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Fulltext Effect of microwave heating with different exposure times on the degradation of corn oil

Abbas Ali, Hadi Mesran, M., Nik Mahmood, N.A., Abd Latip, R.

International Food Research Journal, 2016;23(2):842-848.
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In the present work, the influence of microwave power and heating times on the quality
degradation of corn oil was evaluated. Microwave heating test was carried out using a domestic
microwave oven for different periods at low- and medium-power settings for the corn oil sample.
The changes in physicochemical characteristics related to oil degradation of the samples during
heating were determined by standard methods. In this study, refractive index, free fatty acid
content, peroxide value, p-anisidine value, TOTOX value, viscosity and total polar compound
of the oils all increased with increasing heating power and time of exposure. In GLC analysis,
the percentage of linoleic acid tended to decrease, whereas the percentage of palmitic, stearic
and oleic acids increased. The C18:2/C16:0 ratio decreased in all oil samples with increasing
heating times. Exposing the corn oil to various microwave power settings and heating periods
caused the formation of hydroperoxides and secondary oxidation products. The heating reduced
the various tocopherol isomers in corn oil and highest reduction was detected in γ-tocopherol.
Longer microwave heating times resulted in a greater degree of oil deterioration. Microwave
heating caused the formation of comparatively lower amounts of some degradative products in
the oil samples heated at low-power setting compared to medium-power setting. The present
analysis indicated that oil quality was affected by both microwave power and heating time.
A survey of Sybil attack countermeasures in IoT-based wireless sensor networks

Arshad A, Mohd Hanapi Z, Subramaniam S, Latip R

PeerJ Comput Sci, 2021;7:e673.
PMID: 34712787 DOI: 10.7717/peerj-cs.673

Wireless sensor networks (WSN) have been among the most prevalent wireless innovations over the years exciting new Internet of Things (IoT) applications. IoT based WSN integrated with Internet Protocol IP allows any physical objects with sensors to be connected ubiquitously and send real-time data to the server connected to the Internet gate. Security in WSN remains an ongoing research trend that falls under the IoT paradigm. A WSN node deployed in a hostile environment is likely to open security attacks such as Sybil attack due to its distributed architecture and network contention implemented in the routing protocol. In a Sybil attack, an adversary illegally advertises several false identities or a single identity that may occur at several locations called Sybil nodes. Therefore, in this paper, we give a survey of the most up-to-date assured methods to defend from the Sybil attack. The Sybil attack countermeasures includes encryption, trust, received signal indicator (RSSI), encryption and artificial intelligence. Specifically, we survey different methods, along with their advantages and disadvantages, to mitigate the Sybil attack. We discussed the lesson learned and the future avenues of study and open issues in WSN security analysis.
Fulltext A fault-intrusion-tolerant system and deadline-aware algorithm for scheduling scientific workflow in the cloud

Farid M, Latip R, Hussin M, Abdul Hamid NAW

PeerJ Comput Sci, 2021;7:e747.
PMID: 34805503 DOI: 10.7717/peerj-cs.747

Background: Recent technological developments have enabled the execution of more scientific solutions on cloud platforms. Cloud-based scientific workflows are subject to various risks, such as security breaches and unauthorized access to resources. By attacking side channels or virtual machines, attackers may destroy servers, causing interruption and delay or incorrect output. Although cloud-based scientific workflows are often used for vital computational-intensive tasks, their failure can come at a great cost.
Methodology: To increase workflow reliability, we propose the Fault and Intrusion-tolerant Workflow Scheduling algorithm (FITSW). The proposed workflow system uses task executors consisting of many virtual machines to carry out workflow tasks. FITSW duplicates each sub-task three times, uses an intermediate data decision-making mechanism, and then employs a deadline partitioning method to determine sub-deadlines for each sub-task. This way, dynamism is achieved in task scheduling using the resource flow. The proposed technique generates or recycles task executors, keeps the workflow clean, and improves efficiency. Experiments were conducted on WorkflowSim to evaluate the effectiveness of FITSW using metrics such as task completion rate, success rate and completion time.
Results: The results show that FITSW not only raises the success rate by about 12%, it also improves the task completion rate by 6.2% and minimizes the completion time by about 15.6% in comparison with intrusion tolerant scientific workflow ITSW system.
Fulltext A Novel Texture Extraction-Based Compressive Sensing for Lung Cancer Classification

Irawati ID, Hadiyoso S, Budiman G, Fahmi A, Latip R

J Med Signals Sens, 2022;12(4):278-284.
PMID: 36726419 DOI: 10.4103/jmss.jmss_127_21

