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Evaluation of Chilling Injury in Mangoes Using Multispectral Imaging

Hashim N, Onwude DI, Osman MS

J Food Sci, 2018 May;83(5):1271-1279.
PMID: 29660789 DOI: 10.1111/1750-3841.14127

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.
PRACTICAL APPLICATION: This paper demonstrates a fast, easy, and accurate method of identifying the effect of cold storage on mango, nondestructively. The method presented in this paper can be used industrially to efficiently differentiate different fruits from each other after low temperature storage.
Mechanisation of large-scale agricultural fields in developing countries-a review

Onwude DI, Abdulstter R, Gomes C, Hashim N

J Sci Food Agric, 2016 Mar 4.
PMID: 26940194 DOI: 10.1002/jsfa.7699

Mechanisation of large scale agricultural fields often requires the application of modern technologies such as mechanical power, automation, control and robotics. These technologies are generally associated with relatively well developed economies. The application of these technologies in some developing countries in Africa and Asia is limited by factors such as technology compatibility with the environment, availability of resources to facilitate the technology adoption, cost of technology purchase, government policies, adequacy of technology and appropriateness in addressing the needs of the population. As a result, many of the available resources have been used inadequately by farmers, who continue to rely mostly on conventional means of agricultural production, using traditional tools and equipment in most cases. This has led to low productivity and high cost of production amongst others. Thus, this paper attempts to evaluate the application of present day technology and its limitations to the advancement of large scale mechanisation in developing countries of Africa and Asia. Particular emphasis is given to a general understanding of the various levels of mechanization, present day technology, its management and application to large scale agricultural fields. This review also focuses on/ gives emphasis to future outlook that will enable a gradual, evolutionary, and sustainable technological change. The study concludes that large scale-agricultural farm mechanisation for sustainable food production in Africa and Asia must be anchored on a coherent strategy based on the actual needs and priorities of the large- scale farmers.
A fully coupled multiphase model for infrared-convective drying of sweet potato

Onwude DI, Hashim N, Chen G, Putranto A, Udoenoh NR

J Sci Food Agric, 2021 Jan 30;101(2):398-413.
PMID: 32627847 DOI: 10.1002/jsfa.10649

BACKGROUND: Combined infrared (CIR) and convective drying is a promising technology in dehydrating heat-sensitive foods, such as fruits and vegetables. This novel thermal drying method, which involves the application of infrared energy and hot air during a drying process, can drastically enhance energy efficiency and improve overall product quality at the end of the process. Understanding the dynamics of what goes on inside the product during drying is important for further development, optimization, and upscaling of the drying method. In this study, a multiphase porous media model considering liquid water, gases, and solid matrix was developed for the CIR and hot-air drying (HAD) of sweet potato slices in order to capture the relevant physics and obtain an in-depth insight on the drying process. The model was simulated using Matlab with user-friendly graphical user interface for easy coupling and faster computational time.
RESULTS: The gas pressure for CIR-HAD was higher centrally and decreased gradually towards the surface of the product. This implies that drying force is stronger at the product core than at the product surface. A phase change from liquid water to vapour occurs almost immediately after the start of the drying process for CIR-HAD. The evaporation rate, as expected, was observed to increase with increased drying time. Evaporation during CIR-HAD increased with increasing distance from the centreline of the sample surface. The simulation results of water and vapour flux revealed that moisture transport around the surfaces and sides of the sample is as a result of capillary diffusion, binary diffusion, and gas pressure in both the vertical and horizontal directions. The nonuniform dominant infrared heating caused the heterogeneous distribution of product temperature. These results suggest that CIR-HAD of food occurs in a non-uniform manner with high vapour and water concentration gradient between the product core and the surface.
CONCLUSIONS: This study provides in-depth insight into the physics and phase changes of food during CIR-HAD. The multiphase model has the advantage that phase change and impact of CIR-HAD operating parameters can be swiftly quantified. Such a modelling approach is thereby significant for further development and process optimization of CIR-HAD towards industrial upscaling. © 2020 Society of Chemical Industry.
The potential of computer vision, optical backscattering parameters and artificial neural network modelling in monitoring the shrinkage of sweet potato (Ipomoea batatas L.) during drying

