scholarly journals Multiple Chromatographic Analysis of Urine in the Detection of Bladder Cancer

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1793
Author(s):  
Daniela Džubinská ◽  
Milan Zvarík ◽  
Boris Kollárik ◽  
Libuša Šikurová
Keyword(s):  
Bladder Cancer ◽  
Discriminant Analysis ◽  
High Performance ◽  
Partial Least Square ◽  
Least Square ◽  
Fluorescence Detector ◽  
Multivariate Statistical ◽  
Non Invasive ◽  
Invasive Method ◽  
Chromatographic Peaks

Bladder cancer (BC) is the most common type of carcinoma of the urological system. Recently, there has been an increasing interest in non-invasive diagnostic tumor markers due to the invasive attribute of cystoscopy, which is still considered the gold standard diagnostic method. However, markers published in the literature so far do not meet expectations for replacing cystoscopy due to their low specificity and excessively high false-positive results, which can be mainly caused by frequently occurring hematuria also in benign cases. No reliable non-invasive method has yet been identified that can distinguish patients with bladder cancer and non-malignant hematuria patients. Our work examined the possibilities of non-targeted biomarkers of urine to distinguish patients with malignant and non-malignant diseases of the bladder using 3D HPLC in combination with computer processing of multiple datasets. Urine samples from 47 patients, 23 patients with bladder cancer (BC) and 24 patients with non-malignant hematuria (NMHU), were enrolled in clinical trials. For the separation and subsequent analysis of a large number of urine components, 3D HPLC (high-performance liquid chromatography) with an absorption and fluorescence detector was used. The obtained dataset was further subjected to various uni- and multi-dimensional statistical analyses and mathematical modeling. We found 334 chromatographic peaks, of which 18 peaks were identified as significantly different for BC and NMHU patients. Using receiver operating characteristic (ROC) analysis, we assessed the informative ability of significant chromatographic peaks (90% sensitivity and 74% specificity). By logistic regression, we identified the optimal and simplified set of seven chromatographic peaks (5 absorptions plus 2 fluorescence) with strong classification power (100% sensitivity and 100% specificity) for distinguishing patients with bladder cancer and those with non-malignant hematuria. Partial least square discriminant analysis (PLS-DA) model and orthogonal projection to latent structure discriminant analysis (OPLS-DA) with 100% sensitivity and 96% specificity were used to distinguish BC and NMHU patients. Multivariate statistical analysis of urinary metabolomic profiles of patients revealed that BC patients can be discriminated from NMHU patients and the results can likely contribute to an early and non-invasive diagnosis of BC.

scholarly journals Metabolomics profiling of Polygoni Multiflori Radix and Polygoni Multiflori Radix Preparata extracts using UPLC-Q/TOF-MS

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Zhaoyan Zhang ◽  
Liang Yang ◽  
Xiaoyan Huang ◽  
Yue Gao
Keyword(s):  
High Performance ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Alpha Linolenic Acid ◽  
Multivariate Statistical ◽  
Biochemical Indicators ◽  
Flight Mass Spectrometry ◽  
Underlying Mechanisms ◽  
Tof Ms

Abstract Background The side effects caused by Polygoni Multiflori Radix (PMR) and Polygoni Multiflori Radix Praeparata (PMRP) have often appeared globally. There is no research on the changes of endogenous metabolites among PMR- and PMRP-treated rats. The aim of this study was to evaluate the varying metabolomic effects between PMR- and PMRP-treated rats. We tried to discover relevant differences in biomarkers and endogenous metabolic pathways. Methods Hematoxylin and eosin staining and immunohistochemistry staining were performed to find pathological changes. Biochemical indicators were also measured, one-way analysis of variance with Dunnett’s multiple comparison test was used for biochemical indicators comparison among various groups. Metabolomics analysis based on ultra-high performance liquid chromatography-quadrupole time of flight mass spectrometry (UPLC-Q/TOF-MS) was performed to find the changes in metabolic biomarkers. Multivariate statistical approaches such as principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA) were applied to reveal group clustering trend, evaluate and maximize the discrimination between the two groups. MetaboAnalyst 4.0 was performed to find and confirm the pathways. Results PMR extracts exhibited slight hepatotoxic effects on the liver by increasing aspartate and alanine aminotransferase levels. Twenty-nine metabolites were identified as biomarkers, belonging to five pathways, including alpha-linolenic acid metabolism, taurine and hypotaurine metabolism, glycerophospholipid metabolism, arginine and proline metabolism, and primary bile acid biosynthesis. Conclusion This study provided a comprehensive description of metabolomic changes between PMR- and PMRP-treated rats. The underlying mechanisms require further research.

