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Investigation Of The Relation Between Additive Commonly Used In Meat Products - Soy And Meat By Fluorescence Spectroscopy

Year 2015, Volume: 3 Issue: 1, 1 - 10, 01.05.2015

Abstract

In this study, fluorescence spectroscopy coupled with multivariate chemometrics techniques has been used for the analyzing of meat, sausages and ground meat samples containing various ratios of soybean gel (5 %, 10 %, 20 %, 40 %, 60 %, 80 % and 100%). The emission fluorescence spectra of tryptophan residues (305–400 nm), riboflavin (410–700 nm) and vitamin A (340-540) were recorded with the excitation wavelengths set at 290, 382 and 322 nm, respectively. Chemical characteristics were also determined including humidity, fat and protein content. The obtained experimental data have been processed with chemometrics methods such as PCA, PLSDA and PLS. The mathematical analysis procedure was written in the code of Matlab. From the results, determination ratio of soy in sausages, evaluation the differences between soy and meat and prediction of chemical content of samples (protein and fat with regression coefficients R2 ≈ 0.85 and R2 ≈ 0.89 by the emission spectra of tryptophan and dry matter with regression coefficients R2 ≈ 0.89 by the spectra of riboflavin have been determined) has been shown. Hence, fluorescence spectroscopy in combination with chemometrics methods has been shown to have high potential in the analysis of meat and meat products.

References

  • [1] Алымбеков.К.А. Все о мясе. Научно-технический и производственный журнал. (2005): (43-45)
  • [2] Turhan S., Yazıcı F., Altunkaynak T.B.. Soya protein ürünlerinin et ürünlerinde kullanımı. Gıda Mühendisliği dergisi. GMO. s. 17-21
  • [3] Öner, T., Soya Sektörü Raporu, İstanbul Ticaret Odası, İstanbul. http://www.ito.org.tr/Dokuman/Sektor/1-84.pdf.( 2006). (erişim tarihi 02/01/2013)
  • [4] Özdemir, O. ve Duran M., 2010. Biyoteknolojik Uygulamalara ve Genetiği Değiştirilmiş Organizmalara (GDO) İlişkin Tüketici Davranışları, Akademik Gıda, 8(5) (2010) 20-28.
  • [5] A. Sahar, T. Boubellouta, J. Lepetit, É. Dufour. Front-face fluorescence spectroscopy as a tool to classify seven bovine muscles according to their chemical and rheological characteristics. Meat Science 83, (2009): 672–677
  • [6] Karoui R., E. Dufour, J. De Baerdemaeker. Front face fluorescence spectroscopy coupled with chemometric tools for monitoring the oxidation of semi-hard cheeses throughout ripening. Food Chemistry, 101, (2007): 1305–1314
  • [7] Joseph R. Lakowicz. Principles of Fluorescence Spectroscopy (Third Edition). Center for Fluorescence Spectroscopy. University of Maryland School of Medicine. Baltimor, MD 21201 (2006).
  • [8] Позняковский. В.М. Экспертиза мяса и мясопродуктов, качество и безопасность. (2005).
  • [9] M.A. Sharaf, D.I.İlman, B.R.Kowalski. Chemometrics. John Wiley and Sons. USA. (1986).
  • [10] ГОСТ Р 51479-99 (ИСО 1442-97) Мясо и мясные продукты. Метод определения массовой доли влаги.
  • [11] William Horwitz. Official methods of analysis of AOAC (Association of Official Agricultural Chemists) International. .17th Edition
  • [12] Erdal Dinç. Kemometri, Çok Değişkenli Kalibrasyon Yöntemleri. Hacettepe Üniversitesi, Eczacılık Fakültesi Dergisi. Cilt 27, Sayı 1. (2007): 61-92
  • [13] Moneeb, M. S. Polagraphic chemometric determination of zinc and nickel in aqueous samples. Talanta, 70, (2006): 1035-1043.
  • [14] В.В. Налимов, Н.А. Чернова. Статистические методы планирования экстремальных экспериментов. Издательство Наук, Главная редакция физико-математической литературы. Москва (1968).
  • [15] Monfreda Maria. Principal Component Analysis: A Powerful Interpretative Tool at the Service of Analytical Methodology. Computer and Information Science, (2012): 49-66.
  • [16] G. Destefanis, M.T. Barge, A. Brugiapaglia, S. The use of principal component analysis (PCA) to characterize beef. Meat Science 56, (2000): 255±259.
  • [17] Т. Н. Дребущак. Введение в хемометрику. Практика анализа экспериментальных данных. Учебное пособие. Новосибирск, (2011).
  • [18] Herv´e Abdi. Partial Least Squares (PLS) Regression. The University of Texas at Dallas Program in Cognition and Neurosciences, MS: Gr.4.1. (2003).
  • [19] Vincenzo Esposito Vinzi, Wynne W. Chin, Jorg Henseler, Huiwen Wang. Handbook of Partial Least Squares. Springer. Berlin (2010).
  • [20] А.М. Дубров, В.С. Мхитарян, Л.И. Трошин. Многомерные статистические методы. Финансы и статистика. Москва, (2003).
  • [21] Johan A. Westerhuis, Huub C. J. Hoefsloot, Suzanne Smit, Daniel J. Vis, Age K. Smilde, Ewoud J. J. van Velzen, John P. M. van Duijnhoven, Ferdi A. van Dorsten. Assessment of PLSDA cross validation. Metabolomics, (2008) 4:81–89.
  • [22] Matlab в математическом моделировании химико технологических систем. Введение в систему Matlab. Методические указания. Санкт-Петербург, (2007).
  • [23] Alptekin Günel. Regresyon denkleminin başarısını ölçmede kullanIlan belirleme katsayısı ve kritiği. Doğuş Üniversitesi Dergisi, 4 (2) 2003, 133-140.

