Detection and characterisation of frauds in bovine meat in natura by non-meat ingredient additions using data fusion of chemical parameters and ATR-FTIR spectroscopy

•Meat frauds were characterized using ATR-FTIR and 5 physico-chemical parameters.•Bovine meat samples were obtained from a real police operation.•The injection of non-meat ingredients increased the meat water holding capacity.•Low-level data fusion provided the best model for detecting adulterated s...

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Bibliographic Details
Published in:	Food chemistry Vol. 205; pp. 14 - 22
Main Authors:	Nunes, Karen M., Andrade, Marcus Vinícius O., Santos Filho, Antônio M.P., Lasmar, Marcelo C., Sena, Marcelo M.
Format:	Journal Article
Language:	English
Published:	England Elsevier Ltd 15-08-2016
Subjects:	Animals Brazil Carrageenan - analysis Cattle Data fusion Food Contamination - analysis Forensic analysis Meat - analysis Meat - classification Meat adulteration Mid-infrared spectroscopy Models, Theoretical PLS-DA Polyphosphates - analysis Sodium Chloride - analysis Spectroscopy, Fourier Transform Infrared Water - analysis Data fusion Forensic analysis PLS-DA Meat adulteration Mid-infrared spectroscopy
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Summary:	•Meat frauds were characterized using ATR-FTIR and 5 physico-chemical parameters.•Bovine meat samples were obtained from a real police operation.•The injection of non-meat ingredients increased the meat water holding capacity.•Low-level data fusion provided the best model for detecting adulterated samples.•The best PLS-DA model correctly detected 91% of the adulterated samples. Concerns about meat authenticity are increasing recently, due to great fraud scandals. This paper analysed real samples (43 adulterated and 12 controls) originated from criminal networks dismantled by the Brazilian Police. This fraud consisted of injecting solutions of non-meat ingredients (NaCl, phosphates, carrageenan, maltodextrin) in bovine meat, aiming to increase its water holding capacity. Five physico-chemical variables were determined, protein, ash, chloride, sodium, phosphate. Additionally, infrared spectra were recorded. Supervised classification PLS-DA models were built with each data set individually, but the best model was obtained with data fusion, correctly detecting 91% of the adulterated samples. From this model, a variable selection based on the highest VIPscores was performed and a new data fusion model was built with only one chemical variable, providing slightly lower predictions, but a good cost/performance ratio. Finally, some of the selected infrared bands were specifically associated to the presence of adulterants NaCl, tripolyphosphate and carrageenan.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0308-8146 1873-7072
DOI:	10.1016/j.foodchem.2016.02.158