A comparison of Raman and FT-IR spectroscopy for the prediction of meat spoilage
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @Article{Argyri2012,
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author = "Anthoula A. Argyri and Roger M. Jarvis and
David Wedge and Yun Xu and Efstathios Z. Panagou and
Royston Goodacre and George-John E. Nychas",
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title = "A comparison of Raman and FT-IR spectroscopy for the
prediction of meat spoilage",
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journal = "Food Control",
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volume = "29",
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number = "2",
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pages = "461--470",
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year = "2013",
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note = "Predictive Modelling of Food Quality and Safety",
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ISSN = "0956-7135",
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DOI = "doi:10.1016/j.foodcont.2012.05.040",
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URL = "http://www.sciencedirect.com/science/article/pii/S0956713512002745",
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keywords = "genetic algorithms, genetic programming, Meat
spoilage, Raman spectroscopy, FT-IR, Multivariate
analysis, Evolutionary computing",
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abstract = "In this study, time series spectroscopic,
microbiological and sensory analysis data were obtained
from minced beef samples stored under different
packaging conditions (aerobic and modified atmosphere
packaging) at 5 C. These data were analysed using
machine learning and evolutionary computing methods,
including partial least square regression (PLS-R),
genetic programming (GP), genetic algorithm (GA),
artificial neural networks (ANNs) and support vector
machines regression (SVR) including different kernel
functions [i.e. linear (SVRL), polynomial (SVRP),
radial basis (RBF) (SVRR) and sigmoid functions
(SVRS)]. Models predictive of the microbiological load
and sensory assessment were calculated using these
methods and the relative performance compared. In
general, it was observed that for both FT-IR and Raman
calibration models, better predictions were obtained
for TVC, LAB and Enterobacteriaceae, whilst the FT-IR
models performed in general slightly better in
predicting the microbial counts compared to the Raman
models. Additionally, regarding the predictions of the
microbial counts the multivariate methods (SVM, PLS)
that had similar performances gave better predictions
compared to the evolutionary ones (GA-GP, GA-ANN, GP).
On the other hand, the GA-GP model performed better
from the others in predicting the sensory scores using
the FT-IR data, whilst the GA-ANN model performed
better in predicting the sensory scores using the Raman
data. The results of this study demonstrate for the
first time that Raman spectroscopy as well as FT-IR
spectroscopy can be used reliably and accurately for
the rapid assessment of meat spoilage.",
- }
Genetic Programming entries for
Anthoula A Argyri
Roger M Jarvis
David C Wedge
Yun Xu
Efstathios Z Panagou
Royston Goodacre
George-John E Nychas
Citations