Post-harvest technologies
H. Rezaei; M. Sadeghi
Abstract
IntroductionDue to the disadvantages of using chemical materials as pretreatment before grape drying, the application of non-chemical methods that not only take the environmental issues into account but also increase the drying rate and improve the quality of the produced raisins is vitally important. ...
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IntroductionDue to the disadvantages of using chemical materials as pretreatment before grape drying, the application of non-chemical methods that not only take the environmental issues into account but also increase the drying rate and improve the quality of the produced raisins is vitally important. The high-humidity hot air impingement blanching (HHAIB) is one of the non-chemical methods that can be used as a suitable alternative for chemical pretreatment in grape drying. In this research, the design, construction, and evaluation of a high-humidity hot air impingement blanching system are discussed in terms of the drying kinetics of white seedless grapes. The results are compared against the control and chemical pretreatment.Materials and MethodsHigh-humidity hot air impingement blanching (HHAIB) systemThe HHAIB system is composed of the steam generator, steam transfer pipes, side channel pump, closing and opening valves, air recycling channel, electric air heater, hot-humid air transfer channel, pretreatment chamber, hot-humid air distribution chamber, nozzles, temperature and humidity sensors and controllers. The performance of the system depends on the humid air temperature, the output fluid velocity from the nozzle, the distance of the nozzles from the product surface, as well as the diameter and arrangement of the nozzles. In order to achieve optimal design of the nozzle array, the relationships existed for the heat transfer coefficient, air mass flow, and blowing power were considered.Application of the HHAIB pretreatment and evaluation of its effect on the grape drying processExperiments were conducted to investigate the effect of temperature and duration of HHAIB pretreatment on the kinetics of grape drying. A two-factor completely randomized factorial design with three replications was used to analyze the data.According to the studies, the air at temperatures of 90, 100, and 110°C, a velocity of 10 m s-1, and relative humidity in the range of 40-45% was applied to the product. Pretreatment durations of 30, 60, 90, 120, and 150 s were also considered. Experiments were conducted with three replicates and control treatment and acid pretreatment were used to compare the drying process. Due to the high quality of shade-dried raisins, this method was used to study the process.The effect of the pretreatment duration on the drying kinetics of white seedless grapes was assessed by observing variations in moisture ratio and drying rate over time, as well as determining the effective diffusivity of water.For the color evaluation of the produced raisins, chroma (C), hue angle H°, and total color difference (ΔE) parameters were calculated after measuring L*, a*, and b* values.Results and DiscussionThe comparison of the drying process among the control, chemical, and HHAIB showed the positive efficacy of HHAIB on the drying rate of grapes. Compared to fresh grapes, the increase in drying rate under the influence of HHAIB varied from 8% for a duration of 30 s at 90°C to 68% for a duration of 150 s at 110°C. The values of the diffusion coefficient of grapes for the HHAIB pretreatment at temperatures of 90, 100, and 110°C and durations of 30, 60, 90, 120, and 150 s, as well as for the control and chemical pretreatments were determined. The values of the coefficient changed from 2.28×10-10 m2 s-1 for 30 s of applying pretreatment at 90°C to 3.53×10-10 m2 s-1 for 150 s of applying the pretreatment at 110°C. The highest value of this coefficient (7.46×10-10 m2 s-1) was associated with the chemical pretreatment. The value of the diffusion coefficient increased with increasing temperature and duration of the HHAIB pretreatment. In general, this increase in the drying rate and the diffusion coefficient can be attributed to the effect of the HHAIB pretreatment on the texture and destruction of the cell wall, as well as the microcracks created on the skin of the grapes. Moreover, the findings reveal that, in comparison with the hot air temperature, the duration of the HHAIB pretreatment was more effective in enhancing the drying rate. Additionally, based on the color analysis, a temperature of 110°C and a duration range of 90-150 s were achieved as suitable conditions for applying pretreatment.ConclusionThe HHAIB pretreatment, which combines the benefits of hot air blanching with jet technology, affects the texture and skin of grapes, accelerates the drying process, and increases the quality of the produced raisins. However, the correct application of this pretreatment depends on the proper design of the system and appropriate conditions, including duration, temperature, and relative humidity. The results of drying kinetics showed that the drying rate increased with an increase in the temperature and duration of the pretreatment. The findings indicate that the HHAIB pretreatment could improve the color indices of the raisins, resulting in an increase in the drying rate and acceptable quality of the final product. This provides a basis for the use of HHAIB on larger and industrial scales.
