Literature DB >> 17996542

Determination of the percentage of milk (cow's, ewe's and goat's) in cheeses with different ripening times using near infrared spectroscopy technology and a remote reflectance fibre-optic probe.

I González-Martín1, J M Hernández-Hierro, R Morón-Sancho, J Salvador-Esteban, A Vivar-Quintana, I Revilla.   

Abstract

In the present work we studied the use of near infrared spectroscopy (NIRS) technology employing a remote reflectance fibre-optic probe (with a 5 cm x 5 cm quartz window) for the analysis of the percentage of milk (cow's, ewe's and goat's) used in the elaboration of cheeses with different ripening times. To do so, cheeses with known and varying percentages of cow's, ewe's and goat's milk were elaborated (112 samples with milk collected in winter and 112 samples with milk collected in summer) and used as reference material, and ripening controls were performed over 6 months. The method allows immediate control of the cheese without prior sample treatment or destruction by direct application of the fibre-optic probe to the sample. The regression method employed was modified partial least squares (MPLS). Of all the samples (224), 200 formed to so-called calibration set and the other 24 were used for external validation. The calibration results obtained using 200 samples of cheese allowed the percentage of cow's, ewe's and goat's milk to be measured. The multiple correlation coefficients (RSQ) and prediction corrected standard errors (SEP(C)) obtained were respectively, 0.834 and 11.6% for cow's milk; 0.871 and 9.8% for goat's milk; 0.880 and 10.6% for ewe's milk. The ratio performance deviation (RPD) values obtained indicate that the NIRS equations can be applied to unknown samples.

Entities:  

Year:  2007        PMID: 17996542     DOI: 10.1016/j.aca.2007.10.014

Source DB:  PubMed          Journal:  Anal Chim Acta        ISSN: 0003-2670            Impact factor:   6.558


  2 in total

1.  Accurate Prediction of Sensory Attributes of Cheese Using Near-Infrared Spectroscopy Based on Artificial Neural Network.

Authors:  Belén Curto; Vidal Moreno; Juan Alberto García-Esteban; Francisco Javier Blanco; Inmaculada González; Ana Vivar; Isabel Revilla
Journal:  Sensors (Basel)       Date:  2020-06-24       Impact factor: 3.576

2.  Prediction of meat spectral patterns based on optical properties and concentrations of the major constituents.

Authors:  Gamal ElMasry; Shigeki Nakauchi
Journal:  Food Sci Nutr       Date:  2015-09-23       Impact factor: 2.863

  2 in total

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