Displaying publications 61 - 80 of 104 in total

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  1. Fulltext UV-visible chemometrics approach for the determination of selected adulterants in claimed premixed coffee
    Muhammad Zul Fayyadh Azizo Rahman, Chong, Hui Wen, Lim, Vuanghao
    Malaysian Journal of Medicine and Health Sciences, 2018;14(102):1-6.
    MyJurnal
    Adulterated premixed coffees have turned into an issue in Malaysia lately and have caught the eye of the authorities due to death reports linked to these products. The major cause of this issue is reported that these premixed coffees have passed food inspection test and eventually released to the market for public consumption. These coffees were claimed to be spiked with several sexual enhancers like sildenafil, tadalafil, and vardenafill, which are common drugs used to treat erectile dysfunction. Methods: Chemometrics approach using UV-Vis spectroscopy was developed to detect the selected sexual enhancer drugs found in commercial coffees by employing SIMCA-P software for the multivariate statistical analysis. Seven brands of coffee samples were purchased from local stores, and 30 sachets each were tested, hence totalling to 210 samples. Each sample was named H, J, G, W, N, T, and K, respectively. Results: Three multivariate models were generated, namely principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and partial least squares discriminant analysis (PLS-DA). OPLS-DA was selected as the best model for the overall results as it displayed minimal discriminate. Sildenafil, tadalafil, and vardenafil were detected in sample H, while vardenafil in brand J, and none in samples G, W, N, T, and K. Conclusion: OPLS-DA analysis showed discrimination for the sexual enhancer drugs in two brands of premixed coffee. The UV-Vis spectroscopy-based chemometrics method proved to be reliable and efficient in determining the selected drugs, as well as in saving time and cost.
    Matched MeSH terms: Discriminant Analysis
  2. Discriminant analysis of normal and malignant breast tissue based upon INAA investigation of elemental concentration
    Ng KH, Ong SH, Bradley DA, Looi LM
    Appl Radiat Isot, 1997 Jan;48(1):105-9.
    PMID: 9022216
    Discriminant analysis of six trace element concentrations measured by instrumental neutron activation analysis (INAA) in 26 paired-samples of malignant and histologically normal human breast tissues shows the technique to be a potentially valuable clinical tool for making malignant-normal classification. Nonparametric discriminant analysis is performed for the data obtained. Linear and quadratic discriminant analyses are also carried out for comparison. For this data set a formal analysis shows that the elements which may be useful in distinguishing between malignant and normal tissues are Ca, Rb and Br, providing correct classification for 24 out of 26 normal samples and 22 out of 26 malignant samples.
    Matched MeSH terms: Discriminant Analysis
  3. The Accuracy of Sex Estimation on Metatarsal Bones in a Northeastern Thai Population
    Phatsara M, Das S, Laowatthanaphong S, Tuamsuk P, Mahakkanukrauh P
    Clin Ter, 2016 May-Jun;167(3):72-6.
    PMID: 27424506 DOI: 10.7417/CT.2016.1929
    BACKGROUND: This study was carried out to evaluate the accuracy of sex estimation by discriminant analysis and stepwise discriminant analysis equations generated from metatarsal bones in a Thai population.
    MATERIAL AND METHODS: The testing samples utilized in this study consisted of 50 skeletons (25 males and 25 females) obtained from the Khon Kaen University Skeletal Collection, Department of Anatomy, Faculty of Medicine, Khon Kaen University. Seven measurements of metatarsal bones were measured in centimeters, using either a mini-osteometric board (MOB) or a sliding caliper. The values measured from the Khon Kaen Skeletal Collection were used to determine the accuracy and applicability of sex determination, as predicted by Y1-Y6 equations which were generated from a Chiang Mai Skeletal Collection.
    RESULTS: The percentage of sex determination accuracies predicted from the Y1-Y6 equations demonstrated accuracy rates of 80-95.6.
    CONCLUSIONS: The Chiang Mai sex determination equations, generated from metatarsal bones by discriminant analysis (Y1-Y3) and stepwise discriminant analysis (Y4-Y6), demonstrated high accuracy rates of prediction, suggesting that these equations may be useful for sex determination within the Thai population.
