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Analisis Data Panel ke Atas Fungsi Pengeluaran Sektor Pembuatan Makanan di Malaysia

Abu Hassan Shaari Mohd Nor, Fauziah Maarof

Sains Malaysiana, 2004;33.

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
Application of an optimal class of antisymmetric wavelet filter banks for obstructive sleep apnea diagnosis using ECG signals

Sharma M, Agarwal S, Acharya UR

Comput Biol Med, 2018 09 01;100:100-113.
PMID: 29990643 DOI: 10.1016/j.compbiomed.2018.06.011

Obstructive sleep apnea (OSA) is a sleep disorder caused due to interruption of breathing resulting in insufficient oxygen to the human body and brain. If the OSA is detected and treated at an early stage the possibility of severe health impairment can be mitigated. Therefore, an accurate automated OSA detection system is indispensable. Generally, OSA based computer-aided diagnosis (CAD) system employs multi-channel, multi-signal physiological signals. However, there is a great need for single-channel bio-signal based low-power, a portable OSA-CAD system which can be used at home. In this study, we propose single-channel electrocardiogram (ECG) based OSA-CAD system using a new class of optimal biorthogonal antisymmetric wavelet filter bank (BAWFB). In this class of filter bank, all filters are of even length. The filter bank design problem is transformed into a constrained optimization problem wherein the objective is to minimize either frequency-spread for the given time-spread or time-spread for the given frequency-spread. The optimization problem is formulated as a semi-definite programming (SDP) problem. In the SDP problem, the objective function (time-spread or frequency-spread), constraints of perfect reconstruction (PR) and zero moment (ZM) are incorporated in their time domain matrix formulations. The global solution for SDP is obtained using interior point algorithm. The newly designed BAWFB is used for the classification of OSA using ECG signals taken from the physionet's Apnea-ECG database. The ECG segments of 1 min duration are decomposed into six wavelet subbands (WSBs) by employing the proposed BAWFB. Then, the fuzzy entropy (FE) and log-energy (LE) features are computed from all six WSBs. The FE and LE features are classified into normal and OSA groups using least squares support vector machine (LS-SVM) with 35-fold cross-validation strategy. The proposed OSA detection model achieved the average classification accuracy, sensitivity, specificity and F-score of 90.11%, 90.87% 88.88% and 0.92, respectively. The performance of the model is found to be better than the existing works in detecting OSA using the same database. Thus, the proposed automated OSA detection system is accurate, cost-effective and ready to be tested with a huge database.

Matched MeSH terms: Least-Squares Analysis
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
Applying the least squares method for the titrimetric determination of the concentration of VFA

Lee SK, Yeoh HK, Chua AS, Ngoh GC

Water Sci Technol, 2012;66(3):620-6.
PMID: 22744694 DOI: 10.2166/wst.2012.216

The titrimetric method is used for on-site measurement of the concentration of volatile fatty acids (VFAs) in anaerobic treatment. In current practice, specific and interpolated pH-volume data points are used to obtain the concentration of VFA by solving simultaneous equations iteratively to convergence (denoted as SEq). Here, the least squares method (LSM) is introduced as an elegant alternative. Known concentrations of VFA (acetic acid and/or propionic acid) ranging from to 200 to 1,000 mg/L were determined using SEq and LSM. Using standard numbers of data points, SEq gave more accurate results compared with LSM. However, results favoured LSM when all data points in the range were included without any interpolation. For model refinement, unit monovalent activity coefficient (f(m) = 1) was found reasonable and arithmetic averages of dissociation constants and molecular weight of 80 mol% acetic acid were recommended in the model for VFA determination of mixtures. An accurate result was obtained with a mixture containing more VFA (butyric acid and valeric acid). In a typical VFA measurement of real anaerobic effluent, a satisfactory result with an error of 14% was achieved. LSM appears to be a promising mathematical model solver for determination of concentration of VFA in the titrimetric method. Validation of LSM in the presence of other electrolytes deserves further exploration.

Matched MeSH terms: Least-Squares Analysis*
Approximate bayesian estimates of weibull parameters with Lindley’s method

Chris Bambey Guure, Noor Akma Ibrahim

Sains Malaysiana, 2014;43:1433-1437.

