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 Pilot study on the value of Raman spectroscopy in the entity assignment of salivary gland tumors

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257470
Author(s):  
Till Jasper Meyer ◽  
Elena Gerhard-Hartmann ◽  
Nina Lodes ◽  
Agmal Scherzad ◽  
Rudolf Hagen ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Salivary Gland ◽  
Tissue Sample ◽  
Principal Component ◽  
Salivary Gland Tumors ◽  
Proof Of Concept ◽  
Linear Discriminant ◽  
Adenoid Cystic ◽  
Pleomorphic Adenomas ◽  
Chemometric Algorithms

Background The entity assignment of salivary gland tumors (SGT) based on histomorphology can be challenging. Raman spectroscopy has been applied to analyze differences in the molecular composition of tissues. The aim of this study was to evaluate the suitability of RS for entity assignment in SGT. Methods Raman data were collected in deparaffinized sections of pleomorphic adenomas (PA) and adenoid cystic carcinomas (ACC). Multivariate data and chemometric analysis were completed using the Unscrambler software. Results The Raman spectra detected in ACC samples were mostly assigned to nucleic acids, lipids, and amides. In a principal component-based linear discriminant analysis (LDA) 18 of 20 tumor samples were classified correctly. Conclusion In this proof of concept study, we show that a reliable SGT diagnosis based on LDA algorithm appears possible, despite variations in the entity-specific mean spectra. However, a standardized workflow for tissue sample preparation, measurement setup, and chemometric algorithms is essential to get reliable results.

scholarly journals Identification of new spectral signatures associated with dengue virus infected sera

2017 ◽  
Author(s):  
Ayyaz Amin ◽  
Nimrah Ghouri ◽  
Safdar Ali
Keyword(s):  
Principal Component Analysis ◽  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Dengue Virus ◽  
Linear Discriminant Analysis ◽  
Raman Spectra ◽  
Principal Component ◽  
Component Analysis ◽  
Optical Diagnosis ◽  
Linear Discriminant

In a quest to use Raman spectroscopy as an optical diagnostic tool, we recorded Raman spectra of 32 dengue virus (DENV)-infected and 28 healthy sera samples in the near-infrared spectral range (540 to 1700 cm−1) using laser at 785 nm as the excitation source. We observed clear differences in the Raman spectra of DENV-infected sera as compared with those of healthy individuals. Here, as a result of our study, we report 12 unique Raman bands associated with DENV-infected sera that are not reported earlier. After applying analysis of variance and t-test (p < 0.05) on these 12 bands, six Raman bands at 630 (N-acetylglucosamine), 883 (in-plane bending (ring) of deoxyribose), 1218 (amide III–β conformation from C6H5–C stretching vibrations of tryptophan and phenylalanine), 1273 (amide–III), 1623 (tryptophan) and 1672 cm−1 (ceramide) were found only in the DENV-infected sera. The remaining six Raman bands at 716 (lipids), 780 (Uracil-based ring breathing mode), 828 (ring breathing tyrosine), 840 (α-anomers), 1101 (ν(C–N) of lipids and DNA) and 1150 cm−1(glycogen/carotenoids) were only found in healthy sera. Two types of classification models, principal component analysis and linear discriminant analysis, were employed to develop principal component analysis–linear discriminant analysis model that has provided diagnostic accuracy 96.50%, sensitivity 93.44%, and specificity 100%. This indicates that these 12 Raman bands have the potential to be used as biomarkers for optical diagnosis of DENV infection. This study provides a new insight for future research in the field of optical diagnosis using Raman spectroscopy.

2D Image Matrix-Based Discriminator

2006 ◽  
pp. 258-286
Author(s):  
David Zhang ◽  
Xiao-Yuan Jing ◽  
Jian Yang
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Image Projection ◽  
Projection Techniques

This chapter presents two straightforward image projection techniques — two-dimensional (2D) image matrix-based principal component analysis (IMPCA, 2DPCA) and 2D image matrix-based Fisher linear discriminant analysis (IMLDA, 2DLDA). After a brief introduction, we first introduce IMPCA. Then IMLDA technology is given. As a result, we summarize some useful conclusions.

Investigation of sport supplements quality by Raman spectroscopy and principal component analysis

2016 ◽  
Vol 87 ◽  
pp. 1-7 ◽  
Author(s):  
Mariana R. Almeida ◽  
Letícia P. de Souza ◽  
Rodrigo S. Cesar ◽  
Rafael A. Sousa ◽  
Celly M.S. Izumi
Keyword(s):  
Principal Component Analysis ◽  
Raman Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Sport Supplements

scholarly journals Development of a Piezoelectric-Based Odor Reproduction System

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 870
Author(s):  
Tengteng Wen ◽  
Dehan Luo ◽  
Yongjie Ji ◽  
Pingzhong Zhong
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Component Analysis ◽  
Piezoelectric Transducers ◽  
Linear Discriminant ◽  
Reproduction System ◽  
Machine Olfaction

Odor reproduction, a branch of machine olfaction, is a technology through which a machine represents various odors by blending several odor sources in different proportions and releases them. In this paper, an odor reproduction system is proposed. The system includes an atomization-based odor dispenser using 16 micro-porous piezoelectric transducers. The authors propose the use of an electronic nose combined with a Principal Component Analysis–Linear Discriminant Analysis (PCA–LDA) model to evaluate the effectiveness of the system. The results indicate that the model can be used to evaluate the system.

Monitoring of chemotherapy leukemia treatment using Raman spectroscopy and principal component analysis

2014 ◽  
Vol 29 (3) ◽  
pp. 1241-1249 ◽  
Author(s):  
José Luis González-Solís ◽  
Juan Carlos Martínez-Espinosa ◽  
Juan Manuel Salgado-Román ◽  
Pascual Palomares-Anda
Keyword(s):  
Principal Component Analysis ◽  
Raman Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Leukemia Treatment

Bacillus Spore Classification via Surface-Enhanced Raman Spectroscopy and Principal Component Analysis

2008 ◽  
Vol 62 (3) ◽  
pp. 267-272 ◽  
Author(s):  
J. Guicheteau ◽  
L. Argue ◽  
D. Emge ◽  
A. Hyre ◽  
M. Jacobson ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Raman Spectroscopy ◽  
Bacterial Species ◽  
Principal Component ◽  
Component Analysis ◽  
Surface Enhanced Raman Spectroscopy ◽  
Raman Signal ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Bacillus Spores

Surface-enhanced Raman spectroscopy (SERS) can provide rapid fingerprinting of biomaterial in a nondestructive manner. The adsorption of colloidal silver to biological material suppresses native biofluorescence while providing electromagnetic surface enhancement of the normal Raman signal. This work validates the applicability of qualitative SER spectroscopy for analysis of bacterial species by utilizing principal component analysis (PCA) to show discrimination of biological threat simulants, based upon multivariate statistical confidence limits bounding known data clusters. Gram-positive Bacillus spores ( Bacillus atrophaeus, Bacillus anthracis, and Bacillus thuringiensis) are investigated along with the Gram-negative bacterium Pantoea agglomerans.

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