Modelling fungal growth using radial basis function neural networks: The case of the ascomycetous fungus Monascus ruber van Tieghem

International Journal of Food Microbiology

Volumn 117, Issue 3, 2007, Pages 276-286

  Panagou, E Z ^a,c   Kodogiannis, V ^b   Nychas, G J E ^c  

^a INSTITUTE OF TECHNOLOGY OF AGRICULTURAL PRODUCTS   (Greece)

^b UNIVERSITY OF WESTMINSTER   (United Kingdom)

^c AGRICULTURAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

Artificial neural networks; Monascus ruber; Polynomial regression; Predictive mycology; Radial basis function network

Indexed keywords

ACCURACY; ARTICLE; ASCOMYCETES; BINDING KINETICS; FUNGUS GROWTH; FUNGUS ISOLATION; GROWTH RATE; HEAT TOLERANCE; MATHEMATICAL MODEL; MONASCUS; MONASCUS RUBER; NERVE CELL NETWORK; NONHUMAN; OLIVE; PH MEASUREMENT; RADIAL BASIS FUNCTION NEURAL NETWORK; ROOT MEAN SQUARE ERROR MODEL; SENSITIVITY ANALYSIS; STANDARD ERROR OF PREDICTION; SURFACE PROPERTY; TEMPERATURE MEASUREMENT; TRAINING;

AREA UNDER CURVE; COLONY COUNT, MICROBIAL; FOOD CONTAMINATION; FOOD MICROBIOLOGY; HYDROGEN-ION CONCENTRATION; KINETICS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; MONASCUS; NEURAL NETWORKS (COMPUTER); PREDICTIVE VALUE OF TESTS; TEMPERATURE; WATER;

FUNGI; MONASCUS RUBER; OLEACEAE;

EID: 34250899137     PISSN: 01681605     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijfoodmicro.2007.03.010     Document Type: Article

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