Prediction of seasonal variation of butters by computing the fatty acids composition with artificial neural networks

2011 ◽  
Vol 113 (11) ◽  
pp. 1412-1419 ◽  
Author(s):  
Alessandro Gori ◽  
Chiara Cevoli ◽  
Selenia Melia ◽  
Marco Nocetti ◽  
Angelo Fabbri ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fatty Acids ◽  
Artificial Neural Networks ◽  
Seasonal Variation ◽  
Fatty Acids Composition ◽  
Artificial Neural

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 ◽  
2013 ◽  
Vol 116 ◽  
pp. 50-55 ◽  
Author(s):  
Milton Carlos Soto-Barajas ◽  
Ma Inmaculada González-Martín ◽  
Javier Salvador-Esteban ◽  
José Miguel Hernández-Hierro ◽  
Vidal Moreno-Rodilla ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fatty Acids ◽  
Artificial Neural Networks ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Artificial Neural

Estimation of seasonal variation of ground resistance using Artificial Neural Networks

2013 ◽  
Vol 94 ◽  
pp. 113-121 ◽  
Author(s):  
Fani E. Asimakopoulou ◽  
George J. Tsekouras ◽  
Ioannis F. Gonos ◽  
Ioannis A. Stathopulos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Seasonal Variation ◽  
Ground Resistance ◽  
Artificial Neural

scholarly journals The Use of Artificial Neural Networks and Decision Trees to Predict the Degree of Odor Nuisance of Post-Digestion Sludge in the Sewage Treatment Plant Process

2019 ◽  
Vol 11 (16) ◽  
pp. 4407 ◽  
Author(s):  
Hubert Byliński ◽  
Andrzej Sobecki ◽  
Jacek Gębicki
Keyword(s):  
Neural Networks ◽  
Fatty Acids ◽  
Artificial Neural Networks ◽  
Decision Trees ◽  
Volatile Fatty Acids ◽  
Treatment Plant ◽  
Odor Intensity ◽  
Hedonic Tone ◽  
Input Parameters ◽  
Artificial Neural

This paper presents the application of artificial neural networks and decision trees for the prediction of odor properties of post-fermentation sludge from a biological-mechanical wastewater treatment plant. The input parameters were concentrations of popular compounds present in the sludge, such as toluene, p-xylene, and p-cresol, and process parameters including the concentration of volatile fatty acids, pH, and alkalinity in the fermentation sludge. The analyses revealed that the implementation of artificial neural networks allowed the prediction of the values of odor intensity and the hedonic tone of the post-fermentation sludge at the level of 30% mean absolute percentage error. Application of the decision tree made it possible to determine what input parameters the fermentation feed should have in order to arrive at the post-fermentation sludge with an odor intensity <2 and hedonic tone >−1. It was shown that the aforementioned phenomenon was influenced by the following factors: concentration of p-xylene, pH, concentration of volatile fatty acids, and concentration of p-cresol.

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