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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
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
Authentication analysis of red fruit (Pandanus Conoideus Lam) oil using FTIR spectroscopy in combination with chemometrics

Rohman A, Man YB, Riyanto S

Phytochem Anal, 2011 Sep-Oct;22(5):462-7.
PMID: 22033916 DOI: 10.1002/pca.1304

Red fruit (Pandanus conoideus Lam) is endemic plant of Papua, Indonesia and Papua New Guinea. The price of its oil (red fruit oil, RFO) is 10-15 times higher than that of common vegetable oils; consequently, RFO is subjected to adulteration with lower price oils. Among common vegetable oils, canola oil (CaO) and rice bran oil (RBO) have similar fatty acid profiles to RFO as indicated by the score plot of principal component analysis; therefore, CaO and RBO are potential adulterants in RFO.

Matched MeSH terms: Discriminant Analysis
Statistical validation for the profiling of heroin by associating simulated postcut samples with the corresponding precut sample

Chan KW, Tan GH, Wong RC

J Forensic Sci, 2013 Jan;58 Suppl 1:S199-207.
PMID: 23013257 DOI: 10.1111/j.1556-4029.2012.02285.x

Statistical validation is crucial for the clustering of unknown samples. This study aims to demonstrate how statistical techniques can be optimized using simulated heroin samples containing a range of analyte concentrations that are similar to those of the case samples. Eight simulated heroin distribution links consisting of 64 postcut samples were prepared by mixing one of two mixtures of paracetamol-caffeine-dextromethorphan at different proportions with eight precut samples. Analyte contents and compositional variation of the prepared samples were investigated. A number of data pretreatments were evaluated by associating the postcut samples with the corresponding precut samples using principal component analysis and discriminant analysis. Subsequently, combinations of seven linkage methods and five distance measures were explored using hierarchical cluster analysis. In this study, Ward-Manhattan showed better distinctions between unrelated links and was able to cluster all related samples in very close distance under the known links on a dendogram. A similar discriminative outcome was also achieved by 90 unknown case samples when clustered via Ward-Manhattan.

Matched MeSH terms: Discriminant Analysis
Analysis of geometric moments as features for firearm identification

Md Ghani NA, Liong CY, Jemain AA

Forensic Sci Int, 2010 May 20;198(1-3):143-9.
PMID: 20211535 DOI: 10.1016/j.forsciint.2010.02.011

The task of identifying firearms from forensic ballistics specimens is exacting in crime investigation since the last two decades. Every firearm, regardless of its size, make and model, has its own unique 'fingerprint'. These fingerprints transfer when a firearm is fired to the fired bullet and cartridge case. The components that are involved in producing these unique characteristics are the firing chamber, breech face, firing pin, ejector, extractor and the rifling of the barrel. These unique characteristics are the critical features in identifying firearms. It allows investigators to decide on which particular firearm that has fired the bullet. Traditionally the comparison of ballistic evidence has been a tedious and time-consuming process requiring highly skilled examiners. Therefore, the main objective of this study is the extraction and identification of suitable features from firing pin impression of cartridge case images for firearm recognition. Some previous studies have shown that firing pin impression of cartridge case is one of the most important characteristics used for identifying an individual firearm. In this study, data are gathered using 747 cartridge case images captured from five different pistols of type 9mm Parabellum Vektor SP1, made in South Africa. All the images of the cartridge cases are then segmented into three regions, forming three different set of images, i.e. firing pin impression image, centre of firing pin impression image and ring of firing pin impression image. Then geometric moments up to the sixth order were generated from each part of the images to form a set of numerical features. These 48 features were found to be significantly different using the MANOVA test. This high dimension of features is then reduced into only 11 significant features using correlation analysis. Classification results using cross-validation under discriminant analysis show that 96.7% of the images were classified correctly. These results demonstrate the value of geometric moments technique for producing a set of numerical features, based on which the identification of firearms are made.