One of the most important lifetime distributions that is used for modelling and analysing data in clinical, life sciences and engineering is the Weibull distribution. The main objective of this paper was to determine the best estimator for the two-parameter Weibull distribution. The methods under consideration are the frequentist maximum likelihood estimator, least square regression estimator and the Bayesian estimator by using two loss functions, which are squared error and linear exponential. Lindley approximation is used to obtain the Bayes estimates. Comparisons are made through simulation study to determine the performance of these methods. Based on the results obtained from this simulation study the Bayesian approach used in estimating the Weibull parameters under linear exponential loss function is found to be superior as compared to the conventional maximum likelihood and least squared methods.

Matched MeSH terms: Least-Squares Analysis
Assessing the economic value of urban green spaces in Kuala Lumpur

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
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 Authentication of Nigella sativa seed oil in binary and ternary mixtures with corn oil and soybean oil using FTIR spectroscopy coupled with partial least square

Rohman A, Ariani R

ScientificWorldJournal, 2013;2013:740142.
PMID: 24319381 DOI: 10.1155/2013/740142

Fourier transform infrared spectroscopy (FTIR) combined with multivariate calibration of partial least square (PLS) was developed and optimized for the analysis of Nigella seed oil (NSO) in binary and ternary mixtures with corn oil (CO) and soybean oil (SO). Based on PLS modeling performed, quantitative analysis of NSO in binary mixtures with CO carried out using the second derivative FTIR spectra at combined frequencies of 2977-3028, 1666-1739, and 740-1446 cm(-1) revealed the highest value of coefficient of determination (R (2), 0.9984) and the lowest value of root mean square error of calibration (RMSEC, 1.34% v/v). NSO in binary mixtures with SO is successfully determined at the combined frequencies of 2985-3024 and 752-1755 cm(-1) using the first derivative FTIR spectra with R (2) and RMSEC values of 0.9970 and 0.47% v/v, respectively. Meanwhile, the second derivative FTIR spectra at the combined frequencies of 2977-3028 cm(-1), 1666-1739 cm(-1), and 740-1446 cm(-1) were selected for quantitative analysis of NSO in ternary mixture with CO and SO with R (2) and RMSEC values of 0.9993 and 0.86% v/v, respectively. The results showed that FTIR spectrophotometry is an accurate technique for the quantitative analysis of NSO in binary and ternary mixtures with CO and SO.

Matched MeSH terms: Least-Squares Analysis
Fulltext Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach

Murat M, Chang SW, Abu A, Yap HJ, Yong KT

PeerJ, 2017;5:e3792.
PMID: 28924506 DOI: 10.7717/peerj.3792

Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson's coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99.89% for the Swedish Leaf dataset. In addition, the Relief feature selection method achieved the highest classification accuracy of 98.13% after 80 (or 60%) of the original features were reduced, from 133 to 53 descriptors in the myDAUN dataset with the reduction in computational time. Subsequently, the hybridisation of four descriptors gave the best results compared to others. It is proven that the combination MSD and HOG were good enough for tropical shrubs species classification. Hu and ZM descriptors also improved the accuracy in tropical shrubs species classification in terms of invariant to translation, rotation and scale. ANN outperformed the others for tropical shrub species classification in this study. Feature selection methods can be used in the classification of tropical shrub species, as the comparable results could be obtained with the reduced descriptors and reduced in computational time and cost.

Matched MeSH terms: Least-Squares Analysis
Behavioral pathways explaining oral health disparity in children

Gao XL, Hsu CY, Xu YC, Loh T, Koh D, Hwarng HB

J Dent Res, 2010 Sep;89(9):985-90.
PMID: 20554887 DOI: 10.1177/0022034510372896

Policymakers' understanding of and ability to reduce health disparities are pivotal for health promotion worldwide. This study aimed to verify the behavioral pathways leading to oral health disparities. Oral examinations were conducted for 1782 randomly selected preschoolers (3-6 yrs), and 1576 (88.4%) participants were followed up after 12 months. Parents were surveyed on their knowledge (K), attitude (A), and practices (P) regarding their children's oral health homecare (infant feeding, diet, and oral hygiene) and dental attendance. Structural equation modeling substantiated the links between specific KAs and corresponding practices, while generic KA did not affect practices. KAP pathways partly explained the ethnic and socio-economic disparities in oral health. Deprivation had a direct effect (not mediated by KA) on dental attendance, but not on oral health homecare. Ethnicity directly influenced oral health homecare practices, but not dental attendance. These behavioral pathways, furthering our understanding of health disparity, may have practical implications for health promotion and policy-making.

