Displaying publications 1 - 20 of 107 in total

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  1. 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: Discriminant Analysis
  2. Zakaria A, Shakaff AY, Adom AH, Ahmad MN, Masnan MJ, Aziz AH, et al.
    Sensors (Basel), 2010;10(10):8782-96.
    PMID: 22163381 DOI: 10.3390/s101008782
    An improved classification of Orthosiphon stamineus using a data fusion technique is presented. Five different commercial sources along with freshly prepared samples were discriminated using an electronic nose (e-nose) and an electronic tongue (e-tongue). Samples from the different commercial brands were evaluated by the e-tongue and then followed by the e-nose. Applying Principal Component Analysis (PCA) separately on the respective e-tongue and e-nose data, only five distinct groups were projected. However, by employing a low level data fusion technique, six distinct groupings were achieved. Hence, this technique can enhance the ability of PCA to analyze the complex samples of Orthosiphon stamineus. Linear Discriminant Analysis (LDA) was then used to further validate and classify the samples. It was found that the LDA performance was also improved when the responses from the e-nose and e-tongue were fused together.
    Matched MeSH terms: Discriminant Analysis
  3. Yusuf N, Zakaria A, Omar MI, Shakaff AY, Masnan MJ, Kamarudin LM, et al.
    BMC Bioinformatics, 2015;16:158.
    PMID: 25971258 DOI: 10.1186/s12859-015-0601-5
    Effective management of patients with diabetic foot infection is a crucial concern. A delay in prescribing appropriate antimicrobial agent can lead to amputation or life threatening complications. Thus, this electronic nose (e-nose) technique will provide a diagnostic tool that will allow for rapid and accurate identification of a pathogen.
    Matched MeSH terms: Discriminant Analysis
  4. Yusof NA, Isha A, Ismail IS, Khatib A, Shaari K, Abas F, et al.
    J Sci Food Agric, 2015 Sep;95(12):2533-43.
    PMID: 25371390 DOI: 10.1002/jsfa.6987
    The metabolite changes in three germplasm accessions of Malaysia Andrographis paniculata (Burm. F.) Nees, viz. 11265 (H), 11341 (P) and 11248 (T), due to their different harvesting ages and times were successfully evaluated by attenuated total reflectance (ATR)-Fourier transform infrared (FTIR) spectroscopy and translated through multivariate data analysis of principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA). This present study revealed the feasibility of ATR-FTIR in detecting the trend changes of the major metabolites - andrographolide and neoandrographolide - functional groups in A. paniculata leaves of different accessions. The harvesting parameter was set at three different ages of 120, 150 and 180 days after transplanting (DAT) and at two different time sessions of morning (7:30-10:30 am) and evening (2:30-5.30 pm).
    Matched MeSH terms: Discriminant Analysis
  5. Yusof N, Hamid N, Ma ZF, Lawenko RM, Wan Mohammad WMZ, Collins DA, et al.
    Gut Pathog, 2017;9:75.
    PMID: 29255490 DOI: 10.1186/s13099-017-0224-7
    Background: After an environmental disaster, the affected community is at increased risk for persistent abdominal pain but mechanisms are unclear. Therefore, our study aimed to determine association between abdominal pain and poor water, sanitation and hygiene (WaSH) practices, and if small intestinal bacterial overgrowth (SIBO) and/or gut dysbiosis explain IBS, impaired quality of life (QOL), anxiety and/or depression after a major flood.

    Results: New onset abdominal pain, IBS based on the Rome III criteria, WaSH practices, QOL, anxiety and/or depression, SIBO (hydrogen breath testing) and stools for metagenomic sequencing were assessed in flood victims. Of 211 participants, 37.9% (n = 80) had abdominal pain and 17% (n = 36) with IBS subtyped diarrhea and/or mixed type (n = 27 or 12.8%) being the most common. Poor WaSH practices and impaired quality of life during flood were significantly associated with IBS. Using linear discriminant analysis effect size method, gut dysbiosis was observed in those with anxiety (Bacteroidetes and Proteobacteria, effect size 4.8), abdominal pain (Fusobacteria, Staphylococcus, Megamonas and Plesiomonas, effect size 4.0) and IBS (Plesiomonas and Trabulsiella, effect size 3.0).

