Displaying publications 1 - 20 of 167 in total

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  1. A Samad NS, Abdul-Rahim AS, Mohd Yusof MJ, Tanaka K
    Environ Sci Pollut Res Int, 2020 Apr;27(10):10367-10390.
    PMID: 31939016 DOI: 10.1007/s11356-019-07593-7
    This study assessed the economic value of public urban green spaces (UGSs) in Kuala Lumpur (KL) city by using the hedonic price method (HPM). It involves 1269 house units from eight sub-districts in KL city. Based on the hedonic price method, this study formulates a global and local model. The global model and local model are analyzed using ordinary least square (OLS) regression and geographically weighted regression (GWR). By using the hedonic price method, the house price serves as a proxy for public urban green spaces' economic value. The house price is regressed against the set of three variables which are structural characteristics, neighborhood attributes, and environmental attributes. Measurements of interest in this study are environmental characteristics, including distance to public UGSs and size of public UGSs. The results of the OLS regression illustrated that Taman Rimba Kiara and Taman Tasik Titiwangsa provide the maximum economic value. On average, reducing the distance of the house location to Taman Rimba Kiara by 10 m increased the house price by RM1700. Similarly, increasing the size of the Taman Tasik Titiwangsa by 1000 m2 increases the house price by RM60,000. The advantage of the GWR result is the economic value of public UGSs which can be analyzed by the specific location according to sub-district. From this study, the GWR result exposed that the economic values of Taman Rimba Bukit Kiara and Taman Tasik Titiwangsa were not significant in each of the sub-district within KL city. Taman Rimba Bukit Kiara was negatively significant at all sub-districts except Setapak and certain house locations located at the sub-district of KL. In contrast, Taman Tasik Titiwangsa was positively significant at all sub-districts except certain house locations at the sub-districts of Batu, KL, Setapak, and KL city center. In conclusion, results show that the house price is influenced by the environmental attribute. However, even though both of these public UGSs generate the highest economic value based on distance and size, its significant values with an expected sign are only obtained based on the specific house location as verified by the local model. In terms of model comparison, the local model was better compared with the global model.
    Matched MeSH terms: Least-Squares Analysis
  2. Abbasi AZ, Nisar S, Rehman U, Ting DH
    Front Psychol, 2020;11:1831.
    PMID: 32849078 DOI: 10.3389/fpsyg.2020.01831
    This article aims to uncover novel insights into personality factors and consumer video game engagement modeling. This research empirically validates the role of specific HEXACO personality factors that foster consumer engagement (CE) in electronic sports (eSports) users. Using a survey-based approach, we incorporated the HEXACO 60 items and consumer video game engagement scales for data collection. Data were collected from eSports users, with 250 valid responses. WarpPLS 6.0 was used for partial least squares-structural equation modeling analyses comprising measurement and structural model assessment. The results showed that the reflective measurement model is reliable and sound, whereas the second-order formative measurement model also meets the criteria of indicator weights and collinearity values variance inflation factor (VIF). The results based on the structural model indicate that openness to experience, extraversion, agreeableness, and conscientiousness positively predict CE in eSports. This article is first among others that conceptualizes and validates the HEXACO personality traits as a reflective formative model using the hierarchical component model approach. The research model carries the explanatory capacity for CE in eSports concerning personality dimensions as indicated by the HEXACO model. It highlights the potential benefits of such research especially to marketers who could potentially employ personality modeling to develop tailored strategies to increase CE in video games.
    Matched MeSH terms: Least-Squares Analysis
  3. Abbasi GA, Tiew LY, Tang J, Goh YN, Thurasamy R
    PLoS One, 2021;16(3):e0247582.