Matched MeSH terms: Least-Squares Analysis
Fulltext Changes in the urinary metabolites in losartan-treated spontaneously hypertensive rats

Norasikin Ab Azis, Mohd Saleh Ahmad Kamal, Zurain Radjeni, Ahmed Mediani, Renu Agarwal

Journal of Clinical and Health Sciences, 2021;6(1):32-45.
MyJurnal

Introduction: This study examined the association of losartan induced changes in urinary
metabolomic profile with the changes in blood pressure (BP) and renin-angiotensinaldosterone system (RAAS) in spontaneously hypertensive rats (SHR). Methods: Male SHR
were administered with either 0.5 mL of distilled water (control group, n=6) or 10 mg.kg-1 of
losartan (group 2, n=6) daily by oral gavage for 4 weeks. Body weight, BP, food and water
intake were measured weekly. At week 4, urine was collected for urinary electrolyte analysis
and metabolite profiling, after which the animals were euthanised by decapitation and blood
was collected for analysis of components of RAAS and electrolyte concentrations. Urine
metabolite profile of SHR was determined using proton nuclear magnetic resonance (
1H-NMR)
spectrometry combined with multivariate data analysis. Results: At week 4, losartan-treated
SHR had significantly lower BP than non-treated SHR. There were no differences in water
and food intake, body weight, serum and urinary electrolyte concentrations or in their urinary
excretions between the two groups. No differences were evident in the components of RAAS
except that the angiotensinogen level was significantly higher in losartan-treated SHR
compared to non-treated SHR. Orthogonal partial least squares discriminant analysis (OPLSDA) showed clear separation of urinary metabolites between control and losartan-treated
SHR. Losartan-treated SHR group was separated from the control group by changes in the
intermediates involved in glycine, serine and threonine metabolism. Conclusion:
Antihypertensive effect of losartan in SHR seems to be associated with changes in urinary
metabolite profile, particularly involving the metabolism of glycine, serine and threonine.

Matched MeSH terms: Least-Squares Analysis
Classification and quantification of palm oil adulteration via portable NIR spectroscopy

Basri KN, Hussain MN, Bakar J, Sharif Z, Khir MFA, Zoolfakar AS

Spectrochim Acta A Mol Biomol Spectrosc, 2017 Feb 15;173:335-342.
PMID: 27685001 DOI: 10.1016/j.saa.2016.09.028

Short wave near infrared spectroscopy (NIR) method was used to detect the presence of lard adulteration in palm oil. MicroNIR was set up in two different scan modes to study the effect of path length to the performance of spectral measurement. Pure and adulterated palm oil sample were classified using soft independent modeling class analogy (SIMCA) algorithm with model accuracy more than 0.95 reported for both transflectance and transmission modes. Additionally, by employing partial least square (PLS) regression, the coefficient of determination (R2) of transflectance and transmission were 0.9987 and 0.9994 with root mean square error of calibration (RMSEC) of 0.5931 and 0.6703 respectively. In order to remove the uninformative variables, variable selection using cumulative adaptive reweighted sampling (CARS) has been performed. The result of R2 and RMSEC after variable selection for transflectance and transmission were improved significantly. Based on the result of classification and quantification analysis, the transmission mode has yield better prediction model compared to the transflectance mode to distinguish the pure and adulterated palm oil.

Matched MeSH terms: Least-Squares Analysis
Fulltext Combination of Sensory, Chromatographic, and Chemometrics Analysis of Volatile Organic Compounds for the Discrimination of Authentic and Unauthentic Harumanis Mangoes

