EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 292 (2019) MATEC Web Conf., 292 (2019) 03019 References
Open Access
Issue
MATEC Web Conf.
Volume 292, 2019
23rd International Conference on Circuits, Systems, Communications and Computers (CSCC 2019)
Article Number 03019
Number of page(s) 8
Section Computers
DOI https://doi.org/10.1051/matecconf/201929203019
Published online 24 September 2019
  1. A. Cabrera, J.M. Prieto, Application of artificial neural networks to the prediction of the antioxidant activity of essential oils in two experimental in vitro models, Food Chem., 118, 141 (2010) [CrossRef] [Google Scholar]
  2. C. Cimpoiu, V.-M. Cristea, A. Hosu, M. Sandru, L. Seserman, Antioxidant activity prediction and classification of some teas using artificial neural networks, Food Chem., 127, 1323 (2011) [CrossRef] [Google Scholar]
  3. V. Farzaneh, A. Ghodsvali, H. Bakhshabadi, M. Ganje, Z. Dolatabadi, I.S. Carvalho, Modelling of the selected physical properties of the Fava bean with various moisture contents using Fuzzy Logic Design, Journal of Food Process Eng., 40, 123 (2016) [Google Scholar]
  4. E. Górska-Horczyczak, M. Horczyczak, D. Guzek, I. Wojtasik-Kalinowska, A. Wierzbicka, Chromatographic fingerprints supported by artificial neural network for differentiation of fresh and frozen pork, Food Control, 73, 237 (2017) [CrossRef] [Google Scholar]
  5. M. Hajimahmoodi, M. Khanavi, O. Sadeghpour, M. Ardekani, F. Mazde, M. Khoddami, S. Afzalifard, A. Ranjbar, Application of organic acid based Artificial Neural Network modeling for assessment of commercial vinegar authenticity, Food Anal. Methods, 9, 3451 (2016) [CrossRef] [Google Scholar]
  6. A. Hosu, V.M. Cristea, C. Cimpoiu, Analysis of total phenolic, flavonoids, anthocyanins and tannins content in Romanian red wines: Prediction of antioxidant activities and classification of wines using artificial neural networks, Food Chem., 150, 113 (2014) [CrossRef] [Google Scholar]
  7. S. Keeratipibul, A. Phewpan, C. Lursinsap, Prediction of coliforms and Escherichia coli on tomato fruits and lettuce leaves after sanitizing by using Artificial Neural Networks, Food Sci. Technol., 44, 130 (2011) [Google Scholar]
  8. L. Pan, Q. Zhang, W. Zhang, Y. Sun, P. Hu, K. Tu, Detection of cold injury in peaches by hyperspectral reflectance imaging and artificial neural network, Food Chem., 192, 2016, pp. 134–141. [CrossRef] [Google Scholar]
  9. C. Cevoli, L. Cerretani, A. Gori, M.F. Caboni, T. Gallina Toschi, A. Fabbri, Classification of Pecorino cheeses using electronic nose combined with artificial neural network and comparison with GC–MS analysis of volatile compounds, Food Chem., 129, 1315 (2011) [CrossRef] [Google Scholar]
  10. M. León-Roque, L. Abderrahim, S. Nuñez-Alejos, M. Arribas, L. Condezo-Hoyos, Prediction of fermentation index of cocoa beans (Theobroma cacao L.) based on color measurement and artificial neural networks, Talanta, 161, 31 (2016) [CrossRef] [Google Scholar]
  11. J. Rojas, V. Vásquez, Prediction by Artificial Neural Networks (ANN) of the diffusivity, mass, moisture, volume and solids on osmotically dehydrated yacon (Smallantus sonchifolius), Sci. Agropecu., 3, 201 (2012) [CrossRef] [Google Scholar]
  12. M. Soto-Barajas, Ma Inmaculada González-Martín, J. SalvadorEsteban, J. Hernández-Hierro, V. Moreno-Rodilla, A. Vivar-Quintana, I. Revilla, I. Ortega, R. Morón-Sancho, 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, 116, 50 (2013) [CrossRef] [Google Scholar]
  13. A. Bahmani, S.M. Jafari, S.A. Shahidi, D. Dehnad, Mass transfer kinetics of Eggplant during osmotic dehydration by Neural Networks, Joutnal Food Process. Preserv., 40, 815 (2015) [Google Scholar]
  14. M. Azadbakht, H. Aghili, A. Ziaratban, M.V. Torshizi, Application of artificial neural network method to exergy and energy analyses of fluidized bed dryer for potato cubes, Energy, 120, 947 (2017) [CrossRef] [Google Scholar]
  15. P. Khawas, K.K. Dash, A.J. Das, S.C. Deka, Modeling and optimization of the process parameters in vacuum drying of culinary banana (Musa ABB) slices by application of artificial neural network and genetic algorithm, Dry Technol., 34, 491 (2016) [Google Scholar]
  16. T. Amoriello, R. Ciccoritti, M. Paliotta, K. Carbone, Classification and prediction of early- to-late ripening apricot quality using spectroscopic techniques combined with chemometric tools, Scientia Horticulturae, 240, 310 (2018) [CrossRef] [Google Scholar]
  17. M. Khojastehnazhand, V. Mohammadi, S. Minaei, Maturity detection and volume estimation of apricot using image processing technique, Scientia Horticulturae, 251, 247 (2019) [CrossRef] [Google Scholar]
  18. İ. Sani, Ö. Bülent, Ö. Belgin, Ç. Sarıtepe, H. Aksoy, Rapid, simultaneous and non- destructive assessment of the moisture, water activity, firmness and SO2 content of the intact sulphured-dried apricots using FT-NIRS and hemometrics, Talanta, 186, 467 (2018) [CrossRef] [Google Scholar]
  19. C. Camps, D. Christen, Non-destructive assessment of apricot fruit quality by portable visible-near infrared spectroscopy, LWT - Food Science and Technology, 42, 1125 (2009) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.