    KEYWORDS: Foot; Metatarsal bones; Sex determination; Thailand
    Matched MeSH terms: Discriminant Analysis
  4. In-Line Sorting of Harumanis Mango Based on External Quality Using Visible Imaging
    Ibrahim MF, Ahmad Sa'ad FS, Zakaria A, Md Shakaff AY
    Sensors (Basel), 2016 Oct 27;16(11).
    PMID: 27801799
    The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired t-test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass.
    Matched MeSH terms: Discriminant Analysis
  5. Fulltext The classification of movement intention through machine learning models: the identification of significant time-domain EMG features
    Mohd Khairuddin I, Sidek SN, P P Abdul Majeed A, Mohd Razman MA, Ahmad Puzi A, Md Yusof H
    PeerJ Comput Sci, 2021;7:e379.
    PMID: 33817026 DOI: 10.7717/peerj-cs.379
    Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classification and regression models of muscle activity. In this study, we leverage the information from the EMG signals, to detect the subject's intentions in generating motion commands for a robot-assisted upper limb rehabilitation platform. The EMG signals are recorded from ten healthy subjects' biceps muscle, and the movements of the upper limb evaluated are voluntary elbow flexion and extension along the sagittal plane. The signals are filtered through a fifth-order Butterworth filter. A number of features were extracted from the filtered signals namely waveform length (WL), mean absolute value (MAV), root mean square (RMS), standard deviation (SD), minimum (MIN) and maximum (MAX). Several different classifiers viz. Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) were investigated on its efficacy to accurately classify the pre-intention and intention classes based on the significant features identified (MIN and MAX) via Extremely Randomised Tree feature selection technique. It was observed from the present investigation that the DT classifier yielded an excellent classification with a classification accuracy of 100%, 99% and 99% on training, testing and validation dataset, respectively based on the identified features. The findings of the present investigation are non-trivial towards facilitating the rehabilitation phase of patients based on their actual capability and hence, would eventually yield a more active participation from them.
    Matched MeSH terms: Discriminant Analysis
  6. Fulltext Development of long-term talent in sports model based on anthropometric and motor fitness components using multivariate analysis
    Liyana Daud, Mohamad Razali Abdullah, Siti Musliha Mat-Rasid, Ahmad Bisyri Husin Musawi Maliki, Amr Alnaimat, Muhammad Rabani Hashim, et al.
    Malaysian Journal of Applied Sciences, 2019;4(1):38-47.
    MyJurnal
    The study attempts to use multivariate analysis to evaluate the profile of male player for developments of Long-Term Talent in Sports (LT-TiS) model based on anthropometric and motor fitness components. Data of anthropometric and motor fitness included power, flexibility, coordination and speed were obtained from 2019 respondents aged 7.32±0.52 year. Data interpretations were carried out using multivariate analysis of Principle Components Analysis (PCA) and Discriminant analysis (DA). The adequacy of sampling has been measured using Bartletts tests on sphericity and Kaiser-Meyer-Olkin (KMO) has been used, with this conformance of running the Principal Component Analysis (PCA). Then, Discriminant Analysis (DA) were used to validate the correctness of group classification by LT-TiS model. Then, Discriminant Analysis (DA) were used to validate the correctness of group classification by LT-TiS. As a result, two factors with eigenvalues greater than 1 were extracted which accounted for 55.00% of the variations present in the original variables was found. The two factors were used to obtain the factor score coefficients explained by 27.86% and 27.21% of the variations in player performance respectively. Factor 1 revealed high factor loading on motor fitness compared to factor 2 as it was significantly related to anthropometrics. A model was obtained using standardized coefficient of factor 1. Three clusters of performance were shaped in view by categorizing; LT−TiS≥65%, 40%≤LT−TiS
    Matched MeSH terms: Discriminant Analysis
  7. Fulltext Metabolic profiling of Lactococcus lactis under different culture conditions
    Azizan KA, Baharum SN, Mohd Noor N
    Molecules, 2012 Jul 03;17(7):8022-36.