Zakaria SR, Saim N, Osman R, Abdul Haiyee Z, Juahir H

Molecules, 2018 Sep 16;23(9).
PMID: 30223605 DOI: 10.3390/molecules23092365

This study analyzed the volatile organic compounds (VOCs) of three mango varieties (Harumanis, Tong Dam and Susu) for the discrimination of authentic Harumanis from other mangoes. The VOCs of these mangoes were extracted and analysed nondestructively using Head Space-Solid Phase Micro Extraction (HS-SPME) coupled to Gas Chromatography-Mass Spectrometry (GC-MS). Prior to the analytical method, two simple sensory analyses were carried out to assess the ability of the consumers to differentiate between the Harumanis and Tong Dam mangoes as well as their preferences towards these mangoes. On the other hand, chemometrics techniques, such as principal components analysis (PCA), hierarchical clustering analysis (HCA), and discriminant analysis (DA), were used to visualise grouping tendencies of the volatile compounds detected. These techniques were successful in identifying the grouping tendencies of the mango samples according to the presence of their respective volatile compounds, thus enabling the identification of the groups of substances responsible for the discrimination between the authentic and unauthentic Harumanis mangoes. In addition, three ocimene compounds, namely beta-ocimene, trans beta-ocimene, and allo-ocimene, can be considered as chemical markers of the Harumanis mango, as these compounds exist in all Harumanis mango, regardless the different sources of the mangoes obtained.

Matched MeSH terms: Least-Squares Analysis
Comparison of partial least squares and random forests for evaluating relationship between phenolics and bioactivities of Neptunia oleracea

Lee SY, Mediani A, Maulidiani M, Khatib A, Ismail IS, Zawawi N, et al.

J Sci Food Agric, 2018 Jan;98(1):240-252.
PMID: 28580581 DOI: 10.1002/jsfa.8462

BACKGROUND: Neptunia oleracea is a plant consumed as a vegetable and which has been used as a folk remedy for several diseases. Herein, two regression models (partial least squares, PLS; and random forest, RF) in a metabolomics approach were compared and applied to the evaluation of the relationship between phenolics and bioactivities of N. oleracea. In addition, the effects of different extraction conditions on the phenolic constituents were assessed by pattern recognition analysis.
RESULTS: Comparison of the PLS and RF showed that RF exhibited poorer generalization and hence poorer predictive performance. Both the regression coefficient of PLS and the variable importance of RF revealed that quercetin and kaempferol derivatives, caffeic acid and vitexin-2-O-rhamnoside were significant towards the tested bioactivities. Furthermore, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) results showed that sonication and absolute ethanol are the preferable extraction method and ethanol ratio, respectively, to produce N. oleracea extracts with high phenolic levels and therefore high DPPH scavenging and α-glucosidase inhibitory activities.
CONCLUSION: Both PLS and RF are useful regression models in metabolomics studies. This work provides insight into the performance of different multivariate data analysis tools and the effects of different extraction conditions on the extraction of desired phenolics from plants. © 2017 Society of Chemical Industry.

Matched MeSH terms: Least-Squares Analysis
Comparison of signal-to-noise, blank determination, and linear regression methods for the estimation of detection and quantification limits for volatile organic compounds by gas chromatography

Sanagi MM, Ling SL, Nasir Z, Hermawan D, Ibrahim WA, Abu Naim A

J AOAC Int, 2010 2 20;92(6):1833-8.
PMID: 20166602

LOD and LOQ are two important performance characteristics in method validation. This work compares three methods based on the International Conference on Harmonization and EURACHEM guidelines, namely, signal-to-noise, blank determination, and linear regression, to estimate the LOD and LOQ for volatile organic compounds (VOCs) by experimental methodology using GC. Five VOCs, toluene, ethylbenzene, isopropylbenzene, n-propylbenzene, and styrene, were chosen for the experimental study. The results indicated that the estimated LODs and LOQs were not equivalent and could vary by a factor of 5 to 6 for the different methods. It is, therefore, essential to have a clearly described procedure for estimating the LOD and LOQ during method validation to allow interlaboratory comparisons.

Matched MeSH terms: Least-Squares Analysis
Fulltext Comparison of tobacco import and tobacco control in five countries: lessons learned for Indonesia

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
Fulltext Complex Chemical Data Classification and Discrimination Using Locality Preserving Partial Least Squares Discriminant Analysis

Aminu M, Ahmad NA

ACS Omega, 2020 Oct 20;5(41):26601-26610.
PMID: 33110988 DOI: 10.1021/acsomega.0c03362

