Recent advances of novel thermal combined hot air drying of agricultural crops

Background: Developing an efficient drying system with combined novel thermal and conventional hot-air drying of agricultural crops has become potentially a viable substitute for conventional drying techniques. Due to the synergistic effect, the total energy and time required can be drastically reduced, and the final quality of agricultural crops preserved. The growing interest and research in recent years have already shown that novel thermal with hot-air drying technology can adequately be used in the drying of agricultural crops. Scope and approach: This review attempts to give a summary of recent advances in the research and applications of novel thermal combined hot-air drying technology for agricultural crops, with particular emphasis on the combination mode, process conditions, process-quality interaction, drying kinetics, energy demand and drying efficiency. Key findings and conclusions: The combination of novel thermal with hot-air drying provides distinctive opportunities in the development of advanced agricultural crop drying technologies. The most significant advantages of using the above method were the reduction in the drying time and energy consumption as well as, an increase in the drying rate and overall efficiency. More so, the application of infrared and hot-air drying on agricultural crops is advantageous in obtaining dried products of better quality. In conclusion, the findings suggest that these technologies have great potentials. Therefore, more studies, especially in their industrial and commercial application are indispensable

Evaluation of a suitable thin layer model for drying of pumpkin under forced air convection

The thin layer drying kinetics of pumpkin slices (Cucurbita moschata) were experimentally investigated in a convective hot air dryer. In order to select the appropriate model for predicting the drying kinetics of pumpkin (Cucurbita moschata), twelve thin layer semi theoretical, theoretical and empirical models, widely used in describing the drying behaviour of agricultural products were fitted to the experimental data. The Page and Two term exponential models showed the best fit under certain drying conditions. The Hii et al. (2009) model, which was adopted from a combination of the Page and Two term models was compared to the other 11 selected thin layer models based on the coefficient of determination (R2) and sum of squares error (SSE). Comparison was made between the experimental and model predicted moisture ratio by non-linear regression analysis. Furthermore, the effect of drying temperature and slice thickness on the best model constants was evaluated. Consequently, the Hii et al. (2009) model showed an excellent fit with the experimental data (R2 > 0.99 and SSE < 0.012) for the drying temperatures of 50, 60, 70 and 80 °C and at different sample thicknesses of 3 mm, 5 mm and 7 mm respectively. Thus, the Hii et al. (2009) model can adequately predict the drying kinetics of pumpkin

Development and preliminary testing of a bambara groundnut sheller

A centrifugal bambara groundnut pod shelling machine was designed and constructed to crack various sizes and varieties of bambara groundnut. The sheller was fabricated with locally available materials from the formation of a new idea which aimed at easing the pain, stress, intensive labour, time consumption, undue cost and the cumbersome operation encountered in the traditional method of shelling. The machine consists of three main units, namely the hopper, shelling unit and power transmission unit. The sheller uses impact technique and was designed to shell bambara groundnuts effectively and also to eliminate drudgery associated with the traditional methods of shelling legumes. Five hundred (500) samples of sundried bambara groundnuts at 6% (wet basis) which were randomly selected were shelled at an impeller rotation speed of 1636 rpm. The results of the test showed that the shelling efficiency, seed damage, partially shelled pods, unshelled pods and the machine capacity were 83.2%, 17.4%, 7.8%, 9% and 75000 seeds/hr respectively