    PMID: 22759915 DOI: 10.3390/molecules17078022
    Gas chromatography mass spectrometry (GC-MS) and headspace gas chromatography mass spectrometry (HS/GC-MS) were used to study metabolites produced by Lactococcus lactis subsp. cremoris MG1363 grown at a temperature of 30 °C with and without agitation at 150 rpm, and at 37 °C without agitation. It was observed that L. lactis produced more organic acids under agitation. Primary alcohols, aldehydes, ketones and polyols were identified as the corresponding trimethylsilyl (TMS) derivatives, whereas amino acids and organic acids, including fatty acids, were detected through methyl chloroformate derivatization. HS analysis indicated that branched-chain methyl aldehydes, including 2-methylbutanal, 3-methylbutanal, and 2-methylpropanal are degdradation products of isoleucine, leucine or valine. Multivariate analysis (MVA) using partial least squares discriminant analysis (PLS-DA) revealed the major differences between treatments were due to changes of amino acids and fermentation products.
    Matched MeSH terms: Discriminant Analysis
  8. Fulltext Anthropometric analysis of mandible: an important step for sex determination
    Alias A, Ibrahim A, Abu Bakar SN, Swarhib Shafie M, Das S, Abdullah N, et al.
    Clin Ter, 2018 11 6;169(5):e217-e223.
    PMID: 30393808 DOI: 10.7417/CT.2018.2082
    INTRODUCTION: The first step in the forensic identification is sex determination followed by age and stature estimation, as both are sex-dependent. The mandible is the largest, strongest and most durable bone in the face. Mandible is important for sex confirmation in absence of a complete pelvis and skull.

    AIM: The aim of the present study was to determine sex of human mandible from morphology, morphometric measurements as well as discriminant function analysis from the CT scan.

    MATERIALS AND METHODS: The present retrospective study comprised 79 subjects (48 males, 31 females), with age group between 18 and 74 years, and were obtained from the post mortem computed tomography data in the Hospital Kuala Lumpur. The parameters were divided into three morphologic and nine morphometric parameters, which were measured by using Osirix MD Software 3D Volume Rendering.

    RESULTS: The Chi-square test showed that men were significantly association with square-shaped chin (92%), prominent muscle marking (85%) and everted gonial glare, whereas women had pointed chin (84%), less prominent muscle marking (90%) and inverted gonial glare (80%). All parameter measurements showed significantly greater values in males than in females by independent t-test (p< 0.01). By discriminant analysis, the classification accuracy was 78.5%, the sensitivity was 79.2% and the specificity was 77.4%. The discriminant function equation was formulated based on bigonial breath and condylar height, which were the best predictors.

    CONCLUSION: In conclusion, the mandible could be distinguished according to the sex. The results of the study can be used for identification of damaged and/or unknown mandible in the Malaysian population.

    Matched MeSH terms: Discriminant Analysis
  9. Fulltext Technical and tactical performance indicators discriminating winning and losing team in elite Asian beach soccer tournament
    Muazu Musa R, P P Abdul Majeed A, Abdullah MR, Ab Nasir AF, Arif Hassan MH, Mohd Razman MA
    PLoS One, 2019;14(6):e0219138.
    PMID: 31247012 DOI: 10.1371/journal.pone.0219138
    The present study aims to identify the essential technical and tactical performance indicators that could differentiate winning and losing performance in the Asian elite beach soccer competition. A set of 20 technical and tactical performance indicators namely; shot back-third, shot mid-third, shot front-third, pass back-third, pass mid-third, pass front-third, shot in box, shot outbox, chances created, interception, turnover, goals scored 1st period, goals scored 2nd period, goals scored 3rd period, goals scored extra time, tackling, fouls committed, complete save, incomplete save and passing error were observed during the beach soccer Asian Football Confederation tournament 2017 held in Malaysia. A total of 23 matches from 12 teams were notated using StatWatch application in real-time. Discriminant analysis (DA) of standard, backward as well stepwise modes were used to develop a model for the winning (WT) and losing team (LT) whilst Mann-Whitney U test was utilized to ascertain the differences between the WT and LT with respect to the performance indicators evaluated. The standard backward, forward and stepwise discriminates the WT and the LT with an excellent accuracy of 95.65%, 91.30% and 89.13%, respectively. The standard DA model discriminated the teams from seven performance indicators whilst both the backward and forward stepwise identified two performance indicators. The Mann-Whitney U test analysis indicated that the WT is statistically significant from the LT based on the performance indicators determined from the standard mode model of the DA. It was demonstrated that seven performance indicators namely; shot front-third, pass front-third, chances created, goals scores at the 1st period, goals scored at the 2nd period, goals scored at 3rd period were directly linked to a successful performance whilst the incomplete save by the keeper attribute towards the poor performance of the team. The present finding could serve useful to the coaches as well as performance analysts as a measure of profiling successful performance and enables team improvement with respect to the associated performance indicators.
