Browse
Submit Data
Databases
API
Help

Dataset Information

25 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Authentication of Antibiotics Using Portable Near-Infrared Spectroscopy and Multivariate Data Analysis.

ABSTRACT: Counterfeit medicines represent a global public health threat warranting the development of accurate, rapid, and nondestructive methods for their identification. Portable near-infrared (NIR) spectroscopy offers this advantage. This work sheds light on the potential of combining NIR spectroscopy with principal component analysis (PCA) and soft independent modelling of class analogy (SIMCA) for authenticating branded and generic antibiotics. A total of 23 antibiotics were measured "nondestructively" using a portable NIR spectrometer. The antibiotics corresponded to six different active pharmaceutical ingredients being: amoxicillin trihydrate and clavulanic acid, azithromycin dihydrate, ciprofloxacin hydrochloride, doxycycline hydrochloride, and ofloxacin. NIR spectra were exported into Matlab R2018b where data analysis was applied. The results showed that the NIR spectra of the medicines showed characteristic features that corresponded to the main excipient(s). When combined with PCA, NIR spectroscopy could distinguish between branded and generic medicines and could classify medicines according to their manufacturing sources. The PCA scores showed the distinct clusters corresponding to each group of antibiotics, whereas the loadings indicated which spectral features were significant. SIMCA provided more accurate classification over PCA for all antibiotics except ciprofloxacin which products shared many overlapping excipients. In summary, the findings of the study demonstrated the feasibility of portable NIR as an initial method for screening antibiotics.

SUBMITTER: Assi S

PROVIDER: S-EPMC8645310 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Intracranial Traumatic Hematoma Detection in Children Using a Portable Near-infrared Spectroscopy Device.

Project description:IntroductionWe sought to validate a handheld, near-infrared spectroscopy (NIRS) device for detecting intracranial hematomas in children with head injury.MethodsEligible patients were those <18 years old who were admitted to the emergency department at three academic children's hospitals with head trauma and who received a clinically indicated head computed tomography (HCT). Measurements were obtained by a blinded operator in bilateral frontal, temporal, parietal, and occipital regions. Qualifying hematomas were a priori determined to be within the brain scanner's detection limits of >3.5 milliliters in volume and <2.5 centimeters from the surface of the brain. The device's measurements were positive if the difference in optical density between hemispheres was >0.2 on three successive scans. We calculated diagnostic performance measures with corresponding exact two-sided 95% Clopper-Pearson confidence intervals (CI). Hypothesis test evaluated whether predictive performance exceeded chance agreement (predictive Youden's index > 0).ResultsA total of 464 patients were enrolled and 344 met inclusion for primary data analysis: 10.5% (36/344) had evidence of a hematoma on HCT, and 4.7% (16/344) had qualifying hematomas. The handheld brain scanner demonstrated a sensitivity of 58.3% (21/36) and specificity of 67.9% (209/308) for hematomas of any size. For qualifying hematomas the scanner was designed to detect, sensitivity was 81% (13/16) and specificity was 67.4% (221/328). Predictive performance exceeded chance agreement with a predictive Youden's index of 0.11 (95% CI, 0.10 - 0.15; P < 0.001) for all hematomas, and 0.09 (95% CI, 0.08 - 0.12; P < 0.001) for qualifying hematomas.ConclusionThe handheld brain scanner can non-invasively detect a subset of intracranial hematomas in children and may serve an adjunctive role to head-injury neuroimaging decision rules that predict the risk of clinically significant intracranial pathology after head trauma.

| S-EPMC8203002 | biostudies-literature

Profiling Mannheimia haemolytica infection in dairy calves using near infrared spectroscopy (NIRS) and multivariate analysis (MVA).

Project description:Bovine respiratory disease (BRD) linked with Mannheimia haemolytica is the principal cause of pneumonia in cattle. Diagnosis of BRD traditionally relies on visual assessment, which can be untimely, insensitive, and nonspecific leading to inadequate treatment and further spread of disease. Near Infrared Spectroscopy (NIRS) is a rapid acquisition vibrational spectroscopy that can profile changes in biofluids, and when used in combination with multivariate analysis, has potential for disease diagnosis. This study characterizes the NIR spectral profile of blood plasma from dairy calves infected with M. haemolytica and validates the spectral biochemistry using standardized clinical and hematological reference parameters. Blood samples were collected for four days prior to (baseline), and 23 days after, a controlled intrabronchial challenge. NIR spectral profiles of blood plasma discriminated and predicted Baseline and Infected states of animal disease progression with accuracy, sensitivity, and specificity ≥ 90% using PCA-LDA models. These results show that physiological and biochemical changes occurring in the bloodstream of dairy calves during M. haemolytica infection are reflected in the NIR spectral profiles, demonstrating the potential of NIRS as a diagnostic and monitoring tool of BRD over time.

| S-EPMC7809125 | biostudies-literature

Differentiation between Normal and White Striped Turkey Breasts by Visible/Near Infrared Spectroscopy and Multivariate Data Analysis.

