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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
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
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
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
Fulltext Robust Wavelength Selection Using Filter-Wrapper Method and Input Scaling on Near Infrared Spectral Data

Silalahi DD, Midi H, Arasan J, Mustafa MS, Caliman JP

Sensors (Basel), 2020 Sep 03;20(17).
PMID: 32899292 DOI: 10.3390/s20175001

The extraction of relevant wavelengths from a large dataset of Near Infrared Spectroscopy (NIRS) is a significant challenge in vibrational spectroscopy research. Nonetheless, this process allows the improvement in the chemical interpretability by emphasizing the chemical entities related to the chemical parameters of samples. With the complexity in the dataset, it may be possible that irrelevant wavelengths are still included in the multivariate calibration. This yields the computational process to become unnecessary complex and decreases the accuracy and robustness of the model. In multivariate analysis, Partial Least Square Regression (PLSR) is a method commonly used to build a predictive model from NIR spectral data. However, in the PLSR method and common commercial chemometrics software, there is no standard wavelength selection procedure applied to screen the irrelevant wavelengths. In this study, a new robust wavelength selection procedure called the modified VIP-MCUVE (mod-VIP-MCUVE) using Filter-Wrapper method and input scaling strategy is introduced. The proposed method combines the modified Variable Importance in Projection (VIP) and modified Monte Carlo Uninformative Variable Elimination (MCUVE) to calculate the scale matrix of the input variable. The modified VIP uses the orthogonal components of Partial Least Square (PLS) in investigating the informative variable in the model by applying the amount of variation both in X and y{SSX,SSY}, simultaneously. The modified MCUVE uses a robust reliability coefficient and a robust tolerance interval in the selection procedure. To evaluate the superiority of the proposed method, the classical VIP, MCUVE, and autoscaling procedure in classical PLSR were also included in the evaluation. Using artificial data with Monte Carlo simulation and NIR spectral data of oil palm (Elaeis guineensis Jacq.) fruit mesocarp, the study shows that the proposed method offers advantages to improve model interpretability, to be computationally extensive, and to produce better model accuracy.

Matched MeSH terms: Least-Squares Analysis
Fulltext Does military spending stifle economic growth? The empirical evidence from non-OECD countries

Azam M

Heliyon, 2020 Dec;6(12):e05853.
PMID: 33426342 DOI: 10.1016/j.heliyon.2020.e05853

Undeniably, peace and long-term sustainable economic development are the prime agenda of all countries. This study aims to empirically evaluate the impact of military spending on economic growth for a panel of 35 non-OECD countries over 1988-2019. A multivariate regression model based on the augmented production function is used to achieve the objective of the study. The panel autoregressive distributed lag (ARDL)/pooled mean group (PMG) technique is employed, while, in addition the robust least squares and fixed-effect estimators are implemented for the robustness of the results. This study found a clear negative effect of military spending on economic growth. The pairwise Dumitrescu Hurlin panel causality test results exhibit bi-directional causality between military expenses and economic growth. Overall, these estimates provide strong support that military expenditure is not beneficial rather detrimental to economic growth. The empirical findings of this study suggest that policymakers need to redesign the military budget to stimulate economic growth and improve social welfare.

Matched MeSH terms: Least-Squares Analysis
Fulltext The development of Adaptive Neuro-Fuzzy Inference System model to diagnosis diabetes disease data set

Mamuda M, Sathasivam S

MATEMATIKA, 2017;33(1):11-19.
MyJurnal

Medical diagnosis is the extrapolation of the future course and outcome of a disease and a sign of the likelihood of recovery from that disease. Diagnosis is important because it is used to guide the type and intensity of the medication to be administered to patients. A hybrid intelligent system that combines the fuzzy logic qualitative approach and Adaptive Neural Networks (ANNs) with the capabilities of getting a better performance is required. In this paper, a method for modeling the survival of diabetes patient by utilizing the application of the Adaptive NeuroFuzzy Inference System (ANFIS) is introduced with the aim of turning data into knowledge that can be understood by people. The ANFIS approach implements the hybrid learning algorithm that combines the gradient descent algorithm and a recursive least square error algorithm to update the antecedent and consequent parameters. The combination of fuzzy inference that will represent knowledge in an interpretable manner and the learning ability of neural network that can adjust the membership functions of the parameters and linguistic rules from data will be considered. The proposed framework can be applied to estimate the risk and survival curve between different diagnostic factors and survival time with the explanation capabilities.