    Matched MeSH terms: Discriminant Analysis
  10. 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: Discriminant Analysis
  11. Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques
    Juahir H, Zain SM, Yusoff MK, Hanidza TI, Armi AS, Toriman ME, et al.
    Environ Monit Assess, 2011 Feb;173(1-4):625-41.
    PMID: 20339961 DOI: 10.1007/s10661-010-1411-x
    This study investigates the spatial water quality pattern of seven stations located along the main Langat River. Environmetric methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Twenty-three water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on HACA. These clusters are designated as downstream of Langat river, middle stream of Langat river, and upstream of Langat River regions. Forward and backward stepwise DA managed to discriminate six and seven water quality variables, respectively, from the original 23 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the three clustered regions. Seven principal components (PCs) were obtained with 81% total variation for the high-pollution source (HPS) region, while six PCs with 71% and 79% total variances were obtained for the moderate-pollution source (MPS) and low-pollution source (LPS) regions, respectively. The pollution sources for the HPS and MPS are of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the main sources of pollution. From this study, we can conclude that the application of environmetric methods can reveal meaningful information on the spatial variability of a large and complex river water quality data.
    Matched MeSH terms: Discriminant Analysis
  12. Spatial assessment of Langat River water quality using chemometrics
    Juahir H, Zain SM, Aris AZ, Yusoff MK, Mokhtar MB
    J Environ Monit, 2010 Jan;12(1):287-95.
    PMID: 20082024 DOI: 10.1039/b907306j
    The present study deals with the assessment of Langat River water quality with some chemometrics approaches such as cluster and discriminant analysis coupled with an artificial neural network (ANN). The data used in this study were collected from seven monitoring stations under the river water quality monitoring program by the Department of Environment (DOE) from 1995 to 2002. Twenty three physico-chemical parameters were involved in this analysis. Cluster analysis successfully clustered the Langat River into three major clusters, namely high, moderate and less pollution regions. Discriminant analysis identified seven of the most significant parameters which contribute to the high variation of Langat River water quality, namely dissolved oxygen, biological oxygen demand, pH, ammoniacal nitrogen, chlorine, E. coli, and coliform. Discriminant analysis also plays an important role as an input selection parameter for an ANN of spatial prediction (pollution regions). The ANN showed better prediction performance in discriminating the regional area with an excellent percentage of correct classification compared to discriminant analysis. Multivariate analysis, coupled with ANN, is proposed, which could help in decision making and problem solving in the local environment.
    Matched MeSH terms: Discriminant Analysis
  13. Fulltext Discriminant value of dyspeptic symptoms in peptic ulcer and non-ulcer dyspepsia
    Kudva MV, Zawawi M, Rafee N, Ismail O, Muda JR
    Med J Malaysia, 1989 Sep;44(3):236-42.
    PMID: 2626138
    The objective of the study was to determine whether discriminant analysis of characteristics of dyspepsia can differentiate peptic ulcer from non-ulcer dyspepsia in a Malaysian population. Two hundred and twenty six patients with dyspepsia were interviewed using a standard history questionnaire before undergoing upper gastrointestinal endoscopy. Forty seven patients had peptic ulcer while 149 others were classified as having non-ulcer dyspepsia. Stepwise logistic regression analysis was done on 25 variables. The study showed that only five of these variables could differentiate peptic ulcer from non-ulcer dyspepsia, namely, nocturnal pain, pain before meals or when hungry, absence of nausea, age and sex. A scoring system was devised based on these discriminant symptoms. At a sensitivity of 51%, the specificity for peptic ulcer was 83%, but only prospective studies will determine if this scoring system is of actual clinical value.