BACKGROUND: Lung cancer images require large memory storage and transmission bandwidth for sending the data. Compressive sensing (CS), as a method with a statistical approach in signal sampling, provides different output patterns based on information sources. Thus, it can be considered that CS can be used for feature extraction of compressed information.
METHODS: In this study, we proposed a novel texture extraction-based CS for lung cancer classification. We classify three types of lung cancer, including adenocarcinoma (ACA), squamous cell carcinoma (SCC), and benign lung cancer (N). The classification is carried out based on texture extraction, which is processed in 2 stages, the first stage to detect N and the second to detect ACA and SCC.
RESULTS: The simulation results show that two-stage texture extraction can improve accuracy by an average of 84%. The proposed system is expected to be decision support in assisting clinical diagnosis. In terms of technical storage, this system can save memory resources.
CONCLUSIONS: The proposed two-step texture extraction system combined with CS and K- Nearest Neighbor has succeeded in classifying lung cancer with high accuracy; the system can also save memory storage. It is necessary to examine the complexity of the proposed method so that it can be analyzed further.
Fulltext Block-based compressive sensing in deep learning using AlexNet for vegetable classification

Dyah Irawati I, Budiman G, Saidah S, Rahmadiani S, Latip R

PeerJ Comput Sci, 2023;9:e1551.
PMID: 38077543 DOI: 10.7717/peerj-cs.1551

Vegetables can be distinguished according to differences in color, shape, and texture. The deep learning convolutional neural network (CNN) method is a technique that can be used to classify types of vegetables for various applications in agriculture. This study proposes a vegetable classification technique that uses the CNN AlexNet model and applies compressive sensing (CS) to reduce computing time and save storage space. In CS, discrete cosine transform (DCT) is applied for the sparsing process, Gaussian distribution for sampling, and orthogonal matching pursuit (OMP) for reconstruction. Simulation results on 600 images for four types of vegetables showed a maximum test accuracy of 98% for the AlexNet method, while the combined block-based CS using the AlexNet method produced a maximum accuracy of 96.66% with a compression ratio of 2×. Our results indicated that AlexNet CNN architecture and block-based CS in AlexNet can classify vegetable images better than previous methods.
Fulltext Palm-based diacylglycerol fat dry fractionation: effect of crystallisation temperature, cooling rate and agitation speed on physical and chemical properties of fractions

Ab Latip R, Lee YY, Tang TK, Phuah ET, Lee CM, Tan CP, et al.

PeerJ, 2013;1:e72.
PMID: 23682348 DOI: 10.7717/peerj.72

Fractionation which separates the olein (liquid) and stearin (solid) fractions of oil is used to modify the physicochemical properties of fats in order to extend its applications. Studies showed that the properties of fractionated end products can be affected by fractionation processing conditions. In the present study, dry fractionation of palm-based diacylglycerol (PDAG) was performed at different: cooling rates (0.05, 0.5, 1.0, 1.5, 2.0, 2.5 and 3.0°C/min), end-crystallisation temperatures (30, 35, 40, 45 and 50°C) and agitation speeds (30, 50, 70, 90 and 110 rpm) to determine the effect of these parameters on the properties and yield of the solid and liquid portions. To determine the physicochemical properties of olein and stearin fraction: Iodine value (IV), fatty acid composition (FAC), acylglycerol composition, slip melting point (SMP), solid fat content (SFC), thermal behaviour tests were carried out. Fractionation of PDAG fat changes the chemical composition of liquid and solid fractions. In terms of FAC, the major fatty acid in olein and stearin fractions were oleic (C18:1) and palmitic (C16:0) respectively. Acylglycerol composition showed that olein and stearin fractions is concentrated with TAG and DAG respectively. Crystallization temperature, cooling rate and agitation speed does not affect the IV, SFC, melting and cooling properties of the stearin fraction. The stearin fraction was only affected by cooling rate which changes its SMP. On the other hand, olein fraction was affected by crystallization temperature and cooling rate but not agitation speed which caused changes in IV, SMP, SFC, melting and crystallization behavior. Increase in both the crystallization temperature and cooling rate caused a reduction of IV, increment of the SFC, SMP, melting and crystallization behaviour of olein fraction and vice versa. The fractionated stearin part melted above 65°C while the olein melted at 40°C. SMP in olein fraction also reduced to a range of 26 to 44°C while SMP of stearin fractions increased to (60-62°C) compared to PDAG.
Optimization of palm oil physical refining process for reduction of 3-monochloropropane-1,2-diol (3-MCPD) ester formation