Matched MeSH terms: Discriminant Analysis
Fulltext Vocalisation Repertoire of Female Bluefin Gurnard (Chelidonichthys kumu) in Captivity: Sound Structure, Context and Vocal Activity

Radford CA, Ghazali SM, Montgomery JC, Jeffs AG

PLoS One, 2016;11(2):e0149338.
PMID: 26890124 DOI: 10.1371/journal.pone.0149338

Fish vocalisation is often a major component of underwater soundscapes. Therefore, interpretation of these soundscapes requires an understanding of the vocalisation characteristics of common soniferous fish species. This study of captive female bluefin gurnard, Chelidonichthys kumu, aims to formally characterise their vocalisation sounds and daily pattern of sound production. Four types of sound were produced and characterised, twice as many as previously reported in this species. These sounds fit two aural categories; grunt and growl, the mean peak frequencies for which ranged between 129 to 215 Hz. This species vocalized throughout the 24 hour period at an average rate of (18.5 ± 2.0 sounds fish-1 h-1) with an increase in vocalization rate at dawn and dusk. Competitive feeding did not elevate vocalisation as has been found in other gurnard species. Bluefin gurnard are common in coastal waters of New Zealand, Australia and Japan and, given their vocalization rate, are likely to be significant contributors to ambient underwater soundscape in these areas.

Matched MeSH terms: Discriminant Analysis
Fulltext Fully-automated identification of fish species based on otolith contour: using short-time Fourier transform and discriminant analysis (STFT-DA)

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
Analysis of dog meat adulteration in beef meatballs using non-targeted UHPLC-Orbitrap HRMS metabolomics and chemometrics for halal authentication study

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
Sugarcane disease recognition through visible and near-infrared spectroscopy using deep learning assisted continuous wavelet transform-based spectrogram

Ong P, Jian J, Li X, Zou C, Yin J, Ma G

Spectrochim Acta A Mol Biomol Spectrosc, 2025 Jan 05;324:125001.
PMID: 39180971 DOI: 10.1016/j.saa.2024.125001

Utilizing visible and near-infrared (Vis-NIR) spectroscopy in conjunction with chemometrics methods has been widespread for identifying plant diseases. However, a key obstacle involves the extraction of relevant spectral characteristics. This study aimed to enhance sugarcane disease recognition by combining convolutional neural network (CNN) with continuous wavelet transform (CWT) spectrograms for spectral features extraction within the Vis-NIR spectra (380-1400 nm) to improve the accuracy of sugarcane diseases recognition. Using 130 sugarcane leaf samples, the obtained one-dimensional CWT coefficients from Vis-NIR spectra were transformed into two-dimensional spectrograms. Employing CNN, spectrogram features were extracted and incorporated into decision tree, K-nearest neighbour, partial least squares discriminant analysis, and random forest (RF) calibration models. The RF model, integrating spectrogram-derived features, demonstrated the best performance with an average precision of 0.9111, sensitivity of 0.9733, specificity of 0.9791, and accuracy of 0.9487. This study may offer a non-destructive, rapid, and accurate means to detect sugarcane diseases, enabling farmers to receive timely and actionable insights on the crops' health, thus minimizing crop loss and optimizing yields.

Matched MeSH terms: Discriminant Analysis
Fulltext Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques

Nordin H, Abdul Razak B, Mokhtar N, Jamaludin MF, Mehmood A

PLoS One, 2025;20(1):e0316996.
PMID: 39854603 DOI: 10.1371/journal.pone.0316996

Mold defects pose a significant risk to the preservation of valuable fine art paintings, typically arising from fungal growth in humid environments. This paper presents a novel approach for detecting and categorizing mold defects in fine art paintings. The technique leverages a feature extraction method called Derivative Level Thresholding to pinpoint suspicious regions within an image. Subsequently, these regions are classified as mold defects using either morphological filtering or machine learning models such as Classification and Regression Trees (CART) and Linear Discriminant Analysis (LDA). The efficacy of these methods was evaluated using the Mold Features Dataset (MFD) and a separate set of test images. Results indicate that both methods improve the accuracy and precision of mold defect detection compared to no classifier. However, the CART algorithm exhibits superior performance, increasing precision by 32% to 53% while maintaining high accuracy (96%) even with an imbalanced dataset. This innovative method has the potential to transform the approach to managing mold defects in fine art paintings by offering a more precise and efficient means of identification. By enabling early detection of mold defects, this method can play a crucial role in safeguarding these invaluable artworks for future generations.