    Matched MeSH terms: Discriminant Analysis
  14. Fulltext Forensic analysis of accelerant on different fabrics using Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) and chemometrics techniques
    Farah Izza Jais, Sharifah Mastura, Naji Arafat Mahat, Dzulkiflee Ismail, Muhammad Naeim Mohamad Asri
    Malaysian Journal of Medicine and Health Sciences, 2020;16(2):112-117.
    MyJurnal
    Introduction: Accelerants and fabrics are commonly used to spread fire attributable to their highly flammable prop- erties. Hence, having the ability to discriminate the different types of accelerants on various types of fabrics after fire and/or arson using the non-destructive Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spec- troscopy coupled with chemometric techniques appears forensically relevant. Methods: Six types of fabrics viz. cotton, wool, silk, rayon, satin, and polyester, were burnt completely with RON95 and RON97 gasoline as well as diesel. Subsequently, the samples were analyzed by ATR-FTIR spectroscopy followed by Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for discriminating the different types of accelerants on such burned fabrics. Results: RON95 showed the fastest rate of burning on all fabric types. Results also revealed that while wool had the slowest burning rate for all the three different accelerants, polyester, cotton, and satin demon- strated the highest rate of burning in RON95, RON97, and diesel, respectively. FTIR spectra revealed the presence of alkane, alcohol, alkene, alkyne, aromatic, and amine compounds for all fabrics. The two dimensional PCA (PC1 versus PC2) demonstrated 71% of variance and it was improved by cross-validation through the three dimensional LDA technique with correct classification of 77.8%. Conclusion: ATR-FTIR spectroscopy coupled with chemometric techniques had enabled identification of the functional groups, as well as statistically supported discrimination of the different accelerants, a matter of relevance in forensic fire and arson investigations.
    Matched MeSH terms: Discriminant Analysis
  15. Automated characterization of fatty liver disease and cirrhosis using curvelet transform and entropy features extracted from ultrasound images
    Acharya UR, Raghavendra U, Fujita H, Hagiwara Y, Koh JE, Jen Hong T, et al.
    Comput Biol Med, 2016 12 01;79:250-258.
    PMID: 27825038 DOI: 10.1016/j.compbiomed.2016.10.022
    Fatty liver disease (FLD) is reversible disease and can be treated, if it is identified at an early stage. However, if diagnosed at the later stage, it can progress to an advanced liver disease such as cirrhosis which may ultimately lead to death. Therefore, it is essential to detect it at an early stage before the disease progresses to an irreversible stage. Several non-invasive computer-aided techniques are proposed to assist in the early detection of FLD and cirrhosis using ultrasound images. In this work, we are proposing an algorithm to discriminate automatically the normal, FLD and cirrhosis ultrasound images using curvelet transform (CT) method. Higher order spectra (HOS) bispectrum, HOS phase, fuzzy, Kapoor, max, Renyi, Shannon, Vajda and Yager entropies are extracted from CT coefficients. These extracted features are subjected to locality sensitive discriminant analysis (LSDA) feature reduction method. Then these LSDA coefficients ranked based on F-value are fed to different classifiers to choose the best performing classifier using minimum number of features. Our proposed technique can characterize normal, FLD and cirrhosis using probabilistic neural network (PNN) classifier with an accuracy of 97.33%, specificity of 100.00% and sensitivity of 96.00% using only six features. In addition, these chosen features are used to develop a liver disease index (LDI) to differentiate the normal, FLD and cirrhosis classes using a single number. This can significantly help the radiologists to discriminate FLD and cirrhosis in their routine liver screening.