Et ürünlerinde yaygın kullanılan katkı maddesi soya ile et arasındaki bağıntının floresans spektroskopisiyle araştırılması

Year 2015, Volume: 3 Issue: 1, 1 - 10, 01.05.2015

Abstract

Bu çalışmada, et, sucuk ve çeşitli oranda soya jeli (% 5, % 10, % 20, % 40, % 60, % 80 ve % 100) içeren kıyma örneklerinin analizinde; spektrofluorometrik yöntemle birlikte çok boyutlu kemometri teknikleri kullanılmıştır. Triptofan, riboflavin (В2 vitamini) ve A vitamin floresans emisyon spektrumları 305-400, 410-700, 340-540 nm diyapazonlarında ve uyandırma dalga boyu 290, 382 ve 322 nm seviyelerinde kaydedilmiştir. İncelenen et ve et ürünlerinin rutubet, yağ ve protein içeriği dahil olmak üzere kimyasal özellikleri tespit edilmiştir. Elde edilen bütün deneysel bilgiler; Matlab programlama dili kullanılarak yazılan PCA, PLSDA ve PLS metotları ile kemometrik analizler değerlendirilmiştir. Analiz sonuçlarından, sucuktaki soya oranının belirlenebileceği, soya ile et arasındaki farkın değerlendirebileceği ve örneklerin kimyasal içeriğinin de tahmin edilebileceği (proteini R2 ≈ 0,85 ve yağı R2 ≈ 0,89 regresyon katsayısıyla triptofan emisyon spektrumları, kuru maddeyi ise R2 ≈ 0,89 regresyon katsayısıyla riboflavin emisyon spektrumları belirlemiştir) gösterilmiştir. Sonuç olarak kemometrik metodlarla birlikte floresans spektroskopisi; et ve et ürünlerinin analizinde yüksek potansiyele sahip olduğu söylenilebilir