    Conclusion: Disturbed gut microbiota because of environmentally-derived organisms may explain persistent abdominal pain and IBS after a major environmental disaster in the presence of poor WaSH practices.

    Matched MeSH terms: Discriminant Analysis
  6. Yusof HM, Ab-Rahim S, Wan Ngah WZ, Nathan S, A Jamal AR, Mazlan M
    Bioimpacts, 2021;11(2):147-156.
    PMID: 33842285 DOI: 10.34172/bi.2021.22
    Introduction:
    Metabolomic studies on various colorectal cancer (CRC) cell lines have improved our understanding of the biochemical events underlying the disease. However, the metabolic profile dynamics associated with different stages of CRC progression is still lacking. Such information can provide further insights into the pathophysiology and progression of the disease that will prove useful in identifying specific targets for drug designing and therapeutics. Thus, our study aims to characterize the metabolite profiles in the established cell lines corresponding to different stages of CRC.
    Methods:
    Metabolite profiling of normal colon cell lines (CCD 841 CoN) and CRC cell lines corresponding to different stages, i.e., SW 1116 (stage A), HT 29 and SW 480 (stage B), HCT 15 and DLD-1 (stage C), and HCT 116 (stage D), was carried out using liquid chromatography-mass spectrometry (LC-MS). Mass Profiler Professional and Metaboanalyst 4.0 software were used for statistical and pathway analysis. METLIN database was used for the identification of metabolites.
    Results:
    We identified 72 differential metabolites compared between CRC cell lines of all the stages and normal colon cells. Principle component analysis and partial least squares discriminant analysis score plot were used to segregate normal and CRC cells, as well as CRC cells in different stages of the disease. Variable importance in projection score identified unique differential metabolites in CRC cells of the different stages. We identified 7 differential metabolites unique to stage A, 3 in stage B, 5 in stage C, and 5 in stage D.
    Conclusion:
    This study highlights the differential metabolite profiling in CRC cell lines corresponding to different stages. The identification of the differential metabolites in CRC cells at individual stages will lead to a better understanding of the pathophysiology of CRC development and progression and, hence, its application in treatment strategies.
    Matched MeSH terms: Discriminant Analysis
  7. Windarsih A, Bakar NKA, Rohman A, Erwanto Y
    Anal Sci, 2024 Mar;40(3):385-397.
    PMID: 38095741 DOI: 10.1007/s44211-023-00470-x
    Due to the different price and high quality, halal meat such as beef can be adulterated with non-halal meat with low price to get an economical price. The objective of this research was to develop an analytical method for halal authentication testing of beef meatballs (BM) from dog meat (DM) using a non-targeted metabolomics approach employing liquid chromatography-high-resolution mass spectrometry (LC-HRMS) and chemometrics. The differentiation of authentic BM from that adulterated with DM was successfully performed using partial least square-discriminant analysis (PLS-DA) with high accuracy (R2X = 0.980, and R2Y = 0.980) and good predictivity (Q2 = 0.517). In addition, partial least square (PLS) and orthogonal PLS (OPLS) were successfully used to predict the DM added (% w/w) in BM with high accuracy (R2 > 0.990). A number of metabolites, potential for biomarker candidates, were identified to differentiate BM and that adulterated with DM. It showed that the combination of a non-targeted LC-HRMS Orbitrap metabolomics and chemometrics could detect up to 0.1% w/w of DM adulteration. The developed method was successfully applied for analysis of commercial meatball samples (n = 28). Moreover, pathway analysis revealed that beta-alanine, histidine, and ether lipid metabolism were significantly affected by dog meat adulteration. In summary, this developed method has great potential to be developed and used as an alternative method for analysis of non-halal meats in halal meat products.
