Samsuzana Abd Aziz

Development and Evaluation of an Impedance Spectroscopy Sensor to Assess Cooking Oil Quality

When the cooking oil is used repeatedly, several unwanted substances are generated, which may cause health problems. This study was conducted to determine the possibility of using the impedance spectroscopy to differentiate among varying cooking oil quality at various intervals of heating time at constant temperature. The frequency has started from 100 Hz to 100kHz. Fresh, 10-hour, 20-hour, 30-hour, and 40-hour heated cooking oil was prepared by using lab oven at temperature of 180oC. In this study, a sensing probe was designed to measure the electrical properties of the oil samples. The oil samples were analyzed using a viscometer to measure the viscosity of the oil, a sensor to measure total polar compound (TPC), and an impedance probe connected to a LCR meter to measure the electrical properties of the oil. The measurements were analyzed and correlated with oil quality parameters obtained from a viscometer and a sensor of TPC. The discrimination between different heated hours of oil samples was examined and the results were compared to their physico-chemical properties such as viscosity and total polar compounds. The effect of heating of frying oils were successfully evaluated and discriminated using the impedance spectroscopy. Significant correlations (r -0.98472) were found between changes in total polar compound properties of oil and the impedance values.

Ultrasonic Sensing for Corn Plant Canopy Characterization

Non-destructive measurement of crop growth stage, canopy development, and height may be useful for more efficient crop management practices. In this study, ultrasonic sensing technology was investigated as one approach for corn plant canopy characterization. Ultrasonic echo signals from corn plant canopies were collected using a lab-based sensor platform. Echo signal peak features were extracted from multiple scans of plant canopies. These features included peak amplitude, scan number, and time of flight. Feature vectors with similarities were clustered together to identify individual leaves of the canopy. The mean height of the clustered data of individual leaves was estimated. The growth stage of each plant was estimated based on the number of leaves detected. Regression analysis was used to describe the relationship between manually measured leaf heights and ultrasonic estimates. A leaf-signal interaction model was developed to predict which

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.

A NOVEL VARIABLE RATE PNEUMATIC FERTILIZER APPLICATOR

The canopy prevents the use of global positioning system based fertilizer applicators in oil palm plantations. Hence, a radio frequency identification triggered variable rate pneumatic fertilizer system was developed for this application. A real-time embedded system was used as the core controller, LabVIEW software was used to program and coordinate the operations of the embedded system and the host computer. A speed measuring unit was used to provide feedback to the system. A field test was conducted to examine the response time. The sensors were calibrated in the laboratory and the measurement linearity had regression coefficients close to 1. Two to three seconds were required for the device to respond to changes in application rate. It is expected that this approach will become an alternative in plantations where the canopy hinders proper application of global positioning systems.

Stochastic modelling of seasonal and yearly rainfalls with low-frequency variability

Stochastic rainfall models are important for many hydrological applications due to their appealing ability to simulate synthetic series that resemble the statistical characteristics of the observed series for a location of interest. However, an important limitation of stochastic rainfall models is their inability to preserve the low-frequency variability of rainfall. Accordingly, this study presents a simple yet efficient stochastic rainfall model for a tropical area that attempts to incorporate seasonal and inter-annual variabilities in simulations. The performance of the proposed stochastic rainfall model, the tropical climate rainfall generator (TCRG), was compared with a stochastic multivariable weather generator (MV-WG) in various aspects. Both models were applied on 17 rainfall stations at the Kelantan River Basin, Malaysia, with tropical climate. The validations were carried out on seasonal (monsoon and inter-monsoon) and annual basis. The third-order Markov chain of the TCRG was found to perform better in simulating the rainfall occurrence and preserving the low-frequency variability of the wet spells. The log-normal distribution of the TCRG was consistently better in modelling the rainfall amounts. Both models tend to underestimate the skewness and kurtosis coefficient of the rainfall. The spectral correction approach adopted in the TCRG successfully preserved the seasonal and inter-annual variabilities of rainfall amounts, whereas the MV-WG tends to underestimate the variability bias of rainfall amounts. Overall, the TCRG performed reasonably well in the Kelantan River Basin, as it can represent the key statistics of rainfall occurrence and amounts successfully, as well as the low-frequency variability.

Using spatial uncertainty of prior measurements to design adaptive sampling of elevation data.