    Matched MeSH terms: Discriminant Analysis
  16. Fulltext Metabolite profiles of callus and cell suspension cultures of mangosteen
    Jamil SZMR, Rohani ER, Baharum SN, Noor NM
    3 Biotech, 2018 Aug;8(8):322.
    PMID: 30034986 DOI: 10.1007/s13205-018-1336-6
    Callus was induced from mangosteen (Garcinia mangostana L.) young purple-red leaves on Murashige and Skoog basal medium with various combinations of plant growth regulators. Murashige and Skoog medium with 4.44 µM 6-benzylaminopurine and 4.52 µM 2,4-dichlorophenoxyacetic acid was the best for friable callus induction. This friable callus was used for the initiation of cell suspension culture. The effects of different combinations of 6-benzylaminopurine and 2,4-dichlorophenoxyacetic acid, carbon sources and inoculum sizes were tested. It was found that combination of 2.22 µM 6-benzylaminopurine + 2.26 µM 2,4-dichlorophenoxyacetic acid, glucose (30 g/l) and 1.5 g/50 ml inoculum size was the best for cell growth. Callus and cell suspension cultures were then treated either with 100 µM methyl jasmonate as an elicitor for 5 days, or 0.5 g/l casein hydrolysate as an organic supplement for 7 days. Metabolites were then extracted and profiled using liquid chromatography-time of flight mass spectrometry. Multivariate discriminant analyses revealed significant metabolite differences (P ≤ 0.05) for callus and suspension cells treated either with methyl jasmonate or casein hydrolysate. Based on MS/MS data, methyl jasmonate stimulated the production of an alkaloid (thalsimine) and fatty acid (phosphatidyl ethanolamine) in suspension cells while in callus, an alkaloid (thiacremonone) and glucosinolate (7-methylthioheptanaldoxime) was produced. Meanwhile casein hydrolysate stimulated the production of alkaloids such as 3ß,6ß-dihydroxynortropane and cis-hinokiresinol and triterpenoids such as schidigerasaponin and talinumoside in suspension cells. This study provides evidence on the potential of secondary metabolite production from in vitro culture of mangosteen.
    Matched MeSH terms: Discriminant Analysis
  17. Fulltext The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN
    Rashid M, Bari BS, Hasan MJ, Razman MAM, Musa RM, Ab Nasir AF, et al.
    PeerJ Comput Sci, 2021;7:e374.
    PMID: 33817022 DOI: 10.7717/peerj-cs.374
    Brain-computer interface (BCI) is a viable alternative communication strategy for patients of neurological disorders as it facilitates the translation of human intent into device commands. The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. More often than not, high dimensional feature set contains redundant features that may degrade a given classifier's performance. In the present investigation, an ensemble learning-based classification algorithm, namely random subspace k-nearest neighbour (k-NN) has been proposed to classify the motor imagery (MI) data. The common spatial pattern (CSP) has been applied to extract the features from the MI response, and the effectiveness of random forest (RF)-based feature selection algorithm has also been investigated. In order to evaluate the efficacy of the proposed method, an experimental study has been implemented using four publicly available MI dataset (BCI Competition III dataset 1 (data-1), dataset IIIA (data-2), dataset IVA (data-3) and BCI Competition IV dataset II (data-4)). It was shown that the ensemble-based random subspace k-NN approach achieved the superior classification accuracy (CA) of 99.21%, 93.19%, 93.57% and 90.32% for data-1, data-2, data-3 and data-4, respectively against other models evaluated, namely linear discriminant analysis, support vector machine, random forest, Naïve Bayes and the conventional k-NN. In comparison with other classification approaches reported in the recent studies, the proposed method enhanced the accuracy by 2.09% for data-1, 1.29% for data-2, 4.95% for data-3 and 5.71% for data-4, respectively. Moreover, it is worth highlighting that the RF feature selection technique employed in the present study was able to significantly reduce the feature dimension without compromising the overall CA. The outcome from the present study implies that the proposed method may significantly enhance the accuracy of MI data classification.
    Matched MeSH terms: Discriminant Analysis
  18. The Heritability of Palatal Rugae Morphology Among Siblings*, †
    Chong JA, Syed Mohamed AMF, Marizan Nor M, Pau A
    J Forensic Sci, 2020 Nov;65(6):2000-2007.