Matched MeSH terms: Least-Squares Analysis
Fulltext The impact of task characteristics on the performance of nursing teams

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
The use of Fourier transform mid infrared (FT-MIR) spectroscopy for detection and quantification of adulteration in virgin coconut oil

Rohman A, Che Man YB

Food Chem, 2011 Nov 15;129(2):583-588.
PMID: 30634271 DOI: 10.1016/j.foodchem.2011.04.070

Currently, the authentication of virgin coconut oil (VCO) has become very important due to the possible adulteration of VCO with cheaper plant oils such as corn (CO) and sunflower (SFO) oils. Methods involving Fourier transform mid infrared (FT-MIR) spectroscopy combined with chemometrics techniques (partial least square (PLS) and discriminant analysis (DA)) were developed for quantification and classification of CO and SFO in VCO. MIR spectra of oil samples were recorded at frequency regions of 4000-650cm-1 on horizontal attenuated total reflectance (HATR) attachment of FTIR. DA can successfully classify VCO and that adulterated with CO and SFO using 10 principal components. Furthermore, PLS model correlates the actual and FTIR estimated values of oil adulterants (CO and SFO) with coefficient of determination (R2) of 0.999.

Matched MeSH terms: Least-Squares Analysis
Fulltext Efficient model selection of collector efficiency in solar dryer using hybrid of LASSO and robust regression

Javaid, Anam, Mohd. Tahir Ismail, Ali, Majid Khan Majahar

Pertanika Journal of Science & Technology, 2020;28(1):193-210.
MyJurnal

There are many variables involved in the real life problem so it is difficult to choose an efficient model out of all possible models relating to analytical factors. Interaction terms affecting the model also need to be addressed because of its vital role in the actual dataset. The current study focused on efficient model selection for collector efficiency of solar dryer. For this purpose, collector efficiency of solar dryer was used as a dependent variable with time, inlet temperature, collector average temperature and solar radiation as independent variables. Hybrid of the least absolute shrinkage and selection operator (LASSO) and robust regression were proposed for the identification of efficient model selection. The comparison was made with the ordinary least square (OLS) after performing a multicollinearity and coefficient test and with a ridge regression analysis. The final selected model was obtained using eight selection criteria (8SC). To forecast the efficient model, the mean absolute percentage error (MAPE) was used. As compared to other methods, the proposed method provides a more efficient model with minimum MAPE.

Matched MeSH terms: Least-Squares Analysis
Fulltext Monitoring of CO2 Absorption Solvent in Natural Gas Process Using Fourier Transform Near-Infrared Spectrometry

