scholarly journals Detection and classification of Bacteria using Raman Spectroscopy Combined with Multivariate Analysis

2017 ◽  
Vol 2 (4) ◽  
pp. 435 ◽  
Author(s):  
S. Sil ◽  
R. Mukherjee ◽  
N. S. Kumar ◽  
Aravind S. ◽  
J. Kingston ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Multivariate Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Spectroscopic Techniques ◽  
Hybrid Technique ◽  
Chemometric Method ◽  
Linear Discriminant ◽  
Linear Discrimination

<p class="p1">Vibrational spectroscopic techniques have advantages over conventional microbiological approaches towards identification &amp; detection of pathogens. Since unique spectral fingerprint is obtained, one can identify very closely related bacteria using such methods. In this study Raman microspectroscopy in combination with chemometric method has been used to classify four strains of <em>E</em>. <em>coli </em>(two pathogenic &amp; two non-pathogenic). Different multivariate approaches such as hierarchical cluster analysis, principal component analysis &amp; linear discriminant analysis were explored to obtain efficient classification of the Raman signals obtained from the four strains of <em>E.coli</em>. It was observed that multivariate analysis was able to classify the bacteria at strain level. Linear discrimination analysis using PC scores (PC-LDA) was found to give very good result with as high as 100% accuracy. This hybrid technique (Raman spectroscopy &amp; multivariate analysis) has tremendous potential to be developed as a tool for bacterial identification.<span class="Apple-converted-space"> </span></p>

Raman Analysis Reveals Biochemical Differences in Plasma of Crohn’s Disease Patients

2020 ◽  
Vol 14 (11) ◽  
pp. 1572-1580
Author(s):  
Carlo Morasso ◽  
Marta Truffi ◽  
Renzo Vanna ◽  
Sara Albasini ◽  
Serena Mazzucchelli ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Aromatic Amino Acids ◽  
Principal Component ◽  
Plasma Samples ◽  
Spectra Analysis ◽  
Linear Discrimination ◽  
Β Sheet

Abstract Backgrounds and Aims There is no accurate and reliable circulating biomarker to diagnose Crohn’s disease [CD]. Raman spectroscopy is a relatively new approach that provides information on the biochemical composition of samples in minutes and virtually without any sample preparation. We aimed to test the use of Raman spectroscopy analysis of plasma samples as a potential diagnostic tool for CD. Methods We analysed by Raman spectroscopy dry plasma samples obtained from 77 CD patients [CD] and 45 healthy controls [HC]. In the dataset obtained, we analysed spectra differences between CD and HC, as well as among CD patients with different disease behaviours. We also developed a method, based on principal component analysis followed by a linear discrimination analysis [PCA-LDA], for the automatic classification of individuals based on plasma spectra analysis. Results Compared with HC, the CD spectra were characterised by less intense peaks corresponding to carotenoids [p &lt;10–4] and by more intense peaks corresponding to proteins with β-sheet secondary structure [p &lt;10–4]. Differences were also found on Raman peaks relative to lipids [p = 0.0007] and aromatic amino acids [p &lt;10–4]. The predictive model we developed was able to classify CD and HC subjects with 83.6% accuracy [sensitivity 80.0% and specificity 85.7%] and F1-score of 86.8%. Conclusions Our results indicate that Raman spectroscopy of blood plasma can identify metabolic variations associated with CD and it could be a rapid pre-screening tool to use before further specific evaluation.

scholarly journals RIGHT-ANGLE FLUORESCENCE SPECTROSCOPY FOR DIFFERENTIATION OF DISTILLED ALCOHOLIC BEVERAGES

2013 ◽  
Vol 12 (2) ◽  
pp. 83-92 ◽  
Author(s):  
Veronika Uríčková ◽  
Jana Sádecká ◽  
Pavel Májek
Keyword(s):  
Fluorescence Spectroscopy ◽  
Emission Spectra ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Alcoholic Beverages ◽  
Front Face ◽  
Excitation Wavelength ◽  
Spectroscopic Techniques ◽  
Excitation Spectra ◽  
Linear Discriminant

Abstract Total luminescence and synchronous scanning fluorescence spectroscopic techniques were investigated for differentiating brandies from mixed wine spirits. The studies were performed on 16 brandies from 3 different producers and 30 mixed wine spirits from 5 different producers. Differentiation between samples was accomplished by multivariate data analysis methods (principal component analysis, hierarchical cluster analysis, and linear discriminant analysis). Correct classification was obtained using emission spectra (400-650 nm) recorded at excitation wavelength 390 nm, excitation spectra (225-460 nm) obtained at emission wavelength 470 nm and synchronous fluorescence spectra (200-700 nm) collected at wavelength interval 80 nm. These results indicate that right-angle fluorescence spectroscopy offers a promising approach for the authentication of brandies as neither sample preparation nor special qualification of the personnel are required, and data acquisition and analysis are relatively simple when compared to front-face technique.

