scholarly journals ATR-FTIR spectroscopy for virus identification: A powerful alternative

2020 ◽  
Vol 9 (3-4) ◽  
pp. 103-118 ◽  
Author(s):  
Marfran C.D. Santos ◽  
Camilo L.M. Morais ◽  
Kássio M.G. Lima
Keyword(s):  
Ftir Spectroscopy ◽  
Scientific Community ◽  
Principal Component ◽  
Attenuated Total Reflection ◽  
Successive Projections Algorithm ◽  
Virus Identification ◽  
Advantages And Disadvantages ◽  
The One ◽  
Spectroscopic Tool ◽  
Successive Projections

In pandemic times, like the one we are witnessing for COVID-19, the discussion about new efficient and rapid techniques for diagnosis of diseases is more evident. In this mini-review, we present to the virological scientific community the potential of attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy as a diagnosis technique. Herein, we explain the operation of this technique, as well as its advantages over standard methods. In addition, we also present the multivariate analysis tools that can be used to extract useful information from the data towards classification purposes. Tools such as Principal Component Analysis (PCA), Successive Projections Algorithm (SPA), Genetic Algorithm (GA) and Linear and Quadratic Discriminant Analysis (LDA and QDA) are covered, including examples of published studies. Finally, the advantages and disadvantages of ATR-FTIR spectroscopy are emphasized, as well as future prospects in this field of study that is only growing. One of the main aims of this paper is to encourage the scientific community to explore the potential of this spectroscopic tool to detect changes in biological samples such as those caused by the presence of viruses.

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.

Characteristics and discrimination of five types of wood-plastic composites by FTIR spectroscopy combined with principal component analysis

Holzforschung ◽  
2010 ◽  
Vol 64 (6) ◽  
Author(s):  
Chia-Huang Lee ◽  
Tung-Lin Wu ◽  
Yong-Long Chen ◽  
Jyh-Horng Wu
Keyword(s):  
Principal Component Analysis ◽  
Ftir Spectroscopy ◽  
Principal Component ◽  
Core Layer ◽  
Component Analysis ◽  
Attenuated Total Reflection ◽  
Distribution Profile ◽  
Data Set ◽  
Wood Plastic Composites ◽  
Plastic Composites

Abstract The analytical potential of attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy has been tested on the following wood-plastic composites (WPCs): high and low density polyethylene (HDPE and LDPE), polypropylene (PP), polystyrene (PS), and a recycled plastic (rHDPE). The data set of ATR-FTIR spectra has been analyzed by principal component analysis (PCA) and the studied samples could be grouped according to their polymeric matrixes. Additionally, ATR-FTIR spectroscopy proved to be a useful tool for determining the distribution profile of wood and plastic materials within different types of WPCs. Accordingly, the plastic content of the surface layers of HDPE, rHDPE, and PP composites was significantly higher than that of the core layer, whereas homogenous dispersion was observed in the LDPE composite. Among all WPCs, the PS composite displayed the worst dispersion.

scholarly journals Multivariate classification techniques and mass spectrometry as a tool in the screening of patients with fibromyalgia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marcelo V. S. Alves ◽  
Lanaia I. L. Maciel ◽  
Ruver R. F. Ramalho ◽  
Leomir A. S. Lima ◽  
Boniek G. Vaz ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Principal Component ◽  
Depression And Anxiety ◽  
Multivariate Classification ◽  
Successive Projections Algorithm ◽  
Linear Discriminant ◽  
Paper Spray Ionization ◽  
Chemical Work ◽  
Successive Projections

