Displaying publications 61 - 80 of 167 in total

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  1. Chau KY, Lam MHS, Cheung ML, Tso EKH, Flint SW, Broom DR, et al.
    Health Psychol Res, 2019 Mar 11;7(1):8099.
    PMID: 31583292 DOI: 10.4081/hpr.2019.8099
    Technological advancement and personalized health information has led to an increase in people using and responding to wearable technology in the last decade. These changes are often perceived to be beneficial, providing greater information and insights about health for users, organizations and healthcare and government. However, to date, understanding the antecedents of its adoption is limited. Seeking to address this gap, this cross-sectional study examined what factors influence users' adoption intention of healthcare wearable technology. We used self-administrated online survey to explore adoption intentions of healthcare wearable devices in 171 adults residing in Hong Kong. We analyzed the data by Partial least squares - structural equation modelling (PLS-SEM). The results reveal that perceived convenience and perceived irreplaceability are key predictors of perceived usefulness, which in turn strengthens users' adoption intention. Additionally, the results also reveal that health belief is one of the key predictors of adoption intention. This paper contributes to the extant literature by providing understanding of how to strengthen users' intention to adopt healthcare wearable technology. This includes the strengthening of perceived convenience and perceived irreplaceability to enhance the perceived usefulness, incorporating the extensive communication in the area of healthcare messages, which is useful in strengthening consumers' adoption intention in healthcare wearable technology.
    Matched MeSH terms: Least-Squares Analysis
  2. Ahmad SJ, Mohamad Zin N, Mazlan NW, Baharum SN, Baba MS, Lau YL
    PeerJ, 2021;9:e10816.
    PMID: 33777509 DOI: 10.7717/peerj.10816
    Background: Antiplasmodial drug discovery is significant especially from natural sources such as plant bacteria. This research aimed to determine antiplasmodial metabolites of Streptomyces spp. against Plasmodium falciparum 3D7 by using a metabolomics approach.

    Methods: Streptomyces strains' growth curves, namely SUK 12 and SUK 48, were measured and P. falciparum 3D7 IC50 values were calculated. Metabolomics analysis was conducted on both strains' mid-exponential and stationary phase extracts.

    Results: The most successful antiplasmodial activity of SUK 12 and SUK 48 extracts shown to be at the stationary phase with IC50 values of 0.8168 ng/mL and 0.1963 ng/mL, respectively. In contrast, the IC50 value of chloroquine diphosphate (CQ) for antiplasmodial activity was 0.2812 ng/mL. The univariate analysis revealed that 854 metabolites and 14, 44 and three metabolites showed significant differences in terms of strain, fermentation phase, and their interactions. Orthogonal partial least square-discriminant analysis and S-loading plot putatively identified pavettine, aurantioclavine, and 4-butyldiphenylmethane as significant outliers from the stationary phase of SUK 48. For potential isolation, metabolomics approach may be used as a preliminary approach to rapidly track and identify the presence of antimalarial metabolites before any isolation and purification can be done.

    Matched MeSH terms: Least-Squares Analysis
  3. Veerasamy R, Rajak H
    Turk J Pharm Sci, 2021 04 20;18(2):151-156.
    PMID: 33900700 DOI: 10.4274/tjps.galenos.2020.45556
    Objectives: The present study aimed to establish significant and validated quantitative structure-activity relationship (QSAR) models for neuraminidase inhibitors and correlate their physicochemical, steric, and electrostatic properties with their anti-influenza activity.

    Materials and Methods: We have developed and validated 2D and 3D QSAR models by using multiple linear regression, partial least square regression, and k-nearest neighbor-molecular field analysis methods.

    Results: 2D QSAR models had q2: 0.950 and pred_r2: 0.877 and 3D QSAR models had q2: 0.899 and pred_r2: 0.957. These results showed that the models werere predictive.

    Conclusion: Parameters such as hydrogen count and hydrophilicity were involved in 2D QSAR models. The 3D QSAR study revealed that steric and hydrophobic descriptors were negatively contributed to neuraminidase inhibitory activity. The results of this study could be used as platform for design of better anti-influenza drugs.

    Matched MeSH terms: Least-Squares Analysis
  4. Ahsan A, Wiyono NH, Veruswati M, Adani N, Kusuma D, Amalia N
    Global Health, 2020 07 18;16(1):65.
    PMID: 32682431 DOI: 10.1186/s12992-020-00595-y
    BACKGROUND: With a 264 million population and the second highest male smoking prevalence in the world, Indonesia hosted over 60 million smokers in 2018. However, the government still has not ratified the Framework Convention on Tobacco Control. In the meantime, tobacco import increases rapidly in Indonesia. These create a double, public health and economic burden for Indonesia's welfare.