Matched MeSH terms: Discriminant 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: Discriminant Analysis
Variation of δ2H, δ18O & δ13C in crude palm oil from different regions in Malaysia: Potential of stable isotope signatures as a key traceability parameter

Muhammad SA, Seow EK, Mohd Omar AK, Rodhi AM, Mat Hassan H, Lalung J, et al.

Sci Justice, 2018 Jan;58(1):59-66.
PMID: 29332695 DOI: 10.1016/j.scijus.2017.05.008

A total of 33 crude palm oil samples were randomly collected from different regions in Malaysia. Stable carbon isotopic composition (δ13C) was determined using Flash 2000 elemental analyzer while hydrogen and oxygen isotopic compositions (δ2H and δ18O) were analyzed by Thermo Finnigan TC/EA, wherein both instruments were coupled to an isotope ratio mass spectrometer. The bulk δ2H, δ18O and δ13C of the samples were analyzed by Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA). Unsupervised HCA and PCA methods have demonstrated that crude palm oil samples were grouped into clusters according to respective state. A predictive model was constructed by supervised OPLS-DA with good predictive power of 52.60%. Robustness of the predictive model was validated with overall accuracy of 71.43%. Blind test samples were correctly assigned to their respective cluster except for samples from southern region. δ18O was proposed as the promising discriminatory marker for discerning crude palm oil samples obtained from different regions. Stable isotopes profile was proven to be useful for origin traceability of crude palm oil samples at a narrower geographical area, i.e. based on regions in Malaysia. Predictive power and accuracy of the predictive model was expected to improve with the increase in sample size. Conclusively, the results in this study has fulfilled the main objective of this work where the simple approach of combining stable isotope analysis with chemometrics can be used to discriminate crude palm oil samples obtained from different regions in Malaysia. Overall, this study shows the feasibility of this approach to be used as a traceability assessment of crude palm oils.

Matched MeSH terms: Discriminant Analysis
Fulltext Exposure to environmental microbiota explains persistent abdominal pain and irritable bowel syndrome after a major flood

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
Metabolomics profiling and pathway analysis of human plasma and urine reveal further insights into the multifactorial nature of coronary artery disease

Amin AM, Mostafa H, Arif NH, Abdul Kader MAS, Kah Hay Y

Clin Chim Acta, 2019 Jun;493:112-122.
PMID: 30826371 DOI: 10.1016/j.cca.2019.02.030

BACKGROUND: Coronary artery disease (CAD) claims lives yearly. Nuclear magnetic resonance (1H NMR) metabolomics analysis is efficient in identifying metabolic biomarkers which lend credence to diagnosis. We aimed to identify CAD metabotypes and its implicated pathways using 1H NMR analysis.
METHODS: We analysed plasma and urine samples of 50 stable CAD patients and 50 healthy controls using 1H NMR. Orthogonal partial least square discriminant analysis (OPLS-DA) followed by multivariate logistic regression (MVLR) models were developed to indicate the discriminating metabotypes. Metabolic pathway analysis was performed to identify the implicated pathways.
RESULTS: Both plasma and urine OPLS-DA models had specificity, sensitivity and accuracy of 100%, 96% and 98%, respectively. Plasma MVLR model had specificity, sensitivity, accuracy and AUROC of 92%, 86%, 89% and 0.96, respectively. The MVLR model of urine had specificity, sensitivity, accuracy and AUROC of 90%, 80%, 85% and 0.92, respectively. 35 and 12 metabolites were identified in plasma and urine metabotypes, respectively. Metabolic pathway analysis revealed that urea cycle, aminoacyl-tRNA biosynthesis and synthesis and degradation of ketone bodies pathways were significantly disturbed in plasma, while methylhistidine metabolism and galactose metabolism pathways were significantly disturbed in urine. The enrichment over representation analysis against SNPs-associated-metabolite sets library revealed that 85 SNPs were significantly enriched in plasma metabotype.
CONCLUSIONS: Cardiometabolic diseases, dysbiotic gut-microbiota and genetic variabilities are largely implicated in the pathogenesis of CAD.