AbstractFibromyalgia is a rheumatological disorder that causes chronic pain and other symptomatic conditions such as depression and anxiety. Despite its relevance, the disease still presents a complex diagnosis where the doctor needs to have a correct clinical interpretation of the symptoms. In this context, it is valid to study tools that assist in the screening of this disease, using chemical work techniques such as mass spectroscopy. In this study, an analytical method is proposed to detect individuals with fibromyalgia (n = 20, 10 control samples and 10 samples with fibromyalgia) from blood plasma samples analyzed by mass spectrometry with paper spray ionization and subsequent multivariate classification of the spectral data (unsupervised and supervised), in addition to the treatment of selected variables with possible associations with metabolomics. Exploratory analysis with principal component analysis (PCA) and supervised analysis with successive projections algorithm with linear discriminant analysis (SPA-LDA) showed satisfactory results with 100% accuracy for sample prediction in both groups. This demonstrates that this combination of techniques can be used as a simple, reliable and fast tool in the development of clinical diagnosis of Fibromyalgia.

scholarly journals Spectrochemical differentiation of meningioma tumours based on attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy

2019 ◽  
Vol 412 (5) ◽  
pp. 1077-1086
Author(s):  
Taha Lilo ◽  
Camilo L. M. Morais ◽  
Katherine M. Ashton ◽  
Ana Pardilho ◽  
Charles Davis ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Discriminant Analysis ◽  
Ftir Spectroscopy ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Total Reflection ◽  
Attenuated Total Reflection ◽  
Tissue Samples ◽  
Linear Discriminant ◽  
Grade Ii

AbstractMeningiomas are the commonest types of tumours in the central nervous system (CNS). It is a benign type of tumour divided into three WHO grades (I, II and III) associated with tumour growth rate and likelihood of recurrence, where surgical outcomes and patient treatments are dependent on the meningioma grade and histological subtype. The development of alternative approaches based on attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy could aid meningioma grade determination and its biospectrochemical profiling in an automated fashion. Herein, ATR-FTIR in combination with chemometric techniques is employed to distinguish grade I, grade II and grade I meningiomas that re-occurred. Ninety-nine patients were investigated in this study where their formalin-fixed paraffin-embedded (FFPE) brain tissue samples were analysed by ATR-FTIR spectroscopy. Subsequent classification was performed via principal component analysis plus linear discriminant analysis (PCA-LDA) and partial least squares plus discriminant analysis (PLS-DA). PLS-DA gave the best results where grade I and grade II meningiomas were discriminated with 79% accuracy, 80% sensitivity and 73% specificity, while grade I versus grade I recurrence and grade II versus grade I recurrence were discriminated with 94% accuracy (94% sensitivity and specificity) and 97% accuracy (97% sensitivity and 100% specificity), respectively. Several wavenumbers were identified as possible biomarkers towards tumour differentiation. The majority of these were associated with lipids, protein, DNA/RNA and carbohydrate alterations. These findings demonstrate the potential of ATR-FTIR spectroscopy towards meningioma grade discrimination as a fast, low-cost, non-destructive and sensitive tool for clinical settings.

scholarly journals Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm

Agriculture ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1274
Author(s):  
Xingpeng Li ◽  
Hongzhe Jiang ◽  
Xuesong Jiang ◽  
Minghong Shi
Keyword(s):  
Hyperspectral Imaging ◽  
Geographical Origin ◽  
Principal Component ◽  
Brand Value ◽  
Support Vector ◽  
Successive Projections Algorithm ◽  
One Dimensional ◽  
Machine Learning Methods ◽  
Origin Identification ◽  
Successive Projections

