title: Spectral region optimization for Raman-based optical biopsy of inflammatory lesions.
abstract: OBJECTIVE
  The biochemical alterations between inflammatory fibrous hyperplasia (IFH) and normal tissues of buccal mucosa were probed by using the FT-Raman spectroscopy technique. The aim was to find the minimal set of Raman bands that would furnish the best discrimination.
  
  
  BACKGROUND
  Raman-based optical biopsy is a widely recognized potential technique for noninvasive real-time diagnosis. However, few studies had been devoted to the discrimination of very common subtle or early pathologic states as inflammatory processes that are always present on, for example, cancer lesion borders.
  
  
  METHODS
  Seventy spectra of IFH from 14 patients were compared with 30 spectra of normal tissues from six patients. The statistical analysis was performed with principal components analysis and soft independent modeling class analogy cross-validated, leave-one-out methods.
  
  
  RESULTS
  Bands close to 574, 1,100, 1,250 to 1,350, and 1,500 cm(-1) (mainly amino acids and collagen bands) showed the main intragroup variations that are due to the acanthosis process in the IFH epithelium. The 1,200 (C-C aromatic/DNA), 1,350 (CH(2) bending/collagen 1), and 1,730 cm(-1) (collagen III) regions presented the main intergroup variations. This finding was interpreted as originating in an extracellular matrix-degeneration process occurring in the inflammatory tissues. The statistical analysis results indicated that the best discrimination capability (sensitivity of 95% and specificity of 100%) was found by using the 530-580 cm(-1) spectral region.
  
  
  CONCLUSIONS
  The existence of this narrow spectral window enabling normal and inflammatory diagnosis also had useful implications for an in vivo dispersive Raman setup for clinical applications.
authors.affiliation: Departamento de Biociências e Diagnóstico Bucal, Faculdade de Odontologia de São José dos Campos-FOSJC, Brazil.
authors.forename: Luis Felipe das Chagas E Silva
authors.initials: LF
authors.lastname: de Carvalho
authors.forename: Renata Andrade
authors.initials: RA
authors.lastname: Bitar
authors.forename: Emília Angela Loschiavo
authors.initials: EA
authors.lastname: Arisawa
authors.forename: Adriana Aigotti Haberbeck
authors.initials: AA
authors.lastname: Brandão
authors.forename: Kathia Maria
authors.initials: KM
authors.lastname: Honório
authors.forename: Luiz Antônio Guimarães
authors.initials: LA
authors.lastname: Cabral
authors.forename: Airton Abrahão
authors.initials: AA
authors.lastname: Martin
authors.forename: Herculano da Silva
authors.initials: Hda S
authors.lastname: Martinho
authors.forename: Janete Dias
authors.initials: JD
authors.lastname: Almeida
mesh_terms: D002610:Cheek / Q000473:pathology; D007249:Inflammation / Q000473:pathology*; D009061:Mouth Mucosa / Q000737:chemistry / Q000166:cytology / Q000473:pathology*; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D012680:Sensitivity and Specificity; D013059:Spectrum Analysis, Raman / Q000379:methods; D014732:Vibration