S. M. Mir-ahmadi; S. A. Mireei; M. Sadeghi; A. Hemmat
Abstract
Introduction: Iran is one of the main producers of kiwifruit in the world. Unfortunately, the sorting and grading of the kiwifruits are manual, which is a time consuming and labor intensive task. Due to the lack of appropriate devices for sorting and grading of kiwifruit based on the quality parameters, ...
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Introduction: Iran is one of the main producers of kiwifruit in the world. Unfortunately, the sorting and grading of the kiwifruits are manual, which is a time consuming and labor intensive task. Due to the lack of appropriate devices for sorting and grading of kiwifruit based on the quality parameters, only 10% of total production is exported (Mohammadian & Esehaghi Teymouri, 1999).
One of the main quality attribute for evaluating the kiwifruits is weight. Based on the standards, the minimum weight for an excellent kiwifruit is 90 g, while these values for the first and second classes should be 70 and 65 g, respectively (Abedini, 2003). Therefore, developing a device for fast weighing of fruits in the sorting lines can be useful in packaging, storage, exporting and distributing kiwifruit to the consumer markets.
In the past, the mechanical-based systems were commonly used for online weighing of the agricultural materials, but they did not lead to the promising accuracy and speed in sorting lines. Today, electrical instruments equipped with the precise load cells are substituted for fast weighing in the sorting lines. The dropping impact method, in which a free falling fruit drops on a load cell, is one of the suitable techniques for this purpose.
Different studies have addressed the application of dropping impact for fast weighing of agricultural materials (Rohrbach et al., 1982; Calpe et al., 2002; Gilman & Bailey, 2005; Stropek & Gołacki, 2007; Elbeltagi, 2011). The aim of this study reported here was to develop an on-line system for fast weighing of kiwifruit and compare the accuracy of different methods for extracting the weight predictive models.
Materials and Methods:
Sample selection: A total of 232 samples with the weight range of 40 to 120 g were selected. Before conducting the main experiments, the weight and dimensions of the sample were measured using a digital balance and caliper, with the precisions of 0.001 g and 0.01 mm, respectively.
Impact measuring system: The impact signals of kiwifruits in an online situation were acquired using a system, including conveying and ejecting unit, a load cell and data acquisition unit (Fig.1). The load cell was a single point load cell with 5 kg capacity. The load cell was connected to the data acquisition unit (Fig.2) in order to record the impact signal of the device in time domain of 0-5 s.
Before performing the main experiments, the load cell was calibrated using 100, 200, 500 and 1000 g standard masses. All the tests were carried out on three different forward speeds of conveyor, including 1, 1.5 and 2 m s-1 in order to obtain the optimum forward speed.
Data Analysis: In this study, two different methods were applied to build the weight predictive models. In the first method, the main components of the impact signal, including the force value at the first peak Fp, time required to peak force Dp, and the impulse or area under the first peak Ip were calculated and used as independent variables to develop the weight predictive models. In the second method, the impact components were calculated for the 40 successive peaks. Multiple linear regression (MLR) analyses were used to correlate the independent (impact components) and dependent (weight) variables.
Results and Discussion: The weight statistical characteristics of the samples, including the maximum, minimum, average, standard deviation and coefficient of variability in total data, calibration and test sets are shown in Table 1. As depicted, almost the same range and variability were observed for calibration and test data sets, indicating the proper distribution of the samples.
Table 2 summarizes the results of simple and multiple linear regressions for predicting the weight from the signal components (Fp, Dp, Ip) of the first peak at different speeds of 1, 1.5 and 2 m s-1. As shown, at the forward speeds of 1 and 2 m s-1, the multiple regression models based on all three signal components, and at forward speed of 1.5 m s-1, the model based on the combination of Fp and Ip, resulted to the best prediction powers. Among different forward speeds, the forward speed of 1 m s-1 gave the best model with SDR value of 2.180. Fig.4 depicted the predicted versus true values of weight obtained from the best linear regression models using components of Fp, Dp, Ip, Fp-Ip, and multiple of the first peak of impact signal.
