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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
Fulltext Chromatographic fingerprint and Chemometric approach for quality control of Tongkat Ali (Eurycoma Longifolia)

Nor Nasriah Zaini, Mardiana Saaid, Hafizan Juahir, Rozita Osman

Pertanika Journal of Science & Technology, 2017;25(103):345-354.
MyJurnal

Tongkat Ali (Eurycoma longifolia) is one of the most popular tropical herbal plants as it is believed to enhance virility and sexual prowess. This study looked examined chromatographic fingerprint of Tongkat Ali roots and its products generated using online solid phase-extraction liquid chromatography (SPE-LC) combined with chemometric approaches. The aim was to determine its quality. Pressurised liquid extraction (PLE) technique was used prior to online SPE-LC using polystyrene divinyl benzene (PSDVB) and C18 columns. Seventeen Tongkat Ali roots and 10 products (capsules) were analysed. Chromatographic dataset was subjected to chemometric techniques, namely cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) using 37 selected peaks. The samples were grouped into three clusters based on their quality. The PCA resulted in 11 latent factors describing 90.8% of the whole variance. Pattern matching analysis showed no significant difference (p>0.05) between the roots and products within the same CA grouping. The findings showed the combination of chromatographic fingerprint and chemometric techniques provided comprehensive evaluation for efficient quality control of Tongkat Ali formulation.

Matched MeSH terms: Discriminant Analysis
Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps

Lee LC, Liong CY, Jemain AA

Analyst, 2018 Jul 23;143(15):3526-3539.
PMID: 29947623 DOI: 10.1039/c8an00599k

Partial least squares-discriminant analysis (PLS-DA) is a versatile algorithm that can be used for predictive and descriptive modelling as well as for discriminative variable selection. However, versatility is both a blessing and a curse and the user needs to optimize a wealth of parameters before reaching reliable and valid outcomes. Over the past two decades, PLS-DA has demonstrated great success in modelling high-dimensional datasets for diverse purposes, e.g. product authentication in food analysis, diseases classification in medical diagnosis, and evidence analysis in forensic science. Despite that, in practice, many users have yet to grasp the essence of constructing a valid and reliable PLS-DA model. As the technology progresses, across every discipline, datasets are evolving into a more complex form, i.e. multi-class, imbalanced and colossal. Indeed, the community is welcoming a new era called big data. In this context, the aim of the article is two-fold: (a) to review, outline and describe the contemporary PLS-DA modelling practice strategies, and (b) to critically discuss the respective knowledge gaps that have emerged in response to the present big data era. This work could complement other available reviews or tutorials on PLS-DA, to provide a timely and user-friendly guide to researchers, especially those working in applied research.

Matched MeSH terms: Discriminant Analysis
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
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
Predictive modelling of colossal ATR-FTIR spectral data using PLS-DA: empirical differences between PLS1-DA and PLS2-DA algorithms

Lee LC, Jemain AA

Analyst, 2019 Apr 08;144(8):2670-2678.
PMID: 30849143 DOI: 10.1039/c8an02074d

In response to our review paper [L. C. Lee et al., Analyst, 2018, 143, 3526-3539], we present a study that compares empirical differences between PLS1-DA and PLS2-DA algorithms in modelling a colossal ATR-FTIR spectral dataset. Over the past two decades, partial least squares-discriminant analysis (PLS-DA) has gained wide acceptance and huge popularity in the field of applied research, partly due to its dimensionality reduction capability and ability to handle multicollinear and correlated variables. To solve a K-class problem (K > 2) using PLS-DA and high-dimensional data like infrared spectra, one can construct either K one-versus-all PLS1-DA models or only one PLS2-DA model. The aim of this work is to explore empirical differences between the two PLS-DA algorithms in modeling a colossal ATR-FTIR spectral dataset. The practical task is to build a prediction model using the imbalanced, high dimensional, colossal and multi-class ATR-FTIR spectra of blue gel pen inks. Four different sub-datasets were prepared from the principal dataset by considering the raw and asymmetric least squares (AsLS) preprocessed forms: (a) Raw-global region; (b) Raw-local region; (c) AsLS-global region; and (d) AsLS-local region. A series of 50 models which includes the first 50 PLS components incrementally was constructed repeatedly using the four sub-datasets. Each model was evaluated using six different variants of v-fold cross validation, autoprediction and external testing methods. As a result, each PLS-DA algorithm was represented by a number of figures of merit. The differences between PLS1-DA and PLS2-DA algorithms were assessed using hypothesis tests with respect to model accuracy, stability and fitting. On the other hand, confusion matrices of the two PLS-DA algorithms were inspected carefully for assessment of model parsimony. Overall, both the algorithms presented satisfactory model accuracy and stability. Nonetheless, PLS1-DA models showed significantly higher accuracy rates than PLS2-DA models, whereas PLS2-DA models seem to be much more stable compared to PLS1-DA models. Eventually, PLS2-DA also proved to be less prone to overfitting and is more parsimonious than PLS1-DA. In conclusion, the relatively high accuracy of the PLS1-DA algorithm is achieved at the cost of rather low parsimony and stability, and with an increased risk of overfitting.

