Differentiation of organic and non-organic ewe's cheeses using main mineral composition or near infrared spectroscopy coupled to chemometric tools: A comparative study

Two independent methodologies were investigated to achieve the differentiation of ewes’ cheeses from different systems of production (organic and non-organic). Eighty cheeses (40 organic and 40 non-organic) from two systems of production, two different breeds of ewe, different sizes, seasons (summer...

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Bibliographic Details
Published in:Talanta (Oxford) 2011-09, Vol.85 (4), p.1915-1919
Main Authors: González-Martín, M. Inmaculada, Hernández-Hierro, José Miguel, Revilla, Isabel, Vivar-Quintana, Ana, González-Pérez, Claudio, García, Lorena Gómez, Riocerezo, Carlos Palacios, Ortega, Iris A. Lobos
Format: Article
Language:English
Subjects:
Analytical chemistry
Animals
Cheese - analysis
Chemistry
Discriminant Analysis
Exact sciences and technology
Female
Least-Squares Analysis
Main mineral composition
Minerals - chemistry
Near infrared spectroscopy
Organic Chemicals - analysis
Organic ewe cheese
Sheep
Spectrometric and optical methods
Spectrophotometry, Infrared - methods
Supervised pattern recognition methods
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Summary:Two independent methodologies were investigated to achieve the differentiation of ewes’ cheeses from different systems of production (organic and non-organic). Eighty cheeses (40 organic and 40 non-organic) from two systems of production, two different breeds of ewe, different sizes, seasons (summer and winter) and ripening times up to 9 months were elaborated. Their mineral composition or the information provided by their spectra in the near infrared zone (NIR) coupled to chemometric tools were used in order to differentiate between organic and non-organic cheeses. Main mineral composition (Ca, K, Mg, Na and P) of cheeses and stepwise lineal discriminant analysis were used to develop a discriminant model. The results from canonical standardised coefficients indicated that the most important mineral was Mg (1.725) followed by P (0.764) and K (0.742). The percentage of correctly classified samples was 88% in internal validation and 90% in external validation, selecting Mg, K and P as variables.Spectral information in the NIR zone was used coupled to a discriminant analysis based on a regression by partial least squares in order to obtain a model which allowed a rate of samples correctly classified of 97% in internal validation and 85% in external validation.
ISSN:0039-9140
1873-3573
DOI:10.1016/j.talanta.2011.07.021