Field sampling can be a major expense for planning within-field management in precision agriculture. An efficient sampling strategy should address knowledge gaps, rather than exhaustively collect redundant data. Modification of existing schemes is possible by incorporating prior knowledge of spatial patterns within the field. In this study, spatial uncertainty of prior digital elevation model (DEM) estimates was used to locate adaptive re-survey regions in the field. An agricultural vehicle equipped with RTK-DGPS was driven across a 2.3 ha field area to measure the field elevation in a continuous fashion. A geostatistical simulation technique was used to simulate field DEMs using measurements with different pass intervals and to quantitatively assess the spatial uncertainty of the DEM estimates. The high-uncertainty regions for each DEM were classified using image segmentation methods, and an adaptive re-survey was performed on those regions. The addition of adaptive re-surveying substantially reduced the time required to resample and resulted in DEMs with lower error. For the widest sampling pass width, the RMSE of 0.46 m of the DEM produced from an initial coarse sampling survey was reduced to 0.25 m after an adaptive re-survey, which was close to that (0.22 m) of the DEM produced with an all-field re-survey. The estimated sampling time for the adaptive re-survey was less than 50% of that for all-field re-survey. These results indicate that spatial uncertainty models are useful in an adaptive sampling design to help reduce sampling cost while maintaining the accuracy of the measurements. The method is general and thus not limited to elevation data but can be extended to other spatially variable field data.

A process variation aware system-level framework to model on-chip communication system in support of fault tolerant analysis

On-chip interconnect communication system consists of the drivers, interconnect wires and receivers. Several on-chip communication system models have been developed for the purpose of on-chip fault-tolerant communication research. While most of these models improved the channel modeling, the effects of the drivers and receivers to the whole communication system were largely ignored. In this paper, we introduce a comprehensive, system-level framework, to capture and integrate the characteristics of the channel as well as the drivers and receivers. The proposed framework offers a methodology to model the on-chip interconnect communication system and can provide a flexible and updateable platform to evaluate fault-tolerant communication approaches. Furthermore, the current deterministic paradigm which end is worst case analysis pessimism is avoided by shifting towards statistical design flow to reduce uncertainties caused by process variation.

Generation of a stochastic precipitation model for the tropical climate

A tropical country like Malaysia is characterized by intense localized precipitation with temperatures remaining relatively constant throughout the year. A stochastic modeling of precipitation in the flood-prone Kelantan River Basin is particularly challenging due to the high intermittency of precipitation events of the northeast monsoons. There is an urgent need to have long series of precipitation in modeling the hydrological responses. A single-site stochastic precipitation model that includes precipitation occurrence and an intensity model was developed, calibrated, and validated for the Kelantan River Basin. The simulation process was carried out separately for each station without considering the spatial correlation of precipitation. The Markov chains up to the fifth-order and six distributions were considered. The daily precipitation data of 17 rainfall stations for the study period of 1954–2013 were selected. The results suggested that second- and third-order Markov chains were suitable for simulating monthly and yearly precipitation occurrences, respectively. The fifth-order Markov chain resulted in overestimation of precipitation occurrences. For the mean, distribution, and standard deviation of precipitation amounts, the exponential, gamma, log-normal, skew normal, mixed exponential, and generalized Pareto distributions performed superiorly. However, for the extremes of precipitation, the exponential and log-normal distributions were better while the skew normal and generalized Pareto distributions tend to show underestimations. The log-normal distribution was chosen as the best distribution to simulate precipitation amounts. Overall, the stochastic precipitation model developed is considered a convenient tool to simulate the characteristics of precipitation in the Kelantan River Basin.

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

Lard Detection in Edible Oil Using Dielectric Spectroscopy

Food adulteration is the process of adding or mixing of substance(s) that should not be into food products for financial gain or other motives. Food adulteration is a serious problem worldwide not just because it is a fraud to consumer, but it can also harm the health and causes serious consequences to the well being of people. Among the food products, edible oil has been identified as the top ingredient involved in food fraud. Studies show that adulteration in fats and oils are mainly economically motivated and in some cases intended to enhance food flavor. In general, adulteration of edible oil causes two great concerns to the consumer. First, it concerns consumers that practice vegetarian diet or followers of religions that prohibit from consuming pig, pork or any of its products. Second, it may cause serious health related issues particularly those who have allergies to certain types of substances or consumers who suffer coronary disease. Several conventional techniques have been utilized in order to study food adulteration particularly on fats and oil, such as polymerase chain reaction, differential scanning calorimetry, electronic nose and chromatography. Until recently, dielectric spectroscopy technique that have been widely used to analyze food products, attain research community’s attention in the study of adulteration on fats and oil. In this chapter, a review on conventional techniques and dielectric spectroscopy approach for analyzing food products are presented. In particular, a review on dielectric spectroscopy to study adulteration in fats and oils and recent work on lard detection at both low and high frequency range are discussed. Results show that dielectric sensing can be a great technology to detect lard adulterated edible oil and applying data analysis technique can further enhance the detection ability.