Prediction of quality properties of dried cranberries with combination method of ultrasound-osmotic-microwave using artificial neural networks model

Transcription

1 Prediction of quality properties of dried cranberries with combination method of ultrasound-osmotic-microwave using artificial neural networks model Samira Shamaei a, Zahra Emam-Djomeh b a Transfer Phenomena Laboratory (TPL), Faculty of agricultural engineering and technology, University of Tehran, Karadj, Iran (samira_shamaei63@yahoo.com) b Transfer Phenomena Laboratory (TPL), Faculty of agricultural engineering and technology, University of Tehran, Karadj, Iran (emamj@ut.ac.ir) ABSTRACT This study uses artificial neural network analysis (ANN) to predict quality properties of dried cranberries. Drying techniques were used including osmotic pretreatments (with or without ultrasound) in combination with hot-air and microwave assisted hot-air. To carry out osmotic process, a ternary solution (Sucrose-Nacl- Water) was used. This model mathematically correlates five processing variables (concentration of sucrose, concentration of Nacl, temperature of osmotic solution, frequency of ultrasound (0, 35,130 khz) and power of microwave (0, 180 and 300 W)) with color, water activity, texture, total anthocyanins, polymeric color and antioxidant activity. Quality properties of dried cranberries were examined by a Hunterlab Colorimeter for color, a w meter for water activity, Texture analyser for texture, spectrophotometer for total anthocyanins, polymeric color. Antioxidant activity was measured using EC50 method, too. Texture of f ultrasonic (130 khz) pretreated samples when dried with MW (300 W), was similar to samples dried with hot-air. When using microwave, steam pressure caused severe collapse of textural layers in frequency of 130 khz that it was because of severe destruction created in this frequency. Therefore, higher maximum force (N) was seen. Optimized ANN models were developed based on neurons per hidden layer. ANN models were then tested against an independent dataset. The optimal ANN consisted of 2 hidden layers with 18 neurons. Measured values of outputs were predicted with an R 2 >0.87. Keywords: Artificial Neural Networks Model; Cranberry; Quality properties; Texture; Ultrasound-Osmotic-Microwave drying INTRODUCTION Drying is the oldest method of preserving of food. The main purpose of drying is to allow longer of storage, minimize packaging requirements and reduce shipping weights [8]. Osmotic dehydration is considered as a preservation method, which makes obtaining high-quality product possible by means of water removal from foodstuffs without phase change [4] furthermore, the osmotic treatment would energy consumption during the dehydration processes, the nutritional and organoleptic characteristics of the products, which usually undergo further drying by conventional means; or still to produce intermediate moisture foods of improved shelf-life stability with the help of combined preservation methods [8]. Due to the slowness of the solid-liquid mass transfer processes, several methods have been considered to accelerate the kinetics. One of these methods is ultrasound process. Ultrasound products a series of effects when if travels across a medium that can affect mass transfer. The high intensity of the waves can generate the growth and collapse of bubbles in side liquids. The asymmetric implosions of the cavitation bubbles close to a solid surface generate micro jets in the direction of the surface that can affect mass transfer. Some other effects of ultrasound are heating of the medium, the microstreaming at interfaces and several structural effects such as sponge effect or the generation of micro channels [3]. After this initial osmotic step, due to increasing shelf-life of samples, a finishing drying step is necessary, select of finish drying method is important because it effect on quality properties of dried cranberries. Food drying process involves simultaneous heat and mass transfer, which have been modelled with different levels of complexity [2]. Analytical drying models are usually used to model drying process, but these models are generally based on differential equations and the final result may be very complicated and difficult to use in actual drying systems. Artificial neural networks (ANN) models have been successfully used in the prediction of problems in bio-processing and chemical engineering [8].

