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Please use this identifier to cite or link to this item: http://hdl.handle.net/11019/575

Title: Detection of offal adulteration in beefburgers using near infrared reflectance spectroscopy and multivariate modelling
Authors: Zhao, Ming
O'Donnell, Colm P.
Downey, Gerard
Keywords: Near infrared reflectance spectroscopy
Partial least squares (PLS) regression
Principal component analysis (PCA)
Soft independent modelling of class analogy (SIMCA)
Offal-adulterated beefburgers
Authenticity
Issue Date: 2013
Publisher: IM Publications
Citation: M. Zhao, C. O'Donnell and G. Downey, “Detection of offal adulteration in beefburgers using near infrared reflectance spectroscopy and multivariate modelling”, J. Near Infrared Spectrosc. 21(4), 237–248 (2013). doi: 10.1255/jnirs.1057
Series/Report no.: Journal of Near Infrared Spectroscopy;vol 21
Abstract: The main aim of this study was to develop a rapid and reliable tool using near infrared (NIR) reflectance spectroscopy to confirm beefburger authenticity in the context of offal (kidney, liver, heart and lung) adulteration. An experimental design was used to develop beefburger formulations to simultaneously maximise the variable space describing offal-adulterated samples and minimise the number of experiments required. Authentic (n = 36) and adulterated (n = 46) beefburger samples were produced using these formulations. Classification models (partial least squares discriminant analysis, PLS1-DA) and class-modelling tools (soft independent modelling of class analogy, SIMCA) were developed using raw and pre-treated NIR reflectance spectra (850-1098 nm wavelength range) to detect authentic and adulterated beefburgers in (1) fresh, (2) frozen-then-thawed and (3) fresh or frozen-then-thawed states. In the case of authentic samples, the best PLS1-DA models achieved 100% correct classification for fresh, frozen-then-thawed and fresh or frozen-then-thawed samples. SIMCA models correctly identified all the fresh samples but not all the frozen-then-thawed and fresh or frozen-then-thawed samples. For the adulterated samples, PLS1-DA models correctly classified 95.5% of fresh, 91.3% of frozen-then-thawed and 88.9% of fresh or frozen-then-thawed beefburgers. SIMCA models exhibited specificity values of 1 for both fresh and frozen-then-thawed samples, 0.99 for fresh or frozen-then-thawed samples; sensitivity values of 1, 0.88 and 0.97 were obtained for fresh, frozen-then-thawed and fresh or frozen-then-thawed products respectively. Quantitative models (PLS1 regression) using both 850-1098 nm and 1100-2498 nm wavelength ranges were developed to quantify (1) offal adulteration and (2) added fat in adulterated beefburgers, both fresh and frozen-then-thawed. Models predicted added fat in fresh samples with acceptable accuracy (RMSECV = 2.0; RPD = 5.9); usefully-accurate predictions of added fat in frozen-then-thawed samples were not obtained nor was prediction of total offal possible in either sample form.
Description: peer-reviewed
URI: http://hdl.handle.net/11019/575
http://dx.doi.org/10.1255/jnirs.1057
ISSN: 0967-0335
Appears in Collections:Food Chemistry & Technology

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