Yusop Nurida M, Norfadilah D, Siti Aishah MR, Zhe Phak C, Saleh SM

Int J Anal Chem, 2020;2020:9830685.
PMID: 32089691 DOI: 10.1155/2020/9830685

The analytical methods for the determination of the amine solvent properties do not provide input data for real-time process control and optimization and are labor-intensive, time-consuming, and impractical for studies of dynamic changes in a process. In this study, the potential of nondestructive determination of amine concentration, CO2 loading, and water content in CO2 absorption solvent in the gas processing unit was investigated through Fourier transform near-infrared (FT-NIR) spectroscopy that has the ability to readily carry out multicomponent analysis in association with multivariate analysis methods. The FT-NIR spectra for the solvent were captured and interpreted by using suitable spectra wavenumber regions through multivariate statistical techniques such as partial least square (PLS). The calibration model developed for amine determination had the highest coefficient of determination (R2) of 0.9955 and RMSECV of 0.75%. CO2 calibration model achieved R2 of 0.9902 with RMSECV of 0.25% whereas the water calibration model had R2 of 0.9915 with RMSECV of 1.02%. The statistical evaluation of the validation samples also confirmed that the difference between the actual value and the predicted value from the calibration model was not significantly different and acceptable. Therefore, the amine, CO2, and water models have given a satisfactory result for the concentration determination using the FT-NIR technique. The results of this study indicated that FT-NIR spectroscopy with chemometrics and multivariate technique can be used for the CO2 solvent monitoring to replace the time-consuming and labor-intensive conventional methods.

Matched MeSH terms: Least-Squares Analysis
Fulltext Identifying Significant Metabolic Pathways Using Multi-Block Partial Least-Squares Analysis

Deng L, Guo F, Cheng KK, Zhu J, Gu H, Raftery D, et al.

J Proteome Res, 2020 05 01;19(5):1965-1974.
PMID: 32174118 DOI: 10.1021/acs.jproteome.9b00793

In metabolomics, identification of metabolic pathways altered by disease, genetics, or environmental perturbations is crucial to uncover the underlying biological mechanisms. A number of pathway analysis methods are currently available, which are generally based on equal-probability, topological-centrality, or model-separability methods. In brief, prior identification of significant metabolites is needed for the first two types of methods, while each pathway is modeled separately in the model-separability-based methods. In these methods, interactions between metabolic pathways are not taken into consideration. The current study aims to develop a novel metabolic pathway identification method based on multi-block partial least squares (MB-PLS) analysis by including all pathways into a global model to facilitate biological interpretation. The detected metabolites are first assigned to pathway blocks based on their roles in metabolism as defined by the KEGG pathway database. The metabolite intensity or concentration data matrix is then reconstructed as data blocks according to the metabolite subsets. Then, a MB-PLS model is built on these data blocks. A new metric, named the pathway importance in projection (PIP), is proposed for evaluation of the significance of each metabolic pathway for group separation. A simulated dataset was generated by imposing artificial perturbation on four pre-defined pathways of the healthy control group of a colorectal cancer study. Performance of the proposed method was evaluated and compared with seven other commonly used methods using both an actual metabolomics dataset and the simulated dataset. For the real metabolomics dataset, most of the significant pathways identified by the proposed method were found to be consistent with the published literature. For the simulated dataset, the significant pathways identified by the proposed method are highly consistent with the pre-defined pathways. The experimental results demonstrate that the proposed method is effective for identification of significant metabolic pathways, which may facilitate biological interpretation of metabolomics data.

Matched MeSH terms: Least-Squares Analysis
Fulltext Does It Matter Who Exhibits More Green Purchase Behavior of Cosmetic Products in Asian Culture? A Multi-Group Analysis Approach

Quoquab F, Jaini A, Mohammad J

Int J Environ Res Public Health, 2020 07 21;17(14).
PMID: 32708199 DOI: 10.3390/ijerph17145258

This study attempts to investigate the moderating effect of gender on value-belief-norm relationships. In addition, this study aims to investigate the factors that affect green purchase behavior of cosmetics products. Particularly, this study investigates the causal relationships between values and pro-environmental beliefs, pro-environmental beliefs and personal norms and personal norms and green purchase behavior. An online survey was carried out which yielded 240 usable responses among which 79 responses were obtained from males and 161 from females. Data were analyzed using structural equation modeling, partial least square (PLS-SEM) approach and multi-group analysis (MGA) technique. Results revealed that all direct relationships were supported by the data. It was also found that gender moderates the relationships between altruistic values and pro-environmental beliefs, pro-environmental beliefs and personal norms and personal norms and green purchase behavior. Nevertheless, gender did not moderate the link between hedonic value and pro-environmental beliefs. This study contributes to the existing literature by considering gender as a moderator, which is comparatively new in the green purchase behavior literature. In addition, this study examines few new linkages: more specifically, incorporating hedonic value in value-belief link and adapting value-belief-norm (VBN) theory in measuring consumers' green purchase behavior.