scholarly journals Estimation of Cholesterol in the Presence of Urea and Other Constituents in Blood Tissue

2020 ◽  
Vol 9 (3) ◽  
pp. 1831-1834
Keyword(s):  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Blood Cholesterol ◽  
Processing Unit ◽  
Non Invasive ◽  
Body Tissues ◽  
Invasive Method ◽  
Rf Signal

The determination of cholesterol in pathological laboratories is more painful and may lead to infections if no proper precautions are taken into consideration. The manuscript presented here describes a non-invasive method to estimate blood cholesterol using Radio Frequency (RF) probe signal in the range of 10MHz – 500MHz. The method uses Scalar Network Analyzer and injects RF signal less than -30dbm, which is quite safe for human tissue and the frequency range falls in non-ionizing radiation wavelengths. Due to the absorption of RF signal by various body tissues including cholesterol, the received signal by the spectrum analyzer bears the signatures of percentage of the cholesterol present in the blood. These signatures are then analyzed using multivariate approach to estimate the Cholesterol. The Signal Processing Unit consists of Partial Least Square Regression (PLSR) tool based on the SIMPLS Algorithm. The results show that there is a good agreement between the predicted percentages of Cholesterol as compared to the actual. Though the study here uses only 5 components, the technique can also be used to include other blood components for the estimation of Cholesterol.

scholarly journals A Novel Validated Injectable Colistimethate Sodium Analysis Combining Advanced Chemometrics and Design of Experiments

Molecules ◽  
2021 ◽  
Vol 26 (6) ◽  
pp. 1546
Author(s):  
Ioanna Dagla ◽  
Anthony Tsarbopoulos ◽  
Evagelos Gikas
Keyword(s):  
Design Of Experiments ◽  
High Performance ◽  
Similarity Index ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Distance Measures ◽  
Linear Regression Models ◽  
Colistimethate Sodium

Colistimethate sodium (CMS) is widely administrated for the treatment of life-threatening infections caused by multidrug-resistant Gram-negative bacteria. Until now, the quality control of CMS formulations has been based on microbiological assays. Herein, an ultra-high-performance liquid chromatography coupled to ultraviolet detector methodology was developed for the quantitation of CMS in injectable formulations. The design of experiments was performed for the optimization of the chromatographic parameters. The chromatographic separation was achieved using a Waters Acquity BEH C8 column employing gradient elution with a mobile phase consisting of (A) 0.001 M aq. ammonium formate and (B) methanol/acetonitrile 79/21 (v/v). CMS compounds were detected at 214 nm. In all, 23 univariate linear-regression models were constructed to measure CMS compounds separately, and one partial least-square regression (PLSr) model constructed to assess the total CMS amount in formulations. The method was validated over the range 100–220 μg mL−1. The developed methodology was employed to analyze several batches of CMS injectable formulations that were also compared against a reference batch employing a Principal Component Analysis, similarity and distance measures, heatmaps and the structural similarity index. The methodology was based on freely available software in order to be readily available for the pharmaceutical industry.

scholarly journals A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

2021 ◽  
pp. 096703352098731
Author(s):  
Adenilton C da Silva ◽  
Lívia PD Ribeiro ◽  
Ruth MB Vidal ◽  
Wladiana O Matos ◽  
Gisele S Lopes
Keyword(s):  
Quality Control ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Linear Discriminant ◽  
Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

Identification and Quantification of Metamizole in Traditional Herbal Medicines using Spectroscopy FTIR-ATR combined with Chemometrics