Partial least squares discriminant analysis (PLS-DA) is a well-known technique for feature extraction and discriminant analysis in chemometrics. Despite its popularity, it has been observed that PLS-DA does not automatically lead to extraction of relevant features. Feature learning and extraction depends on how well the discriminant subspace is captured. In this paper, discriminant subspace learning of chemical data is discussed from the perspective of PLS-DA and a recent extension of PLS-DA, which is known as the locality preserving partial least squares discriminant analysis (LPPLS-DA). The objective is twofold: (a) to introduce the LPPLS-DA algorithm to the chemometrics community and (b) to demonstrate the superior discrimination capabilities of LPPLS-DA and how it can be a powerful alternative to PLS-DA. Four chemical data sets are used: three spectroscopic data sets and one that contains compositional data. Comparative performances are measured based on discrimination and classification of these data sets. To compare the classification performances, the data samples are projected onto the PLS-DA and LPPLS-DA subspaces, and classification of the projected samples into one of the different groups (classes) is done using the nearest-neighbor classifier. We also compare the two techniques in data visualization (discrimination) task. The ability of LPPLS-DA to group samples from the same class while at the same time maximizing the between-class separation is clearly shown in our results. In comparison with PLS-DA, separation of data in the projected LPPLS-DA subspace is more well defined.

Matched MeSH terms: Least-Squares Analysis
Fulltext Consolidated backpropagation neural network for Malaysian construction costs indices data with outliers problem

Saadi Ahmad Kamaruddin, Nor Azura Md Ghani, Norazan Mohamed Ramli

Pertanika Journal of Science & Technology, 2018;26(1):353-366.
MyJurnal

Neurocomputing has been adjusted effectively in time series forecasting activities, yet the vicinity of exceptions that frequently happens in time arrangement information might contaminate the system preparing information. This is because of its capacity to naturally realise any example without earlier suspicions and loss of sweeping statement. In principle, the most widely recognised calculation for preparing the system is the backpropagation (BP) calculation, which inclines toward minimisation of standard slightest squares (OLS) estimator, particularly the mean squared mistake (MSE). Regardless, this calculation is not by any stretch of the imagination strong when the exceptions are available, and it might prompt bogus expectation of future qualities. In this paper, we exhibit another calculation which controls the firefly algorithm of least median squares (FFA-LMedS) estimator for neural system nonlinear autoregressive moving average (ANN-NARMA) model enhancement to provide betterment for the peripheral issue in time arrangement information. Moreover, execution of the solidified model in correlation with another hearty ANN-NARMA models, utilising M-estimators, Iterative LMedS and Particle Swarm Optimisation on LMedS (PSO-LMedS) with root mean squared blunder (RMSE) qualities, is highlighted in this paper. In the interim, the actual monthly information of Malaysian Aggregate, Sand and Roof Materials value was taken from January 1980 to December 2012 (base year 1980=100) with various levels of anomaly issues. It was found that the robustified ANN-NARMA model utilising FFA-LMedS delivered the best results, with the RMSE values having almost no mistakes at all in all the preparation, testing and acceptance sets for every single distinctive variable. Findings of the studies are hoped to assist the regarded powers including the PFI development tasks to overcome cost overwhelms.

Matched MeSH terms: Least-Squares Analysis
Fulltext Consumer’s preferences for eggs attributes in Malaysia: Evidence from conjoint survey

Ahmad Hanis, I.A.H., Mad Nasir, S., Jinap, S., Alias, R., Ab Karim, M.S.

International Food Research Journal, 2013;20(5):2865-2872.
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
Convergence in CO 2 emissions, carbon footprint and ecological footprint: evidence from OECD countries

Solarin SA

Environ Sci Pollut Res Int, 2019 Feb;26(6):6167-6181.
PMID: 30617875 DOI: 10.1007/s11356-018-3993-8

The aim of this paper is to augment the existing literature on convergence of CO2 emissions, by adding carbon footprint per capita and ecological footprint per capita to the convergence debate. We use the residual augmented least squares regression to examine the stochastic convergence of the environmental indices in 27 OECD countries. Furthermore, in contrast to the previous studies which mainly used the conventional beta-convergence approach to examine conditional convergence, we use a beta-convergence method that is capable of identifying the actual number of countries that contribute to conditional convergence. The sigma-convergence of the environmental indices is also examined. The results suggest that conditional convergence exists in 12 countries for CO2 emissions per capita, 15 countries for carbon footprint per capita and also 13 countries for ecological footprint per capita. There is evidence for sigma-convergence for all the three indicators. The policy implications of the results are discussed in the body of the paper.

Matched MeSH terms: Least-Squares Analysis

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