    PMID: 33684120 DOI: 10.1371/journal.pone.0247582
    In recent years, the growth of cryptocurrency has undergone an enormous increase in cryptocurrency markets all around the world. Sadly, only insignificant heed has been paid to the unveiling of determinants of cryptocurrency adoption globally, particularly in emerging markets like Malaysia. The purpose of the study is to examine whether the application of deep learning-based dual-stage Partial Least Square-Structural Equation Modelling (PLS-SEM) & Artificial Neural Network (ANN) analysis enable better in-depth research results as compared to single-step PLS-SEM approach and to excavate factors which can predict behavioural intention to adopt cryptocurrency. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model were extended with the inclusion of trust and personnel innovativeness. The model was further validated by introducing a new path model compared to the original UTAUT2 model and the moderating role of personal innovativeness between performance expectancy and price value, with a sample of 314 respondents. Contrary to previous technology adoption studies that used PLS-SEM & ANN as single-stage analysis, this study further enhanced the analysis by applying a deep learning-based dual-stage PLS-SEM and ANN method. The application of deep learning-based dual-stage PLS-SEM & ANN analysis is a novel methodological approach, detecting both linear and non-linear associations among constructs. At the same time, it is regarded as a superior statistical approach as compared to traditional hybrid shallow SEM & ANN single-stage analysis. Also, sensitivity analysis provides normalised importance using multi-layer perceptron with the feed-forward-back-propagation algorithm. Furthermore, the deep learning-based dual-stage PLS-SEM & ANN revealed that trust proved to be the strongest predictor in driving user intention. The introduction of this new methodology and the theoretical contribution opens the vistas of the extant body of knowledge in technology-adoption related literature. This study also provides theoretical, practical and methodological contributions.
    Matched MeSH terms: Least-Squares Analysis
  4. Abdu Masanawa Sagir, Saratha Sathasivam
    MyJurnal
    Medical diagnosis is the process of determining which disease or medical condition explains a person’s determinable signs and symptoms. Diagnosis of most diseases is very expensive as many tests are required for predictions. This paper aims to introduce an improved hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). It incorporates hybrid learning algorithms least square estimates with Levenberg-Marquardt algorithm using analytic derivation for computation of Jacobian matrix, as well as code optimisation technique, which indexes membership functions. The goal is to investigate how certain diseases are affected by patient’s characteristics and measurement such as abnormalities or a decision about the presence or absence of a disease. In order to achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system to classify and predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. The proposed hybridised intelligent technique was tested with Statlog heart disease and Hepatitis disease datasets obtained from the University of California at Irvine’s (UCI) machine learning repository. The robustness of the performance measuring total accuracy, sensitivity and specificity was examined. In comparison, the proposed method was found to achieve superior
    performance when compared to some other related existing methods.
    Matched MeSH terms: Least-Squares Analysis
  5. Abdul-Hamid NA, Abas F, Ismail IS, Shaari K, Lajis NH
    J Food Sci, 2015 Nov;80(11):H2603-11.
    PMID: 26457883 DOI: 10.1111/1750-3841.13084
    This study aimed to examine the variation in the metabolite profiles and nitric oxide (NO) inhibitory activity of Ajwa dates that were subjected to 2 drying treatments and different extraction solvents. (1)H NMR coupled with multivariate data analysis was employed. A Griess assay was used to determine the inhibition of the production of NO in RAW 264.7 cells treated with LPS and interferon-γ. The oven dried (OD) samples demonstrated the absence of asparagine and ascorbic acid as compared to the freeze dried (FD) dates. The principal component analysis showed distinct clusters between the OD and FD dates by the second principal component. In respect of extraction solvents, chloroform extracts can be distinguished by the absence of arginine, glycine and asparagine compared to the methanol and 50% methanol extracts. The chloroform extracts can be clearly distinguished from the methanol and 50% methanol extracts by first principal component. Meanwhile, the loading score plot of partial least squares analysis suggested that beta glucose, alpha glucose, choline, ascorbic acid and glycine were among the metabolites that were contributing to higher biological activity displayed by FD and methanol extracts of Ajwa. The results highlight an alternative method of metabolomics approach for determination of the metabolites that contribute to NO inhibitory activity.