    OBJECTIVE: Our study analyzed the trend of tobacco import in five countries: Indonesia, Pakistan, Bangladesh, Zimbabwe, and Mozambique. Also, we analyze the tobacco control policies implemented in these countries and determine some lessons learn for Indonesia.

    METHODS: We conducted quantitative analyses on tobacco production, consumption, export, and import during 1990-2016 in the five countries. Data were analyzed using simple ordinary least square regressions, correcting for time series autocorrelation. We also conducted a desk review on the tobacco control policies implemented in the five countries.

    RESULTS: While local production decreased by almost 20% during 1990-2016, the proportion of tobacco imports out of domestic production quadrupled from 17 to 65%. Similarly, the ratio of tobacco imports to exports reversed from 0.7 (i.e., exports were higher) to 2.9 (i.e., import were 2.9 times higher than export) in 1990 and 2016, respectively. This condition is quite different from the other four respective countries in the observation where their tobacco export is higher than the import. From the tobacco control point of view, the four other countries have ratified the Framework Convention on Tobacco Control (FCTC).

    CONCLUSION: The situation is unlikely for Indonesia to either reduce tobacco consumption or improve the local tobacco farmer's welfare, considering that the number of imports continued to increase. Emulating from the four countries, Indonesia must ratify the FCTC and implement stricter tobacco control policies to decrease tobacco consumption and import.

    Matched MeSH terms: Least-Squares Analysis
  5. Al-Okaily M, Alqudah H, Matar A, Lutfi A, Taamneh A
    Data Brief, 2020 Aug 18.
    PMID: 32837976 DOI: 10.1016/j.dib.2020.106176
    The COVID-19 pandemic has produced an unprecedented change in the educational system worldwide. Besides the economic and social impacts, there is a dilemma of accepting the new educational system "e-learning" by students within educational institutions. In particular, universities students have to handle several kinds of environmental, electronic and mental struggles due to COVID-19. To catch the current circumstances of more than two hundred thousand Jordanian university student during COVID-19. 2,500 students have been randomly selected to respond on an online survey using universities' portals and websites between March and April 2020. At the end of the data gathering process, we have received 587 records. The dataset includes 1) Demographics of students; 2) students' perspectives concerning the factors influencing their intention to use e-learning system within the Jordanian universities context. Data were analyzed using Partial Least Squares - Structural Equation Modelling (PLS-SEM). Next, the result has confirmed the positive of direct effect variables (subjective norm, perceived ease of use, and perceived usefulness) on the students' intention to use e-learning system. Next, the result has also confirmed the mediating effect of perceived usefulness and perceived ease of use between subjective norm and the behavioral intention to use the e-learning system with partially supported.
    Matched MeSH terms: Least-Squares Analysis
  6. Shahzad A, Hassan R, Aremu AY, Hussain A, Lodhi RN
    Qual Quant, 2020 Aug 04.
    PMID: 32836471 DOI: 10.1007/s11135-020-01028-z
    In response to the emerging and ever solution to the COVID-19 outbreak. This study proposes a theoretical framework based on literature and model to determined E-learning portal success. The study compared males and females to E-learning portal usage. The study objective is to check the difference between male and female E-learning portals' accessibility among the students' perspective. The study included service quality, system quality, information quality, user satisfaction, system use, and E-learning portal success. The empirical data of 280 students participated from the different universities of Malaysia through google surveys analyzed using the Partial Least Squares Structural Equation Modelling. The study further divided the full model into two domains, which are female and male. In the male model, information quality and system quality have direct relationships with user satisfaction. Information quality also supported the relationship with system use. At the same time, there is a positive relationship between user satisfaction and E-learning portals. Likewise, in the female model, E-service quality and Information quality both are supported by system use and user satisfaction. Similarly, system quality has a positive relationship with user satisfaction, and user satisfaction has a positive relationship with E-learning portals. The study will be further helpful for the Malaysian universities policy-makers such as top management, ministry of higher education, Malaysian universities union in designing the policies and programs on E-learning Portal Success in the country. The findings of the study reveal that males and females have a different level of in terms of usage of towards E-learning portals in Malaysian Universities.
    Matched MeSH terms: Least-Squares Analysis
  7. Lim V, Gorji SG, Daygon VD, Fitzgerald M
    Metabolites, 2020 Mar 19;10(3).
    PMID: 32204361 DOI: 10.3390/metabo10030114
    Selected Australian native fruits such as Davidson's plum, finger lime and native pepperberry have been reported to demonstrate potent antioxidant activity. However, comprehensive metabolite profiling of these fruits is limited, therefore the compounds responsible are unknown, and further, the compounds of nutritional value in these native fruits are yet to be described. In this study, untargeted and targeted metabolomics were conducted using the three fruits, together with assays to determine their antioxidant activities. The results demonstrate that targeted free and hydrolysed protein amino acids exhibited high amounts of essential amino acids. Similarly, important minerals like potassium were detected in the fruit samples. In antioxidant activity, Davidson's plum reported the highest activity in ferric reducing power (FRAP), finger lime in antioxidant capacity (ABTS), and native pepperberry in free radical scavenging (DPPH) and phosphomolybdenum assay. The compounds responsible for the antioxidant activity were tentatively identified using untargeted GC×GC-TOFMS and UHPLC-QqQ-TOF-MS/MS metabolomics. A clear discrimination into three clusters of fruits was observed using principal component analysis (PCA) and partial least squares (PLS) analysis. The correlation study identified a number of compounds that provide the antioxidant activities. GC×GC-TOFMS detected potent aroma compounds of limonene, furfural, and 1-R-α-pinene. Based on the untargeted and targeted metabolomics, and antioxidant assays, the nutritional potential of these Australian bush fruits is considerable and supports these indigenous fruits in the nutraceutical industry as well as functional ingredients for the food industry, with such outcomes benefiting Indigenous Australian communities.
    Matched MeSH terms: Least-Squares Analysis
  8. Saadatian-Elahi M, Alexander N, Möhlmann T, Langlois-Jacques C, Suer R, Ahmad NW, et al.
    Trials, 2021 May 30;22(1):374.
    PMID: 34053466 DOI: 10.1186/s13063-021-05298-2
    BACKGROUND: In common with many South East Asian countries, Malaysia is endemic for dengue. Dengue control in Malaysia is currently based on reactive vector management within 24 h of a dengue case being reported. Preventive rather than reactive vector control approaches, with combined interventions, are expected to improve the cost-effectiveness of dengue control programs. The principal objective of this cluster randomized controlled trial is to quantify the effectiveness of a preventive integrated vector management (IVM) strategy on the incidence of dengue as compared to routine vector control efforts.

