Nazmi Mat Nawi

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

Abstract 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.

Visible and shortwave near infrared spectroscopy for predicting sugar content of sugarcane based on a cross-sectional scanning method

The need for a reliable in-field quality measurement in the sugarcane industry is growing as the quality of sugarcane could vary significantly across the field. However, current monitoring systems in this industry only monitor crop yield and do not have the ability to measure the product quality. Thus, the potential of the visible/shortwave near infrared (vis/SW-NIR) spectroscopic technique as a low-cost alternative to predict sugar content from sugarcane stalks was investigated. Two hundred and ninety-two internode samples were extracted from three different sugarcane varieties to assess the ability of this technique. Each sample was cut into four sections and the spectra collected from the cross-sectional surface of each section were later correlated with its sugar content (°Brix). Partial least square (PLS) models were developed using calibration samples. The best model predicted samples in a prediction set had a coefficient of determination (r2) of 0.87 and root means square error of prediction (RMSEP) of 1.45°Brix. The value of the ratio of the standard deviation to the standard error of prediction (RPD) was 2. The variations of °Brix and prediction accuracy along the individual internode were 8.7 and 13%, respectively. These results indicated the vis/SW-NIR spectroscopy could be applied to predict °Brix values from sugarcane stalks based on a cross-sectional scanning method.

In-field measurement and sampling technologies for monitoring quality in the sugarcane industry: a review

Reliable in-field quality measurement and sampling techniques are needed in the sugarcane industry to accommodate spatial variability in crop quality during harvesting. Existing in-field monitoring systems only monitor the crop yield and do not have the ability to measure product quality. This is a serious limitation for the industry in dealing with a significant quality variation across a field. Conventional technologies for measuring sugarcane quality in a laboratory have severe limitations for field use because they require complex sample preparation procedures especially to have clarified juice samples for each measurement. This review focuses on the use of current and new emerging precision agricultural sensing technologies for measuring product quality and describes their potential application and limitation for field use in the sugarcane industry. Optical spectroscopy is among the most promising technologies for measuring sugarcane quality on a harvester. The key considerations for development of a measurement method and sampling mechanism in the field are also discussed.

Application of visible and shortwave near infrared spectrometer to predict sugarcane quality from different sample forms

Spectroscopic methods have been proposed to predict sugarcane quality in the field. There are different sample forms could be used to predict sugar content using spectroscopic methods; raw juice (RJ), clear juice (CJ), fibrated samples (FS), stalk cross sectional surface (SCS) and stalk skin (SS). Thus, this study was conducted to identify the optimum sample form for predicting quality using a low-cost and portable spectrometer. A total of 100 samples from each sample form were scanned using a visible-shortwave near infrared (Vis/SWNIR) spectrometer. The experiment was conducted under the same experimental setup and all data were treated using the same statistical methods. All spectral data were calibrated against brix value. The coefficient of determination (R2) for SCS, FS, CJ, SS and RJ were 0.88, 0.86, 0.84, 0.84, 0.81 and 0.80, respectively. The study found that a Vis/SWNIR spectrometer could be used to predict sugar content from all sample forms. The stalk samples scanned on cross sectional surface was found to be the optimum sample form for quality prediction using a Vis/SWNIR spectrometer.

Human energy expenditure in lowland rice cultivation in Malaysia

A study was undertaken to evaluate the human energy consumption of various field operations involved in lowland rice cultivation in Malaysia. Based on recorded average heart rates, fertilizing was found to be the most strenuous operation, with an average heart rate of 138 beats min(-1). There were no significant differences in the average heart rates of the subjects among the individual tasks within the first plowing, second plowing, and harvesting operations, with the average heart rates for these three tasks being 116, 106, and 106 beats min(-1), respectively. The corresponding energy expenditures were 3.90, 3.43, and 3.35 kcal min(-1). Loading the seed into the blower tank and broadcasting the seed were the most critical tasks for the seed broadcasting operation, with average heart rates of 124 and 136 beats min(-1), respectively. The highest energy expenditure of 418.38 kcal ha(-1) was observed for seed broadcasting, and the lowest energy expenditure of 127.96 kcal ha(-1) was for second plowing. The total seasonal human energy expenditure for rice cultivation was estimated to be 5810.71 kcal ha(-1), 55.7% of which was spent on pesticide spraying. Although the sample size in this study was relatively small, the results indicated that human energy expenditure per unit area (kcal ha(-1)) was positively linked to the average heart rate of the subjects and negatively linked to the field capacity. Thus, mechanization of certain tasks could decrease worker physical effort and fatigue and increase production.

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

ABSTRACT 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.

Spectral features selection and classification of oil palm leaves infected by Basal stem rot (BSR) disease using dielectric spectroscopy

Abstract Basal stem rot (BSR) is a prominent plant disease caused by Ganoderma boninense fungus, which infects oil palm plantations leading to large economic losses in palm oil production. There is need for novel disease detection techniques that can be used to reduce the oil palm losses due to BSR. Thus, this paper investigated the feasibility of utilizing electrical properties such as impedance, capacitance, dielectric constant, and dissipation factor in early detection of BSR disease in oil palm tree. Leaf samples from different oil palm trees (healthy, mild, moderate, and severely-infected) were collected and measured using a solid test fixture (16451B, Keysight Technologies, Japan) connected to an impedance analyzer (4294A, Agilent Technologies, Japan) at a frequency range of 100 kHz–30 MHz with 300 spectral interval. Genetic algorithm (GA), random forest (RF), and support vector machine-feature selection (SVM-FS) were used to analyze the electrical properties of the dataset and the most significant frequencies were selected. Following the selection of significant frequencies, the features were evaluated using two classifiers, support vector machine (SVM) and artificial neural networks (ANN) to determine the overall and individual class classification accuracies. The selection model comparative feature analysis demonstrated that the best statistical indicators with overall accuracy (88.64%), kappa (0.8480) and low mean absolute error (0.1652) were obtained using significant frequencies produced by SVM-FS model. The results indicated that the SVM classifier shows better performance as compared to ANN classifier. The results also showed that the classes, features selection models, and the electrical properties were found to be significantly different (p