Optimization of computational intelligence approach for the prediction of glutinous rice dehydration.

BACKGROUND: Five computational intelligence approaches, namely Gaussian process regression (GPR), artificial neural network (ANN), decision tree (DT), ensemble of trees (EoT) and support vector machine (SVM), were used to describe the evolution of moisture during the dehydration process of glutinous rice. The hyperparameters of the models were optimized with three strategies: Bayesian optimization, grid search and random search. To understand the parameters that facilitate intelligence model adaptation to the dehydration process, global sensitivity analysis (GSA) was used to compute the impact of the input variables on the model output. RESULT: The result shows that the optimum computational intelligence techniques include the 3-9-1 topology trained with Bayesian regulation function for ANN, Gaussian kernel function for SVM, Matérn covariance function combined with zero mean function for GPR, boosting method for EoT and 4 minimum leaf size for DT. GPR has the highest performance with R2 of 100% and 99.71% during calibration and testing of the model, respectively. GSA reveals that all the models significantly rely on the variation in time as the main factor that affects the model outputs. CONCLUSION: Therefore, the computational intelligence models, especially GPR, can be applied for an effective description of moisture evolution during small-scale and industrial dehydration of glutinous rice. © 2024 Society of Chemical Industry.

Towards a Multispectral Imaging System for Spatial Mapping of Chemical Composition in Fresh-Cut Pineapple (Ananas comosus).

With increasing public demand for ready-to-eat fresh-cut fruit, the postharvest industry requires the development and adaptation of monitoring technologies to provide customers with a product of consistent quality. The fresh-cut trade of pineapples (Ananas comosus) is on the rise, favored by the sensory quality of the product and mechanization of the cutting process. In this paper, a multispectral imaging-based approach is introduced to provide distribution maps of moisture content, soluble solids content, and carotenoids content in fresh-cut pineapple. A dataset containing hyperspectral images (380-1690 nm) and reference measurements in 10 regions of interest of 60 fruit (n = 600) was prepared. Ranking and uncorrelatedness (based on ReliefF algorithm) and subset selection (based on CfsSubset algorithm) approaches were applied to find the most informative wavelengths in which bandpass optical filters or light sources are commercially available. The correlation coefficient and error metrics obtained by cross-validated multilayer perceptron neural network models indicated that the superior selected wavelengths (495, 500, 505, 1215, 1240, and 1425 nm) resulted in prediction of moisture content with R = 0.56, MAPE = 1.92%, soluble solids content with R = 0.52, MAPE = 14.72%, and carotenoids content with R = 0.63, MAPE = 43.99%. Prediction of chemical composition in each pixel of the multispectral images using the calibration models yielded spatially distributed quantification of the fruit slice, spatially varying according to the maturation of single fruitlets in the whole pineapple. Calibration models provided reliable responses spatially throughout the surface of fresh-cut pineapple slices with a constant error. According to the approach to use commercially relevant wavelengths, calibration models could be applied in classifying fruit segments in the mechanized preparation of fresh-cut produce.

Role of Honey as a Bifunctional Reducing and Capping/Stabilizing Agent: Application for Silver and Zinc Oxide Nanoparticles.

The use of natural reducing and capping agents has gained importance as a way to synthesize nanoparticles (NPs) in an environmentally sustainable manner. Increasing numbers of studies have been published on the green synthesis of NPs using natural sources such as bacteria, fungi, and plants. In recent years, the use of honey in the synthesis of metal and metal oxide NPs has become a new and promising area of research. Honey acts as both a stabilizing and reducing agent in the NP synthesis process and serves as a precursor. This review focuses on the use of honey in the synthesis of silver NPs (Ag-NPs) and zinc oxide NPs (ZnO-NPs), emphasizing its role as a reducing and capping agent. Additionally, a comprehensive examination of the bio-based reducing and capping/stabilizing agents used in the honey-mediated biosynthesis mechanism is provided. Finally, the review looks forward to environmentally friendly methods for NP synthesis.

Recent Advances in Honey-Based Nanoparticles for Wound Dressing: A Review.

Wounds with impaired healing, including delayed acute injuries and chronic injuries, generally fail to progress through normal healing stages. A deeper understanding of the biochemical processes involved in chronic wound cures is necessary to correct the microenvironmental imbalances in the wound treatment designs of products. The therapeutic benefits of honey, particularly its antimicrobial activity, make it a viable option for wound treatment in a variety of situations. Integration with nanotechnology has opened up new possibilities not only for wound healing but also for other medicinal applications. In this review, recent advances in honey-based nanoparticles for wound healing are discussed. This also covers the mechanism of the action of nanoparticles in the wound healing process and perspectives on the challenges and future trends of using honey-based nanoparticles. The underlying mechanisms of wound healing using honey are believed to be attributed to hydrogen peroxide, high osmolality, acidity, non-peroxide components, and phenols. Therefore, incorporating honey into various wound dressings has become a major trend due to the increasing demand for combination dressings in the global wound dressing market because these dressings contain two or more types of chemical and physical properties to ensure optimal functionality. At the same time, their multiple features (low cost, biocompatibility, and swelling index) and diverse fabrication methods (electrospun fibres, hydrogels, etc.) make them a popular choice among researchers.