    METHODS: The trial is conducted in randomly allocated clusters of low- and medium-cost housing located in the Federal Territory of Kuala Lumpur and Putrajaya. The IVM approach combines: targeted outdoor residual spraying with K-Othrine Polyzone, deployment of mosquito traps as auto-dissemination devices, and community engagement activities. The trial includes 300 clusters randomly allocated in a 1:1 ratio. The clusters receive either the preventive IVM in addition to the routine vector control activities or the routine vector control activities only. Epidemiological data from monthly confirmed dengue cases during the study period will be obtained from the Vector Borne Disease Sector, Malaysian Ministry of Health e-Dengue surveillance system. Entomological surveillance data will be collected in 12 clusters randomly selected from each arm. To measure the effectiveness of the IVM approach on dengue incidence, a negative binomial regression model will be used to compare the incidence between control and intervention clusters. To quantify the effect of the interventions on the main entomological outcome, ovitrap index, a modified ordinary least squares regression model using a robust standard error estimator will be used.

    DISCUSSION: Considering the ongoing expansion of dengue burden in Malaysia, setting up proactive control strategies is critical. Despite some limitations of the trial such as the use of passive surveillance to identify cases, the results will be informative for a better understanding of effectiveness of proactive IVM approach in the control of dengue. Evidence from this trial may help justify investment in preventive IVM approaches as preferred to reactive case management strategies.

    TRIAL REGISTRATION: ISRCTN ISRCTN81915073 . Retrospectively registered on 17 April 2020.

