P. Shatadal, and D. E. Gerrard. 2000. Evaluation of pork color by using computer vision. Meat Science 56: 57-60.
26.Martens and Næs. 1989. Multivariate Calibration. 4th ed., New York: John Wiley.
27.Savenije, B., G. H. Geesink, J. G. P. van der Palen, and G. Hemke. 2006. Prediction of pork quality using visible/near-infrared reflectance spectroscopy. Meat Science 73(1): 181–184.
28.Tan, J. 2004. Meat quality evaluation by computer vision. Journal of Food Engineering 61(1): 27-35.
29.Tang J., C. Faustman, and T. A. Hoagland. 2004. Krzywicki revisited: equations for spectrophotometric determination of myoglobin redox forms in aqueous meat extracts. Journal of Food Science 69(9): C717-C720.
30.Williams, P. C. 2001. Chapter 8: Implementation of near-infrared technology. In 'Near-Infrared Technology in the Agricultural and Food Industries. 2nd ed. ', eds. P. Williams and K. Norris, 145-169. Minnesota: American Association of Cereal Chemists, Inc.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/27369-
dc.description.abstract有鑑於近年來斃死豬肉的問題不斷被披露,不但影響消費者的信心,更對養豬業者造成難以估計的損失。為了避免不肖業者以斃死豬肉欺騙消費者而謀取暴利,保障生產者,及維持市場秩序,有必要研究發展客觀及快速判別斃死豬之大里肌肉的方法。探討可見光/近紅外光技術、機器視覺、pH値與肉色標準(PCS)作為判別斃死大里肌肉之可行性。
以2nm間隔的可見光/近紅外光光譜值,利用Unscrambler7.6軟體之部份最小平方迴歸,搭配虛擬迴歸技術分析,選擇400-2198nm作為變數可以96.8%判別冷藏和斃死豬大里肌肉;選擇波長472-524nm作為變數,可以87.5%判別冷凍再解凍與斃死豬大里肌肉;選擇波長400-2198nm作為變數,可以96.8%的判別冷藏和正常冷凍再解凍豬大里肌肉。
使用RGB值、HSI值及Lab值作為變數,利用最小平方迴歸搭配虛擬迴歸技術分析,可以96.7%判別冷藏和斃死豬大里肌肉、90.3%判別冷凍再解凍與斃死豬大里肌肉,以及81.2%的判別冷藏和正常冷凍再解凍豬大里肌肉。
冷藏、冷凍再解凍和斃死豬大里肌肉之平均pH值分別為5.89±0.21、5.91±0.23和5.80±0.13。正常冷藏、正常冷凍再解凍和斃死大里肌肉之平均肉色標準分別為2.33±0.58、2.53±0.58和1.94±0.96。
zh_TW
dc.description.abstractFor the last few years, the problem of cadaveric pork has been published in the newspaper continuously. Consequently, this may result in affecting consumer confidence, and unpredictable loss on the pork industry. To avoid illegal cheating, to protect producers and to maintain market order, it is necessary to develop a rapid method to validate cadaveric longissimus. The feasibility of validating cadaveric longissimus has been studied by visible/near-infrared spectroscopy, machine vision, pH value, and Pork Color Standard (PCS).
By the visible/near-infrared spectra with 2nm interval, partial least square regression from Unscrambler7.6 software and dummy regression techniques, the validation rate of chilled and cadaveric pork longissimus was 96.8% in the 400-2198 nm range. The validation rate of frozen-then-thawed and cadaveric pork longissimus was 87.5% in the 472-524 nm range. In the 400-2198 nm range, the validation rate of chilled and frozen-then-thawed pork longissimus was 96.8%.
Using RGB, HSI and Lab values, the validation rate of chilled and cadaveric pork longissimus was 96.7%. The validation rate of frozen-then-thawed and cadaveric pork longissimus was 90.3%, and 81.2% for chilled and frozen-then-thawed pork longissimus.
The average pH values of chilled, frozen-then-thawed and cadaveric pork longissimus were 5.89 ± 0.21, 5.91 ± 0.23, and 5.80 ± 0.13, respectively. The pork color standard (PCS) of chilled, frozen-then-thawed and cadaveric pork longissimus were 2.33 ± 0.58, 2.53 ± 0.58, and 1.94 ± 0.96, respectively.
en
dc.description.provenanceMade available in DSpace on 2021-06-12T18:02:42Z (GMT). No. of bitstreams: 1
ntu-97-R94631020-1.pdf: 1365949 bytes, checksum: 602e08caf3addaf82ce5afadce87c8ca (MD5)
Previous issue date: 2008
en
dc.description.tableofcontents口試委員審定書 ................................ i
誌謝 ......................................... ii
中文摘要………………………………………………………. iii
英文摘要………………………………………………………. iv
目錄……………………………………………………….........vi
圖目錄………………………………………………………… vii
表目錄…………………………………………………………. ix
第一章 前言…………………………………………………. 1
第二章 文獻探討……………………………………………. 2
第三章 材料與方法…………………………………………. 9
3-1 大里肌肉樣本之來源與前處理…………………..... 9
3-2 設備與方法………………………………………… 10
第四章 結果與討論………………………………………... 20
第五章 結論………………………………………………... 40
參考文獻…………………………………….……………….. 41
dc.language.isozh-TW
dc.subjectpH值zh_TW
dc.subject豬肉zh_TW
dc.subject大里肌肉zh_TW
dc.subject光譜值zh_TW
dc.subject機器視覺zh_TW
dc.subjectpH valueen
dc.subjectLongissimusen
dc.subjectSpectraen
dc.subjectMachine visionen
dc.subjectPorken
dc.title冷藏、冷凍和斃死豬大里肌肉的判別zh_TW
dc.titleValidation of Chilled, Frozen and Cadaveric Pork Longissimusen
dc.typeThesis
dc.date.schoolyear96-1
dc.description.degree碩士
dc.contributor.oralexamcommittee陳世銘(Suming Chen),周瑞仁(Jui-Jen Chou)
dc.subject.keyword豬肉,大里肌肉,光譜值,機器視覺,pH值,zh_TW
dc.subject.keywordPork,Longissimus,Spectra,Machine vision,pH value,en
dc.relation.page44
dc.rights.note有償授權
dc.date.accepted2008-01-23
dc.contributor.author-college生物資源暨農學院zh_TW
dc.contributor.author-dept生物產業機電工程學研究所zh_TW
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