    PMID: 32692413 DOI: 10.1111/1556-4029.14507
    Although there is clinical applicability of the palatal rugae as an identification tool in forensic odontology, controversy exists whether the palatal rugae patterns are stable or variable. The greater the genetic component, the higher the probability that palatal rugae patterns are stable. The aim of this study was to compare the palatal rugae morphology between full siblings and the proportion of variability due to genetic component. This cross-sectional study was conducted on digital models of 162 siblings aged 15-30 years old. The palatal rugae patterns were assessed with Thomas and Kotze (1983) classification using Geomagic Studio software (3D Systems, Rock Hill, SC). The palatal rugae morphology between siblings showed significantly similar characteristics for total number of left rugae (p = 0.001), left primary rugae (p = 0.017), secondary rugae for right (p = 0.024) and left sides (p = 0.001), right straight rugae (p = 0.010), and right convergent rugae (p = 0.005) accounting for at least 6.25%-12.8% of the variability due to heredity. Despite the similarities found, the palatal rugae patterns showed significant differences between siblings of at least 46.9% (p = 0.001). Zero heritability was found in 9 of the 14 rugae patterns. Meanwhile, total number of rugae, primary, backward, and convergent rugae showed moderate heritability (h2  > 0.3) and total number of secondary rugae showed high heritability (h2  > 0.6). In conclusion, despite the individuality characteristics, an appreciable hereditary component is observed with significant similarities found between sibling pairs and the palatal rugae patterns were both environmentally and genetically influenced.
    Matched MeSH terms: Discriminant Analysis
  19. Fulltext Cross-cultural adaptation of modified dental pain screening questionnaire (M-DePaQ) in a primary dental care clinic in Kuala Lumpur
    Kia ACL, Dalia Abdullah, Seong JS, Chiang SC, Pau A
    Malaysian Journal of Public Health Medicine, 2017;17(1):38-45.
    A validated screening tool for patient triage based on the pain symptoms, could potentially optimize the resources and expertise available in dental pain management. The aim of this study was to translate and validate the Modified Dental Pain Questionnaire (M-DePaQ) for use in categorizing patients with pain into three groups of common dental conditions. Forward Malay and Chinese translation was performed, followed by backward English translation. The translation was reviewed by an expert panel and pre-tested on patients who are native speakers.Consecutive patients aged 18 years and older experiencing pain and attending the primary dental care clinic completed the questionnaires. Four calibrated dentists made clinical diagnoses independent of the questionnaire responses. For data analysis, the cases were split randomly into Random Sample 1 (RS1) and Random Sample 2 (RS2). Discriminant analysis was performed on RS1 to develop a model for classifying dental pain cases into three groups. The model was applied to cases in RS2, and a cross-validated accuracy rate was obtained. Criterion validity was assessed using measures such as sensitivity, specificity, positive predictive value, and kappa. Of the 234 questionnaires distributed, 216 (92.3%) were returned. Classification rates were recorded at 73.8% for RS1, 75.0% for RS2, and 71.1% for all cases. The sensitivity values were 0.72, 0.39, and 0.43 for Groups 1, 2, and 3, respectively. The corresponding specificity values were 0.42, 0.87, and 0.94. The discriminant validity of the adapted questionnaire was satisfactory, but the criterion validity could not be established because of biases incorporated in the study.
    Matched MeSH terms: Discriminant Analysis
  20. Quantitative Tetraplex Real-Time Polymerase Chain Reaction Assay with TaqMan Probes Discriminates Cattle, Buffalo, and Porcine Materials in Food Chain
    Hossain MAM, Ali ME, Sultana S, Asing, Bonny SQ, Kader MA, et al.
    J Agric Food Chem, 2017 May 17;65(19):3975-3985.
    PMID: 28481513 DOI: 10.1021/acs.jafc.7b00730
    Cattle, buffalo, and porcine materials are widely adulterated, and their quantification might safeguard health, religious, economic, and social sanctity. Recently, conventional polymerase chain reaction (PCR) and PCR-restriction fragment length polymorphism (RFLP) assays have been documented but they are just suitable for identification, cannot quantify adulterations. We described here a quantitative tetraplex real-time PCR assay with TaqMan Probes to quantify contributions from cattle, buffalo, and porcine materials simultaneously. Amplicon-sizes were very short (106-, 90-, and 146-bp for cattle, buffalo, and porcine) because longer targets could be broken down, bringing serious ambiguity in molecular diagnostics. False negative detection was eliminated through an endogenous control (141-bp site of eukaryotic 18S rRNA). Analysis of 27 frankfurters and 27 meatballs reflected 84-115% target recovery at 0.1-10% adulterations. Finally, a test of 36 commercial products revealed 71% beef frankfurters, 100% meatballs, and 85% burgers contained buffalo adulteration, but no porcine was found in beef products.
    Matched MeSH terms: Discriminant Analysis
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