Matched MeSH terms: Least-Squares Analysis
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
Fulltext FTIR spectroscopy combined with multivariate calibration for analysis of cod liver oil in binary mixture with corn oil

Rohman, A., Che Man, Y.B., Ismail, A., Puziah, H.

International Food Research Journal, 2011;18(2):757-761.
MyJurnal

FTIR spectroscopy in combination with multivariate calibrations, i.e. partial least square (PLS) and principle component regression (PCR) was developed for quantitative analysis of cod liver oil (CLO) in binary mixture with corn oil (CO). The spectra of CLO, CO and their blends with certain concentrations were scanned using horizontal attenuated total reflectance (HATR) accessory at mid infrared (MIR) region of 4,000 – 650 cm-1. The optimal spectral treatments selected for calibration models were based on its ability to provide the highest values of coefficient of determination (R2) and the lowest values of root mean error of calibration (RMSEC). PLS was slightly well suited for quantitative analysis of CLO compared to PCR. FTIR spectroscopy in combination with multivariate calibration offers rapid, no excessive chemical reagent, and easy in operational to be applied for determination of CLO in binary mixture with other oils.

Matched MeSH terms: Least-Squares Analysis
Fulltext Authentication analysis of butter from beef fat using Fourier Transform Infrared (FTIR) spectroscopy coupled with chemometrics

Nurrulhidayah, A.F., Che Man, Y.B., Shuhaimi, M., Rohman, A., Khatib, A., Amin, I.

International Food Research Journal, 2013;20(3):1383-1388.
MyJurnal

The use of Fourier transform infrared (FTIR) spectroscopy coupled with chemometric techniques to differentiate butter from beef fat (BF) was investigated. The spectral bands associated with butter, BF, and their mixtures were scanned, interpreted, and identified by relating them to those spectroscopically representative to pure butter and BF. For quantitative analysis, partial least square (PLS) regression was used to develop a calibration model at the selected fingerprint regions of 1500-1000 cm-1, with the values of coefficient of determination (R2) and root mean square error of calibration (RMSEC) are 0.999 and 0.89% (v/v), respectively. The PLS calibration model was subsequently used for the prediction of independent samples containing butter in the binary mixtures with BF. Using 6 principal components, root mean square error of prediction (RMSEP) is 2.42% (v/v). These results proved that FTIR spectroscopy in combination with multivariate calibration can be used for the detection and quantification of BF in butter formulation for authentication use.

Matched MeSH terms: Least-Squares Analysis
Fulltext Relating descriptive sensory analysis to gas chromatography/mass spectrometry of palm sugars using partial least squares regression

Wan Aida, W.M., Ho, C.W., Maskat, M.Y., Osman, H.

International Food Research Journal, 2008;15(1):35-45.
MyJurnal

Sensory attributes of four different palm sugars were related to gas chromatography/mass spectrometry (GC/MS) analysis using partial least squares regression (PLS). The sweet caramel and burnt-like sensory attributes were strongly associated with 2-furfural and 2-furan methanol volatile compounds. The sensory scores for roasty and nutty were also associated with the GC/MS ratings for roasty and nutty-like aroma by its highest scores obtained from 2-ethyl-5-methyl pyrazine, 2,5-dimethyl pyrazine and 2,3-dimethyl pyrazine volatile compounds along the PC1 dimension. PLS analysis did not show correlation for the character impact compound furaneol, 2-ethyl-3,5-dimethyl pyrazine (EDMP) and 2,3-diethyl-5-methyl pyrazine (DEMP), which are perceived to be responsible for the sweet caramel-like and roasty/nutty attributes of palm sugars, respectively. This lack of relationship could partially be explained by covariance among the sensory ratings for the samples.

