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Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks

Abdollahi Y, Sairi NA, Said SB, Abouzari-lotf E, Zakaria A, Sabri MF, et al.

Spectrochim Acta A Mol Biomol Spectrosc, 2015 Nov 5;150:892-901.
PMID: 26119355 DOI: 10.1016/j.saa.2015.06.036

It is believe that 80% industrial of carbon dioxide can be controlled by separation and storage technologies which use the blended ionic liquids absorber. Among the blended absorbers, the mixture of water, N-methyldiethanolamine (MDEA) and guanidinium trifluoromethane sulfonate (gua) has presented the superior stripping qualities. However, the blended solution has illustrated high viscosity that affects the cost of separation process. In this work, the blended fabrication was scheduled with is the process arranging, controlling and optimizing. Therefore, the blend's components and operating temperature were modeled and optimized as input effective variables to minimize its viscosity as the final output by using back-propagation artificial neural network (ANN). The modeling was carried out by four mathematical algorithms with individual experimental design to obtain the optimum topology using root mean squared error (RMSE), R-squared (R(2)) and absolute average deviation (AAD). As a result, the final model (QP-4-8-1) with minimum RMSE and AAD as well as the highest R(2) was selected to navigate the fabrication of the blended solution. Therefore, the model was applied to obtain the optimum initial level of the input variables which were included temperature 303-323 K, x[gua], 0-0.033, x[MDAE], 0.3-0.4, and x[H2O], 0.7-1.0. Moreover, the model has obtained the relative importance ordered of the variables which included x[gua]>temperature>x[MDEA]>x[H2O]. Therefore, none of the variables was negligible in the fabrication. Furthermore, the model predicted the optimum points of the variables to minimize the viscosity which was validated by further experiments. The validated results confirmed the model schedulability. Accordingly, ANN succeeds to model the initial components of the blended solutions as absorber of CO2 capture in separation technologies that is able to industries scale up.
Fulltext Physical and Physiological Monitoring on Red Palm Weevil-Infested Oil Palms

Harith-Fadzilah N, Haris-Hussain M, Abd Ghani I, Zakaria A, Amit S, Zainal Z, et al.

Insects, 2020 Jun 30;11(7).
PMID: 32630104 DOI: 10.3390/insects11070407

The red palm weevil (RPW) is a stem boring Coleoptera that decimates host palm trees from within. The challenge of managing this pest is due to a lack of physical symptoms during the early stages of infestation. Investigating the physiological changes that occur within RPW-infested palm trees may be useful in establishing a new approach in RPW detection. In this study, the effects of RPW infestation were investigated in Elaeis guineensis by observing changes in physical and physiological parameters during the progress of infestation by visual inspection and the comparison of growth, gas exchange, stomatal conductance, and chlorophyll content between the non-infested control, physically wounded, and RPW-infested E. guineensis groups. During the study period, four distinct levels of physical infestation were observed and recorded. The RPW-infested group displayed significantly lower maximum photosynthesis activity (Amax) starting from the third week post-infestation. However, growth in terms of change in plant height and stem circumference, leaves' stomatal conductance, and chlorophyll content were not significantly different between the three groups during the duration of the study. The significant drop in photosynthesis was observed one week before physical changes appeared. This suggests the promising utilisation of photosynthesis activity as a signal for detecting RPW infestation at the early stage of attacks, which could be useful for integration in integrated pest management (IPM).
Seroprevalence and contributing factors of transboundary animal diseases in sheep and goats: a study in Peninsular Malaysia

Jimale YA, Jesse FFA, Paul BT, Chung ELT, Zakaria A, Azhar NA, et al.

Trop Anim Health Prod, 2024 Jul 13;56(6):212.
PMID: 39002035 DOI: 10.1007/s11250-024-04061-4

Diseases caused by small ruminant lentiviruses, Mycobacterium avium ssp. paratuberculosis (MAP), Schmallenberg virus, and peste des petits ruminants virus (PPR) is globally recognised as serious threats to the ruminant industry due to their potential to spread rapidly across boundaries. Despite their global distribution and negative impacts on ruminant production, there is a gap in knowledge of the current trends in their epidemiology among sheep and goat populations in Peninsular Malaysia. This study was therefore designed to fill the gap of knowledge concerning the seroprevalence and contributing factors of CAEV, paratuberculosis, SBV, and PPRV among small ruminants from selected flocks in Selangor, Negeri Sembilan, and Pahang states in Peninsular Malaysia. A cross-sectional study design was used to collect animal data and blood samples for serological assays simultaneously. The ID Screen (ID.VET, France) indirect ELISA screening tests were used to detect serum antibodies directed against CAEV/MVV (VISNAS Ver 0922), paratuberculosis (PARAS Ver 0516), SBV (SBVC Ver 1114) and PPRV (PPRC Ver 0821). There was 45.4% (95% CI = 40.74-50.74), 6.8% (95% CI = 4.66-9.69), 27.8% (95% CI = 23.35-32.77), and 2.6% (95% CI = 1.11-0.51) true seroprevalence for CAEV, paratuberculosis, SBV, and PPR, respectively. Geographical location and species were the risk factors for CAEV and paratuberculosis, while the management system and age of small ruminants were the risk factors for SBV. The present study is the first to document a large-scale seroprevalence of MAP and PPR infection among sheep and goat flocks in Peninsular Malaysia. The presence of PPRV and MAP antibodies among small ruminant flocks is signalling current or previous exposure to the pathogens or cross reactions with similar antigens. This finding further suggests the potential for future outbreaks of these devastating diseases among sheep and goats in Malaysia. The high seroprevalence of CAEV and SBV among small ruminants indicates high levels of exposure to the viruses in the environment, which is a potential threat to production.
Fulltext Semi-empirical study of ortho-cresol photo degradation in manganese-doped zinc oxide nanoparticles suspensions