Matched MeSH terms: Discriminant Analysis
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
Differentiation of Commercially Available Kratom by Purported Country of Origin using Inductively Coupled Plasma-Mass Spectrometry

Braley C, Hondrogiannis EM

J Forensic Sci, 2020 Mar;65(2):428-437.
PMID: 31560807 DOI: 10.1111/1556-4029.14201

Kratom is a plant material exhibiting both analgesic and stimulant effects and is also forensically relevant since it is abused as a "legal high." It is regulated in several countries but not scheduled in the United States at the federal level. This study used inductively coupled plasma-mass spectrometry (ICP-MS) to measure the concentrations of 13 elements in 19 kratom samples obtained from an online distributor selling kratom, from Borneo, Malaysia, Indonesia, Thailand, and Vietnam, for the purpose of using the elements to discriminate among purported country of origin, "suborigin," and strain. Analysis of variance revealed statistical differences in concentrations of elements from each group, while discriminant function analysis (using leave-one-out classification) successfully classified kratom samples by their purported country of origin (100%), "suborigin," (100%), and strain (86%). Our method illustrates the possibility of utilizing ICP-MS for determination of commercially available kratom samples by purported origin, "subororign," or by product line.

Matched MeSH terms: Discriminant Analysis
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
Fulltext 3-Dimensional facial expression recognition in human using multi-points warping

Agbolade O, Nazri A, Yaakob R, Ghani AA, Cheah YK

BMC Bioinformatics, 2019 Dec 02;20(1):619.
PMID: 31791234 DOI: 10.1186/s12859-019-3153-2

BACKGROUND: Expression in H-sapiens plays a remarkable role when it comes to social communication. The identification of this expression by human beings is relatively easy and accurate. However, achieving the same result in 3D by machine remains a challenge in computer vision. This is due to the current challenges facing facial data acquisition in 3D; such as lack of homology and complex mathematical analysis for facial point digitization. This study proposes facial expression recognition in human with the application of Multi-points Warping for 3D facial landmark by building a template mesh as a reference object. This template mesh is thereby applied to each of the target mesh on Stirling/ESRC and Bosphorus datasets. The semi-landmarks are allowed to slide along tangents to the curves and surfaces until the bending energy between a template and a target form is minimal and localization error is assessed using Procrustes ANOVA. By using Principal Component Analysis (PCA) for feature selection, classification is done using Linear Discriminant Analysis (LDA).
RESULT: The localization error is validated on the two datasets with superior performance over the state-of-the-art methods and variation in the expression is visualized using Principal Components (PCs). The deformations show various expression regions in the faces. The results indicate that Sad expression has the lowest recognition accuracy on both datasets. The classifier achieved a recognition accuracy of 99.58 and 99.32% on Stirling/ESRC and Bosphorus, respectively.
CONCLUSION: The results demonstrate that the method is robust and in agreement with the state-of-the-art results.

Matched MeSH terms: Discriminant Analysis
Fulltext Differentiation of ripe and unripe fruit flour using mineral composition data-Statistical assessment

Alkarkhi AFM, Alqaraghuli WAA, Mohamed Zam NR, Manan DMA, Mahmud MN, Huda N

Data Brief, 2020 Jun;30:105414.
PMID: 32258278 DOI: 10.1016/j.dib.2020.105414

Data on the mineral composition and content of one heavy metal measured in three different fruit flours prepared from ripe and unripe fruits (pulp and peel) are presented. The mineral composition (sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), zinc (Zn), copper (Cu), iron (Fe) and manganese (Mn)) and content of one heavy metal (lead (Pb)) of the flours were analyzed by atomic absorption spectrophotometry. The analysis showed that the data can be used for differentiation between different fruits and stages of ripeness, as revealed by discriminant analysis and cluster analysis. The data provided can be used by researchers and scientists in the differentiation of fruits based on major and minor mineral elements.

Matched MeSH terms: Discriminant Analysis
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

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