    Matched MeSH terms: Least-Squares Analysis
  9. Mat Dawi N, Namazi H, Maresova P
    Front Psychol, 2021;12:616749.
    PMID: 34093307 DOI: 10.3389/fpsyg.2021.616749
    Preventive behavior adoption is the key to reduce the possibility of getting COVID-19 infection. This paper aims to examine the determinants of intention to adopt preventive behavior by incorporating perception of e-government information and services and perception of social media into the theory of reasoned action. A cross-sectional online survey was carried out among Malaysian residents. Four hundred four valid responses were obtained and used for data analysis. A partial least-square-based path analysis revealed direct effects of attitude and subjective norm in predicting intention to adopt preventive behavior. In addition, perception of e-government information and services and perception of social media were found to be significant predictors of attitude toward preventive behavior. The findings highlight the importance of digital platforms in improving people's attitudes toward preventive behavior and in turn contain the spread of the infectious disease.
    Matched MeSH terms: Least-Squares Analysis
  10. Hashim N, Onwude DI, Osman MS
    J Food Sci, 2018 May;83(5):1271-1279.
    PMID: 29660789 DOI: 10.1111/1750-3841.14127
    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.

    Matched MeSH terms: Least-Squares Analysis
  11. Geethaavacini G, Poh GP, Yan LY, Deepashini R, Shalini S, Harish R, et al.
    Med Chem, 2018;14(7):733-740.
    PMID: 29807521 DOI: 10.2174/1573406414666180529091618
    BACKGROUND: The development of severe drug resistance caused by the extensive use of anti-HIV agents has resulted in a greatly extensive reduction in these drugs efficacy.

    OBJECTIVES: To identify the important pharmacophoric features and correlate 3D chemical structure of benzothiazinimines with their anti-HIV potential using 2D, 3D-QSAR and pharmacophore modeling studies.

    METHODS: QSAR and pharmacophore mapping studies have been used to relate structural features. 2D QSAR and 3D QSAR studies were performed using partial least square and k-nearest neighbor methodology, coupled with various feature selection methods, viz. stepwise, genetic algorithm, and simulated annealing, to derive QSAR models which were further validated for statistical significance.

    RESULTS: The physicochemical descriptor XAHydrophilicArea and SsOHE-index, and alignmentindependent descriptor T_C_Cl_6 showed significant correlation with the anti-HIV activity of benzothiazinimines in 2D QSAR. 3D QSAR results showed the significant effect of electrostatic and steric field descriptors in the anti-HIV potential of benzothiazinimines. The generated pharmacophore hypothesis demonstrated the importance of aromaticity and hydrogen bond acceptors.

    CONCLUSION: The significant models obtained in this study suggested that these techniques could be used as a guidance for designing new benzothiazinimines with enhanced anti-HIV potential.

    Matched MeSH terms: Least-Squares Analysis
  12. Lee SY, Mediani A, Maulidiani M, Khatib A, Ismail IS, Zawawi N, et al.
    J Sci Food Agric, 2018 Jan;98(1):240-252.
    PMID: 28580581 DOI: 10.1002/jsfa.8462
    BACKGROUND: Neptunia oleracea is a plant consumed as a vegetable and which has been used as a folk remedy for several diseases. Herein, two regression models (partial least squares, PLS; and random forest, RF) in a metabolomics approach were compared and applied to the evaluation of the relationship between phenolics and bioactivities of N. oleracea. In addition, the effects of different extraction conditions on the phenolic constituents were assessed by pattern recognition analysis.

    RESULTS: Comparison of the PLS and RF showed that RF exhibited poorer generalization and hence poorer predictive performance. Both the regression coefficient of PLS and the variable importance of RF revealed that quercetin and kaempferol derivatives, caffeic acid and vitexin-2-O-rhamnoside were significant towards the tested bioactivities. Furthermore, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) results showed that sonication and absolute ethanol are the preferable extraction method and ethanol ratio, respectively, to produce N. oleracea extracts with high phenolic levels and therefore high DPPH scavenging and α-glucosidase inhibitory activities.

    CONCLUSION: Both PLS and RF are useful regression models in metabolomics studies. This work provides insight into the performance of different multivariate data analysis tools and the effects of different extraction conditions on the extraction of desired phenolics from plants. © 2017 Society of Chemical Industry.