    Matched MeSH terms: Discriminant Analysis
  8. Wang J, Chen T, Zhang W, Zhao Y, Yang S, Chen A
    Food Chem, 2020 May 30;313:126093.
    PMID: 31927205 DOI: 10.1016/j.foodchem.2019.126093
    Multivariate stable isotope analysis combined with chemometrics was used to investigate and discriminate rice samples from six rice producing provinces in China (Heilongjiang, Jilin, Jiangsu, Zhejiang, Hunan and Guizhou) and four other Asian rice producing countries (Thailand, Malaysia, Philippines, and Pakistan). The stable isotope characteristics were analyzed for rice of different species cultivated with varied farming methods at different altitudes and latitudes/longitudes. The index groups of δ13C, δ15N, δ18O, 207/206Pb and 208/207Pb were screened and established for the selected samples with different geographical features by means of principal component analysis (PCA) and discriminant analysis (DA), which would provide a sound technical solution for rice traceability and serve as a template for further research on the traceability of other agricultural products, especially plant-derived products.
    Matched MeSH terms: Discriminant Analysis
  9. Uncini A, Ippoliti L, Shahrizaila N, Sekiguchi Y, Kuwabara S
    Clin Neurophysiol, 2017 07;128(7):1176-1183.
    PMID: 28521265 DOI: 10.1016/j.clinph.2017.03.048
    OBJECTIVE: To optimize the electrodiagnosis of Guillain-Barré syndrome (GBS) subtypes at first study.

    METHODS: The reference electrodiagnosis was obtained in 53 demyelinating and 45 axonal GBS patients on the basis of two serial studies and results of anti-ganglioside antibodies assay. We retrospectively employed sparse linear discriminant analysis (LDA), two existing electrodiagnostic criteria sets (Hadden et al., 1998; Rajabally et al., 2015) and one we propose that additionally evaluates duration of motor responses, sural sparing pattern and defines reversible conduction failure (RCF) in motor and sensory nerves at second study.

    RESULTS: At first study the misclassification error rates, compared to reference diagnoses, were: 15.3% for sparse LDA, 30% for our criteria, 45% for Rajabally's and 48% for Hadden's. Sparse LDA identified seven most powerful electrophysiological variables differentiating demyelinating and axonal subtypes and assigned to each patient the diagnostic probability of belonging to either subtype. At second study 46.6% of axonal GBS patients showed RCF in two motor and 8.8% in two sensory nerves.

    CONCLUSIONS: Based on a single study, sparse LDA showed the highest diagnostic accuracy. RCF is present in a considerable percentage of axonal patients.

    SIGNIFICANCE: Sparse LDA, a supervised statistical method of classification, should be introduced in the electrodiagnostic practice.

    Matched MeSH terms: Discriminant Analysis
  10. Tan M, Mariapun S, Yip CH, Ng KH, Teo SH
    Phys Med Biol, 2019 01 31;64(3):035016.
    PMID: 30577031 DOI: 10.1088/1361-6560/aafabd
    Historically, breast cancer risk prediction models are based on mammographic density measures, which are dichotomous in nature and generally categorize each voxel or area of the breast parenchyma as 'dense' or 'not dense'. Using these conventional methods, the structural patterns or textural components of the breast tissue elements are not considered or ignored entirely. This study presents a novel method to predict breast cancer risk that combines new texture and mammographic density based image features. We performed a comprehensive study of the correlation of 944 new and conventional texture and mammographic density features with breast cancer risk on a cohort of Asian women. We studied 250 breast cancer cases and 250 controls matched at full-field digital mammography (FFDM) status for age, BMI and ethnicity. Stepwise regression analysis identified relevant features to be included in a linear discriminant analysis (LDA) classifier model, trained and tested using a leave-one-out based cross-validation method. The area under the receiver operating characteristic (AUC) and adjusted odds ratios (ORs) were used as the two performance assessment indices in our study. For the LDA trained classifier, the adjusted OR was 6.15 (95% confidence interval: 3.55-10.64) and for Volpara volumetric breast density, 1.10 (0.67-1.81). The AUC for the LDA trained classifier was 0.68 (0.64-0.73), compared to 0.52 (0.47-0.57) for Volpara volumetric breast density (p   
    Matched MeSH terms: Discriminant Analysis
  11. Syed Mohd Hamdan SN, Rahmat RA, Abdul Razak F, Abd Kadir KA, Mohd Faizal Abdullah ER, Ibrahim N
    Leg Med (Tokyo), 2023 Sep;64:102275.