Matched MeSH terms: Least-Squares Analysis
Fulltext Demand for beef in Malaysia: preference for quantity, quality or lean?

Tey, Y.S., Mad Nasir, S., Alias, R., Zainalabidin, M., Amin, M.A.

International Food Research Journal, 2008;15(3):347-353.
MyJurnal

Using the Malaysian Household Expenditure Survey 2004/2005 data, this study investigated Malaysian consumers’ preference for beef quantity, quality, and lean beef. Demand and price models that incorporated consumer socio-economic variables were estimated via two-stage least squares (2SLS). This study showed that Malaysian consumers tend to demand for more quantity rather than quality of beef products. Malaysian consumers are also more responsive to price changes rather than fat reduction in beef products. It is more profitable for beef market players to increase their production as Malaysian consumers are expected to consume increasing amounts of beef products.

Matched MeSH terms: Least-Squares Analysis
Fulltext Simultaneous quantitative analysis of two functional food oils, extra virgin olive oil and virgin coconut oil using FTIR spectroscopy and multivariate calibration

Rohman, A., Che Man, Y.B.

International Food Research Journal, 2011;18(4):1231-1235.
MyJurnal

Two functional food oils, namely extra virgin olive oil (EVOO) and virgin coconut oil (VCO) have been analyzed simultaneously using Fourier transform infrared (FTIR) spectroscopy. The performance of multivariate calibration of principle component regression (PCR) and partial least square regression (PLSR) was evaluated in order to give the best prediction model for such determination. FTIR spectra were treated with several treatments including mean centering (MC), derivatization, and standard normal variate (SNV) at the combined frequency regions of 3050 – 3000, 1660 – 1650, and 1200 – 900 cm-1. Based on its capability to give the highest values of coefficient of correlation (R) for the relationship between actual value of EVOO/VCO and FTIR predicted value together with the lowest values of root mean square error of calibration (RMSEC), PLSR with mean centered-first derivative spectra was chosen for simultaneous determination of EVOO and VCO. It can be concluded that FTIR spectroscopy combined with multivariate calibration of PLSR was successfully applied to simultaneously quantify EVOO and VCO with acceptable parameters.

Matched MeSH terms: Least-Squares Analysis
Fulltext Application of partial least squares discriminant analysis for discrimination of palm oils

Mas Ezatul Nadia Mohd Ruah, Nor Fazila Rasaruddin, Fong, Sim Siong, Mohd Zuli Jaafar

Scientific Research Journal, 2014;11(1):0-0.
MyJurnal

This paper outlines the application of chemometrics and pattern recognition tools to classify palm oil using Fourier Transform Mid Infrared spectroscopy (FT-MIR). FT-MIR spectroscopy is used as an effective analytical tool in order to categorise the oil into the category of unused palm oil and used palm oil for frying. The samples used in this study consist of 28 types of pure palm oil, and 28 types of frying palm oils. FT-MIR spectral was obtained in absorbance mode at the spectral range from 650 cm -1 to 4000 cm -1 using FT-MIR-ATR sample handling. The aim of this work is to develop fast method in discriminating the palm oils by implementing Partial Least Square Discriminant Analysis (PLS-DA), Learning Vector Quantisation (LVQ) and Support Vector Machine (SVM). Raw FT-MIR spectra were subjected to Savitzky-Golay smoothing and standardized before developing the classification models. The classification model was validated through finding the value of percentage correctly classified by test set for every model in order to show which classifier provided the best classification. In order to improve the performance of the classification model, variable selection method known as t-statistic method was applied. The significant variable in developing classification model was selected through this method. The result revealed that PLSDA classifier of the standardized data with application of t-statistic showed the best performance with highest percentage correctly classified among the classifiers.

Matched MeSH terms: Least-Squares Analysis

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