Abdollahi Y, Zakaria A, Abdullah AH, Fard Masoumi HR, Jahangirian H, Shameli K, et al.

Chem Cent J, 2012 Aug 21;6(1):88.
PMID: 22909072 DOI: 10.1186/1752-153X-6-88

The optimization processes of photo degradation are complicated and expensive when it is performed with traditional methods such as one variable at a time. In this research, the condition of ortho-cresol (o-cresol) photo degradation was optimized by using a semi empirical method. First of all, the experiments were designed with four effective factors including irradiation time, pH, photo catalyst's amount, o-cresol concentration and photo degradation % as response by response surface methodology (RSM). The RSM used central composite design (CCD) method consists of 30 runs to obtain the actual responses. The actual responses were fitted with the second order algebraic polynomial equation to select a model (suggested model). The suggested model was validated by a few numbers of excellent statistical evidences in analysis of variance (ANOVA). The used evidences include high F-value (143.12), very low P-value (<0.0001), non-significant lack of fit, the determination coefficient (R2 = 0.99) and the adequate precision (47.067). To visualize the optimum, the validated model simulated the condition of variables and response (photo degradation %) be using a few number of three dimensional plots (3D). To confirm the model, the optimums were performed in laboratory. The results of performed experiments were quite close to the predicted values. In conclusion, the study indicated that the model is successful to simulate the optimum condition of o-cresol photo degradation under visible-light irradiation by manganese doped ZnO nanoparticles.
Automatic alkaloid removal system

Yahaya MR, Hj Razali MH, Abu Bakar CA, Ismail WI, Muda WM, Mat N, et al.

Pak J Biol Sci, 2014 Jan 01;17(1):141-5.
PMID: 24783795

This alkaloid automated removal machine was developed at Instrumentation Laboratory, Universiti Sultan Zainal Abidin Malaysia that purposely for removing the alkaloid toxicity from Dioscorea hispida (DH) tuber. It is a poisonous plant where scientific study has shown that its tubers contain toxic alkaloid constituents, dioscorine. The tubers can only be consumed after it poisonous is removed. In this experiment, the tubers are needed to blend as powder form before inserting into machine basket. The user is need to push the START button on machine controller for switching the water pump ON by then creating turbulence wave of water in machine tank. The water will stop automatically by triggering the outlet solenoid valve. The powders of tubers are washed for 10 minutes while 1 liter of contaminated water due toxin mixture is flowing out. At this time, the controller will automatically triggered inlet solenoid valve and the new water will flow in machine tank until achieve the desire level that which determined by ultra sonic sensor. This process will repeated for 7 h and the positive result is achieved and shows it significant according to the several parameters of biological character ofpH, temperature, dissolve oxygen, turbidity, conductivity and fish survival rate or time. From that parameter, it also shows the positive result which is near or same with control water and assuming was made that the toxin is fully removed when the pH of DH powder is near with control water. For control water, the pH is about 5.3 while water from this experiment process is 6.0 and before run the machine the pH of contaminated water is about 3.8 which are too acid. This automated machine can save time for removing toxicity from DH compared with a traditional method while less observation of the user.
Fulltext A biomimetic sensor for the classification of honeys of different floral origin and the detection of adulteration

Zakaria A, Shakaff AY, Masnan MJ, Ahmad MN, Adom AH, Jaafar MN, et al.