References

  • [1] Алымбеков.К.А. Все о мясе. Научно-технический и производственный журнал. (2005): (43-45)
  • [2] Turhan S., Yazıcı F., Altunkaynak T.B.. Soya protein ürünlerinin et ürünlerinde kullanımı. Gıda Mühendisliği dergisi. GMO. s. 17-21
  • [3] Öner, T., Soya Sektörü Raporu, İstanbul Ticaret Odası, İstanbul. http://www.ito.org.tr/Dokuman/Sektor/1-84.pdf.( 2006). (erişim tarihi 02/01/2013)
  • [4] Özdemir, O. ve Duran M., 2010. Biyoteknolojik Uygulamalara ve Genetiği Değiştirilmiş Organizmalara (GDO) İlişkin Tüketici Davranışları, Akademik Gıda, 8(5) (2010) 20-28.
  • [5] A. Sahar, T. Boubellouta, J. Lepetit, É. Dufour. Front-face fluorescence spectroscopy as a tool to classify seven bovine muscles according to their chemical and rheological characteristics. Meat Science 83, (2009): 672–677
  • [6] Karoui R., E. Dufour, J. De Baerdemaeker. Front face fluorescence spectroscopy coupled with chemometric tools for monitoring the oxidation of semi-hard cheeses throughout ripening. Food Chemistry, 101, (2007): 1305–1314
  • [7] Joseph R. Lakowicz. Principles of Fluorescence Spectroscopy (Third Edition). Center for Fluorescence Spectroscopy. University of Maryland School of Medicine. Baltimor, MD 21201 (2006).
  • [8] Позняковский. В.М. Экспертиза мяса и мясопродуктов, качество и безопасность. (2005).
  • [9] M.A. Sharaf, D.I.İlman, B.R.Kowalski. Chemometrics. John Wiley and Sons. USA. (1986).
  • [10] ГОСТ Р 51479-99 (ИСО 1442-97) Мясо и мясные продукты. Метод определения массовой доли влаги.
  • [11] William Horwitz. Official methods of analysis of AOAC (Association of Official Agricultural Chemists) International. .17th Edition
  • [12] Erdal Dinç. Kemometri, Çok Değişkenli Kalibrasyon Yöntemleri. Hacettepe Üniversitesi, Eczacılık Fakültesi Dergisi. Cilt 27, Sayı 1. (2007): 61-92
  • [13] Moneeb, M. S. Polagraphic chemometric determination of zinc and nickel in aqueous samples. Talanta, 70, (2006): 1035-1043.
  • [14] В.В. Налимов, Н.А. Чернова. Статистические методы планирования экстремальных экспериментов. Издательство Наук, Главная редакция физико-математической литературы. Москва (1968).
  • [15] Monfreda Maria. Principal Component Analysis: A Powerful Interpretative Tool at the Service of Analytical Methodology. Computer and Information Science, (2012): 49-66.
  • [16] G. Destefanis, M.T. Barge, A. Brugiapaglia, S. The use of principal component analysis (PCA) to characterize beef. Meat Science 56, (2000): 255±259.
  • [17] Т. Н. Дребущак. Введение в хемометрику. Практика анализа экспериментальных данных. Учебное пособие. Новосибирск, (2011).
  • [18] Herv´e Abdi. Partial Least Squares (PLS) Regression. The University of Texas at Dallas Program in Cognition and Neurosciences, MS: Gr.4.1. (2003).
  • [19] Vincenzo Esposito Vinzi, Wynne W. Chin, Jorg Henseler, Huiwen Wang. Handbook of Partial Least Squares. Springer. Berlin (2010).
  • [20] А.М. Дубров, В.С. Мхитарян, Л.И. Трошин. Многомерные статистические методы. Финансы и статистика. Москва, (2003).
  • [21] Johan A. Westerhuis, Huub C. J. Hoefsloot, Suzanne Smit, Daniel J. Vis, Age K. Smilde, Ewoud J. J. van Velzen, John P. M. van Duijnhoven, Ferdi A. van Dorsten. Assessment of PLSDA cross validation. Metabolomics, (2008) 4:81–89.
  • [22] Matlab в математическом моделировании химико технологических систем. Введение в систему Matlab. Методические указания. Санкт-Петербург, (2007).
  • [23] Alptekin Günel. Regresyon denkleminin başarısını ölçmede kullanIlan belirleme katsayısı ve kritiği. Doğuş Üniversitesi Dergisi, 4 (2) 2003, 133-140.
There are 23 citations in total.

Details

Other ID JA54NC46VA
Journal Section Research Article
Authors

Z. Ozbekova This is me

A. Kulmyrzaev This is me

Publication Date May 1, 2015
Published in Issue Year 2015 Volume: 3 Issue: 1

Cite

APA Ozbekova, Z., & Kulmyrzaev, A. (2015). Investigation Of The Relation Between Additive Commonly Used In Meat Products - Soy And Meat By Fluorescence Spectroscopy. MANAS Journal of Engineering, 3(1), 1-10.
AMA Ozbekova Z, Kulmyrzaev A. Investigation Of The Relation Between Additive Commonly Used In Meat Products - Soy And Meat By Fluorescence Spectroscopy. MJEN. May 2015;3(1):1-10.
Chicago Ozbekova, Z., and A. Kulmyrzaev. “Investigation Of The Relation Between Additive Commonly Used In Meat Products - Soy And Meat By Fluorescence Spectroscopy”. MANAS Journal of Engineering 3, no. 1 (May 2015): 1-10.
EndNote Ozbekova Z, Kulmyrzaev A (May 1, 2015) Investigation Of The Relation Between Additive Commonly Used In Meat Products - Soy And Meat By Fluorescence Spectroscopy. MANAS Journal of Engineering 3 1 1–10.
IEEE Z. Ozbekova and A. Kulmyrzaev, “Investigation Of The Relation Between Additive Commonly Used In Meat Products - Soy And Meat By Fluorescence Spectroscopy”, MJEN, vol. 3, no. 1, pp. 1–10, 2015.
ISNAD Ozbekova, Z. - Kulmyrzaev, A. “Investigation Of The Relation Between Additive Commonly Used In Meat Products - Soy And Meat By Fluorescence Spectroscopy”. MANAS Journal of Engineering 3/1 (May 2015), 1-10.
JAMA Ozbekova Z, Kulmyrzaev A. Investigation Of The Relation Between Additive Commonly Used In Meat Products - Soy And Meat By Fluorescence Spectroscopy. MJEN. 2015;3:1–10.
MLA Ozbekova, Z. and A. Kulmyrzaev. “Investigation Of The Relation Between Additive Commonly Used In Meat Products - Soy And Meat By Fluorescence Spectroscopy”. MANAS Journal of Engineering, vol. 3, no. 1, 2015, pp. 1-10.
Vancouver Ozbekova Z, Kulmyrzaev A. Investigation Of The Relation Between Additive Commonly Used In Meat Products - Soy And Meat By Fluorescence Spectroscopy. MJEN. 2015;3(1):1-10.

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