    PMID: 37229938 DOI: 10.1016/j.legalmed.2023.102275
    Sex estimation is crucial in biological profiling of skeletal human remains. Methods used for sex estimation in adults are less effective for sub-adults due to varied cranium patterns during the growth period. Hence, this study aimed to develop a sex estimation model for Malaysian sub-adults using craniometric measurements obtained through multi-slice computed tomography (MSCT). A total of 521 cranial MSCT dataset of sub-adult Malaysians (279 males, 242 females; 0-20 years old) were collected. Mimics software version 21.0 (Materialise, Leuven, Belgium) was used to construct three-dimensional (3D) models. A plane-to-plane (PTP) protocol was utilised to measure 14 selected craniometric parameters. Discriminant function analysis (DFA) and binary logistic regression (BLR) were used to statistically analyze the data. In this study, low level of sexual dimorphism was observed in cranium below 6 years old. The level was then increased with age. For sample validation data, the accuracy of DFA and BLR in estimating sex improved with age from 61.6% to 90.3%. All age groups except 0-2 and 3-6 showed high accuracy percentage (≥75%) when tested using DFA and BLR. DFA and BLR can be utilised to estimate sex for Malaysian sub-adult using MSCT craniometric measurements. However, BLR showed higher accuracy than DFA in sex estimation of sub-adults.
    Matched MeSH terms: Discriminant Analysis
  12. Syed Abdul Mutalib SN, Juahir H, Azid A, Mohd Sharif S, Latif MT, Aris AZ, et al.
    Environ Sci Process Impacts, 2013 Sep;15(9):1717-28.
    PMID: 23831918 DOI: 10.1039/c3em00161j
    The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.
    Matched MeSH terms: Discriminant Analysis
  13. Sweeti, Joshi D, Panigrahi BK, Anand S, Santhosh J
    J Healthc Eng, 2018;2018:9213707.
    PMID: 29808111 DOI: 10.1155/2018/9213707
    This paper presents a classification system to classify the cognitive load corresponding to targets and distractors present in opposite visual hemifields. The approach includes the study of EEG (electroencephalogram) signal features acquired in a spatial attention task. The process comprises of EEG feature selection based on the feature distribution, followed by the stepwise discriminant analysis- (SDA-) based channel selection. Repeated measure analysis of variance (rANOVA) is applied to test the statistical significance of the selected features. Classifiers are developed and compared using the selected features to classify the target and distractor present in visual hemifields. The results provide a maximum classification accuracy of 87.2% and 86.1% and an average classification accuracy of 76.5 ± 4% and 76.2 ± 5.3% over the thirteen subjects corresponding to the two task conditions. These correlates present a step towards building a feature-based neurofeedback system for visual attention.
    Matched MeSH terms: Discriminant Analysis
  14. Subari N, Mohamad Saleh J, Md Shakaff AY, Zakaria A
    Sensors (Basel), 2012;12(10):14022-40.
    PMID: 23202033 DOI: 10.3390/s121014022
    This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data.
    Matched MeSH terms: Discriminant Analysis
  15. Siti Waznah Abdurahman, Mohd Azmi Ambak, Shahreza Md Sheriff, Ying GS, Ahmad Azfar Mohamed, Ahmed Jalal Khan Chowdhury
    Sains Malaysiana, 2016;45:1-7.