scholarly journals RAMAN SPECTROSCOPIC STUDY ON PREDICTION OF TREATMENT RESPONSE IN CERVICAL CANCERS

2013 ◽  
Vol 06 (02) ◽  
pp. 1350014 ◽  
Author(s):  
S. RUBINA ◽  
M. S. VIDYASAGAR ◽  
C. MURALI KRISHNA
Keyword(s):  
Raman Spectroscopy ◽  
Tumor Response ◽  
Locally Advanced ◽  
Principal Component ◽  
Diagnostic Tools ◽  
Spectroscopic Techniques ◽  
Predictive Tool ◽  
Linear Discriminant ◽  
Cervical Cancers

Concurrent chemoradiotherapy (CCRT) is the choice of treatment for locally advanced cervical cancers; however, tumors exhibit diverse response to treatment. Early prediction of tumor response leads to individualizing treatment regimen. Response evaluation criteria in solid tumors (RECIST), the current modality of tumor response assessment, is often subjective and carried out at the first visit after treatment, which is about four months. Hence, there is a need for better predictive tool for radioresponse. Optical spectroscopic techniques, sensitive to molecular alteration, are being pursued as potential diagnostic tools. Present pilot study aims to explore the fiber-optic-based Raman spectroscopy approach in prediction of tumor response to CCRT, before taking up extensive in vivo studies. Ex vivo Raman spectra were acquired from biopsies collected from 11 normal (148 spectra), 16 tumor (201 spectra) and 13 complete response (151 CR spectra), one partial response (8 PR spectra) and one nonresponder (8 NR spectra) subjects. Data was analyzed using principal component linear discriminant analysis (PC-LDA) followed by leave-one-out cross-validation (LOO-CV). Findings suggest that normal tissues can be efficiently classified from both pre- and post-treated tumor biopsies, while there is an overlap between pre- and post-CCRT tumor tissues. Spectra of CR, PR and NR tissues were subjected to principal component analysis (PCA) and a tendency of classification was observed, corroborating previous studies. Thus, this study further supports the feasibility of Raman spectroscopy in prediction of tumor radioresponse and prospective noninvasive in vivo applications.

scholarly journals Structured Laser Illumination Planar Imaging Based Classification of Ground Coffee Using Multivariate Chemometric Analysis

2016 ◽  
Vol 8 (3) ◽  
pp. 32 ◽  
Author(s):  
Olivier K. Bagui ◽  
Kenneth A. Kaduki ◽  
Edouard Berrocal ◽  
Jeremie T. Zoueu
Keyword(s):  
Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Planar Imaging ◽  
Reliable Measurement ◽  
Laser Illumination ◽  
Coffee Beans ◽  
Chemometric Techniques ◽  
Ground Coffee

<p class="1Body">Most commercially available ground coffees are processed from Robusta or Arabica coffee beans. In this work, we report on the potential of Structured Laser Illumination Planar Imaging (SLIPI) technique for the classification of five types of Robusta and Arabica commercial ground coffee samples (Familial, Belier, Brazil, Colombia and Malaga). This classification is made, here, from the measurement of the extinction coefficient µ<sub>e</sub> and of the optical depth OD by means of SLIPI. The proposed technique offers the advantage of eliminating the light intensity from photons which have been multiply scattered in the coffee solution, leading to an accurate and reliable measurement of µ<sub>e</sub>. Data analysis uses the chemometric techniques of Principal Component Anaysis (PCA) for variable selection and Hierarchical Cluster Analysis (HCA) for classification. The chemometric model demonstrates the potential of this approach for practical assessment of coffee grades by correctly classifying the coffee samples according to their species.</p>

scholarly journals Determination of HPLC-UV Fingerprints of Spanish Paprika (Capsicum annuum L.) for Its Classification by Linear Discriminant Analysis

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4479 ◽  
Author(s):  
Xavier Cetó ◽  
Núria Serrano ◽  
Miriam Aragó ◽  
Alejandro Gámez ◽  
Miquel Esteban ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Reversed Phase ◽  
Classification Rate ◽  
Phenolic Profile ◽  
Linear Discriminant ◽  
Sample Extraction