2021 ◽  
pp. 4413-4419
Author(s):  
Dharmastuti Cahya Fatmarahmi ◽  
Ratna Asmah Susidarti ◽  
Respati Tri Swasono ◽  
Abdul Rohman
Keyword(s):  
Discriminant Analysis ◽  
Herbal Medicine ◽  
Ftir Spectroscopy ◽  
Herbal Medicines ◽  
Principal Component ◽  
Attenuated Total Reflection ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Precision Data

The study aims to develop an effective, efficient, and reliable method using Fourier Transform Infrared (FTIR) spectroscopy with Attenuated Total Reflection (ATR) combined with chemometric for identifying the synthetic drug in Indonesian herbal medicine known as Jamu. Jamu powders, Metamizole, and the binary mixture of Jamu and Metamizole were measured using FTIR-ATR at the mid-infrared region (4000-650 cm-1). The obtained spectra profiles were further analyzed by Principal Component Analysis, Partial Least Square Regression, Principal Component Regression, and Discriminant Analysis. Jamu Pegel Linu (JPL), Jamu Encok (JE), Jamu Sakit Pinggang (JSP), Metamizole (M), and adulterated Jamu by Metamizole were discriminated well on PCA score plot. PLSR and PCR showed the accuracy and precision data to quantify JPL, JE, and JSP, and each adulterated by M with R2 value > 0,995 and low value of RMSEC and RMSEP. Discriminant Analysis (DA) was successfully grouping Jamu and Metamizole without any misclassification. A combination of FTIR spectroscopy and chemometrics offered useful tools for detecting Metamizole in traditional herbal medicine.

Authentication of geographical origin of palm oil by chromatographic fingerprinting of triacylglycerols and partial least square-discriminant analysis

Talanta ◽  
2013 ◽  
Vol 116 ◽  
pp. 788-793 ◽  
Author(s):  
Cristina Ruiz-Samblás ◽  
Cristina Arrebola-Pascual ◽  
Alba Tres ◽  
Saskia van Ruth ◽  
Luis Cuadros-Rodríguez
Keyword(s):  
Discriminant Analysis ◽  
Palm Oil ◽  
Geographical Origin ◽  
Partial Least Square ◽  
Least Square ◽  
Chromatographic Fingerprinting

Near-Infrared Spectroscopy for Discrimination of Charcoal from Eucalyptus and Native Cerrado Species—Contribution to a Database for Forestry Supervision

2021 ◽  
Author(s):  
Silvana Nisgoski ◽  
Thaís A P Gonçalves ◽  
Júlia Sonsin-Oliveira ◽  
Adriano W Ballarin ◽  
Graciela I B Muñiz
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
New Technologies ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Eucalyptus Species ◽  
Linear Discriminant

Abstract The illegal charcoal trade is an internationally well-known forest crime. In Brazil, government agents try to control it using the document of forest origin (DOF). To confirm a load’s legality, the agents must compare it with the declared content of the DOF. However, to identify charcoal is difficult even for specialists in wood anatomy. Hence, new technologies would facilitate the agents’ work. Near-infrared spectroscopy (NIR) provides a rapid and precise response to differentiate carbonized species. Considering the rich Brazilian flora, NIR studies are still underdeveloped. Our work aimed to differentiate charcoals of seven eucalypts and 10 Cerrado species based on NIR analysis and to add information to a charcoal database. Data were collected with a spectrophotometer in reflectance mode. Partial least square regression with discriminant analysis (PLS-DA) and a linear discriminant analysis (LDA) was applied to confirm the performance and potential of NIR spectra to distinguish native Cerrado species from eucalyptus species. Wavenumbers from 4,000 to 6,000 cm−1 and transversal surface presented the best results. NIR had the potential to distinguish eucalypt charcoals from Cerrado species and in comparison to reference samples. NIR is a potential tool for forestry supervision to guarantee the sustainability of the charcoal supply in Brazil and countries with similar conditions. Study Implications It is a challenge to protect the Cerrado biome against deforestation for charcoal production. The application of new technologies such as near-infrared spectroscopy (NIR) for charcoal identification might improve the work of government agents. In this article, we studied the spectra of Cerrado and eucalypt species. Our results present good separation between the analyzed groups. The main goal is to develop a reliable NIR database that would be useful in the practical work of agents. The database will be available for all control agencies, and future training will be done for a rapid initial evaluation in the field.