    Ariid catfishes, belong to family Ariidae is considered as one of the taxonomically problematic groups, which is still under review by fish taxonomist globally. Species level identification of some ariids often resulted in species misidentification because of their complex characters and very similar morphological characters within genera. A vigilant and detail observation is very important during the species level identification of ariid species. In these contexts, this study was carried out in order to determine the morphological variations of one of the ariid genera, Plicofollis, which have been giving misleading taxonomic information in the south-east Asian countries. A Truss network technique was used throughout the study period. The study was conducted based on 20 truss measurements using 22 to 23 specimens per species, namely P. argyropleuron, P. nella and P. tenuispinis found in Peninsular Malaysian waters. Morphological variations were determined using a multivariate technique of discriminant function analysis (DFA). The results obtained in this study showed that discriminant analysis using truss network measurements has produced very clear separations of all the species in Plicofollis group. Several important morphological characters have been identified, which represent body depth and caudal regions of the fish. The documentary evidences of these variables could be considered as the constructive functional features, which could enable us to assess more accurately to distinguish the species within this complex Ariidae family.
    Matched MeSH terms: Discriminant Analysis
  16. Sharin SN, Sani MSA, Jaafar MA, Yuswan MH, Kassim NK, Manaf YN, et al.
    Food Chem, 2021 Jun 01;346:128654.
    PMID: 33461823 DOI: 10.1016/j.foodchem.2020.128654
    Identification of honey origin based on specific chemical markers is important for honey authentication. This study is aimed to differentiate Malaysian stingless bee honey from different entomological origins (Heterotrigona bakeri, Geniotrigona thoracica and Tetrigona binghami) based on physicochemical properties (pH, moisture content, ash, total soluble solid and electrical conductivity) and volatile compound profiles. The discrimination pattern of 75 honey samples was observed using Principal Component Analysis (PCA), Hierarchical Clustering Analysis (HCA), Partial Least Square-Discriminant Analysis (PLS-DA), and Support Vector Machine (SVM). The profiles of H. bakeri and G. thoracica honey were close to each other, but clearly separated from T. binghami honey, consistent with their phylogenetic relationship. T. binghami honey is marked by significantly higher electrical conductivity, moisture and ash content, and high abundance of 2,6,6-trimethyl-1-cyclohexene-1-carboxaldehyde, 2,6,6-trimethyl-1-cyclohexene-1-acetaldehyde and ethyl 2-(5-methyl-5-vinyltetrahydrofuran-2-yl)propan-2-yl carbonate. Copaene was proposed as chemical marker for G. thoracica honey. The potential of different parameters that aid in honey authentication was highlighted.
    Matched MeSH terms: Discriminant Analysis
  17. Shamsudin S, Selamat J, Sanny M, A R SB, Jambari NN, Khatib A
    Molecules, 2019 Oct 29;24(21).
    PMID: 31671885 DOI: 10.3390/molecules24213898
    Stingless bee honey produced by Heterotrigona itama from different botanical origins was characterised and discriminated. Three types of stingless bee honey collected from acacia, gelam, and starfruit nectars were analyzed and compared with Apis mellifera honey. The results showed that stingless bee honey samples from the three different botanical origins were significantly different in terms of their moisture content, pH, free acidity, total soluble solids, colour characteristics, sugar content, amino acid content and antioxidant properties. Stingless bee honey was significantly different from Apis mellifera honey in terms of physicochemical and antioxidant properties. The amino acid content was further used in the chemometrics analysis to evaluate the role of amino acid in discriminating honey according to botanical origin. Partial least squares-discriminant analysis (PLS-DA) revealed that the stingless bee honey was completely distinguishable from Apis mellifera honey. Notably, a clear distinction between the stingless bee honey types was also observed. The specific amino acids involved in the distinction of honey were cysteine for acacia and gelam, phenylalanine and 3-hydroxyproline for starfruit, and proline for Apis mellifera honey. The results showed that all honey samples were successfully classified based on amino acid content.
    Matched MeSH terms: Discriminant Analysis
  18. Sarpeshkar V, Mann DL, Spratford W, Abernethy B
    Hum Mov Sci, 2017 Aug;54:82-100.