Evaluation of chilling injury in mangoes using multispectral imaging

Commodities originating from tropical and subtropical climes are prone to chilling injury (CI). This injury could affect the quality and marketing potential of mango after harvest. This will later affect the quality of the produce and subsequent consumer acceptance. In this study, the appearance of CI symptoms in mango was evaluated non-destructively using multispectral imaging. The fruit were stored at 4 °C to induce CI and 12 °C to preserve the quality of the control samples for 4 days before they were taken out and stored at ambient temperature for 24 hr. Measurements using multispectral imaging and standard reference methods were conducted before and after storage. The performance of multispectral imaging was compared using standard reference properties including moisture content (MC), total soluble solids (TSS) content, firmness, pH, and color. Least square support vector machine (LS-SVM) combined with principal component analysis (PCA) were used to discriminate CI samples with those of control and before storage, respectively. The statistical results demonstrated significant changes in the reference quality properties of samples before and after storage. The results also revealed that multispectral parameters have a strong correlation with the reference parameters of L* , a* , TSS, and MC. The MC and L* were found to be the best reference parameters in identifying the severity of CI in mangoes. PCA and LS-SVM analysis indicated that the fruit were successfully classified into their categories, that is, before storage, control, and CI. This indicated that the multispectral imaging technique is feasible for detecting CI in mangoes during postharvest storage and processing

Modelling the convective drying process of pumpkin (Cucurbita moschata) using an artificial neural network

This study investigated the drying kinetic of pumpkin under different drying temperatures (50, 60, 70 and 80°C), samples thickness (3, 4, 5 and 7mm), air velocity (1.2m/s) and relative humidity (40 - 50%). Kinetic models were developed using semi-theoretical thin layer models and multi-layer feed-forward artificial neural network (ANN) method. The Hii et al. (2009) semi-theoretical model was found to be the most suitable thin layer model while two hidden layers with 20 neurons was the best for the ANN method. The selections were based on the statistical indicators of coefficient of determination (R2), root mean square error (RMSE) and sum of squares error (SSE). Results indicated that the ANN demonstrated better prediction than those of the theoretical models with R2, RMSE and SSE values of 0.992, 0.036 and 0.207 as compared to the Hii et al. (2009) model values of 0.902, 0.088 and 1.734 respectively. The validation result also showed good agreement between the predicted values obtained from the ANN model and the experimental moisture ratio data. This indicates that an ANN can effectively describe the drying process of pumpkin

Modeling the thin-layer drying of fruits and vegetables: a review

The drying of fruits and vegetables is a complex operation that demands much energy and time. In practice, the drying of fruits and vegetables increases product shelf-life and reduces the bulk and weight of the product, thus simplifying transport. Occasionally, drying may lead to a great decrease in the volume of the product, leading to a decrease in storage space requirements. Studies have shown that dependence purely on experimental drying practices, without mathematical considerations of the drying kinetics, can significantly affect the efficiency of dryers, increase the cost of production, and reduce the quality of the dried product. Thus, the use of mathematical models in estimating the drying kinetics, the behavior, and the energy needed in the drying of agricultural and food products becomes indispensable. This paper presents a comprehensive review of modeling thin-layer drying of fruits and vegetables with particular focus on thin-layer theories, models, and applications since the year 2005. The thin-layer drying behavior of fruits and vegetables is also highlighted. The most frequently used of the newly developed mathematical models for thin-layer drying of fruits and vegetables in the last 10 years are shown. Subsequently, the equations and various conditions used in the estimation of the effective moisture diffusivity, shrinkage effects, and minimum energy requirement are displayed. The authors hope that this review will be of use for future research in terms of modeling, analysis, design, and the optimization of the drying process of fruits and vegetables

Modelling effective moisture diffusivity of pumpkin (Cucurbita moschata) slices under convective hot air drying condition

This study seeks to investigate the effects of temperature (50, 60, 70 and 80 °C) and material thickness (3, 5 and 7 mm), on the drying characteristics of pumpkin (Cucurbita moschata). Experimental data were used to estimate the effective moisture diffusivities and activation energy of pumpkin by using solutions of Fick’s second law of diffusion or its simplified form. The calculated value of moisture diffusivity with and without shrinkage effect varied from a minimum of 1.942 × 10–8 m2/s to a maximum of 9.196 × 10–8 m2/s, while that of activation energy varied from 5.02158 to 32.14542 kJ/mol with temperature ranging from 50 to 80 °C and slice thickness of 3 to 7 mm at constant air velocity of 1.16 m/s, respectively. The results indicated that with increasing temperature, and reduction of slice thickness, the drying time was reduced by more than 30 %. The effective moisture diffusivity increased with an increase in drying temperature with or without shrinkage effect. An increase in the activation energy was observed due to an increase in the slice thickness of the pumpkin samples

