Machine Learning Models for Predicting Shelf Life of Processed Cheese

Sumit Goyal, Gyanendra Kumar Goyal

Abstract


Feedforward multilayer machine learning artificial neural network (ANN) models were established for predicting shelf life of processed cheese stored at 7-8o C. Soluble nitrogen, pH, standard plate count, yeast & mould count, and spore count were input variables, and sensory score was the output variable. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash–Sutcliffe Coefficient were used for comparing the prediction ability of the developed models. Feedforward ANN model with combination of 5à16à16à1 simulated best with high R2: 0.998717294, suggesting that multilayer machine learning models can predict shelf life of processed cheese.

Full Text:

PDF

References


Learnartificialneuralnetworks Website: http://www.learnartificialneuralnetworks.com/ (accessed on 1.4.2011).

Wikipedia ANN Website: http://en.wikipedia.org/wiki/Artificial_neural_network (accessed on 28.5.2011)

Wikipedia Feedforward Website: http://en.wikipedia.org/wiki/Feedforward_neural_network (accessed on 30.1.2011)

Medlabs Website: http://www.medlabs.com/Downloads/food_product_shelf_life_web.pdf (accessed on 21.5.2011)

T. Marique, A. Kharoubi, P. Bauffe, and C. Ducattillon, “ Modeling of fried potato chips color classification using image analysis and artificial neural network,” Journal of Food Science, vol.68, no.7, pp. 2263-2266, 2003.

Sumit, Goyal, S. Kar, and G.K. Goyal, “Artificial neural networks for analyzing solubility index of roller dried goat whole milk powder,” International Journal of Mechanical Engineering and Computer Applications, vol.1, no.1, pp. 1-4, 2013.

Sumit Goyal and G.K. Goyal, “ Radial basis artificial neural network models for predicting solubility index of roller dried goat whole milk powder,” In: V. Snášel et al. eds. Soft Computing in Industrial Applications. Advances in Intelligent Systems and Computing 223, DOI: 10.1007/978-3-319-00930-8_21. Chapter No.: 21, Book ID: 311964_1_En Book. ISBN: 978-3-319-00929-2. Publisher: Springer International Publishing, Switzerland, 2013.

M. Bahramparvar, S. Fakhreddin, and S. Razavi, “Predicting total acceptance of ice cream using artificial neural network,” Journal of Food Processing and Preservation, doi: 10.1111/jfpp.12066, 2013.

A.A. Argyri, R.M. Jarvis, D. Wedge, Y. Xu, E.Z. Panagou, R. Goodacre, and G.J.E. Nychas, “ A comparison of Raman and FT-IR spectroscopy for the prediction of meat spoilage,” Food Control, vol. 29, no.2, pp. 461-470, 2013.

Sumit Goyal and G.K. Goyal, “Intelligent artificial neural network computing models for predicting shelf life of processed cheese,” Intelligent Decision Technologies, vol.7, no.2, pp. 107-111, 2013.

M.C. Soto-Barajas, M.I. González-Martín, J. Salvador-Esteban,J.M. Hernández-Hierro, V. Moreno-Rodilla, A.M. Vivar-Quintana, I. Revilla, I.L. Ortega, R. Morón-Sancho, and B. Curto-Diego, “ Prediction of the type of milk and degree of ripening in cheeses by means of artificial neural networks with data concerning fatty acids and near infrared spectroscopy,” Talanta, vol.116, pp. 50-55, 2013.

M.H. Saeidirad, A. Rohani, and S. Zarifneshat, “Predictions of viscoelastic behavior of pomegranate using artificial neural network and Maxwell model,” Computers and Electronics in Agriculture, vol. 98, pp. 1-7, 2013.

Sumit Goyal, and G.K. Goyal, “Artificial vision for estimating shelf life of burfi,” Journal of Nutritional Ecology and Food Research, vol.1, no.2, pp. 134-136, 2013.

S. Taghadomi‐Saberi, M. Omid, Z. Emam‐Djomeh, and H. Ahmadi, “ Evaluating the potential of artificial neural network and neuro‐fuzzy techniques for estimating antioxidant activity and anthocyanin content of sweet cherry during ripening by using image processing,” Journal of the Science of Food and Agriculture. doi: 10.1002/jsfa.6202, 2013

Sumit Goyal, “Artificial neural networks (ANNs) in food science–A review,” International Journal of Scientific World, vol.1, no.2, pp. 19-28, 2013.

Sumit Goyal, “Artificial neural networks in vegetables: A comprehensive review,” Scientific Journal of Crop Science, vol.2, no.7, pp. 75-94, 2013.


Refbacks

  • There are currently no refbacks.


Abava   Servletsuite

ISSN: 2307-8162