    PMID: 28410536 DOI: 10.1016/j.humov.2017.04.003
    Successful interception relies on the use of perceptual information to accurately guide an efficient movement strategy that allows performers to be placed at the right place at the right time. Although previous studies have highlighted the differences in the timing and coordination of movement that underpin interceptive expertise, very little is known about how these movement patterns are adapted when intercepting targets that follow a curvilinear flight-path. The aim of this study was to examine how curvilinear ball-trajectories influence movement patterns when intercepting a fast-moving target. Movement timing and coordination was examined when four groups of cricket batters, who differed in their skill level and/or age, hit targets that followed straight or curvilinear flight-paths. The results revealed that when compared to hitting straight trials, (i) mixing straight with curvilinear trials altered movement coordination and when the ball was hit, (ii) curvilinear trajectories reduced interceptive performance and significantly delayed the timing of all kinematic moments, but there were (iii) larger decrease in performance when the ball swung away from (rather than in towards) the performer. Movement coordination differed between skill but not age groups, suggesting that skill-appropriate movement patterns that are apparent in adults may have fully emerged by late adolescence.
    Matched MeSH terms: Discriminant Analysis
  19. Samsudin MS, Azid A, Khalit SI, Sani MSA, Lananan F
    Mar Pollut Bull, 2019 Apr;141:472-481.
    PMID: 30955758 DOI: 10.1016/j.marpolbul.2019.02.045
    The prediction models of MWQI in mangrove and estuarine zones were constructed. The 2011-2015 data employed in this study entailed 13 parameters from six monitoring stations in West Malaysia. Spatial discriminant analysis (SDA) had recommended seven significant parameters to develop the MWQI which were DO, TSS, O&G, PO4, Cd, Cr and Zn. These selected parameters were then used to develop prediction models for the MWQI using artificial neural network (ANN) and multiple linear regressions (MLR). The SDA-ANN model had higher R2 value for training (0.9044) and validation (0.7113) results than SDA-MLR model and was chosen as the best model in mangrove estuarine zone. The SDA-ANN model had also demonstrated lower RMSE (5.224) than the SDA-MLR (12.7755). In summary, this work suggested that ANN was an effective tool to compute the MWQ in mangrove estuarine zone and a powerful alternative prediction model as compared to the other modelling methods.
    Matched MeSH terms: Discriminant Analysis
  20. Salimi N, Loh KH, Kaur Dhillon S, Chong VC
    PeerJ, 2016;4:e1664.
    PMID: 26925315 DOI: 10.7717/peerj.1664
    Background. Fish species may be identified based on their unique otolith shape or contour. Several pattern recognition methods have been proposed to classify fish species through morphological features of the otolith contours. However, there has been no fully-automated species identification model with the accuracy higher than 80%. The purpose of the current study is to develop a fully-automated model, based on the otolith contours, to identify the fish species with the high classification accuracy. Methods. Images of the right sagittal otoliths of 14 fish species from three families namely Sciaenidae, Ariidae, and Engraulidae were used to develop the proposed identification model. Short-time Fourier transform (STFT) was used, for the first time in the area of otolith shape analysis, to extract important features of the otolith contours. Discriminant Analysis (DA), as a classification technique, was used to train and test the model based on the extracted features. Results. Performance of the model was demonstrated using species from three families separately, as well as all species combined. Overall classification accuracy of the model was greater than 90% for all cases. In addition, effects of STFT variables on the performance of the identification model were explored in this study. Conclusions. Short-time Fourier transform could determine important features of the otolith outlines. The fully-automated model proposed in this study (STFT-DA) could predict species of an unknown specimen with acceptable identification accuracy. The model codes can be accessed at http://mybiodiversityontologies.um.edu.my/Otolith/ and https://peerj.com/preprints/1517/. The current model has flexibility to be used for more species and families in future studies.
    Matched MeSH terms: Discriminant Analysis
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