    Matched MeSH terms: Least-Squares Analysis
  6. Abu Bakar Sajak A, Azlan A, Abas F, Hamzah H
    Nutrients, 2021 Oct 12;13(10).
    PMID: 34684574 DOI: 10.3390/nu13103573
    An herbal mixture composed of lemon, apple cider, garlic, ginger and honey as a polyphenol-rich mixture (PRM) has been reported to contain hypolipidemic activity on human subjects and hyperlipidemic rats. However, the therapeutic effects of PRM on metabolites are not clearly understood. Therefore, this study aimed to provide new information on the causal impact of PRM on the endogenous metabolites, pathways and serum biochemistry. Serum samples of hyperlipidemic rats treated with PRM were subjected to biochemistry (lipid and liver profile) and hydroxymethylglutaryl-CoA enzyme reductase (HMG-CoA reductase) analyses. In contrast, the urine samples were subjected to urine metabolomics using 1H NMR. The serum biochemistry revealed that PRM at 500 mg/kg (PRM-H) managed to lower the total cholesterol level and low-density lipoprotein (LDL-C) (p < 0.05) and reduce the HMG-CoA reductase activity. The pathway analysis from urine metabolomics reveals that PRM-H altered 17 pathways, with the TCA cycle having the highest impact (0.26). Results also showed the relationship between the serum biochemistry of LDL-C and HMG-CoA reductase and urine metabolites (trimethylamine-N-oxide, dimethylglycine, allantoin and succinate). The study's findings demonstrated the potential of PRM at 500 mg/kg as an anti-hyperlipidemic by altering the TCA cycle, inhibiting HMG-CoA reductase and lowering the LDL-C in high cholesterol rats.
    Matched MeSH terms: Least-Squares Analysis
  7. Abu Hassan Shaari Mohd Nor, Fauziah Maarof
    The main purpose of this article is to introduce the technique of panel data analysis in econometrics modeling. The elasticity of labour, capital and economic of scale for twenty two food manufacturing firms covering from 1989 to 1993 is estimated using the Cobb-Douglas model. The three main techniques of panel data analysis discussed are least square dummy variables (LSDV), analysis of covariance (ANCOVA) and generalized least square (GLS). Ordinary Least Square (OLS) method is included as the basis of comparison.
    Matched MeSH terms: Least-Squares Analysis
  8. Adeleke AQ, Bahaudin AY, Kamaruddeen AM, Bamgbade JA, Salimon MG, Khan MWA, et al.
    Saf Health Work, 2018 Mar;9(1):115-124.
    PMID: 30363069 DOI: 10.1016/j.shaw.2017.05.004
    Background: Substantial empirical research has shown conflicting results regarding the influence of organizational external factors on construction risk management, suggesting the necessity to introduce a moderator into the study. The present research confirmed whether rules and regulations matter on the relationships between organizational external factors and construction risk management.

    Methods: Based on discouragement and organizational control theory, this research examined the effects of organizational external factors and rules and regulations on construction risk management among 238 employees operating in construction companies in Abuja and Lagos, Nigeria. A personally administered questionnaire was used to acquire the data. The data were analyzed using partial least squares structural equation modeling.

    Results: A significant positive relationship between organizational external factors and construction risk management was asserted. This study also found a significant positive relationship between rules and regulations and construction risk management. As anticipated, rules and regulations were found to moderate the relationship between organizational external factors and construction risk management, with a significant positive result. Similarly, a significant interaction effect was also found between rules and regulations and organizational external factors. Implications of the research from a Nigerian point of view have also been discussed.

    Conclusion: Political, economy, and technology factors helped the construction companies to reduce the chance of risk occurrence during the construction activities. Rules and regulations also helped to lessen the rate of accidents involving construction workers as well as the duration of the projects. Similarly, the influence of the organizational external factors with rules and regulations on construction risk management has proven that most of the construction companies that implement the aforementioned factors have the chance to deliver their projects within the stipulated time, cost, and qualities, which can be used as a yardstick to measure a good project.