The adulteration in Chinese chestnuts affects the quality, taste, and brand value. The objective of this study was to explore the feasibility of the hyperspectral imaging (HSI) technique to determine the geographical origin of Chinese chestnuts. An HSI system in spectral range of 400–1000 nm was applied to identify a total of 417 Chinese chestnuts from three different geographical origins. Principal component analysis (PCA) was preliminarily used to investigate the differences of average spectra of the samples from different geographical origins. A deep-learning-based model (1D-CNN, one-dimensional convolutional neural network) was developed first, and then the model based on full spectra and optimal wavelengths were established for various machine learning methods, including partial least squares-discriminant analysis (PLS-DA) and particle swarm optimization-support vector machine (PSO-SVM). The optimal results based on full spectra for 1D-CNN, PLS-DA, and PSO-SVM models were 97.12%, 97.12%, and 95.68%, respectively. Competitive adaptive reweighted sampling (CARS) and a successive projections algorithm (SPA) were individually utilized for wavelengths selection, and the results of simplified models generally improved. The contrasting results demonstrated that the prediction accuracies of SPA-PLS-DA and 1D-CNN both reached 97.12%, but 1D-CNN presented a higher Kappa coefficient value than SPA-PLS-DA. Meanwhile, the sensitivities and specificities of SPA-PLS-DA and 1D-CNN models were both above 90% for the samples from each geographical origin. These results indicated that both SPA-PLS-DA and 1D-CNN models combined with HSI have great potential for the geographical origin identification of Chinese chestnuts.

Assessment of poultry eggs freshness using FTIR spectroscopy combined with HCA and PCA methods

2018 ◽  
Vol 2 (1) ◽  
pp. 7-12
Author(s):  
Ewelina Michalczyk ◽  
Rafał Kurczab
Keyword(s):  
Chemical Composition ◽  
Ftir Spectroscopy ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Culture Method ◽  
Egg Yolk ◽  
Chemometric Analysis ◽  
Attenuated Total Reflection ◽  
Ftir Spectra ◽  
Chemometric Methods

The main aim of this study was to investigate the use of Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR FTIR) and selected chemometric methods to classify eggs in terms of the laying hen farming method, as well as to identify changes in the individual egg compositions during storage. In total, 50 eggs were used for the study; 10 eggs per classes: 0, 1, 2, 3 and rural. Eggs were stored by 29 days period, which was divided on the 10 measuring days in which one egg from each class was tested by recording two FTIR spectra for the shell, albumen and egg yolk. The chemometric analysis, including Hierarchical Cluster Analysis (HCA) and the Principal Component Analysis (PCA), was performed based on the recorded FTIR spectra. Changes in chemical composition during the experiment in individual egg elements were analyzed. Furthermore, by analyzing the graphs (HCA and PCA) obtained by the chemometric analysis, it was noted that the largest changes in the chemical composition of eggs occurred in the shell and yolk, while in the albumen it was less insignificant. The chemometric analysis of the recorded spectra also showed that combination of chemometric methods and FTIR spectroscopy can potentially be used to develop a non-destructive method for classifying eggs in terms of the hen culture method and to monitor of their freshness.

scholarly journals The use of ATR-FTIR spectroscopy to characterise crude heparin samples by composition and structural features

2019 ◽  
Author(s):  
Anthony Devlin ◽  
Lucio Mauri ◽  
Marco Guerrini ◽  
Edwin A. Yates ◽  
Mark A. Skidmore
Keyword(s):  
Ftir Spectroscopy ◽  
Conformational Changes ◽  
Spectroscopic Method ◽  
Principal Component ◽  
Analytical Techniques ◽  
Structural Features ◽  
Attenuated Total Reflection ◽  
Quality Analysis ◽  
Set Up ◽  
High Set

AbstractProduction of the major anticoagulant drug, heparin, is a complex process that begins with the collection of crude material from a dispersed network of suppliers with poor traceability, an issue that was made apparent in 2007-2008, when batches of heparin were contaminated deliberately in the supply chain, resulting in over 100 deaths in the US alone. Several analytical techniques are used currently for the characterisation of pharmaceutical grade heparin, but few have been applied to its crude counterpart. One exception is NMR spectroscopy which was used to study crude heparin (2017), however, owing to the high set-up and running costs, as well as the need for skilled technical operators, the use of NMR at crude heparin production plants is unviable. An alternative, practical, spectroscopic method is attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) that is user-friendly, economical and, importantly, requires little specialised training or sample preparation. Using a top-down chemometric approach employing principal component analysis, ATR-FTIR spectroscopy was able to distinguish crude heparins based on their similarity to pharmaceutical heparin, as well as on their compositional and structural features, which included levels of sulphation, the extent of related conformational changes, as well as the quantities of chondroitin and dermatan sulphate present. This approach lends itself to automation and will enable users and regulators to undertake quality control of crude heparin during manufacture. The method requires only economical, portable equipment and little specialised training, bringing the high-quality analysis of crude heparin within reach of both manufacturers and regulators for the first time.