The development of a simple HPLC-UV method towards the evaluation of Spanish paprika’s phenolic profile and their discrimination based on the former is reported herein. The approach is based on C18 reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identified in paprika. Paprika samples were subjected to a sample extraction stage by sonication and centrifugation; extracting procedure and conditions were optimized to maximize the generation of enough discriminant fingerprints. Finally, chromatograms were baseline corrected, compressed employing fast Fourier transform (FFT), and then analyzed by means of principal component analysis (PCA) and LDA to carry out the classification of paprika samples. Under the developed procedure, a total of 96 paprika samples were analyzed, achieving a classification rate of 100% for the test subset (n = 25).

scholarly journals Discrimination of the Geographical Origin of the Lateral Roots of Aconitum carmichaelii Using the Fingerprint, Multicomponent Quantification, and Chemometric Methods

Molecules ◽  
2019 ◽  
Vol 24 (22) ◽  
pp. 4124 ◽  
Author(s):  
Lu-Lin Miao ◽  
Qin-Mei Zhou ◽  
Cheng Peng ◽  
Chun-Wang Meng ◽  
Xiao-Ya Wang ◽  
...  
Keyword(s):  
High Performance ◽  
Geographical Origin ◽  
Lateral Roots ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Similarity Analysis ◽  
Diterpenoid Alkaloids ◽  
Linear Discriminant ◽  
Hplc Fingerprint ◽  
Aconitum Carmichaelii

Fuzi is a well-known traditional Chinese medicine developed from the lateral roots of Aconitum carmichaelii Debx. It is rich in alkaloids that display a wide variety of bioactivities, and it has a strong cardiotoxicity and neurotoxicity. In order to discriminate the geographical origin and evaluate the quality of this medicine, a method based on high-performance liquid chromatography (HPLC) was developed for multicomponent quantification and chemical fingerprint analysis. The measured results of 32 batches of Fuzi from three different regions were evaluated by chemometric analysis, including similarity analysis (SA), hierarchical cluster analysis (HCA), principal component analysis (PCA), and linear discriminant analysis (LDA). The content of six representative alkaloids of Fuzi (benzoylmesaconine, benzoylhypaconine, benzoylaconine, mesaconitine, hypaconitine, and aconitine) were varied by geographical origin, and the content ratios of the benzoylmesaconine/mesaconitine and diester-type/monoester-type diterpenoid alkaloids may be potential traits for classifying the geographical origin of the medicine. In the HPLC fingerprint similarity analysis, the Fuzi from Jiangyou, Sichuan, was distinguished from the Fuzi from Butuo, Sichuan, and the Fuzi from Yunnan. Based on the HCA and PCA analyses of the content of the six representative alkaloids, all of the batches were classified into two categories, which were closely related to the plants’ geographical origins. The Fuzi samples from Jiangyou were placed into one category, while the Fuzi samples from Butuo and Yunnan were put into another category. The LDA analysis provided an efficient and satisfactory prediction model for differentiating the Fuzi samples from the above-mentioned three geographical origins. Thus, the content of the six representative alkaloids and the fingerprint similarity values were useful markers for differentiating the geographical origin of the Fuzi samples.

Raman Spectroscopy for Inverted Papilloma: A Proof-of-Concept Study

2018 ◽  
Vol 159 (3) ◽  
pp. 587-589 ◽  
Author(s):  
Marco A. Mascarella ◽  
Abdulaziz Alrasheed ◽  
Naif Fnais ◽  
Ophelie Gourgas ◽  
Ghulam Jalani ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Raman Spectroscopy ◽  
Chronic Rhinosinusitis ◽  
Principal Component ◽  
Inverted Papilloma ◽  
Proof Of Concept ◽  
Linear Discriminant ◽  
Acceptable Accuracy ◽  
Sinonasal Mucosa ◽  
Inverted Papillomas

Inverted papillomas are tumors of the sinonasal tract with a propensity to recur. Raman spectroscopy can potentially identify inverted papillomas from other tissue based on biochemical signatures. A pilot study comparing Raman spectroscopy to histopathology for 3 types of sinonasal tissue was performed. Spectral data of biopsies from patients with normal sinonasal mucosa, chronic rhinosinusitis, and inverted papillomas are compared to histopathology using principal component analysis and linear discriminant analysis after data preprocessing. A total of 18 normal, 15 chronic rhinosinusitis, and 18 inverted papilloma specimens were evaluated. The model distinguished normal sinonasal mucosa, chronic rhinosinusitis, and inverted papilloma tissue with an overall accuracy of 90.2% (95% confidence interval, 0.86-0.94). In conclusion, Raman spectroscopy can distinguish inverted papilloma, normal sinonasal mucosa, and chronically rhinosinusitis tissue with acceptable accuracy.