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
Infrared-metabolomics approach in detecting changes in Andrographis paniculata metabolites due to different harvesting ages and times

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
Fulltext Reliability and validity of the Nigerian (Hausa) version of the Stroke Impact Scale (SIS) 3.0 index

Mohammad AH, Al-Sadat N, Siew Yim L, Chinna K

Biomed Res Int, 2014;2014:302097.
PMID: 25276774 DOI: 10.1155/2014/302097

This study aims to test the translated Hausa version of the stroke impact scale SIS (3.0) and further evaluate its psychometric properties. The SIS 3.0 was translated from English into Hausa and was tested for its reliability and validity on a stratified random sample adult stroke survivors attending rehabilitation services at stroke referral hospitals in Kano, Nigeria. Psychometric analysis of the Hausa-SIS 3.0 involved face, content, criterion, and construct validity tests as well as internal and test-retest reliability. In reliability analyses, the Cronbach's alpha values for the items in Strength, Hand function, Mobility, ADL/IADL, Memory and thinking, Communication, Emotion, and Social participation domains were 0.80, 0.92, 0.90, 0.78, 0.84, 0.89, 0.58, and 0.74, respectively. There are 8 domains in stroke impact scale 3.0 in confirmatory factory analysis; some of the items in the Hausa-SIS questionnaire have to be dropped due to lack of discriminate validity. In the final analysis, a parsimonious model was obtained with two items per construct for the 8 constructs (Chi-square/df < 3, TLI and CFI > 0.9, and RMSEA < 0.08). Cross validation with 1000 bootstrap samples gave a satisfactory result (P = 0.011). In conclusion, the shorter 16-item Hausa-SIS seems to measure adequately the QOL outcomes in the 8 domains.

Matched MeSH terms: Discriminant Analysis
Fulltext UltiMatch-NL: a Web service matchmaker based on multiple semantic filters

Mohebbi K, Ibrahim S, Zamani M, Khezrian M

PLoS One, 2014;9(8):e104735.
PMID: 25157872 DOI: 10.1371/journal.pone.0104735

In this paper, a Semantic Web service matchmaker called UltiMatch-NL is presented. UltiMatch-NL applies two filters namely Signature-based and Description-based on different abstraction levels of a service profile to achieve more accurate results. More specifically, the proposed filters rely on semantic knowledge to extract the similarity between a given pair of service descriptions. Thus it is a further step towards fully automated Web service discovery via making this process more semantic-aware. In addition, a new technique is proposed to weight and combine the results of different filters of UltiMatch-NL, automatically. Moreover, an innovative approach is introduced to predict the relevance of requests and Web services and eliminate the need for setting a threshold value of similarity. In order to evaluate UltiMatch-NL, the repository of OWLS-TC is used. The performance evaluation based on standard measures from the information retrieval field shows that semantic matching of OWL-S services can be significantly improved by incorporating designed matching filters.

Matched MeSH terms: Discriminant Analysis
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
Fulltext Improved classification of Orthosiphon stamineus by data fusion of electronic nose and tongue sensors

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
A discrimination method for the detection of pneumonia using chest radiograph

Noor NM, Rijal OM, Yunus A, Abu-Bakar SA

Comput Med Imaging Graph, 2010 Mar;34(2):160-6.
PMID: 19758785 DOI: 10.1016/j.compmedimag.2009.08.005

This paper presents a statistical method for the detection of lobar pneumonia when using digitized chest X-ray films. Each region of interest was represented by a vector of wavelet texture measures which is then multiplied by the orthogonal matrix Q(2). The first two elements of the transformed vectors were shown to have a bivariate normal distribution. Misclassification probabilities were estimated using probability ellipsoids and discriminant functions. The result of this study recommends the detection of pneumonia by constructing probability ellipsoids or discriminant function using maximum energy and maximum column sum energy texture measures where misclassification probabilities were less than 0.15.