Project description:The appearance of white striations over breast meat is an emerging and growing problem. The main purpose of this study was to employ the reflectance of visible-near infrared (VIS/NIR) spectroscopy to differentiate between normal and white striped turkey breasts. Accordingly, 34 turkey breast fillets were selected representing a different level of white striping (WS) defects (normal, moderate and severe). The findings of VIS/NIR were analyzed by principal component (PC1) analysis (PCA). It was found that the first PC1 for VIS, NIR and VIS/NIR region explained 98%, 97%, and 96% of the total variation, respectively. PCA showed high performance to differentiate normal meat from abnormal meat (moderate and severe WS). In conclusion, the results of this research showed that VIS/NIR spectroscopy was satisfactory to differentiate normal from severe WS turkey fillets by using several quality traits.

| S-EPMC6957443 | biostudies-literature

Indexing cerebrovascular health using near-infrared spectroscopy.

Project description:Near-infrared spectroscopy (NiRS) is a relatively new technology of brain imaging with its potential in the assessment of cerebrovascular health only recently discovered. Encouraging early results suggest that NiRS can be used as an inexpensive and portable cerebrovascular health tracking device using a recently proposed pulse relaxation function (PReFx). In this paper, we propose a new NiRS timing index, [Formula: see text], of cerebrovascular health. [Formula: see text] is a novel use of the NiRS technology. [Formula: see text] is motivated by the previously proved relationship of the timing of the reflected wave with vascular resistance and compliance in the context of pressure waveforms. We correlated both [Formula: see text] and PReFx against age, a non-exercise cardiorespiratory fitness (CRF) index, and two existing indices of cerebrovascular health, namely transcranial Doppler (TCD) augmentation index, [Formula: see text], and magnetic resonance imaging (MRI) blood flow pulsatility index, [Formula: see text]. The [Formula: see text] correlations with Age, CRF, [Formula: see text] and [Formula: see text] all are significant, i.e., [Formula: see text] ([Formula: see text]), [Formula: see text] ([Formula: see text]), [Formula: see text] ([Formula: see text]) and [Formula: see text] ([Formula: see text]), respectively. PReFx, however, did not have significant correlations with any of the vascular health factors. The proposed timing index is a reliable indicator of cerebrovascular aging factors in the NiRS waveform.

| S-EPMC8292377 | biostudies-literature

Assessing functional impulsivity using functional near-infrared spectroscopy

Project description: Not available

| S-EPMC10790886 | biostudies-literature

Portable robot for autonomous venipuncture using 3D near infrared image guidance.

Project description:Venipuncture is pivotal to a wide range of clinical interventions and is consequently the leading cause of medical injury in the U.S. Complications associated with venipuncture are exacerbated in difficult settings, where the rate of success depends heavily on the patient's physiology and the practitioner's experience. In this paper, we describe a device that improves the accuracy and safety of the procedure by autonomously establishing a peripheral line for blood draws and IV's. The device combines a near-infrared imaging system, computer vision software, and a robotically driven needle within a portable shell. The device operates by imaging and mapping in real-time the 3D spatial coordinates of subcutaneous veins in order to direct the needle into a designated vein. We demonstrate proof of concept by assessing imaging performance in humans and cannulation accuracy on an advanced phlebotomy training model.

| S-EPMC4482475 | biostudies-literature

Multivariate method for prediction of fumonisins B1 and B2 and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR).