The results of simple and multiple linear regression for predicting the weight from the signal components (Fp, Dp, Ip) of the first forty peaks at different speeds of 1, 1.5 and 2 m s-1 are summarized in Table 3. The best models were obtained by multiple combination of all three impact signals at the forward speed of 1 and 2 m s-1, and combination of Fpi-Ipi (i=1,...,40) at 1.5 m s-1 speed. Compared with the first peak results, the accuracy of prediction reached to 84%, 60% and 52% at forward speeds of 1, 1.5 and 2 m s-1, respectively. The best results were obtained at a forward speed of 2 m s-1, in which the SDR reached to a satisfactory value of 2.857 by applying the Ipi (i=1,...,40) values. The predicted versus true values of weight obtained from the best linear regression models using components of Fp, Dp, Ip, Fp-Ip, and multiple of the first forty peaks of impact signal are illustrated in Fig.5.
Conclusions: The results of this study revealed that among different impact component, Ip was the best predictor of the kiwifruits weight. Moreover, the developed models based on impact components of the first forty successive peaks gave the best accuracy with respect to the first peak components.
Modeling
M. Kamali; S. J. Razavi; M. Sadeghi; S. M. Shafaei
Abstract
Introduction: Barley is one of the most important grains with high digestible starch making it a main source of energy in human nutrition as well as in livestock rations formulation and feeding. Starch is the main part of barley grain and it has an inverse relation with its protein. It has a digestible ...
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Introduction: Barley is one of the most important grains with high digestible starch making it a main source of energy in human nutrition as well as in livestock rations formulation and feeding. Starch is the main part of barley grain and it has an inverse relation with its protein. It has a digestible foodstuff of 80 to 84 percent of its dry matter content. Barley as livestock foodstuff should be processed and it is done in several ways. A customary method for processing barley in dairy farms is its size reduction by milling (Hunt, 1996). An alternative method of barley processing is steam rolling. However, because of the high cost of steam generators a method of soaking with heating has been considered as an alternative method for steam rolling (Yang et al., 2000). The rate of moisture absorption by grains during the soaking process varies considerably and depends on the size of the grain, water temperature and the length of soaking. High temperature water soaking is an ordinary way to reduce the time duration for reaching a high rate of moisture absorption during the soaking process (Kashaninejad et al., 2009). Various studies have shown that these models have adequate accuracy in analyzing drying and moisture absorption processes for most agricultural products (Abu-Ghannam and McKenna, 1997). Some researchers have modeled beans moisture absorption behavior using 14 mathematical models and found that the Weibull model had the most conformity with variations in experimental data (Shafaei and Masoumi, 2014c). Observations made by researchers indicate that the moisture absorption process in various materials encompasses a primary phase with a fast rate and a second phase with a lower rate. The second phase in moisture absorption is called the relaxation phase. The main problem with all the mathematical and experimental models is the lack of the model’s ability to evaluate the rate of moisture absorption in the secondary phase. Artificial Neural Network (ANN) as an important artificial intelligent method comparable to human brain capabilities is applied to train and store data in the form of weighted networks (Dayhoff, 1990). This method has superiority to many ordinary statistical and model making methods. In comparison to linear regression models, ANN does not require placing estimated values around mean values and for this reason it retains actual variations in the data being analyzed. Prediction by using trained ANN enables the researchers to decrease or increase input and output variables.Therefore, it is possible to produce a multivariate model with an output even more than the objectives deemed necessary (Heristev, 1998). The goal of this research was to predict instant moisture content of three barley varieties (Reyhan3, Fajr and MB862) during the soaking process under three temperature levels (10, 20 and 45 ◦C) using two conventional ANN methods of multilayer perceptron (MLP) and radial basis function (RBF) in comparison with viscoelastic mathematical model and reporting the results. Materials and method: Barley varieties were collected from the Isfahan Province Agriculture Organization grain depository and were cleaned and the debris were separated before the experiments. The selected grains were sorted to three groups of small, medium and large grains sizes. To exclude the effect of grain size during moisture absorption, the medium size grains were used. The moisture content of the grains was determined based on the ASAE S352.2 DEC97 (ASAE, 1999) which were %8.23, %8.62 and %8.89 on a dry basis for Reyhan3, Fajr and MB862, respectively with no significant difference at %5 probability level (p>0.05). Experiments were conducted