Matched MeSH terms: Discriminant Analysis
Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features

Acharya UR, Mookiah MR, Koh JE, Tan JH, Noronha K, Bhandary SV, et al.

Comput Biol Med, 2016 06 01;73:131-40.
PMID: 27107676 DOI: 10.1016/j.compbiomed.2016.04.009

Age-related Macular Degeneration (AMD) affects the central vision of aged people. It can be diagnosed due to the presence of drusen, Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in the fundus images. It is labor intensive and time-consuming for the ophthalmologists to screen these images. An automated digital fundus photography based screening system can overcome these drawbacks. Such a safe, non-contact and cost-effective platform can be used as a screening system for dry AMD. In this paper, we are proposing a novel algorithm using Radon Transform (RT), Discrete Wavelet Transform (DWT) coupled with Locality Sensitive Discriminant Analysis (LSDA) for automated diagnosis of AMD. First the image is subjected to RT followed by DWT. The extracted features are subjected to dimension reduction using LSDA and ranked using t-test. The performance of various supervised classifiers namely Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) are compared to automatically discriminate to normal and AMD classes using ranked LSDA components. The proposed approach is evaluated using private and public datasets such as ARIA and STARE. The highest classification accuracy of 99.49%, 96.89% and 100% are reported for private, ARIA and STARE datasets. Also, AMD index is devised using two LSDA components to distinguish two classes accurately. Hence, this proposed system can be extended for mass AMD screening.

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
Geographical variation of the skull of the lesser mouse deer

Endo H, Fukuta K, Kimura J, Sasaki M, Stafford BJ

J Vet Med Sci, 2004 Oct;66(10):1229-35.
PMID: 15528854

We examined the geographical variation of the skull size and shape of the lesser mouse deer (Tragulus javanicus) from Laos, Thailand, Peninsular Malaysia, Sumatra, Java, Borneo, Langkawi and some Islands of Tenasserim in Myanmar. Although the influence of the climatic condition on skull size was not confirmed in the mainland populations, the skull became rostro-caudally longer in the populations of Tenasserim and Sumatra because of island isolation effect. The skull size was classified into the following three clusters of localities from the matrix of Q-mode correlation coefficients: 1) Langkawi and Tenasserim, 2) Laos and Thailand, 3) Sumatra and Borneo. The skulls in the population of Java belong to the cluster of Langkawi and Tenasserim in male, however were morphologically similar to those in the cluster of Borneo and Sumatra. The canonical discriminant analysis pointed out that the Laos and Tenasserim populations were separated from the other ones and that the populations of Sumatra, Java and Borneo were intermingled each other.

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 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
Fulltext Metabolite profiling of endophytic Streptomyces spp. and its antiplasmodial potential

Ahmad SJ, Mohamad Zin N, Mazlan NW, Baharum SN, Baba MS, Lau YL

PeerJ, 2021;9:e10816.
PMID: 33777509 DOI: 10.7717/peerj.10816

Background: Antiplasmodial drug discovery is significant especially from natural sources such as plant bacteria. This research aimed to determine antiplasmodial metabolites of Streptomyces spp. against Plasmodium falciparum 3D7 by using a metabolomics approach.
Methods: Streptomyces strains' growth curves, namely SUK 12 and SUK 48, were measured and P. falciparum 3D7 IC50 values were calculated. Metabolomics analysis was conducted on both strains' mid-exponential and stationary phase extracts.
Results: The most successful antiplasmodial activity of SUK 12 and SUK 48 extracts shown to be at the stationary phase with IC50 values of 0.8168 ng/mL and 0.1963 ng/mL, respectively. In contrast, the IC50 value of chloroquine diphosphate (CQ) for antiplasmodial activity was 0.2812 ng/mL. The univariate analysis revealed that 854 metabolites and 14, 44 and three metabolites showed significant differences in terms of strain, fermentation phase, and their interactions. Orthogonal partial least square-discriminant analysis and S-loading plot putatively identified pavettine, aurantioclavine, and 4-butyldiphenylmethane as significant outliers from the stationary phase of SUK 48. For potential isolation, metabolomics approach may be used as a preliminary approach to rapidly track and identify the presence of antimalarial metabolites before any isolation and purification can be done.

Matched MeSH terms: Discriminant Analysis

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