Literature DB >> 20416640

Front-face fluorescence spectroscopy as a tool to classify seven bovine muscles according to their chemical and rheological characteristics.

A Sahar1, T Boubellouta, J Lepetit, E Dufour.   

Abstract

Potential of front-face fluorescence spectroscopy was evaluated to classify muscles according to their chemical and rheological characteristics. Seven bovine muscles (Semitendinosus, Semimembranosus, Tensor fasciae latae, Rectus abdominis, Longissimus thoracis et lumborum, Triceps branchii and Infraspinatus) were taken from 14 animals of the Charolais breed. Chemical characteristics and rheological properties of the meat were determined including dry matter, fat, collagen, protein, peak load, energy required to rupture and cooking loss. Emission spectra in the 305-400nm, 340-540nm and 410-700nm ranges were recorded using front-face fluorescence spectroscopy by fixing the excitation wavelengths at 290, 322 and 382nm, respectively. Analysis of variance (ANOVA) applied on chemical and rheological parameters showed that these muscles were significantly different (P<0.01) from each other. Chemical and rheological data were divided into low, medium and high range groups for each variable. The results of PLSDA showed that 305-400nm spectra were responsible for 67% (calibration), 53% (validation), 96% (calibration) and 55% (validation) of good classification for protein and cooking loss, respectively, while 340-540nm spectra allowed 75% of good classification (validation samples) for fat content.

Entities:  

Year:  2009        PMID: 20416640     DOI: 10.1016/j.meatsci.2009.08.002

Source DB:  PubMed          Journal:  Meat Sci        ISSN: 0309-1740            Impact factor:   5.209


  3 in total

1.  Determination of Adulteration of Chicken Meat into Minced Beef Mixtures using Front Face Fluorescence Spectroscopy Coupled with Chemometric.

Authors:  Asima Saleem; Amna Sahar; Imran Pasha; Muhammad Shahid
Journal:  Food Sci Anim Resour       Date:  2022-07-01

2.  Analysis of muscle tissue in vivo using fiber-optic autofluorescence and diffuse reflectance spectroscopy.

Authors:  Christopher J Davey; Emily R Vasiljevski; Alexandra K O'Donohue; Simon C Fleming; Aaron Schindeler
Journal:  J Biomed Opt       Date:  2021-12       Impact factor: 3.170

3.  Online Prediction of Physico-Chemical Quality Attributes of Beef Using Visible-Near-Infrared Spectroscopy and Chemometrics.

Authors:  Amna Sahar; Paul Allen; Torres Sweeney; Jamie Cafferky; Gerard Downey; Andrew Cromie; Ruth M Hamill
Journal:  Foods       Date:  2019-10-23
  3 in total

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