Project description:Fumonisins (FBs) and zearalenone (ZEN) are mycotoxins which occur naturally in grains and cereals, especially maize, causing negative effects on animals and humans. Along with the need for constant monitoring, there is a growing demand for rapid, non-destructive methods. Among these, Near Infrared Spectroscopy (NIR) has made great headway for being an easy-to-use technology. NIR was applied in the present research to quantify the contamination level of total FBs, i.e., fumonisin B1+fumonisin B2 (FB1+FB2), and ZEN in Brazilian maize. From a total of six hundred and seventy-six samples, 236 were analyzed for FBs and 440 for ZEN. Three regression models were defined: one with 18 principal components (PCs) for FB1, one with 10 PCs for FB2, and one with 7 PCs for ZEN. Partial least square regression algorithm with full cross-validation was applied as internal validation. External validation was performed with 200 unknown samples (100 for FBs and 100 for ZEN). Correlation coefficient (R), determination coefficient (R2), root mean square error of prediction (RMSEP), standard error of prediction (SEP) and residual prediction deviation (RPD) for FBs and ZEN were, respectively: 0.809 and 0.991; 0.899 and 0.984; 659 and 69.4; 682 and 69.8; and 3.33 and 2.71. No significant difference was observed between predicted values using NIR and reference values obtained by Liquid Chromatography Coupled to Tandem Mass Spectrometry (LC-MS/MS), thus indicating the suitability of NIR to rapidly analyze a large numbers of maize samples for FBs and ZEN contamination. The external validation confirmed a fair potential of the model in predicting FB1+FB2 and ZEN concentration. This is the first study providing scientific knowledge on the determination of FBs and ZEN in Brazilian maize samples using NIR, which is confirmed as a reliable alternative methodology for the analysis of such toxins.

| S-EPMC7790530 | biostudies-literature

Quantification of irrigated lesion morphology using near-infrared spectroscopy.

Project description:There are currently limited means by which lesion formation can be confirmed during radiofrequency ablation procedures. The purpose of this study was to evaluate the use of NIRS-integrated RFA catheters for monitoring irrigated lesion progression, ex vivo and in vivo. Open-irrigated NIRS-ablation catheters with optical fibers were fabricated to sample tissue diffuse reflectance. Spectra from 44 irrigated lesions and 44 non-lesion sites from ex vivo swine hearts (n = 15) were used to train and evaluate a predictive model for lesion dimensions based on key spectral features. Additional studies were performed in diluted blood to assess NIRS signatures of catheter-tissue contact status. Finally, the potential of NIRS-RFA catheters for guiding lesion delivery was evaluated in a set of in vivo pilot studies conducted in healthy pigs (n = 4). Model predictions for lesion depth (R = 0.968), width (R = 0.971), and depth percentage (R = 0.924) correlated well with measured lesion dimensions. In vivo deployment in preliminary trials showed robust translational consistency of contact discrimination (P < 0.0001) and lesion depth parameters (< 3% error). NIRS empowered catheters are well suited for monitoring myocardial response to RF ablation and may provide useful intraprocedural feedback for optimizing treatment efficacy alongside current practices.

| S-EPMC8505541 | biostudies-literature

Origin Identification of Hungarian Honey Using Melissopalynology, Physicochemical Analysis, and Near Infrared Spectroscopy.

Project description:The objective of the study was to check the authenticity of Hungarian honey using physicochemical analysis, near infrared spectroscopy, and melissopalynology. In the study, 87 samples from different botanical origins such as acacia, bastard indigo, rape, sunflower, linden, honeydew, milkweed, and sweet chestnut were collected. The samples were analyzed by physicochemical methods (pH, electrical conductivity, and moisture), melissopalynology (300 pollen grains counted), and near infrared spectroscopy (NIRS:740-1700 nm). During the evaluation of the data PCA-LDA models were built for the classification of different botanical and geographical origins, using the methods separately, and in combination (low-level data fusion). PC number optimization and external validation were applied for all the models. Botanical origin classification models were >90% and >55% accurate in the case of the pollen and NIR methods. Improved results were obtained with the combination of the physicochemical, melissopalynology, and NIRS techniques, which provided >99% and >81% accuracy for botanical and geographical origin classification models, respectively. The combination of these methods could be a promising tool for origin identification of honey.

| S-EPMC8658813 | biostudies-literature

Combining multivariate analysis and monosaccharide composition modeling to identify plant cell wall variations by Fourier Transform Near Infrared spectroscopy.

Project description:We outline a high throughput procedure that improves outlier detection in cell wall screens using FT-NIR spectroscopy of plant leaves. The improvement relies on generating a calibration set from a subset of a mutant population by taking advantage of the Mahalanobis distance outlier scheme to construct a monosaccharide range predictive model using PLS regression. This model was then used to identify specific monosaccharide outliers from the mutant population.

| S-EPMC3168417 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data