    Matched MeSH terms: Least-Squares Analysis
  9. 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
  10. Ahirwal MK, Kumar A, Singh GK
    IEEE/ACM Trans Comput Biol Bioinform, 2013 Nov-Dec;10(6):1491-504.
    PMID: 24407307 DOI: 10.1109/TCBB.2013.119
    This paper explores the migration of adaptive filtering with swarm intelligence/evolutionary techniques employed in the field of electroencephalogram/event-related potential noise cancellation or extraction. A new approach is proposed in the form of controlled search space to stabilize the randomness of swarm intelligence techniques especially for the EEG signal. Swarm-based algorithms such as Particles Swarm Optimization, Artificial Bee Colony, and Cuckoo Optimization Algorithm with their variants are implemented to design optimized adaptive noise canceler. The proposed controlled search space technique is tested on each of the swarm intelligence techniques and is found to be more accurate and powerful. Adaptive noise canceler with traditional algorithms such as least-mean-square, normalized least-mean-square, and recursive least-mean-square algorithms are also implemented to compare the results. ERP signals such as simulated visual evoked potential, real visual evoked potential, and real sensorimotor evoked potential are used, due to their physiological importance in various EEG studies. Average computational time and shape measures of evolutionary techniques are observed 8.21E-01 sec and 1.73E-01, respectively. Though, traditional algorithms take negligible time consumption, but are unable to offer good shape preservation of ERP, noticed as average computational time and shape measure difference, 1.41E-02 sec and 2.60E+00, respectively.
    Matched MeSH terms: Least-Squares Analysis
  11. Ahmad Hanis, I.A.H., Mad Nasir, S., Jinap, S., Alias, R., Ab Karim, M.S.
    MyJurnal
    As Malaysian economies grow, Malaysian per capita income is likely to increase. From economics point of view, it is expected that better-off consumers will move to better quality of food attributes such as freshness, food safety, quality and healthfulness in their food intake. This study aimed to investigate the demand for eggs attributes by Malaysian consumers. The study considers the conjoint analysis technique as a method for acquiring insights into preferences for eggs product. The technique was applied to establish the trade-offs that Malaysian consumers make between size, colour, size of packaging, functional attribute and price in the purchasing of eggs for 202 respondents. Least squares regression was utilized to estimate the relative importance of attributes for eggs. The results revealed that the ideal characteristic of egg was one with large size (grade A), omega eggs, brown, and ten per packs. We also found that consumers were also willing to pay more for their preferred attributes. The results found in the study provide valuable inputs to producers or marketers to improve their marketing efforts as well as market positioning, in line with the demanded eggs attributes.
    Matched MeSH terms: Least-Squares Analysis
  12. Ahmad J, Al Mamun A, Reza MNH, Makhbul ZKM, Ali KAM
    Environ Sci Pollut Res Int, 2023 Aug;30(37):87938-87957.
    PMID: 37432578 DOI: 10.1007/s11356-023-28624-4
    This study investigates the effect of green human resource management practices on green competitive advantage and the mediating role of competitive advantage between the green human resource management practices and green ambidexterity. This study also examined the effect of green competitive advantage on green ambidexterity and the moderating effect of firm size on green competitive advantage and ambidexterity. The results reveal that green recruitment and green training and involvement are not sufficient, but they are necessary for any outcome level of green competitive advantage. The other three constructs (green performance management and compensation, green intellectual capital, and green transformational leadership) are sufficient and necessary; however, green performance management and compensation is necessary only at an outcome level of more than or equal to 60%. The findings revealed that the mediating effect of green competitive advantage is significant only between three constructs (green performance management and compensation, green intellectual capital, and green transformational leadership) and green ambidexterity. The results also indicate that a green competitive advantage has a significant positive effect on green ambidexterity. Exploring the necessary and sufficient factors using a combination of partial least squares structural equation modeling and necessary condition analysis provides valuable guidance for practitioners to optimize firm outcomes.
    Matched MeSH terms: Least-Squares Analysis
  13. Ahmad Mahir R, Arfah A, Rozaimah Z, Siti Adyani S, Khairiah J, Ismail B
    Sains Malaysiana, 2017;46:2305-2313.
    The study was conducted to determine the best model suitable for the determination of ferrum uptake in Brassica chinensis (flowering white cabbage). A nonlinear regression model was selected to determine the amount of ferrum absorbed by each part of the Brassica chinensis plant namely the leaves, stems and roots. The Levenberg-Marquardt method was used to perform the nonlinear least square fit. This method employs information on the gradients and hence requires specification of the partial derivatives. A suitable model was obtained from the exponential regression model. The polynomial model was found to be appropriate for leaves, the mono-exponential model was suitable for stems and the simple exponential model for roots. The residual plots and the normal probability plots from each of the models indicated no substantial diagnostic problems, so it can be concluded that the polynomial and exponential regression models provide adequate fit to determine data on heavy metal uptake by the flowering white cabbage.
    Matched MeSH terms: Least-Squares Analysis
  14. Ahmad R, Lim CK, Marzuki NF, Goh YK, Azizan KA, Goh YK, et al.
    Molecules, 2020 Dec 16;25(24).
    PMID: 33339375 DOI: 10.3390/molecules25245965
    In solving the issue of basal stem rot diseases caused by Ganoderma, an investigation of Scytalidium parasiticum as a biological control agent that suppresses Ganoderma infection has gained our interest, as it is more environmentally friendly. Recently, the fungal co-cultivation has emerged as a promising method to discover novel antimicrobial metabolites. In this study, an established technique of co-culturing Scytalidium parasiticum and Ganoderma boninense was applied to produce and induce metabolites that have antifungal activity against G. boninense. The crude extract from the co-culture media was applied to a High Performance Liquid Chromatography (HPLC) preparative column to isolate the bioactive compounds, which were tested against G. boninense. The fractions that showed inhibition against G. boninense were sent for a Liquid Chromatography-Time of Flight-Mass Spectrometry (LC-TOF-MS) analysis to further identify the compounds that were responsible for the microbicidal activity. Interestingly, we found that eudistomin I, naringenin 7-O-beta-D-glucoside and penipanoid A, which were present in different abundances in all the active fractions, except in the control, could be the antimicrobial metabolites. In addition, the abundance of fatty acids, such as oleic acid and stearamide in the active fraction, also enhanced the antimicrobial activity. This comprehensive metabolomics study could be used as the basis for isolating biocontrol compounds to be applied in oil palm fields to combat a Ganoderma infection.
    Matched MeSH terms: Least-Squares Analysis
  15. 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
  16. Ahmad Zawawi A, Nasurdin AM
    Int J Nurs Sci, 2017 Jul 10;4(3):285-290.
    PMID: 31406754 DOI: 10.1016/j.ijnss.2017.03.009
    Purpose: This study sought to examine the relationship between team task features and team task performance. Team task performance revolved around the team's technical knowledge and the technical core activities of the organization. On the other hand, team task characteristics include task identity, task significance, and task interdependence.

    Methods: This study involved a total of 300 nursing teams (1436 individual nurses) from seven state hospitals in Peninsular Malaysia. Data were collected using two sets of questionnaires which were initially distributed to 320 teams. One set was given to the team members and another set was given to the team leaders. Of the 320 sets sent out, 300 sets were returned. Responses were then combined and aggregated to the team level to get the team's final score. Analyses of the hypotheses were done using Partial Least Squares (PLS) through assessment of the measurement and structural model.

    Results: Results from the path analysis revealed that of the three dimensions of team task attributes, only task significance was positively and significantly related to team task performance (β = 0.076, P > 0.05), while task identity (β = 0.076, P > 0.05) and task interdependence (β = -0.037, P > 0.05) were found unrelated to team task performance.

    Conclusions: This study demonstrated that task significance is important to predict team task performance. Task significance reflects meaningfulness and nobility of tasks, thus elevate the desire to perform better in each assigned task.

    Matched MeSH terms: Least-Squares Analysis
  17. Ahmed Qasim Turki, Nashiren Farzilah Mailah, Ahmed H. Sabry
    MyJurnal
    This paper presents a transmission line (TL) modelling which is based upon vector fitting algorithm
    and RLC passive filter design. Frequency Response Analysis (FRA) is utilised for behaviour prediction and fault diagnosis. The utilities of the measured FRA data points need to be enhanced with suitable modelling category to facilitate the modelling and analysis process. This research proposes a new method for modelling the transmission line based on a rational approximation function which can be extracted through the Vector Fitting (VF) method, based on the frequency response measured data points. A set of steps needs to be implemented to achieve this by setting up an extracted partial fraction approximation, which results from a least square RMS error via VF. Active and passive filter design circuits are used to construct the model of the Transmission line. The RLC design representation was implemented for modelling the system physically while MATLAB Simulink was used to verify the results.
    Matched MeSH terms: Least-Squares Analysis
  18. 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
  19. Akhtar MT, Samar M, Shami AA, Mumtaz MW, Mukhtar H, Tahir A, et al.
    Molecules, 2021 Jul 30;26(15).
    PMID: 34361796 DOI: 10.3390/molecules26154643
    Meat is a rich source of energy that provides high-value animal protein, fats, vitamins, minerals and trace amounts of carbohydrates. Globally, different types of meats are consumed to fulfill nutritional requirements. However, the increasing burden on the livestock industry has triggered the mixing of high-price meat species with low-quality/-price meat. This work aimed to differentiate different meat samples on the basis of metabolites. The metabolic difference between various meat samples was investigated through Nuclear Magnetic Resonance spectroscopy coupled with multivariate data analysis approaches like principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA). In total, 37 metabolites were identified in the gluteal muscle tissues of cow, goat, donkey and chicken using 1H-NMR spectroscopy. PCA was found unable to completely differentiate between meat types, whereas OPLS-DA showed an apparent separation and successfully differentiated samples from all four types of meat. Lactate, creatine, choline, acetate, leucine, isoleucine, valine, formate, carnitine, glutamate, 3-hydroxybutyrate and α-mannose were found as the major discriminating metabolites between white (chicken) and red meat (chevon, beef and donkey). However, inosine, lactate, uracil, carnosine, format, pyruvate, carnitine, creatine and acetate were found responsible for differentiating chevon, beef and donkey meat. The relative quantification of differentiating metabolites was performed using one-way ANOVA and Tukey test. Our results showed that NMR-based metabolomics is a powerful tool for the identification of novel signatures (potential biomarkers) to characterize meats from different sources and could potentially be used for quality control purposes in order to differentiate different meat types.
    Matched MeSH terms: Least-Squares Analysis
  20. Al-Haddad, S.A.R., Samad, S.A., Hussain, A., Ishak, K.A., Noor, A.O.A.
    ASM Science Journal, 2008;2(1):75-81.
    MyJurnal
    Robustness is a key issue in speech recognition. A speech recognition algorithm for Malay digits from zero to nine and an algorithm for noise cancellation by using recursive least squares (RLS) is proposed in this article. This system consisted of speech processing inclusive of digit margin and recognition using zero crossing and energy calculations. Mel-frequency cepstral coefficient vectors were used to provide an estimate of the vocal tract filter. Meanwhile dynamic time warping was used to detect the nearest recorded voice with appropriate global constraint. The global constraint was used to set a valid search region because the variation of the speech rate of the speaker was considered to be limited in a reasonable range which meant that it could prune the unreasonable search space. The algorithm was tested on speech samples that were recorded as part of a Malay corpus. The results showed that the algorithm managed to recognize almost 80.5% of the Malay digits for all recorded words. The addition of a RLS noise canceller in the preprocessing stage increased the accuracy to 94.1%.
    Matched MeSH terms: Least-Squares Analysis
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