Artificial neural network modelling of the chemical composition of carrots submitted to different pre-drying treatments

The effect of various pre-drying treatments on the quality of dried carrots was evaluated by assessing the values of moisture, ash, protein, fibre, sugars and colour. The pre-drying treatments under investigation were dipping, either in ascorbic acid or sodium metabisulphite at different concentrations and pre-treatment times, as well as blanching. The experimental data was analysed using neural networks, so that relevant patterns in the data were found and conclusions drawn about each variable. The results showed that the type of pre-drying treatment (chemical or physical) had variable impact on the nutritional composition of the dried carrots but not on the colour parameters, which were found to be mostly unaffected by the pre-treatment procedure. Pre-treatment with chemical agents such as ascorbic acid or metabisulphite seem to have the least impact on the parameters studied. The results of the analysis by artificial neural networks confirmed these findings.

[1]  Sakamon Devahastin,et al.  Neural network prediction of physical property changes of dried carrot as a function of fractal dimension and moisture content , 2006 .

[2]  A. Tecante,et al.  Chia (Salvia hispanica): A Review of Native Mexican Seed and its Nutritional and Functional Properties. , 2015, Advances in food and nutrition research.

[3]  Sangeeta Prakash,et al.  Performance evaluation of blanched carrots dried by three different driers , 2004 .

[4]  D. Barrett,et al.  Influence of Pre-drying treatments on Quality and Safety of Sun-dried Tomatoes. Part II. Effects of Storage on Nutritional and Sensory Quality of Sun-dried Tomatoes Pretreated with Sulfur, Sodium Metbisulfite, or Salt , 2006 .

[5]  Mahmoud Omid,et al.  Prediction of Energy and Exergy of Carrot Cubes in a Fluidized Bed Dryer by Artificial Neural Networks , 2011 .

[6]  Hosahalli S. Ramaswamy,et al.  A neuro-computing approach for modeling of residence time distribution (RTD) of carrot cubes in a vertical scraped surface heat exchanger (SSHE) , 2000 .

[7]  R. Guiné Drying of Pears: Experimental Study and Process Simulation , 2011 .

[8]  Effect of processing parameters on physico-chemical and culinary quality of dried carrot slices , 2011, Journal of food science and technology.

[9]  A. Mujumdar,et al.  Drying Kinetics and β‐Carotene Degradation in Carrot Undergoing Different Drying Processes , 2005 .

[10]  S. Simal,et al.  Optimisation of the addition of carrot dietary fibre to a dry fermented sausage (sobrassada) using artificial neural networks. , 2013, Meat science.

[11]  Marek Markowski,et al.  Color Characteristics of Carrots: Effect of Drying and Rehydration , 2012 .

[12]  Raquel Guiné,et al.  Effect of drying treatments on texture and color of vegetables (pumpkin and green pepper) , 2012 .

[13]  T. Labuza,et al.  Preservation of β-Carotene from Carrots , 1998 .

[14]  C. Chang,et al.  Effect of adding ascorbic acid and glucose on the antioxidative properties during storage of dried carrot , 2008 .

[15]  F. Murr,et al.  Influence of pre-treatments on the drying kinetics during vacuum drying of carrot and pumpkin , 2007 .

[16]  P. Negi,et al.  The effect of blanching on quality attributes of dehydrated carrots during long-term storage , 2001 .

[17]  R. Guiné,et al.  Effect of drying temperatures on the phenolic composition and antioxidant activity of pears of Rocha variety (Pyrus communis L.) , 2014, Journal of Food Measurement and Characterization.

[18]  Zacharias B. Maroulis,et al.  KINETICS ON COLOR CHANGES DURING DRYING OF SOME FRUITS AND VEGETABLES , 1998 .

[19]  Mateus Mendes,et al.  Convective Drying of Apples: Kinetic Study, Evaluation of Mass Transfer Properties and Data Analysis using Artificial Neural Networks , 2014 .

[20]  J. Rufián‐Henares,et al.  Effect of red sweet pepper dehydration conditions on Maillard reaction, ascorbic acid and antioxidant activity , 2013 .

[21]  P. Lewicki Effect of pre‐drying treatment, drying and rehydration on plant tissue properties: A review , 1998 .

[22]  E. Fialho,et al.  Polyphenol Oxidase: Characteristics and Mechanisms of Browning Control , 2008 .

[23]  Mortaza Aghbashlo,et al.  Optimization of an Artificial Neural Network Topology for Predicting Drying Kinetics of Carrot Cubes Using Combined Response Surface and Genetic Algorithm , 2011 .

[24]  R. Guiné,et al.  Convective drying of onion: Kinetics and nutritional evaluation , 2010 .

[25]  Koksal Erenturk,et al.  Comparison of genetic algorithm and neural network approaches for the drying process of carrot , 2007 .

[26]  G. Barbosa‐Cánovas,et al.  Inhibition of polyphenoloxidase in mango puree with 4-hexylresorcinol, cysteine and ascorbic acid , 2005 .

[27]  Jude W. Shavlik,et al.  Using neural networks for data mining , 1997, Future Gener. Comput. Syst..

[28]  A. Bayındırlı,et al.  Inhibition of enzymic browning in cloudy apple juice with selected antibrowning agents , 2002 .

[29]  Zhengfu Wang,et al.  Two-Stage Intermittent Microwave Coupled with Hot-Air Drying of Carrot Slices: Drying Kinetics and Physical Quality , 2014, Food and Bioprocess Technology.

[30]  D. Pustaka,et al.  AOAC, 1999. Official Method of Analysis of AOAC International. The Association of The Official Analytical Chemists 11 th Edition, , 2008 .

[31]  Emilía Martinsdóttir,et al.  Evaluation of Shelf Life of Superchilled Cod (Gadus morhua) Fillets and the Influence of Temperature Fluctuations During Storage on Microbial and Chemical Quality Indicators , 2006 .