Zulkurnain M, Lai OM, Tan SC, Abdul Latip R, Tan CP

J Agric Food Chem, 2013 Apr 3;61(13):3341-9.
PMID: 23464796 DOI: 10.1021/jf4009185

The reduction of 3-monochloropropane-1,2-diol (3-MCPD) ester formation in refined palm oil was achieved by incorporation of additional processing steps in the physical refining process to remove chloroester precursors prior to the deodorization step. The modified refining process was optimized for the least 3-MCPD ester formation and acceptable refined palm oil quality using response surface methodology (RSM) with five processing parameters: water dosage, phosphoric acid dosage, degumming temperature, activated clay dosage, and deodorization temperature. The removal of chloroester precursors was largely accomplished by increasing the water dosage, while the reduction of 3-MCPD esters was a compromise in oxidative stability and color of the refined palm oil because some factors such as acid dosage, degumming temperature, and deodorization temperature showed contradictory effects. The optimization resulted in 87.2% reduction of 3-MCPD esters from 2.9 mg/kg in the conventional refining process to 0.4 mg/kg, with color and oil stability index values of 2.4 R and 14.3 h, respectively.
Fulltext A Fault Tolerant Surveillance System for Fire Detection and Prevention Using LoRaWAN in Smart Buildings

Safi A, Ahmad Z, Jehangiri AI, Latip R, Zaman SKU, Khan MA, et al.

Sensors (Basel), 2022 Nov 01;22(21).
PMID: 36366109 DOI: 10.3390/s22218411

In recent years, fire detection technologies have helped safeguard lives and property from hazards. Early fire warning methods, such as smoke or gas sensors, are ineffectual. Many fires have caused deaths and property damage. IoT is a fast-growing technology. It contains equipment, buildings, electrical systems, vehicles, and everyday things with computing and sensing capabilities. These objects can be managed and monitored remotely as they are connected to the Internet. In the Internet of Things concept, low-power devices like sensors and controllers are linked together using the concept of Low Power Wide Area Network (LPWAN). Long Range Wide Area Network (LoRaWAN) is an LPWAN product used on the Internet of Things (IoT). It is well suited for networks of things connected to the Internet, where terminals send a minute amount of sensor data over large distances, providing the end terminals with battery lifetimes of years. In this article, we design and implement a LoRaWAN-based system for smart building fire detection and prevention, not reliant upon Wireless Fidelity (Wi-Fi) connection. A LoRa node with a combination of sensors can detect smoke, gas, Liquefied Petroleum Gas (LPG), propane, methane, hydrogen, alcohol, temperature, and humidity. We developed the system in a real-world environment utilizing Wi-Fi Lora 32 boards. The performance is evaluated considering the response time and overall network delay. The tests are carried out in different lengths (0-600 m) and heights above the ground (0-2 m) in an open environment and indoor (1st Floor-3rd floor) environment. We observed that the proposed system outperformed in sensing and data transfer from sensing nodes to the controller boards.
Fulltext Influence of the Degumming Process Parameters on the Formation of Glyceryl Esters and 3-MCPDE in Refined Palm Oil: Optimization and Palm Oil Quality Analyses

Nidzam MS, Hossain MS, Ismail N, Abdul Latip R, Mohammad Ilias MK, Mobin Siddique MB, et al.

Foods, 2022 Jan 05;11(1).
PMID: 35010250 DOI: 10.3390/foods11010124

The presence of glyceryl esters (GE) and 3-monochloropropane-1,2-diol esters (3-MCPDE) in refined, bleached, and deodorized (RBD) palm oil is severely concerning to the palm oil consumer. In the present study, the influence of the phosphoric acid degumming process on the formation of GE and 3-MCDE and in the RBD palm oil was determined with varying the acid dose (0.03-0.06 wt%), temperature (70-100 °C), and reaction time (15-45 min). The experimental conditions of the acid degumming process were designed following the central composite design of experiments, and they were optimized using Response Surface Methodology (RSM) based on the minimal formation of GE and 3-MCDE in the RBD palm oil. The optimal experimental conditions of the acid degumming process were a reaction time of 30 min, phosphoric acid concentration of 0.06 wt%, and temperature of 90 °C. Under these experimental conditions, the minimal GE and 3-MCDE formation in RBD palm oil were determined to be 0.61 mg/kg and 0.59 mg/kg; respectively. Several analytical methods were employed to determine RBD palm oil quality, including color, phosphorus, free fatty acids (FFAs), peroxide values, and fatty acid properties. It was found that the phosphoric acid degumming of CPO effectively removed the phosphorus and hydroperoxide content without conceding the quality of palm oil.