Spearman's Law of Diminishing Returns

2006 ◽  
Vol 27 (4) ◽  
pp. 199-207 ◽  
Author(s):  
Peter Hartmann
Keyword(s):  
Principal Axis ◽  
Age Groups ◽  
Principal Component ◽  
Longitudinal Survey ◽  
Male And Female ◽  
Two Samples ◽  
Diminishing Returns ◽  
The One ◽  
Armed Services ◽  
Principal Axis Factor

Spearman's Law of Diminishing Returns (SLODR) with regard to age was tested in two different databases from the National Longitudinal Survey of Youth. The first database consisted of 6,980 boys and girls aged 12–16 from the 1997 cohort ( NLSY 1997 ). The subjects were tested with a computer-administered adaptive format (CAT) of the Armed Services Vocational Aptitude Battery (ASVAB) consisting of 12 subtests. The second database consisted of 11,448 male and female subjects aged 15–24 from the 1979 cohort ( NLSY 1979 ). These subjects were tested with the older 10-subtest version of the ASVAB. The hypothesis was tested by dividing the sample into Young and Old age groups while keeping IQ fairly constant by a method similar to the one developed and employed by Deary et al. (1996) . The different age groups were subsequently factor-analyzed separately. The eigenvalue of the first principal component (PC1) and the first principal axis factor (PAF1), and the average intercorrelation of the subtests were used as estimates of the g saturation and compared across groups. There were no significant differences in the g saturation across age groups for any of the two samples, thereby pointing to no support for this aspect of Spearman's “Law of Diminishing Returns.”

Hybrid polymer composite materials. Part 2. Preparation technology

2020 ◽  
pp. 8-13
Keyword(s):  
Composite Materials ◽  
Impact Toughness ◽  
Polymer Composites ◽  
The Other ◽  
Polymer Materials ◽  
Hybrid Polymer ◽  
Preparation Technology ◽  
Advantages And Disadvantages ◽  
Hybrid Polymer Composite ◽  
The One

The main methods (pressing and winding) of the processing of hybrid polymer composites to obtain items were examined. Advantages and disadvantages of the methods were noted. Good combinations of different-module fibers (carbon, glass, boron, organic) in hybrid polymer materials are described, which allow one to prepare materials with high compression strength on the one hand, and to increase fracture energy of samples and impact toughness on the other hand.

A Review of Drug Side Effect Identification Methods

2020 ◽  
Vol 26 (26) ◽  
pp. 3096-3104 ◽  
Author(s):  
Shuai Deng ◽  
Yige Sun ◽  
Tianyi Zhao ◽  
Yang Hu ◽  
Tianyi Zang
Keyword(s):  
Side Effects ◽  
Future Research ◽  
Drug Side Effects ◽  
Important Indicator ◽  
Advantages And Disadvantages ◽  
Key Points ◽  
Network Methods ◽  
Future Research Directions ◽  
The One ◽  
Approved Drugs

Drug side effects have become an important indicator for evaluating the safety of drugs. There are two main factors in the frequent occurrence of drug safety problems; on the one hand, the clinical understanding of drug side effects is insufficient, leading to frequent adverse drug reactions, while on the other hand, due to the long-term period and complexity of clinical trials, side effects of approved drugs on the market cannot be reported in a timely manner. Therefore, many researchers have focused on developing methods to identify drug side effects. In this review, we summarize the methods of identifying drug side effects and common databases in this field. We classified methods of identifying side effects into four categories: biological experimental, machine learning, text mining and network methods. We point out the key points of each kind of method. In addition, we also explain the advantages and disadvantages of each method. Finally, we propose future research directions.

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