    Matched MeSH terms: Least-Squares Analysis
  13. Maheshwari S, Pachori RB, Kanhangad V, Bhandary SV, Acharya UR
    Comput Biol Med, 2017 Sep 01;88:142-149.
    PMID: 28728059 DOI: 10.1016/j.compbiomed.2017.06.017
    Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images.
    Matched MeSH terms: Least-Squares Analysis
  14. Sharma M, Agarwal S, Acharya UR
    Comput Biol Med, 2018 09 01;100:100-113.
    PMID: 29990643 DOI: 10.1016/j.compbiomed.2018.06.011
    Obstructive sleep apnea (OSA) is a sleep disorder caused due to interruption of breathing resulting in insufficient oxygen to the human body and brain. If the OSA is detected and treated at an early stage the possibility of severe health impairment can be mitigated. Therefore, an accurate automated OSA detection system is indispensable. Generally, OSA based computer-aided diagnosis (CAD) system employs multi-channel, multi-signal physiological signals. However, there is a great need for single-channel bio-signal based low-power, a portable OSA-CAD system which can be used at home. In this study, we propose single-channel electrocardiogram (ECG) based OSA-CAD system using a new class of optimal biorthogonal antisymmetric wavelet filter bank (BAWFB). In this class of filter bank, all filters are of even length. The filter bank design problem is transformed into a constrained optimization problem wherein the objective is to minimize either frequency-spread for the given time-spread or time-spread for the given frequency-spread. The optimization problem is formulated as a semi-definite programming (SDP) problem. In the SDP problem, the objective function (time-spread or frequency-spread), constraints of perfect reconstruction (PR) and zero moment (ZM) are incorporated in their time domain matrix formulations. The global solution for SDP is obtained using interior point algorithm. The newly designed BAWFB is used for the classification of OSA using ECG signals taken from the physionet's Apnea-ECG database. The ECG segments of 1 min duration are decomposed into six wavelet subbands (WSBs) by employing the proposed BAWFB. Then, the fuzzy entropy (FE) and log-energy (LE) features are computed from all six WSBs. The FE and LE features are classified into normal and OSA groups using least squares support vector machine (LS-SVM) with 35-fold cross-validation strategy. The proposed OSA detection model achieved the average classification accuracy, sensitivity, specificity and F-score of 90.11%, 90.87% 88.88% and 0.92, respectively. The performance of the model is found to be better than the existing works in detecting OSA using the same database. Thus, the proposed automated OSA detection system is accurate, cost-effective and ready to be tested with a huge database.
    Matched MeSH terms: Least-Squares Analysis
  15. Ahadzadeh AS, Rafik-Galea S, Alavi M, Amini M
    Health Psychol Open, 2018 06 10;5(1):2055102918774251.
    PMID: 29977587 DOI: 10.1177/2055102918774251
    This study examined the correlation between body mass index as independent variable, and body image and fear of negative evaluation as dependent variables, as well as the moderating role of self-esteem in these correlations. A total of 318 Malaysian young adults were conveniently recruited to do the self-administered survey on the demographic characteristics body image, fear of negative evaluation, and self-esteem. Partial least squares structural equation modeling was used to test the research hypotheses. The results revealed that body mass index was negatively associated with body image, while no such correlation was found with fear of negative evaluation. Meanwhile, the negative correlation of body mass index with body image was stronger among those with lower self-esteem, while a positive association of body mass index with fear of negative evaluation was significant only among individuals with low self-esteem.
    Matched MeSH terms: Least-Squares Analysis
  16. Chang CC, Saad B, Surif M, Ahmad MN, Md Shakaff AY
    Sensors (Basel), 2008 Jun 01;8(6):3665-3677.
    PMID: 27879900
    A disposable screen-printed e-tongue based on sensor array and pattern recognition that is suitable for the assessment of water quality in fish tanks is described. The characteristics of sensors fabricated using two kinds of sensing materials, namely (i) lipids (referred to as Type 1), and (ii) alternative electroactive materials comprising liquid ion-exchangers and macrocyclic compounds (Type 2) were evaluated for their performance stability, sensitivity and reproducibility. The Type 2 e-tongue was found to have better sensing performance in terms of sensitivity and reproducibility and was thus used for application studies. By using a pattern recognition tool i.e. principal component analysis (PCA), the e-tongue was able to discriminate the changes in the water quality in tilapia and catfish tanks monitored over eight days. E-tongues coupled with partial least squares (PLS) was used for the quantitative analysis of nitrate and ammonium ions in catfish tank water and good agreement were found with the ion-chromatography method (relative error, ±1.04- 4.10 %).
    Matched MeSH terms: Least-Squares Analysis
  17. Azizan A, Xin LA, Abdul Hamid NA, Maulidiani M, Mediani A, Abdul Ghafar SZ, et al.
    Foods, 2020 Feb 11;9(2).
    PMID: 32053982 DOI: 10.3390/foods9020173