Sensors (Basel), 2011;11(8):7799-822.
PMID: 22164046 DOI: 10.3390/s110807799

The major compounds in honey are carbohydrates such as monosaccharides and disaccharides. The same compounds are found in cane-sugar concentrates. Unfortunately when sugar concentrate is added to honey, laboratory assessments are found to be ineffective in detecting this adulteration. Unlike tracing heavy metals in honey, sugar adulterated honey is much trickier and harder to detect, and traditionally it has been very challenging to come up with a suitable method to prove the presence of adulterants in honey products. This paper proposes a combination of array sensing and multi-modality sensor fusion that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours and aromas. Conversely, analytical instruments are based on chemical separations which may alter the properties of the volatiles or flavours of a particular honey. The present work is focused on classifying 18 samples of different honeys, sugar syrups and adulterated samples using data fusion of electronic nose (e-nose) and electronic tongue (e-tongue) measurements. Each group of samples was evaluated separately by the e-nose and e-tongue. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to separately discriminate monofloral honey from sugar syrup, and polyfloral honey from sugar and adulterated samples using the e-nose and e-tongue. The e-nose was observed to give better separation compared to e-tongue assessment, particularly when LDA was applied. However, when all samples were combined in one classification analysis, neither PCA nor LDA were able to discriminate between honeys of different floral origins, sugar syrup and adulterated samples. By applying a sensor fusion technique, the classification for the 18 different samples was improved. Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification. An improvement in performance was also observed using a Probabilistic Neural Network classifier when the e-nose and e-tongue data were fused.
Fulltext In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology

Yusuf N, Zakaria A, Omar MI, Shakaff AY, Masnan MJ, Kamarudin LM, et al.

BMC Bioinformatics, 2015;16:158.
PMID: 25971258 DOI: 10.1186/s12859-015-0601-5

Effective management of patients with diabetic foot infection is a crucial concern. A delay in prescribing appropriate antimicrobial agent can lead to amputation or life threatening complications. Thus, this electronic nose (e-nose) technique will provide a diagnostic tool that will allow for rapid and accurate identification of a pathogen.
Fulltext Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm

Goh CC, Kamarudin LM, Zakaria A, Nishizaki H, Ramli N, Mao X, et al.

Sensors (Basel), 2021 Jul 21;21(15).
PMID: 34372192 DOI: 10.3390/s21154956

This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers' drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO2, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R2 of 0.9981.
Fulltext A study on volatile organic compounds emitted by in-vitro lung cancer cultured cells using gas sensor array and SPME-GCMS

Thriumani R, Zakaria A, Hashim YZH, Jeffree AI, Helmy KM, Kamarudin LM, et al.

BMC Cancer, 2018 04 02;18(1):362.
PMID: 29609557 DOI: 10.1186/s12885-018-4235-7

BACKGROUND: Volatile organic compounds (VOCs) emitted from exhaled breath from human bodies have been proven to be a useful source of information for early lung cancer diagnosis. To date, there are still arguable information on the production and origin of significant VOCs of cancer cells. Thus, this study aims to conduct in-vitro experiments involving related cell lines to verify the capability of VOCs in providing information of the cells.
METHOD: The performances of e-nose technology with different statistical methods to determine the best classifier were conducted and discussed. The gas sensor study has been complemented using solid phase micro-extraction-gas chromatography mass spectrometry. For this purpose, the lung cancer cells (A549 and Calu-3) and control cell lines, breast cancer cell (MCF7) and non-cancerous lung cell (WI38VA13) were cultured in growth medium.
RESULTS: This study successfully provided a list of possible volatile organic compounds that can be specific biomarkers for lung cancer, even at the 24th hour of cell growth. Also, the Linear Discriminant Analysis-based One versus All-Support Vector Machine classifier, is able to produce high performance in distinguishing lung cancer from breast cancer cells and normal lung cells.
CONCLUSION: The findings in this work conclude that the specific VOC released from the cancer cells can act as the odour signature and potentially to be used as non-invasive screening of lung cancer using gas array sensor devices.
Influence of pH Adjustment Parameter for Sol-Gel Modification on Structural, Microstructure, and Magnetic Properties of Nanocrystalline Strontium Ferrite

Azis RS, Sulaiman S, Ibrahim IR, Zakaria A, Hassan J, Muda NNC, et al.

Nanoscale Res Lett, 2018 May 23;13(1):160.
PMID: 29796949 DOI: 10.1186/s11671-018-2562-x

Synthesis of nanocrystalline strontium ferrite (SrFe12O19) via sol-gel is sensitive to its modification parameters. Therefore, in this study, an attempt of regulating the pH as a sol-gel modification parameter during preparation of SrFe12O19 nanoparticles sintered at a low sintering temperature of 900 °C has been presented. The relationship of varying pH (pH 0 to 8) on structural, microstructures, and magnetic behaviors of SrFe12O19 nanoparticles were characterized by X-ray diffraction (XRD), field emission scanning microscope (FESEM), and vibrating sample magnetometer (VSM). Varying the pH of precursor exhibited a strong effect on the sintered density, crystal structure and magnetic properties of the SrFe12O19 nanoparticles. As the pH is 0, the SrFe12O19 produced relatively largest density, saturation magnetization, Ms, and coercivity, Hc, at a low sintering temperature of 900 °C. The grain size of SrFe12O19 is obtained in the range of 73.6 to 133.3 nm. The porosity of the sample affected the density and the magnetic properties of the SrFe12O19 ferrite. It is suggested that the low-temperature sintered SrFe12O19 at pH 0 displayed Ms of 44.19 emu/g and Hc of 6403.6 Oe, possessing a significant potential for applying in low-temperature co-fired ceramic permanent magnet.

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