2 In this study, fresh cranberries were dried by using two pre-treatments: Osmotic dehydration without and with ultrasonic process in two frequencies of 35 and 130 khz then finish drying was performed by two methods: Hot air and MW assisted hot air in two powers 180 and 300 W and finally the quality properties of dried samples were modelled using artificial neural networks. MATERIALS & METHODS The cranberries were purchased from a local market. They were sorted visually for maturity and size, and then they washed with tap water and dried with a filter paper. To increase permeability of skin cranberries, they were dipped in NaOH (0.5 Molar) for 2 minutes. Cranberries weighted and placed in to an osmotic solution in dynamic condition provided by agitation 150 rpm at ambient temperature. The sample/solution ratio was 1:10 (w/w), because of decreasing of ratio of sample to solution avoids significant dilution of the medium by water removal and subsequent decrease of osmotic driving force during the process. Samples were then removed from the solution at different time intervals (2, 4, 6, 8, 10 and 12 hour), then were washed by distilled water in order to remove the excess osmotic solution on the surface and dried with a filter paper. Experiments with ultrasound application were carried out with ultrasound bath (Elma, D Singen/Htw, Germany). Water temperature inside the ultrasonic bath was maintained constant during the osmotic experiments. Two levels of frequency were tested: 35 and 130 KHz. The electrical power output was 100%. Samples were removed from the solution at different time intervals (10, 20, 30, 40, 50, 60, 70, 80 minutes), then were washed by distilled water and dried with a filter paper. Samples were then dried using two different methods: Hot air drying: Cranberries were dried in a pilot plant hot-air drier (Tray dryer, Armfield, England). The drying was operated at an air velocity of 1.5 m/s parallel to the drying surface of the slices at 70 ºC dry bulb temperature. Hot-air drying was conducted until a moisture content of 30% (wet basis) was reached. Microwave-assisted hot-air drying: Hot-air drying was conducted as previously described until a moisture content of 45% (wet basis) was reached. Initial observation revealed that using higher moisture content produced a lower quality product, thus, samples with a low moisture content of 45% (wet basis) were used. Next, the samples were transferred to a programmable domestic microwave oven (Butane MR-1, Iran, maximum output of 1000 W at 2450 MHz.) for the microwave treatment. It was observed that charring and sample boiling occurred at 900 and600 W, respectively. Thus, only the 300 W and 180 W power levels were chosen for 30s on and 60s off. Samples were placed at the centre of the turntable in the microwave. The use of the turntable was necessary to achieve uniform heating of the samples and to reduce the level of microwave power on the magnetron. After the microwave treatment, the maximum recorded moisture content was 30% (wet basis). Quality properties of dried cranberries were examined by a Hunterlab Colorimeter (Dp-900 USA) for color, a w meter (TH-500, Novasina, Switzerland) for water activity, Texture analyser (H5KS, Hounsfield, UK) for texture, spectrophotometer (Cecil, CE 2502, UK) for total anthocyanins, and polymeric color. Antioxidant activity was measured using EC50 method [6]. Artificial neural networks were developed using Matlab 7 software, with the network toolbox nftool (The mathworks, Inc., Natick, MA, USA). The networks were simulated based on a multilayer feed forward neural network. This type of network is very powerful in function optimization modeling. A back propagation algorithm was used to implement supervised training of the network. A hyperbolic tangent was used as the transfer function in first layer, and a linear transfer function was used in second layer. Minimization of error was accomplished using the leven berg-marquardt (LM) algorithm. Training was finished when the mean square error (MSE) converged and was less than The inputs included the concentration of sucrose, salt and temperature ( C) frequency of ultrasound (khz) and power of MW (W). The output layer consisted of water activity, texture (N) total difference index of color, total anthocyanins, polymeric color and antioxidant activity. The optimized configurations from training for each neuron were selected based on neural network performance, which gave the minimum error from the training process. The average mean square error (MAE) and percentage of relative mean square error (%MRE) were used to compare the performances of various ANN models, and were calculated as: (1) (2)

3 Where, P P is the predicted output, and P E the experimentally measured outputs [7]. RESULTS & DISCUSSION Texture of dried samples became softer with increase of solution concentration (sucrose and salt) and temperature because increasing of concentration of osmotic solution and temperature increased water loss (WL) and solid gain (SG). Higher solid gain promoted viscosity of texture matrix and prevented from destruction of texture layers during finish drying, Treatment of sucrose and salt, prevented texture from collapse in addition to pre-treatment of osmosis leaded to leakage calcium in medium and Calcium content in dried samples was decreased by increase of WL. Ultrasonic pre-treatment decreased max force (N) of samples and with increasing of frequency of ultrasound, texture of samples become softer because this phenomenon increase SG and WL and leaded to create fractures and micro-channels in samples that caused decreased max force. The method of MW-assisted hot-air caused that texture of samples became softer than hot-air because this method create more porosity in this samples [1]. Texture of samples of ultrasonic pre-treatment with frequency of 130 khz when dried with MW (300 W), was similar to dried samples with hot-air. When using of microwave, steam pressure caused severe collapse of textural layers in frequency of 130 khz that it was because of severe destruction created in this frequency. Therefore higher max force (N) was seen. Due to osmotic pressure gradient, in during process epidermal and sub epidermal layers could be ruptured and anthocyanins leakage to medium. Although ultrasonic pre-treatment increased rupture of surface but anthocyanin content in this samples were high. It is because of reducing time of process and negative influence of oxygen [5]. Figure 1. The effect of changing sucrose concentration and frequency of ultrasound (A), changing NaCl concentration and frequency of ultrasound (B), changing temperature and frequency of ultrasound (C) and changing method of finish drying and frequency of ultrasound (D) on total anthocyanins of dried cranberries.

