Raman spectroscopy for classification of neoplastic and non-neoplastic CAM colon tumors
B. Esteves,
S. Pimenta,
M.J. Maciel,
M. Costa,
F. Baltazar,
M.F. Cerqueira,
P. Alpuim,
C.A. Silva,
J.H. Correia
Affiliations
B. Esteves
CMEMS-UMinho, Department of Industrial Electronics, University of Minho, Guimarães, Portugal
S. Pimenta
CMEMS-UMinho, Department of Industrial Electronics, University of Minho, Guimarães, Portugal; LABBELS – Associate Laboratory, Braga, Guimarães, Portugal; Corresponding author. CMEMS-UMinho, Department of Industrial Electronics, University of Minho, Guimarães, Portugal.
M.J. Maciel
CMEMS-UMinho, Department of Industrial Electronics, University of Minho, Guimarães, Portugal; LABBELS – Associate Laboratory, Braga, Guimarães, Portugal
M. Costa
Life and Health Sciences Research Institute (ICVS), University of Minho, Campus of Gualtar, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga, Guimarães, Portugal
F. Baltazar
Life and Health Sciences Research Institute (ICVS), University of Minho, Campus of Gualtar, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga, Guimarães, Portugal
M.F. Cerqueira
International Iberian Nanotechnology Laboratory (INL), Braga, Portugal; Centre of Physics of Minho and Porto Universities (CF-UM-UP), University of Minho, Braga, Portugal
P. Alpuim
International Iberian Nanotechnology Laboratory (INL), Braga, Portugal; Centre of Physics of Minho and Porto Universities (CF-UM-UP), University of Minho, Braga, Portugal
C.A. Silva
CMEMS-UMinho, Department of Industrial Electronics, University of Minho, Guimarães, Portugal; LABBELS – Associate Laboratory, Braga, Guimarães, Portugal
J.H. Correia
CMEMS-UMinho, Department of Industrial Electronics, University of Minho, Guimarães, Portugal; LABBELS – Associate Laboratory, Braga, Guimarães, Portugal
This paper demonstrates the potential of Raman spectroscopy for differentiating neoplastic from non-neoplastic colon tumors, obtained with the CAM (chicken chorioallantoic membrane) model. For the CAM model two human cell lines were used to generate two types of tumors, the RKO cell line for neoplastic colon tumors and the NCM460 cell line for non-neoplastic colon tumors. The Raman spectra were acquired with a 785 nm excitation laser. The measured Raman spectra from the CAM samples (n = 14) were processed with several methods for baseline correction and to remove artifacts. The corrected spectra were analyzed with PCA (principal component analysis). Additionally, machine learning based algorithms were used to create a model capable of classifying neoplastic and non-neoplastic tumors. The principal component scores showed a clear differentiation between neoplastic and non-neoplastic colon tumors. The classification model had an accuracy of 93 %. Thus, a complete methodology to process and analyze Raman spectra was validated, using a rapid, accessible, and well-established tumor model that mimics the human tumor pathology with minor ethical concerns.