    Pineapple (Ananascomosus) waste is a promising source of metabolites for therapeutics, functional foods, and cosmeceutical applications. This study strives to characterize the complete metabolite profiles of a variety of MD2 pineapple waste extracts. Metabolomics strategies were utilized to identify bioactive metabolites of this variety prepared with different solvent ratios. Each pineapple waste extract was first screened for total phenolic content, 2,2-diphenyl-1-picrylhydrazyl free radical scavenging, nitric oxide scavenging, and α-glucosidase inhibitory activities. The highest TPC was found in all samples of the peel, crown, and core extracted using a 50% ethanol ratio, even though the results were fairly significant than those obtained for other ethanol ratios. Additionally, crown extracted with a 100% ethanol ratio demonstrated the highest potency in DPPH and NO scavenging activity, with IC50 values of 296.31 and 338.52 µg/mL, respectively. Peel extracted with 100% ethanol exhibited the highest α-glucosidase inhibitory activity with an IC50 value of 92.95 µg/mL. Then, the extracts were analyzed and the data from 1H NMR were processed using multivariate data analysis. A partial least squares and correlogram plot suggested that 3-methylglutaric acid, threonine, valine, and α-linolenic acid were the main contributors to the antioxidant activities, whereas epicatechin was responsible for the α-glucosidase inhibitory activity. Relative quantification further supported that 100% crown extract was among the extracts that possessed the most abundant potential metabolites. The present study demonstrated that the crown and peel parts of MD2 pineapple extracted with 100% ethanol are potentially natural sources of antioxidants and α-glucosidase inhibitors, respectively.
    Matched MeSH terms: Least-Squares Analysis
  18. Muhammad SA, Seow EK, Mohd Omar AK, Rodhi AM, Mat Hassan H, Lalung J, et al.
    Sci Justice, 2018 Jan;58(1):59-66.
    PMID: 29332695 DOI: 10.1016/j.scijus.2017.05.008
    A total of 33 crude palm oil samples were randomly collected from different regions in Malaysia. Stable carbon isotopic composition (δ13C) was determined using Flash 2000 elemental analyzer while hydrogen and oxygen isotopic compositions (δ2H and δ18O) were analyzed by Thermo Finnigan TC/EA, wherein both instruments were coupled to an isotope ratio mass spectrometer. The bulk δ2H, δ18O and δ13C of the samples were analyzed by Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA). Unsupervised HCA and PCA methods have demonstrated that crude palm oil samples were grouped into clusters according to respective state. A predictive model was constructed by supervised OPLS-DA with good predictive power of 52.60%. Robustness of the predictive model was validated with overall accuracy of 71.43%. Blind test samples were correctly assigned to their respective cluster except for samples from southern region. δ18O was proposed as the promising discriminatory marker for discerning crude palm oil samples obtained from different regions. Stable isotopes profile was proven to be useful for origin traceability of crude palm oil samples at a narrower geographical area, i.e. based on regions in Malaysia. Predictive power and accuracy of the predictive model was expected to improve with the increase in sample size. Conclusively, the results in this study has fulfilled the main objective of this work where the simple approach of combining stable isotope analysis with chemometrics can be used to discriminate crude palm oil samples obtained from different regions in Malaysia. Overall, this study shows the feasibility of this approach to be used as a traceability assessment of crude palm oils.
    Matched MeSH terms: Least-Squares Analysis
  19. Murat M, Chang SW, Abu A, Yap HJ, Yong KT
    PeerJ, 2017;5:e3792.
    PMID: 28924506 DOI: 10.7717/peerj.3792
    Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson's coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99.89% for the Swedish Leaf dataset. In addition, the Relief feature selection method achieved the highest classification accuracy of 98.13% after 80 (or 60%) of the original features were reduced, from 133 to 53 descriptors in the myDAUN dataset with the reduction in computational time. Subsequently, the hybridisation of four descriptors gave the best results compared to others. It is proven that the combination MSD and HOG were good enough for tropical shrubs species classification. Hu and ZM descriptors also improved the accuracy in tropical shrubs species classification in terms of invariant to translation, rotation and scale. ANN outperformed the others for tropical shrub species classification in this study. Feature selection methods can be used in the classification of tropical shrub species, as the comparable results could be obtained with the reduced descriptors and reduced in computational time and cost.
    Matched MeSH terms: Least-Squares Analysis
  20. Sabry AH, W Hasan WZ, Ab Kadir MZA, Radzi MAM, Shafie S
    PLoS One, 2018;13(1):e0191478.
    PMID: 29351554 DOI: 10.1371/journal.pone.0191478
    The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system's modeling equations based on the Bode plot equations and the vector fitting (VF) algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model.
    Matched MeSH terms: Least-Squares Analysis
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