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

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 linear discriminant analysis (LDA) with an average accuracy of more than 82%. The study showed that LDA, linear support vector machine, quadratic discriminant analysis and quadratic support vector machine obtained the correct classification of up to 100% for R5, whereas R1, R2, R3 and R4 gave classification accuracies in the range 83.75-91.85%, 85.6-90.25%, 85.75-90.85% and 77.35-87.15%, respectively. This indicates that 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. © 2021 Society of Chemical Industry.

Optimisation of Stingless Bee Honey Nanoemulsions Using Response Surface Methodology.

Nanoemulsions (NEs) have been used in a wide range of products, such as those produced by the food, cosmetics, and pharmaceutical industries, due to their stability and long shelf life. In the present study, stingless bee honey (SBH) NEs were formulated using SBH, oleic acid, tween 80, glycerol, and double-distilled water. SBH NEs were prepared using a high-pressure homogeniser and were characterised by observing their stability and droplet size. Fourier Transform-Infrared (FTIR) analysis was used to observe the functional groups of the SBH NEs after being subjected to high-pressure homogenisation. Transmission Electron Microscopy (TEM) images were then used to confirm the particle size of the SBH NEs and to investigate their morphology. The effects of the independent variables (percentage of oleic acid, storage time, and storage temperature) on the response variables (particle size and polydispersity index) were investigated using the response surface methodology, along with a three-level factorial design. The results showed that the models developed via the response surface methodology were reliable, with a coefficient of determination (R2) of more than 0.90. The experimental validation indicated an error of less than 10% in the actual results compared to the predicted results. The FTIR analysis showed that SBH NEs have the same functional group as SBH. Observation through TEM indicated that the SBH NEs had a similar particle size, which was between 10 and 100 nm. Thus, this study shows that SBH NEs can be developed using a high-pressure homogeniser, which indicates a new direction for SBH by-products.

Effect of Kelulut Honey Nanoparticles Coating on the Changes of Respiration Rate, Ascorbic Acid, and Total Phenolic Content of Papaya (Carica papaya L.) during Cold Storage.

This study evaluated the respiration rate of coated and uncoated (control) papayas (Carica papaya L.) with 15% of Kelulut honey (KH) nanoparticles (Nps) coating solution during cold storage at 12 ± 1 °C for 21 days. The respiration rate of the papayas significantly changed during storage, with an increase in CO2 and a decrease in O2 and C2H4, while the ascorbic acid and total phenolic content was maintained. The changes in respiration rate were rather slower for coated papayas when compared to control ones. A kinetic model was established from the experimental data to describe the changes of O2, CO2, and C2H4 production in papayas throughout the storage period. All O2, CO2, and C2H4 were experimentally retrieved from a closed system method and then represented by the Peleg model. The outcomes indicated the Peleg constant K1 and K2, which were gained from linear regression analysis and coefficients of determination (R2), seemed to fit well with the experimental data, whereby the R2 values exceeded 0.85 for both coated and control papayas. The model confirmed both the capability and predictability aspects of the respiration rate displayed by papayas coated with KH Nps throughout the cold storage period. This is supported by the differences in the stomatal aperture of coated and control papaya shown by microstructural images.

Finite Element Modelling for Predicting the Puncture Responses in Papayas.

This study aims to develop a finite element (FE) model to determine the mechanical responses of Exotica papayas during puncture loads. The FE model of the puncture-test was developed using the ANSYS 19.1 software. The proposed framework combined the finite element method and statistical procedure to validate the simulation with the experimental results. Assuming the elastic-plastic behaviour of papaya, the mechanical properties were measured through tensile test and compression test for both skin and flesh. The geometrical models include a quarter solid of papaya that was subjected to a puncture test with a 2 mm diameter flat-end stainless-steel probe inserted into the fruit tissues at 0.5 mm/s, 1 mm/s, 1.5 mm/s, 2 mm/s, and 2.5 mm/s. The FE results showed good agreement with the experimental data, indicating that the proposed approach was reliable. The FE model was best predicted the bioyield force with the highest relative error of 14.46%. In conclusion, this study contributes to the usage of FE methods for predicting the puncture responses of any perishable fruit and agricultural products.

A fully coupled multiphase model for infrared-convective drying of sweet potato.

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.

Pineapple (Ananas comosus): A comprehensive review of nutritional values, volatile compounds, health benefits, and potential food products.