4 High arrival of sucrose to cranberries could destruct anthocyanins because of destructive effect of sugars on anthocyanins. The other parameters of osmotic process were NaCl concentration. The use of NaCl in 4% concentration increased anthocyanins loss in to solution. It is because ternary osmotic solution (Water- NaCl- Sucrose) has high osmotic pressure that is enhance water loss and solid gain. In concentration of 8% NaCl, Total anthocyanins were higher than concentration of 4% that it may be resulted in a fixed effect of salt on anthocyanins in fruit. With increasing of temperature from 30 C to 40 C, total anthocyanins didn t show a significant difference but in temperature of 50 C, anthocyanin content of fruit was lower due to increasing of water loss and the destructive effect of temperature on anthocyanins. Total anthocyanins were higher in dried samples with method of finish drying of MW-assisted Hot-Air than Hot-Air because it reduced drying time very much. Although ultrasonic pre-treatment had high anthocyanin content but it showed antioxidant activity lower than pretreatments without ultrasound, it is because ultrasound process enhanced percentage of polymeric color. Percentage of polymeric color increased with increasing frequency and caused antioxidant activity became low. With increasing sucrose and NaCl concentration in osmotic process; changes of antioxidant activity of dried samples were similar to changes of total anthocyanins. Increasing of temperature decreases antioxidant activity. This coupled be explained by decreasing total anthocyanins and consequently polymeric color. The use of microwave assisted hot-air method leaded to increasing antioxidant activity because it increased total anthocyanins and decreased polymeric color. A Figure2. The effect of changing sucrose concentration and frequency of ultrasound (A), changing NaCl concentration and frequency of ultrasound (B), changing temperature and frequency of ultrasound (C) and changing method of finish drying and frequency of ultrasound (D) on antioxidant activity of dried cranberries.

5 Figure 3. The effects of changing method of finish drying (A) and frequency of ultrasound (B) on polymeric color of dried cranberries. The ANN optimization Process was performed using a trial and error technique. Each dataset was divided in 70% for training, 15% for testing and 15% for validation. During training, the datasets were used to determine the optimum number of neurons per hidden layer that gave the best predictive power. ANNs with neurons per hidden layer were tested. The results showed that the number of neurons per hidden layer that yielded minimum error was different for each output. Tables 1 illustrated that minimum MRE was found with 18 neurons for total color difference, with 20 neurons for a w, with 18 neurons for texture, with 10 neurons for total anthocyanins, with 16 neurons for polymeric color and with 12 neurons for antioxidant activity. The best prediction in most of outsets contained 18 neurons. Table 1- Errors in the prediction of color (1), a w (2), texture (3), anthocynins (4), polymeric color (5) and antioxidant activity (6) with different number of neurons per layer for dried cranberries. N Neuron MAE 1 MRE 1 MAE 2 MRE 2 MAE 3 MRE 3 MAE 4 MRE 4 MAE 5 MRE 5 MAE 6 MRE CONCLUSION ANN-based model was developed for prediction of quality properties of dried cranberries for a wide range of experimental conditions. Due to complexity this process, traditional regression could not use in this case but ANN is easy to implement for design and control and it could be effectively used for prediction. The optimal models had two hidden layers and 18 neurons in each hidden layer and it could predict quality properties with R 2 values greater than REFERENCES [1] Askari, G. A., Emam-Djomeh. Z. and Mousavi, S. M. (2006). Effect of combined coating and microwave assisted hot-air drying on the texture, microstructure and rehydration characteristics of apple slices. Journal of Food Science and Technology, 12(1): [2] Çam, M., Hışıl Y. and Durmaz, G Classification of eight pomegranate juices based on antioxidant capacity measured by four methods. Food chemistry, 112: [3] Cẚrcel, J.A., Benedito, J., Rossellỏ, C. and Mulet, A. (2007), Influence of ultrasound intensity on mass transfer in apple immersed in a sucrose solution. Journal of Food Engineering, 78: [4] Emam-Djomeh, Z., Dehghannya, J. and Sotudeh Gharabagh, R. (2006), Assessment of Osmotic Process in Combination with Coating on Effective Diffusivities during Drying of Apple Slices. Drying Technology, 24:

6 [5] Fernandes, F.A.N., Linhares, F.E. and Rodrigues, S. (2008). Ultrasound as pre-treatment for drying of pineapple. Ultrasonics Sonochemistry, 15: [6] Hernẚndez, J.A Optimum operating conditions for heat and mass transfer in foodstuffs drying by means of neural network inverse. Food control, 20: [7] Kerdpiboon, S., William L, K. and Devahastin, S Neural network prediction of physical property changes of dried carrot as a function of fractal dimension and moisture content. Food Research International, 39: [8] Movagharnejad, K. and Nikzad, M Modeling of tomato drying using artificial neural network. Computers and Electronics in Agriculture, 59: