Displaying publications 1 - 20 of 167 in total

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  1. Visible and near-infrared spectroscopic determination of sugarcane chlorophyll content using a modified wavelength selection method for multivariate calibration
    Ong P, Jian J, Li X, Yin J, Ma G
    Spectrochim Acta A Mol Biomol Spectrosc, 2024 Jan 15;305:123477.
    PMID: 37804706 DOI: 10.1016/j.saa.2023.123477
    Spectroscopy in the visible and near-infrared region (Vis-NIR) region has proven to be an effective technique for quantifying the chlorophyll contents of plants, which serves as an important indicator of their photosynthetic rate and health status. However, the Vis-NIR spectroscopy analysis confronts a significant challenge concerning the existence of spectral variations and interferences induced by diverse factors. Hence, the selection of characteristic wavelengths plays a crucial role in Vis-NIR spectroscopy analysis. In this study, a novel wavelength selection approach known as the modified regression coefficient (MRC) selection method was introduced to enhance the diagnostic accuracy of chlorophyll content in sugarcane leaves. Experimental data comprising spectral reflectance measurements (220-1400 nm) were collected from sugarcane leaf samples at different growth stages, including seedling, tillering, and jointing, and the corresponding chlorophyll contents were measured. The proposed MRC method was employed to select optimal wavelengths for analysis, and subsequent partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed to establish the relationship between the selected wavelengths and the measured chlorophyll contents. In comparison to full-spectrum modelling and other commonly employed wavelength selection techniques, the proposed simplified MRC-GPR model, utilizing a subset of 291 selected wavelengths, demonstrated superior performance. The MRC-GPR model achieved higher coefficient of determination of 0.9665 and 0.8659, and lower root mean squared error of 1.7624 and 3.2029, for calibration set and prediction set, respectively. Results showed that the GPR model, a nonlinear regression approach, outperformed the PLSR model.
    Matched MeSH terms: Least-Squares Analysis
  2. New approach for sugarcane disease recognition through visible and near-infrared spectroscopy and a modified wavelength selection method using machine learning models
    Ong P, Jian J, Li X, Zou C, Yin J, Ma G
    Spectrochim Acta A Mol Biomol Spectrosc, 2023 Dec 05;302:123037.
    PMID: 37356390 DOI: 10.1016/j.saa.2023.123037
    The proliferation of pathogenic fungi in sugarcane crops poses a significant threat to agricultural productivity and economic sustainability. Early identification and management of sugarcane diseases are therefore crucial to mitigate the adverse impacts of these pathogens. In this study, visible and near-infrared spectroscopy (380-1400 nm) combined with a novel wavelength selection method, referred to as modified flower pollination algorithm (MFPA), was utilized for sugarcane disease recognition. The selected wavelengths were incorporated into machine learning models, including Naïve Bayes, random forest, and support vector machine (SVM). The developed simplified SVM model, which utilized the MFPA wavelength selection method yielded the best performances, achieving a precision value of 0.9753, a sensitivity value of 0.9259, a specificity value of 0.9524, and an accuracy of 0.9487. These results outperformed those obtained by other wavelength selection approaches, including the selectivity ratio, variable importance in projection, and the baseline method of the flower pollination algorithm.
    Matched MeSH terms: Least-Squares Analysis
  3. Determining the main contributing factors to nutrient concentration in rivers in arid northwest China using partial least squares structural equation modeling
    Wang W, Zhang F, Zhao Q, Liu C, Jim CY, Johnson VC, et al.
    J Environ Manage, 2023 Oct 01;343:118249.
    PMID: 37245314 DOI: 10.1016/j.jenvman.2023.118249
    Understanding the main driving factors of oasis river nutrients in arid areas is important to identify the sources of water pollution and protect water resources. Twenty-seven sub-watersheds were selected in the lower oasis irrigated agricultural reaches of the Kaidu River watershed in arid Northwest China, divided into the site, riparian, and catchment buffer zones. Data on four sets of explanatory variables (topographic, soil, meteorological elements, and land use types) were collected. The relationships between explanatory variables and response variables (total phosphorus, TP and total nitrogen, TN) were analyzed by redundancy analysis (RDA). Partial least squares structural equation modeling (PLS-SEM) was used to quantify the relationship between explanatory as well as response variables and fit the path relationship among factors. The results showed that there were significant differences in the TP and TN concentrations at each sampling point. The catchment buffer exhibited the best explanatory power of the relationship between explanatory and response variables based on PLS-SEM. The effects of various land use types, meteorological elements (ME), soil, and topography in the catchment buffer were responsible for 54.3% of TP changes and for 68.5% of TN changes. Land use types, ME and soil were the main factors driving TP and TN changes, accounting for 95.56% and 94.84% of the total effects, respectively. The study provides a reference for river nutrients management in arid oases with irrigated agriculture and a scientific and targeted basis to mitigate water pollution and eutrophication of rivers in arid lands.
    Matched MeSH terms: Least-Squares Analysis
  4. The influence of hazard control and prevention toward safety behaviors and safety outcomes in coal-fired power plants using PLS-SEM
    Arifin K, Ali MXM, Abas A, Ahmad MA, Ahamad MA, Sahimi AS
    J Safety Res, 2023 Sep;86:376-389.
    PMID: 37718065 DOI: 10.1016/j.jsr.2023.07.017
    INTRODUCTION: The electrical utility industry, which plays a vital role in sustaining other sectors, contributes to high occupational accident rates in the utility industries. The high accident rate shows that there has been insufficient effort made to control unsafe actions and conditions in the workplace. This study aims to examine the influence of hazard control and prevention as leading indicators of safety behaviors and outcomes in coal-fired power plants in Malaysia.

    METHODS: This quantitative research was conducted by distributing survey questionnaires randomly to five coal-fired power plants in Peninsular Malaysia. A total of 340 respondents were involved in this research. Partial least squares structural equation modeling (PLS-SEM) analysis was performed using SmartPLS to validate and examine the relationship of the proposed model.

    RESULTS: The results validate the construct of hazard control and prevention consisting of planning, action, managing, and verifying, while the safety outcomes construct consists of occupational accidents, fatal accidents, near misses, and lost time injuries. The results indicate that hazard control and prevention significantly relate to safety compliance, safety participation, safety motivation, and safety knowledge. Moreover, safety outcomes were influenced negatively by hazard control and prevention through safety compliance.

    CONCLUSION: The model provides a better understanding of the influence of hazard control and prevention on safety behavior and outcomes.

    PRACTICAL APPLICATIONS: The model can be used as guidance for practitioners and researchers in planning and implementing hazard control and prevention to improve health and safety in the workplace.

    Matched MeSH terms: Least-Squares Analysis
  5. Discrimination and authentication of lard blending with palm oil in cosmetic soap formulations
    Aziz AA, Abdullah Sani MS, Zakaria Z, Abu Bakar NK
    Int J Cosmet Sci, 2023 Aug;45(4):444-457.
    PMID: 36987749 DOI: 10.1111/ics.12854
    BACKGROUND: The employment of Fourier transforms infrared (FT-IR) spectroscopy combined with chemometrics for determination and quantification of lard in a binary blend with palm oil in a cosmetic soap formulations.

    OBJECTIVE: To determine and quantify lard as an adulterant in a binary blend with palm oil in a cosmetic soap formulations by FT-IR and multivariate analysis.

    METHODS: Fatty acids in lard, palm oil and binary blends were extracted via liquid-liquid extraction and were subjected to FTIR spectrometry, combined with principal component analysis (PCA) and discriminant analysis (DA) for the classification of lard in cosmetic soap formulations via two DA models: Model A (percentage of lard in cosmetic soap) and Model B (porcine and non-porcine cosmetic soap). Linear regression (MLR), partial least square regression (PLS-R) and principal components regression (PCR) were used to assess the degree of adulteration of lard in the cosmetic soap.

    FINDINGS: The FTIR spectrum of palm oil slightly differed from that of lard at the wavenumber range of 1453 cm -1 and 1415 cm -1 in palm oil and lard, respectively, indicating the bending vibrations of CH2 and CH3 aliphatic groups and OH carboxyl group respectively. Both of the DA models could accurately classify 100% of cosmetic soap formulations. Nevertheless, less than 100% of verification value was obtained when it was further used to predict the unknown cosmetic soap sample suspected of containing lard or a different percentage of lard. The PCA for Model A and Model B explained a similar cumulative variability (CV) of 92.86% for the whole dataset. MLR and PCR showed the highest determination coefficient (R2) of 0.996, and the lowest relative standard error (RSE) and mean square error (MSE), indicating that both regression models were effective in quantifying the lard adulterant in cosmetic soap.

    CONCLUSION: FTIR spectroscopy coupled with chemometrics with DA, PCA and MLR or PCR can be used to analyse the presence of lard and quantify its percentage in cosmetic soap formulations.

    Matched MeSH terms: Least-Squares Analysis
  6. Green human resource management, competitive advantages, and green ambidexterity: using partial least squares structural equation modeling and necessary condition analysis
    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
  7. Fulltext Electrospun nanofiber membrane diameter prediction using a combined response surface methodology and machine learning approach
    Pervez MN, Yeo WS, Mishu MMR, Talukder ME, Roy H, Islam MS, et al.
    Sci Rep, 2023 Jun 15;13(1):9679.
    PMID: 37322139 DOI: 10.1038/s41598-023-36431-7
    Despite the widespread interest in electrospinning technology, very few simulation studies have been conducted. Thus, the current research produced a system for providing a sustainable and effective electrospinning process by combining the design of experiments with machine learning prediction models. Specifically, in order to estimate the diameter of the electrospun nanofiber membrane, we developed a locally weighted kernel partial least squares regression (LW-KPLSR) model based on a response surface methodology (RSM). The accuracy of the model's predictions was evaluated based on its root mean square error (RMSE), its mean absolute error (MAE), and its coefficient of determination (R2). In addition to principal component regression (PCR), locally weighted partial least squares regression (LW-PLSR), partial least square regression (PLSR), and least square support vector regression model (LSSVR), some of the other types of regression models used to verify and compare the results were fuzzy modelling and least square support vector regression model (LSSVR). According to the results of our research, the LW-KPLSR model performed far better than other competing models when attempting to forecast the membrane's diameter. This is made clear by the much lower RMSE and MAE values of the LW-KPLSR model. In addition, it offered the highest R2 values that could be achieved, reaching 0.9989.
    Matched MeSH terms: Least-Squares Analysis
  8. Environmental pollution and sustainability of IPO performance: evidence from the Pakistani Market
    Mehmood W, Alsmady AA, Amin S, Mohd-Rashid R, Aman-Ullah A
    Environ Sci Pollut Res Int, 2023 Mar;30(11):30073-30086.
    PMID: 36427127 DOI: 10.1007/s11356-022-23985-8
    This study examined the effect of air pollution on the initial return of IPOs in Pakistan. Cross-sectional data were used to examine 102 listed IPOs on Pakistan Stock Exchange between 1996 and 2019. Ordinary least squares and quantile least squares were employed to examine the influence of air pollution on IPO initial returns. Lastly, stepwise regression was utilised for additional analysis. According to the findings, in the presence of high air pollution, IPO initial returns also increase due to higher uncertainty. The findings demonstrate that air pollution intensifies a company's information environment and financial uncertainty. Therefore, addressing environmental challenges is critical for both public health and capital formation. This study's findings will increase companies' awareness of the economic effect of air pollution, particularly in a country where air pollution is strictly regulated. This study provides businesses with an economic reason to reduce their pollution levels, and it can also help regulators pass environmental laws that are aimed at addressing this issue.
    Matched MeSH terms: Least-Squares Analysis
  9. The rural consumer adoption of sustainable energy: a PLS-SEM-ANN approach of conceptual model development and cross-country validation of Pakistan and Malaysia
    Yin YC, Ahmed J, Nee AYH, Hoe OK
    Environ Sci Pollut Res Int, 2023 Jan;30(3):5881-5902.
    PMID: 35982392 DOI: 10.1007/s11356-022-22271-x
    Sustainable and alternative energy sources of biofuel and solar power panel have been revolutionizing the lives and economy of many countries. However, these changes mainly occur in the urban areas and the rural population section has long been ignored by policy makers and government in the provision of energy. It is only recently that solar and biofuel are finally making in road to provide cheap and clean energy sources to rural population. As a result, literatures on consumer behavior of rural population towards sustainable energy sources are still very scarce. The present research aims to fulfill this gap by developing a conceptual model to investigate the adoption of solar power and biofuel energy resources in the cross-cultural setting of Malaysia and Pakistan. The data was collected from the rural areas of Pakistan and Malaysia. The two-stage data analysis method of partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) have been applied to satisfy both linear and non-linear regression assumptions, respectively. The results show that consumer in rural areas of Pakistan are willing and possess intention to adopt both biofuel and solar power for commercial and domestic use. Additionally, the results confirm that branding, economic, and altruistic factors are important in yielding intention to use towards biofuel and solar power panel in Pakistan which are validated by the results obtained in Malaysia. Other factors such as climate change awareness, retailer services quality, and ease of use are also important. The results offer wide-ranging theoretical and managerial implications.
    Matched MeSH terms: Least-Squares Analysis
  10. Fulltext Prediction of the Nitrogen Content of Rice Leaf Using Multi-Spectral Images Based on Hybrid Radial Basis Function Neural Network and Partial Least-Squares Regression
    Wu Y, Al-Jumaili SJ, Al-Jumeily D, Bian H
    Sensors (Basel), 2022 Nov 09;22(22).
    PMID: 36433222 DOI: 10.3390/s22228626
    This paper's novel focus is predicting the leaf nitrogen content of rice during growing and maturing. A multispectral image processing-based prediction model of the Radial Basis Function Neural Network (RBFNN) model was proposed. Moreover, this paper depicted three primary points as the following: First, collect images of rice leaves (RL) from a controlled condition experimental laboratory and new shoot leaves in different stages in the visible light spectrum, and apply digital image processing technology to extract the color characteristics of RL and the morphological characteristics of the new shoot leaves. Secondly, the RBFNN model, the General Regression Model (GRL), and the General Regression Method (GRM) model were constructed based on the extracted image feature parameters and the nitrogen content of rice leaves. Third, the RBFNN is optimized by and Partial Least-Squares Regression (RBFNN-PLSR) model. Finally, the validation results show that the nitrogen content prediction models at growing and mature stages that the mean absolute error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) of the RFBNN model during the rice-growing stage and the mature stage are 0.6418 (%), 0.5399 (%), 0.0652 (%), and 0.3540 (%), 0.1566 (%), 0.0214 (%) respectively, the predicted value of the model fits well with the actual value. Finally, the model may be used to give the best foundation for achieving exact fertilization control by continuously monitoring the nitrogen nutrition status of rice. In addition, at the growing stage, the RBFNN model shows better results compared to both GRL and GRM, in which MAE is reduced by 0.2233% and 0.2785%, respectively.
    Matched MeSH terms: Least-Squares Analysis
  11. An eco-environmental efficiency analysis of Malaysia sewage treatment plants: an incorporated window-based data envelopment analysis and ordinary least square regression
    Fallahiarezoudar E, Ahmadipourroudposht M, Yakideh K, Ngadiman NA
    Environ Sci Pollut Res Int, 2022 May;29(25):38285-38302.
    PMID: 35075563 DOI: 10.1007/s11356-022-18742-w
    Most human activities that use water produced sewage. As urbanization grows, the overall demand for water grows. Correspondingly, the amount of produced sewage and pollution-induced water shortage is continuously increasing worldwide. Ensuring there are sufficient and safe water supplies for everyone is becoming increasingly challenging. Sewage treatment is an essential prerequisite for water reclamation and reuse. Sewage treatment plants' (STPs) performance in terms of economic and environmental perspective is known as a critical indicator for this purpose. Here, the window-based data envelopment analysis model was applied to dynamically assess the relative annual efficiency of STPs under different window widths. A total of five STPs across Malaysia were analyzed during 2015-2019. The labor cost, utility cost, operation cost, chemical consumption cost, and removal rate of pollution, as well as greenhouse gases' (GHGs) emissions, all were integrated to interpret the eco-environmental efficiency. Moreover, the ordinary least square as a supplementary method was used to regress the efficiency drivers. The results indicated the particular window width significantly affects the average of overall efficiencies; however, it shows no influence on the ranking of STP efficiency. The labor cost was determined as the most influential parameter, involving almost 40% of the total cost incurred. Hence, higher efficiency was observed with the larger-scale plants. Meanwhile, the statistical regression analysis illustrates the significance of plant scale, inflow cBOD concentrations, and inflow total phosphorus concentrations at [Formula: see text] on the performance. Lastly, some applicable techniques were suggested in terms of GHG emission mitigation.
    Matched MeSH terms: Least-Squares Analysis
  12. Identification of Stingless Bee Honey Adulteration Using Visible-Near Infrared Spectroscopy Combined with Aquaphotomics
    Raypah ME, Omar AF, Muncan J, Zulkurnain M, Abdul Najib AR
    Molecules, 2022 Apr 03;27(7).
    PMID: 35408723 DOI: 10.3390/molecules27072324
    Honey is a natural product that is considered globally one of the most widely important foods. Various studies on authenticity detection of honey have been fulfilled using visible and near-infrared (Vis-NIR) spectroscopy techniques. However, there are limited studies on stingless bee honey (SBH) despite the increase of market demand for this food product. The objective of this work was to present the potential of Vis-NIR absorbance spectroscopy for profiling, classifying, and quantifying the adulterated SBH. The SBH sample was mixed with various percentages (10−90%) of adulterants, including distilled water, apple cider vinegar, and high fructose syrup. The results showed that the region at 400−1100 nm that is related to the color and water properties of the samples was effective to discriminate and quantify the adulterated SBH. By applying the principal component analysis (PCA) on adulterants and honey samples, the PCA score plot revealed the classification of the adulterants and adulterated SBHs. A partial least squares regression (PLSR) model was developed to quantify the contamination level in the SBH samples. The general PLSR model with the highest coefficient of determination and lowest root means square error of cross-validation (RCV2=0.96 and RMSECV=5.88 %) was acquired. The aquaphotomics analysis of adulteration in SBH with the three adulterants utilizing the short-wavelength NIR region (800−1100 nm) was presented. The structural changes of SBH due to adulteration were described in terms of the changes in the water molecular matrix, and the aquagrams were used to visualize the results. It was revealed that the integration of NIR spectroscopy with aquaphotomics could be used to detect the water molecular structures in the adulterated SBH.
    Matched MeSH terms: Least-Squares Analysis
  13. The assessment of emerging data-intelligence technologies for modeling Mg+2 and SO4-2 surface water quality
    Jamei M, Ahmadianfar I, Karbasi M, Jawad AH, Farooque AA, Yaseen ZM
    J Environ Manage, 2021 Dec 15;300:113774.
    PMID: 34560461 DOI: 10.1016/j.jenvman.2021.113774
    The concentration of soluble salts in surface water and rivers such as sodium, sulfate, chloride, magnesium ions, etc., plays an important role in the water salinity. Therefore, accurate determination of the distribution pattern of these ions can improve better management of drinking water resources and human health. The main goal of this research is to establish two novel wavelet-complementary intelligence paradigms so-called wavelet least square support vector machine coupled with improved simulated annealing (W-LSSVM-ISA) and the wavelet extended Kalman filter integrated with artificial neural network (W-EKF- ANN) for accurate forecasting of the monthly), magnesium (Mg+2), and sulfate (SO4-2) indices at Maroon River, in Southwest of Iran. The monthly River flow (Q), electrical conductivity (EC), Mg+2, and SO4-2 data recorded at Tange-Takab station for the period 1980-2016. Some preprocessing procedures consisting of specifying the number of lag times and decomposition of the existing original signals into multi-resolution sub-series using three mother wavelets were performed to develop predictive models. In addition, the best subset regression analysis was designed to separately assess the best selective combinations for Mg+2 and SO4-2. The statistical metrics and authoritative validation approaches showed that both complementary paradigms yielded promising accuracy compared with standalone artificial intelligence (AI) models. Furthermore, the results demonstrated that W-LSSVM-ISA-C1 (correlation coefficient (R) = 0.9521, root mean square error (RMSE) = 0.2637 mg/l, and Kling-Gupta efficiency (KGE) = 0.9361) and W-LSSVM-ISA-C4 (R = 0.9673, RMSE = 0.5534 mg/l and KGE = 0.9437), using Dmey mother that outperformed the W-EKF-ANN for predicting Mg+2 and SO4-2, respectively.
    Matched MeSH terms: Least-Squares Analysis
  14. Fulltext The Changes in Endogenous Metabolites in Hyperlipidemic Rats Treated with Herbal Mixture Containing Lemon, Apple Cider, Garlic, Ginger, and Honey
    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
  15. The effects of high-fat diet and metformin on urinary metabolites in diabetes and prediabetes rat models
    Lee YF, Sim XY, Teh YH, Ismail MN, Greimel P, Murugaiyah V, et al.
    Biotechnol Appl Biochem, 2021 Oct;68(5):1014-1026.
    PMID: 32931602 DOI: 10.1002/bab.2021
    High-fat diet (HFD) interferes with the dietary plan of patients with type 2 diabetes mellitus (T2DM). However, many diabetes patients consume food with higher fat content for a better taste bud experience. In this study, we examined the effect of HFD on rats at the early onset of diabetes and prediabetes by supplementing their feed with palm olein oil to provide a fat content representing 39% of total calorie intake. Urinary profile generated from liquid chromatography-mass spectrometry analysis was used to construct the orthogonal partial least squares discriminant analysis (OPLS-DA) score plots. The data provide insights into the physiological state of an organism. Healthy rats fed with normal chow (NC) and HFD cannot be distinguished by their urinary metabolite profiles, whereas diabetic and prediabetic rats showed a clear separation in OPLS-DA profile between the two diets, indicating a change in their physiological state. Metformin treatment altered the metabolomics profiles of diabetic rats and lowered their blood sugar levels. For prediabetic rats, metformin treatment on both NC- and HFD-fed rats not only reduced their blood sugar levels to normal but also altered the urinary metabolite profile to be more like healthy rats. The use of metformin is therefore beneficial at the prediabetes stage.
    Matched MeSH terms: Least-Squares Analysis
  16. Fulltext 1H-NMR-Based Metabolomics: An Integrated Approach for the Detection of the Adulteration in Chicken, Chevon, Beef and Donkey Meat
    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
  17. Prediction of tea theanine content using near-infrared spectroscopy and flower pollination algorithm
    Ong P, Chen S, Tsai CY, Chuang YK
    Spectrochim Acta A Mol Biomol Spectrosc, 2021 Jul 05;255:119657.
    PMID: 33744842 DOI: 10.1016/j.saa.2021.119657
    In this study, near-infrared (NIR) spectroscopy was exploited for non-destructive determination of theanine content of oolong tea. The NIR spectral data (400-2500 nm) were correlated with the theanine level of 161 tea samples using partial least squares regression (PLSR) with different wavelengths selection methods, including the regression coefficient-based selection, uninformative variable elimination, variable importance in projection, selectivity ratio and flower pollination algorithm (FPA). The potential of using the FPA to select the discriminative wavelengths for PLSR was examined for the first time. The analysis showed that the PLSR with FPA method achieved better predictive results than the PLSR with full spectrum (PLSR-full). The developed simplified model using on FPA based on 12 latent variables and 89 selected wavelengths produced R-squared (R2) value and root mean squared error (RMSE) of 0.9542, 0.8794 and 0.2045, 0.3219 for calibration and prediction, respectively. For PLSR-full, the R2 values of 0.9068, 0.8412 and RMSEs of 0.2916, 0.3693, were achieved for calibration and prediction. Also, the optimized model using FPA outperformed other wavelengths selection methods considered in this study. The obtained results indicated the feasibility of FPA to improve the predictability of the PLSR and reduce the model complexity. The nonlinear regression models of support vector machine regression and Gaussian process regression (GPR) were further utilized to evaluate the superiority of using the FPA in the wavelength selection. The results demonstrated that utilizing the wavelength selection method of FPA and nonlinear regression model of GPR could improve the predictive performance.
    Matched MeSH terms: Least-Squares Analysis
  18. Sparse PLS-Based Method for Overlapping Metabolite Set Enrichment Analysis
    Deng L, Ma L, Cheng KK, Xu X, Raftery D, Dong J
    J Proteome Res, 2021 06 04;20(6):3204-3213.
    PMID: 34002606 DOI: 10.1021/acs.jproteome.1c00064
    Metabolite set enrichment analysis (MSEA) has gained increasing research interest for identification of perturbed metabolic pathways in metabolomics. The method incorporates predefined metabolic pathways information in the analysis where metabolite sets are typically assumed to be mutually exclusive to each other. However, metabolic pathways are known to contain common metabolites and intermediates. This situation, along with limitations in metabolite detection or coverage leads to overlapping, incomplete metabolite sets in pathway analysis. For overlapping metabolite sets, MSEA tends to result in high false positives due to improper weights allocated to the overlapping metabolites. Here, we proposed an extended partial least squares (PLS) model with a new sparse scheme for overlapping metabolite set enrichment analysis, named overlapping group PLS (ogPLS) analysis. The weight vector of the ogPLS model was decomposed into pathway-specific subvectors, and then a group lasso penalty was imposed on these subvectors to achieve a proper weight allocation for the overlapping metabolites. Two strategies were adopted in the proposed ogPLS model to identify the perturbed metabolic pathways. The first strategy involves debiasing regularization, which was used to reduce inequalities amongst the predefined metabolic pathways. The second strategy is stable selection, which was used to rank pathways while avoiding the nuisance problems of model parameter optimization. Both simulated and real-world metabolomic datasets were used to evaluate the proposed method and compare with two other MSEA methods including Global-test and the multiblock PLS (MB-PLS)-based pathway importance in projection (PIP) methods. Using a simulated dataset with known perturbed pathways, the average true discovery rate for the ogPLS method was found to be higher than the Global-test and the MB-PLS-based PIP methods. Analysis with a real-world metabolomics dataset also indicated that the developed method was less prone to select pathways with highly overlapped detected metabolite sets. Compared with the two other methods, the proposed method features higher accuracy, lower false-positive rate, and is more robust when applied to overlapping metabolite set analysis. The developed ogPLS method may serve as an alternative MSEA method to facilitate biological interpretation of metabolomics data for overlapping metabolite sets.
    Matched MeSH terms: Least-Squares Analysis
  19. Discrimination of Malaysian stingless bee honey from different entomological origins based on physicochemical properties and volatile compound profiles using chemometrics and machine learning
    Sharin SN, Sani MSA, Jaafar MA, Yuswan MH, Kassim NK, Manaf YN, et al.
    Food Chem, 2021 Jun 01;346:128654.
    PMID: 33461823 DOI: 10.1016/j.foodchem.2020.128654
    Identification of honey origin based on specific chemical markers is important for honey authentication. This study is aimed to differentiate Malaysian stingless bee honey from different entomological origins (Heterotrigona bakeri, Geniotrigona thoracica and Tetrigona binghami) based on physicochemical properties (pH, moisture content, ash, total soluble solid and electrical conductivity) and volatile compound profiles. The discrimination pattern of 75 honey samples was observed using Principal Component Analysis (PCA), Hierarchical Clustering Analysis (HCA), Partial Least Square-Discriminant Analysis (PLS-DA), and Support Vector Machine (SVM). The profiles of H. bakeri and G. thoracica honey were close to each other, but clearly separated from T. binghami honey, consistent with their phylogenetic relationship. T. binghami honey is marked by significantly higher electrical conductivity, moisture and ash content, and high abundance of 2,6,6-trimethyl-1-cyclohexene-1-carboxaldehyde, 2,6,6-trimethyl-1-cyclohexene-1-acetaldehyde and ethyl 2-(5-methyl-5-vinyltetrahydrofuran-2-yl)propan-2-yl carbonate. Copaene was proposed as chemical marker for G. thoracica honey. The potential of different parameters that aid in honey authentication was highlighted.
    Matched MeSH terms: Least-Squares Analysis
  20. Fulltext Measuring the effectiveness of integrated vector management with targeted outdoor residual spraying and autodissemination devices on the incidence of dengue in urban Malaysia in the iDEM trial (intervention for Dengue Epidemiology in Malaysia): study protocol for a cluster randomized controlled trial
    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
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