Pineapple (Ananas comosus) is a tropical fruit that is highly relished for its unique aroma and sweet taste. It is renowned as a flavourful fruit since it contains a number of volatile compounds in small amounts and complex mixtures. Pineapple is also a rich source of minerals and vitamins that offer a number of health benefits. Ranked third behind banana and citrus, the demand for pineapple has greatly increased within the international market. The growth of the pineapple industry in the utilisation of pineapple food-based processing products as well as waste processing has progressed rapidly worldwide. This review discusses the nutritional values, physicochemical composition and volatile compounds, as well as health benefits of pineapples. Pineapple contains considerable amounts of bioactive compounds, dietary fiber, minerals, and nutrients. In addition, pineapple has been proven to have various health benefits including anti-inflammatory, antioxidant activity, monitoring nervous system function, and healing bowel movement. The potential of food products and waste processing of pineapples are also highlighted. The future perspectives and challenges with regard to the potential uses of pineapple are critically addressed. From the review, it is proven that pineapples have various health benefits and are a potential breakthrough in the agricultural and food industries.

Dielectric Spectroscopy of Palm Olein During Batch Deep Frying and Their Relation with Degradation Parameters.

Total polar compounds (TPC) and free fatty acids (FFA) are important indicators in evaluating the quality of frying oil. Conventional methods to determine TPC and FFA are often time consuming, involved laboratory analyses which required skilled personnel and used substantial amount of harmful solvent. In this study, dielectric spectroscopy technique was used to investigate the relation between dielectric property of refined, bleached and deodorized palm olein (RBDPO) during deep frying with TPC and FFA. In total, 150 batches of French fries were intermittently fried at 185 ± 5 °C for 7 hr a day over 5 consecutive days. A total of 30 frying oil samples were collected. The dielectric property of frying oil samples were measured using impedance analyzer with frequencies ranging from 100 Hz to 10 MHz. The TPC of frying oil samples were measured with a Testo 270, while the FFA analysis was done using Malaysian Palm Oil Board (MPOB) test method. Results showed that dielectric constant, TPC and FFA of RBDPO increased as the frying time increased. Dielectric constant increased from 3.09 to 3.17, while TPC and FFA increased from 9.96 to 19.52 and from 0.08% to 0.36%, respectively. Partial least square (PLS) analysis produced good prediction of TPC and FFA with the application of genetic algorithm (GA). Model developed for prediction of TPC and FFA yielded highly significant correlation with R2 of 0.91 and 0.95, respectively and both had root mean square error in cross-validation (RMSECV) of 1.06%. This study demonstrates the potential of dielectric spectroscopy in monitoring palm olein degradation during frying. PRACTICAL APPLICATION: The application of dielectric spectroscopy to detect degradation of palm olein during frying was studied. The dielectric property of palm olein during frying has successfully correlated with TPC and FFA. The model developed in this study could be used for the development of a sensing system for palm olein degradation monitoring.

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. 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.

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.

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.

Determination of the difference on color changes of watermelons by laser light backscattering imaging.

The potential of laser light backscattering imaging was investigated for monitoring color parameters of seeded and seedless watermelons during storage. Two watermelon cultivars were harvested and stored for 3 weeks with seven measuring storage days (0, 4, 8, 12, 15, 18, and 21). The color parameters of watermelons were monitored using the conventional colorimetric methods (L*, a*, b*, C*, H*, and ∆E*) and laser light backscattering imaging system. A laser diode emitting at 658 nm and 30 mW power was used as a light source to obtain the backscattering image. The backscattering images were evaluated by the extraction of backscattering parameters based on the mean pixel values. The results showed that a good color prediction was achieved by the seedless watermelon with the R2 are all above 0.900. Thus, the application of the laser light backscattering imaging can be used for evaluating the color parameters of watermelons during the storage period.

The effect of ultraviolet treatment on enzymatic activity and total phenolic content of minimally processed potato slices.

In this work, potato slices were exposed to different doses of UV-C irradiation (i.e. 2.28, 6.84, 11.41, and 13.68 kJ m-2) with or without pretreatment [i.e. ascorbic acid and calcium chloride (AACCl) dip] and stored at 4 ± 1 °C. Changes in enzymatic activities of polyphenol oxidase (PPO), peroxidase (POD) and phenylalanine ammonia lyase (PAL), as well as total phenolic content (TPC) were investigated after 0, 3, 7 and 10 days of storage. Results showed that untreated and UV-C treated potato slices at 13.68 kJ m-2 dosage level showed significantly higher PPO, POD and PAL activities. Conversely, untreated potato slices showed the lowest TPC during storage period. Potato slices subjected to AACCl dip plus UV-C at 6.84 kJ m-2 produced lower PPO, POD and PAL activities, as well as maintained a high TPC during storage.

Mechanisation of large-scale agricultural fields in developing countries - a review.

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 among others. Therefore 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 mechanisation, 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. © 2016 Society of Chemical Industry.

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.