under three temperatures (10, 20 and 45 ◦C) in the refrigerator, at room temperature and in the oven, respectively for each variety. In each experiment, 10 medium size grains were selected randomly and weighed with an AND laboratory scale model Gf-400 (made in Japan) and placed in foam containers having 200 mg of distilled water. Grains were weighed after a predetermined period of elapsed time (5, 10, 15, 30, 60, 120, etc. minutes). The experiments were conducted with three replications and moisture absorption rates were determined by the equations presented by McWatters et al., 2002. The experiments were conducted on a time table based on which the time for the dissolving of grains was reached. In this case, the moisture content of the grains reaches the saturation point. According to equations presented by Peleg, as water density increases as much as 0.01 gram due to grains dissolving in water, the saturation point has been reached (Peleg, 1988). For this reason, distilled water density was measured and controlled before and after each experiment by a pycnometer. Neural network was designed according to the two methods of multi-layer perceptron (MLP) and radial basis function (RBF) with three neuron layers. The first layer, i.e. input layer, is independent variables of temperature and time.The second layer, i.e. hidden layers, is the networks hidden layer and the third layer, i.e. output layer, is the dependent variable of moisture content which was selected. In each case, the nonlinear reduced gradient, combined gradient and BFGS algorithm, and Trigonometric, Logarithmic, Gaussian, and Logical functions were used to train, test and evaluate the network. To evaluate the predicting viscoelastic model and the network, we used statistical indices maximum value of coefficient of determination (R2) and minimum value of mean square error (RMSE).Results and Discussion: Moisture absorption curves showed that as the temperature increases, moisture absorption rate increases as well. Higher equilibrium moisture levels are obtained in water with higher temperatures. This phenomenon is the result of increased moisture diffusion in grains due to higher temperature levels. Higher water temperatures causes grain internal material which is mainly starch to gelatinize and, thus, the internal tissues resistance to moisture absorption reduces (Ranjbari et al., 2011). The moisture absorption rate increases as immersion temperature and gelatinization temperature reach closer to each other. Conclusions: The results of this study showed that although viscoelastic mathematical model has an adequate accuracy for instant prediction of barley grain moisture content, it has a lower accuracy compared to intelligent models. On the other hand, among the two neural network methods, MLP method has a higher accuracy in predicting moisture content compared to RBF method. MLP obtained the best results for three varieties of barley because of back- propagation learning algorithm with BFGS algorithm and 2-4-1 network structure. According to the prediction of the best neural network which was selected, three-dimensional graphs of moisture content based on temperature and time variables, showed that with an increase in temperature and duration of immersion, moisture absorption increases for three varieties of barley.
S. M. Ataei Ardestani; B. Beheshti; M. Sadeghi; S. Minaei
Abstract
Fluidized bed dryers have not yet been used for drying products such as mint leaves. This could be due to high porosity and low mechanical resistance resulting in poor quality of fluidization. Applying vibration has been recommended to overcome problems such as channeling and defluidization, and hence ...
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Fluidized bed dryers have not yet been used for drying products such as mint leaves. This could be due to high porosity and low mechanical resistance resulting in poor quality of fluidization. Applying vibration has been recommended to overcome problems such as channeling and defluidization, and hence improving the fluidization quality. In this research, a laboratory scale vibro-fluidized bed heat pump dryer was designed and constructed for drying mint leaves. The experiments were conducted at vibration frequency of 80 Hz and amplitude of 3 mm. The velocity and temperature of the inlet air was controlled by an automatic control system. Experiments were carried out at 40, 50 and 60 °C, and two methods: heat pump drying (HPD) and non-heat pump drying (NHPD). The results revealed that drying process primarily occurred in the falling rate period. Effective moisture diffusivity of the samples increased with increase in drying air temperature and varied from 4.26656×10-11 to 2.95872×10-10 m2 s-1 for the HPD method, and 3.71918×10-11 to 1.29196×10-10 m2 s-1 for the NHPD method and was within the reported range of 10-9 to 10-11 m2 s-1 for drying of food materials. The activation energy was determined to be 84 kJ mol-1 for the HPD and 54.34 kJ mol-1 for the NHPD, both have very good agreement with the results of other investigators. The coefficient of performance and specific moisture evaporation rate showed the acceptable performance of the heat pump system. Moreover, the energy consumption of the dryer for the NHPD method was more than the HPD method.
