Screening of the alterations in qualitative characteristics of grape under the impacts of storage and harvest times using artificial neural network
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The tested model showed that high reliability based on the obtained inputs for achieved data for RMSE and correlation coefficient between the obtained experimental and predicted values. An enhancement in the storage time, reduced the pH value and flavor index (fruit maturation), but boosted the acidity value of the fruits. On the other hand retardation in the harvest time led to an increase in pH value, total soluble solids and dextrose contents as well as flavor index of the samples. Artificial neural network design has been applied to predict the process of alterations during storage time. Back propagation feed forward neural network with the arrangement of 5:8:2 with a high correlation coefficient value (˃ 0.989) and low root mean square error value (< 0.0019) as well as sigmoid hyperbolic tangent activation function with Levenberg–Marquardt learning and learning cycle of 1000 were detected as the most reliable and appropriate neural network model in storage process.
KeywordsHarvest time Storage conditions Grape fruit Modeling ANN
The present work is a part of PhD studies of Vahid Farzaneh, We gratefully appreciate Erasmus Mundus Program SALAM for the financial supports and Food Science Lab of University of the Algarve for facilities provided during this study. Our great thanks to Golestan Agricultural and Natural Recourses Research Center, Gorgan, Iran, Agricultural Engineering.
- AOAC (2005) Official methods of analysis of the association of official analytical chemists. Association of Official Analytical Chemists, ArlingtonGoogle Scholar
- Chemists A o A (2006) Official method of analysis of the association of analytical chemists. AOAC, Washington, DCGoogle Scholar
- Farzaneh V, Carvalho SI (2017) Modelling of microwave assisted extraction (MAE) of anthocyanins (TMA). J Appl Res Med Aromat Plants 6(0):92–100Google Scholar
- Farzaneh V, Ghodsvali A, Bakhshabadi H, Ganje M, Dolatabadi Z, Carvalho SI (2016) Modelling of the selected physical properties of the fava bean with various moisture contents using fuzzy logic design. J Food Process Eng 40(2). https://doi.org/10.1111/jfpe.12366
- Fiasche M, Cuzzold M, Cacciola M, Megali G, Fedele R, Iacopino P, Morabito FC (2009) A neural network model for early diagnosis of acute gvhd based on gene expression data. In: 2009 IEEE international workshop on genomic signal processing and statistics, GENSIPS 2009, May 2009. Minneapolis, MN, USA. https://doi.org/10.1109/GENSIPS.2009.5174360
- Lu B, Zhang Y, Wu X, Shi J (2007) Separation and determination of diversiform phytosterols in food materials using supercritical carbon dioxide extraction and ultraperformance liquid chromatography-atmospheric pressure chemical ionization-mass spectrometry. Anal Chim Acta 588(1):50–63CrossRefGoogle Scholar
- Rahemi M (2005) Postharvest physiology (introduction to the physiology and handling fruits and vegetables and ornamental plants). Shiraz University Press, ShirazGoogle Scholar
- Woodroof JG, Luh BS (1986) AVIPublishing Co. Inc. WestportGoogle Scholar