Optical spectroscopy methods combined with multivariate statistical analysis for the classification of C retan thyme, multi‐floral and honeydew honey

2021 ◽  
Author(s):  
Emmanouil Orfanakis ◽  
Michail Markoulidakis ◽  
Aggelos Philippidis ◽  
Aikaterini Zoumi ◽  
Michalis Velegrakis
Keyword(s):  
Statistical Analysis ◽  
Optical Spectroscopy ◽  
Multivariate Statistical Analysis ◽  
Multivariate Statistical

Discrimination of normal and malignant mouse ovarian surface epithelial cells in vitro using Raman microspectroscopy

2015 ◽  
Vol 7 (22) ◽  
pp. 9520-9528 ◽  
Author(s):  
S. Borel ◽  
E. A. Prikryl ◽  
N. H. Vuong ◽  
J. Jonkman ◽  
B. Vanderhyden ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Raman Microspectroscopy ◽  
Live Cells ◽  
Label Free ◽  
Multivariate Statistical ◽  
Ovarian Surface Epithelial ◽  
Free Classification

Raman microspectroscopy in conjunction with multivariate statistical analysis is a powerful technique for label-free classification of live cells based on their molecular composition, which can be correlated to variations in protein, DNA/RNA, and lipid macromolecules.

Classification of Chinese wine varieties using 1H NMR spectroscopy combined with multivariate statistical analysis

Food Control ◽  
2018 ◽  
Vol 88 ◽  
pp. 113-122 ◽  
Author(s):  
Shuangxi Fan ◽  
Qiding Zhong ◽  
Carsten Fauhl-Hassek ◽  
Michael K.-H. Pfister ◽  
Bettina Horn ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Nmr Spectroscopy ◽  
1H Nmr ◽  
Multivariate Statistical Analysis ◽  
1H Nmr Spectroscopy ◽  
Multivariate Statistical ◽  
Chinese Wine

scholarly journals Lymph node pathology using optical spectroscopy in cancer diagnostics

2008 ◽  
Vol 22 (2-3) ◽  
pp. 97-104 ◽  
Author(s):  
M. Isabelle ◽  
N. Stone ◽  
H. Barr ◽  
M. Vipond ◽  
N. Shepherd ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Statistical Analysis ◽  
Lymph Nodes ◽  
Optical Spectroscopy ◽  
Multivariate Statistical Analysis ◽  
Diagnostic Methods ◽  
Spectroscopic Techniques ◽  
Multivariate Statistical ◽  
Analysis Tools ◽  
Raman And Infrared Spectroscopy

Raman and infrared spectroscopy are optical spectroscopic techniques that use light scattering (Raman) and light absorption (infrared) to probe the vibrational energy levels of molecules in tissue samples. Using these techniques, one can gain an insight into the biochemical composition of cells and tissues by looking at the spectra produced and comparing them with spectra obtained from standards such as proteins, nucleic acids, lipids and carbohydrates. As a result of optical spectroscopy being able to measure these biochemical changes, diagnosis of cancer could take place faster than current diagnostic methods, assisting and offering pathologists and cytologists a novel technology in cancer screening and diagnosis.The purpose of this study is to use both spectroscopic techniques, in combination with multivariate statistical analysis tools, to analyze some of the major biochemical and morphological changes taking place during carcinogenesis and metastasis in lymph nodes and to develop a predictive model to correctly differentiate cancerous from benign lymph nodes taken from oesophageal cancer patients.The results of this study showed that Raman and infrared spectroscopy managed to correctly differentiate between cancerous and benign oesophageal lymph nodes with a training performance greater than 94% using principal component analysis (PCA)-fed linear discriminant analysis (LDA). Cancerous nodes had higher nucleic acid but lower lipid and carbohydrate content compared to benign nodes which is indicative of increased cell proliferation and loss of differentiation.With better understanding of the molecular mechanisms of carcinogenesis and metastasis together with use of multivariate statistical analysis tools, these spectroscopic studies will provide a platform for future development of real-time (in surgery) non-invasive diagnostic tools in medical research.

scholarly journals Multivariate Statistical Analysis of Mass Spectra as a Tool for the Classification of the Main Humic Substances According to Their Structural and Conformational Features

2008 ◽  
Vol 56 (14) ◽  
pp. 5480-5487 ◽  
Author(s):  
Roberto Baigorri ◽  
Angel Mari Zamarreño ◽  
Marta Fuentes ◽  
Gustavo González-Gaitano ◽  
Jose Maria García-Mina ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Humic Substances ◽  
Multivariate Statistical Analysis ◽  
Mass Spectra ◽  
Multivariate Statistical
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