Onwude DI, Hashim N, Abdan K, Janius R, Chen G

J Sci Food Agric, 2018 Mar;98(4):1310-1324.
PMID: 28758207 DOI: 10.1002/jsfa.8595

BACKGROUND: Drying is a method used to preserve agricultural crops. During the drying of products with high moisture content, structural changes in shape, volume, area, density and porosity occur. These changes could affect the final quality of dried product and also the effective design of drying equipment. Therefore, this study investigated a novel approach in monitoring and predicting the shrinkage of sweet potato during drying. Drying experiments were conducted at temperatures of 50-70 °C and samples thicknesses of 2-6 mm. The volume and surface area obtained from camera vision, and the perimeter and illuminated area from backscattered optical images were analysed and used to evaluate the shrinkage of sweet potato during drying.
RESULTS: The relationship between dimensionless moisture content and shrinkage of sweet potato in terms of volume, surface area, perimeter and illuminated area was found to be linearly correlated. The results also demonstrated that the shrinkage of sweet potato based on computer vision and backscattered optical parameters is affected by the product thickness, drying temperature and drying time. A multilayer perceptron (MLP) artificial neural network with input layer containing three cells, two hidden layers (18 neurons), and five cells for output layer, was used to develop a model that can monitor, control and predict the shrinkage parameters and moisture content of sweet potato slices under different drying conditions. The developed ANN model satisfactorily predicted the shrinkage and dimensionless moisture content of sweet potato with correlation coefficient greater than 0.95.
CONCLUSION: Combined computer vision, laser light backscattering imaging and artificial neural network can be used as a non-destructive, rapid and easily adaptable technique for in-line monitoring, predicting and controlling the shrinkage and moisture changes of food and agricultural crops during drying. © 2017 Society of Chemical Industry.
Modeling the Thin-Layer Drying of Fruits and Vegetables: A Review

Onwude DI, Hashim N, Janius RB, Nawi NM, Abdan K

Compr Rev Food Sci Food Saf, 2016 May;15(3):599-618.
PMID: 33401820 DOI: 10.1111/1541-4337.12196

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.
Prediction of the ripening stages of papayas using discriminant analysis and support vector machine algorithms

Zulkifli N, Hashim N, Harith HH, Mohamad Shukery MF, Onwude DI

J Sci Food Agric, 2021 Nov 20.
PMID: 34802158 DOI: 10.1002/jsfa.11669

BACKGROUND: Evaluation of the quality properties of papaya becomes essential due to the acceleration of the fruit shelf-life senescence and the deterioration factor of the expected postharvest operations. In this study, the colour features in RGB, normalised RGB, HSV and L*a*b* channels were extracted and correlated with mechanical properties, moisture content (MC), total soluble solids (TSS), and pH for the prediction of quality properties at five ripening stages of papaya (R1- R5).
RESULTS: The mean values of colour features in RGB R m , G m , B m , normalised RGB R nm , G nm , B nm HSV H m , S m , V m , and L*a*b* L m , a m , b m were the best estimator for predicting TSS with R2 ≥ 0.90. All colour channels also showed satisfactory accuracies of R2 ≥ 0.80 in predicting the bioyield force, apparent modulus and mean force. The highest average classification accuracy was obtained using LDA with an average accuracy of more than 82%. The study showed that LDA, LSVM, QDA and QSVM obtained the correct classification of up to 100% for R5, whereas R1, R2, R3 and R4 gave classification accuracies in the range between 83.75-91.85%, 85.6-90.25%, 85.75-90.85% and 77.35-87.15% respectively. This indicates R5 colour information was obviously different from R1-R4. The mean values of the HSV channel indicated the best performance to predict the ripening stages of papaya, compared to RGB, normalised RGB and L*a*b*channels, with an average classification accuracy of more than 80%.
CONCLUSION: The study has shown the versatility of a machine vision system in predicting the quality changes in papaya. The results showed that the machine vision system can be used to predict the ripening stages as well as classifying the fruits into different ripening stages of papayas. This article is protected by copyright. All rights reserved.