Color change kinetics and total carotenoid content of pumpkin as affected by drying temperature

The color changes kinetics of pumpkin slices during convective hot air drying was investigated at drying temperatures of 50, 60, 70 and 80 °C. The hunter lab L* a* and b* color coordinates were used as assessment indicators. The total color change, chroma value, hue angle and brownness index (BI) of the pumpkin slices where also determined. To determine the most suitable kinetics model for the prediction of the color changes of pumpkin, the zero-order, first-order, and fractional conversion models were fitted to the experimental data, using linear regression analysis. The activation energy of the color change parameters (L*, a*, b* and ) was estimated and found to be 41.59 kJ/mol, 16.287 kJ/mol, 63.856 kJ/mol and 73.390 kJ/mol respectively. The fresh pumpkin samples contained a mean total carotenoid content of 25μ g/g, while the total carotenoid content of samples dried at 50 °C, 60 °C,70 °C and 80 °C were 146μ g/g, 56.4μ g/g, 37.9μ g/g and 102.5μ g/g respectively. Further, the results of ANOVA showed there was significant difference between the total carotenoid content of the fresh pumpkin samples and those dried in convective hot air dryer at 5% (p<0.05) significant level

Modeling the thin‐layer drying of fruits and vegetables: a review

The drying of fruits and vegetables is a complex operation that demands much energy and time. In practice, the drying of fruits and vegetables increases product shelf‐life and reduces the bulk and weight of the product, thus simplifying transport. Occasionally, drying may lead to a great decrease in the volume of the product, leading to a decrease in storage space requirements. Studies have shown that dependence purely on experimental drying practices, without mathematical considerations of the drying kinetics, can significantly affect the efficiency of dryers, increase the cost of production, and reduce the quality of the dried product. Thus, the use of mathematical models in estimating the drying kinetics, the behavior, and the energy needed in the drying of agricultural and food products becomes indispensable. This paper presents a comprehensive review of modeling thin‐layer drying of fruits and vegetables with particular focus on thin‐layer theories, models, and applications since the year 2005. The thin‐layer drying behavior of fruits and vegetables is also highlighted. The most frequently used of the newly developed mathematical models for thin‐layer drying of fruits and vegetables in the last 10 years are shown. Subsequently, the equations and various conditions used in the estimation of the effective moisture diffusivity, shrinkage effects, and minimum energy requirement are displayed. The authors hope that this review will be of use for future research in terms of modeling, analysis, design, and the optimization of the drying process of fruits and vegetables

Early detection of diseases in plant tissue using spectroscopy – applications and limitations

Plant diseases can greatly affect the total production of food and agricultural materials, which may lead to high amount of losses in terms of quality, quantity and also in economic sense. To reduce the losses due to plant diseases, early diseases detection either based on a visual inspection or laboratory tests are widely employed. However, these techniques are labor-intensive and time consuming. In a view to overcome the shortcoming of these conventional approaches, several researchers have developed non-invasive techniques. Recently, spectroscopy technique has become one of the most available non-invasive methods utilized in detecting plant diseases. However, most of the studies on the application of this novel technology are still in the experimental stages, and are carried out in isolation with no comprehensive information on the most suitable approach. This problem could affect the advancement and commercialization of spectroscopy technology in early plant disease detection. Here, we review the applications and limitations of spectroscopy techniques (visible/infrared, electrical impedance and fluorescence spectroscopy) in early detection of plant disease. Particular emphasis was given to different spectral level, challenges and future outlook