scholarly journals Characterization and classification of Romanian acacia honey based on its physicochemical parameters and chemometrics

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mihaela Emanuela Crăciun ◽  
Oana Cristina Pârvulescu ◽  
Andreea Cristina Donise ◽  
Tănase Dobre ◽  
Dumitru Radu Stanciu
Keyword(s):  
Physicochemical Parameters ◽  
Stable Carbon Isotopes ◽  
Reducing Sugars ◽  
Activity Ratio ◽  
Sugar Content ◽  
Principal Component ◽  
Reducing Sugar ◽  
Linear Discriminant ◽  
Acacia Honey

AbstractThree groups of Romanian acacia honey, i.e., pure, directly adulterated (by mixing the pure honey with three sugar syrups), and indirectly adulterated (by feeding the bees with the same syrups), were characterized and discriminated based on their physicochemical parameters. Moisture, ash, 5-hydroxymethylfurfural (HMF), reducing sugars (fructose and glucose), and sucrose contents, free acidity, diastase activity, ratio between stable carbon isotopes of honey and its proteins (δ13CH and δ13CP) were evaluated. Adulteration led to a significant increase in sucrose content, HMF level, and Δδ13C = δ13CH‒δ13CP as well a decrease in reducing sugar content and diastase activity. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to experimental data in order to distinguish between pure and adulterated honey. The most relevant discriminative parameters were diastase activity, HMF, sucrose, and reducing sugar contents. Posterior classification probabilities and classification functions obtained by LDA revealed that 100% of honey samples were correctly assigned to their original group.

scholarly journals Cultivar Discrimination of Single Alfalfa (Medicago sativa L.) Seed via Multispectral Imaging Combined with Multivariate Analysis

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6575
Author(s):  
Lingjie Yang ◽  
Zuxin Zhang ◽  
Xiaowen Hu
Keyword(s):  
Multivariate Analysis ◽  
Multispectral Imaging ◽  
Principal Component ◽  
Nondestructive Method ◽  
Morphological Features ◽  
Support Vector ◽  
Linear Discrimination ◽  
Vector Machines ◽  
Alfalfa Seed ◽  
Medicago Sativa L

Rapid and accurate discrimination of alfalfa cultivars is crucial for producers, consumers, and market regulators. However, the conventional routine of alfalfa cultivars discrimination is time-consuming and labor-intensive. In this study, the potential of a new method was evaluated that used multispectral imaging combined with object-wise multivariate image analysis to distinguish alfalfa cultivars with a single seed. Three multivariate analysis methods including principal component analysis (PCA), linear discrimination analysis (LDA), and support vector machines (SVM) were applied to distinguish seeds of 12 alfalfa cultivars based on their morphological and spectral traits. The results showed that the combination of morphological features and spectral data could provide an exceedingly concise process to classify alfalfa seeds of different cultivars with multivariate analysis, while it failed to make the classification with only seed morphological features. Seed classification accuracy of the testing sets was 91.53% for LDA, and 93.47% for SVM. Thus, multispectral imaging combined with multivariate analysis could provide a simple, robust and nondestructive method to distinguish alfalfa seed cultivars.

Use of Gas Chromatograms of the Essential Leaf Oils of the Genus Eucalyptus for Taxonomic Purposes

1997 ◽  
Vol 45 (1) ◽  
pp. 1 ◽  
Author(s):  
Peter J. Dunlop ◽  
Caroline M. Bignell ◽  
D. Brynn Hibbert
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Vacuum Distillation ◽  
Principal Component ◽  
Analytical Techniques ◽  
Hierarchical Cluster ◽  
Eucalyptus Species ◽  
Linear Discriminant ◽  
Genus Eucalyptus

Using morphological observations, botanists have classified Eucalyptus species into characteristic series. A new vacuum distillation technique has been employed to obtain the characteristic leaf oils, which are very close to their in vivo compositions, from 35 species belonging to series Tetrapterae, series Torquatae and series Rufispermae. Accurate gas chromatograms have been obtained for each species and three analytical techniques (principal component analysis (PCA), hierarchical cluster analysis (CA) and linear discriminant analysis (LDA)) have been used to process these chromatograms to see if agreement with these classifications could be achieved without using any auxiliary morphometric data. For the species chosen for the present study, linear discriminant analysis was the most successful in assigning species to their present botanic classifications. These analytical methods were also used with some success in searching for groupings within a series and within a species.

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