Wood discrimination analyses of Pterocarpus tinctorius and endangered Pterocarpus santalinus using DART-FTICR-MS coupled with multivariate statistics

IAWA Journal ◽  
2019 ◽  
Vol 40 (1) ◽  
pp. 58-74 ◽  
Author(s):  
Maomao Zhang ◽  
Guang Jie Zhao ◽  
Bo Liu ◽  
Tuo He ◽  
Juan Guo ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Information ◽  
Ion Cyclotron Resonance ◽  
Timber Species ◽  
Multivariate Statistical ◽  
Fticr Ms ◽  
Pterocarpus Santalinus

ABSTRACT Pterocarpus santalinus, listed in CITES Appendix II, is an endangered timber species as a result of illegal harvesting due to its high value and commercial demand. The growing demand for P. santalinus and timbers with the morphologically similar Pterocarpus tinctorius has resulted in confusion as well as identification problems. Therefore, it is of vital importance to explore reliable ways to accurately discriminate between P. santalinus and P. tinctorius. In this study, the method of direct analysis in real time and fourier transform ion cyclotron resonance mass spectrometry (DART-FTICR-MS), combined with multivariate statistical analysis, was used to extract chemical information from xylarium wood specimens and to explore the feasibility of distinguishing these two species. Significant differences were observed in their DART-FTICR-MS spectra. Orthogonal partial least square-discriminant analysis (OPLS-DA) showed the highest prediction, with an accuracy of 100%. These findings demonstrate the feasibility of authenticating wood types using DART-FTICR-MS coupled with multivariate statistical analysis.

Determination of the viability of retinispora (Hinoki cypress) seeds using shortwave infrared hyperspectral imaging spectroscopy

2020 ◽  
Vol 28 (2) ◽  
pp. 70-80 ◽  
Author(s):  
Perez Mukasa ◽  
Collins Wakholi ◽  
Akbar Faqeerzada Mohammad ◽  
Eunsoo Park ◽  
Jayoung Lee ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Imaging Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Successive Projections Algorithm ◽  
Hinoki Cypress ◽  
Sorting System ◽  
Shortwave Infrared ◽  
Successive Projections

The combination of hyperspectral imaging with multivariate data analysis methods has recently been applied to develop a nondestructive technique, required to determine the seed viability of artificially aged vegetable and cereal seeds. In this study, the potential of shortwave infrared hyperspectral imaging to determine the viability of naturally aged seeds was investigated and thereafter a model for online seed sorting system was developed. The hyperspectral images of 400 Hinoki cypress tree seeds were acquired, and germination tests were conducted for viability confirmation, which indicated 31.5% of the viable seeds. Partial least square discriminant analysis models with 179 variables in the wavelength region of 1000–1800 nm were developed with a maximum model accuracy of 98.4% and 93.8% in both the calibration and validation sets, respectively. The partial least square discriminant analysis beta coefficient revealed the key wavelengths to differentiate viable from nonviable seeds, determined based on the differences in the chemical compositions of the seeds, including their lipid and fatty acid contents, which may control the germination ability of the seeds. The most effective wavelengths were selected using two model-based variable selection methods (i.e., the variable importance of projection (15 variables) and the successive projections algorithm (8 variables)) to develop the model. The successive projections algorithm wavelength selection method was considered to develop a viability model, and its application to the raw data resulted in a prediction accuracy of 94.7% in the calibration set and 92.2% in the validation set. These results demonstrate the potential of shortwave infrared hyperspectral imaging spectroscopy as a powerful nondestructive method to determine the viability of Hinoki cypress seeds. This method could be applied to develop an online seed sorting system for seed companies and nurseries.

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