| Title: | Headspace-programmed temperature vaporizer-mass spectrometry and pattern recognition techniques for the analysis of volatiles in saliva samples |
| Author(s): | Perez Anton A; del Nogal Sanchez M; Crisolino Pozas AP; Perez Pavon JL; Moreno Cordero B; |
| Address: | "Departamento de Quimica Analitica, Nutricion y Bromatologia, Facultad de Ciencias Quimicas, Universidad de Salamanca, 37008 Salamanca, Spain. Departamento de Quimica Analitica, Nutricion y Bromatologia, Facultad de Ciencias Quimicas, Universidad de Salamanca, 37008 Salamanca, Spain. Electronic address: mns@usal.es. Servicio de Medicina Interna, Hospital Virgen de la Vega, Complejo Asistencial Universitario de Salamanca, 37007 Salamanca, Spain" |
| DOI: | 10.1016/j.talanta.2016.06.061 |
| ISSN/ISBN: | 1873-3573 (Electronic) 0039-9140 (Linking) |
| Abstract: | "A rapid method for the analysis of volatiles in saliva samples is proposed. The method is based on direct coupling of three components: a headspace sampler (HS), a programmable temperature vaporizer (PTV) and a quadrupole mass spectrometer (qMS). Several applications in the biomedical field have been proposed with electronic noses based on different sensors. However, few contributions have been developed using a mass spectrometry-based electronic nose in this field up to date. Samples of 23 patients with some type of cancer and 32 healthy volunteers were analyzed with HS-PTV-MS and the profile signals obtained were subjected to pattern recognition techniques with the aim of studying the possibilities of the methodology to differentiate patients with cancer from healthy controls. An initial inspection of the contained information in the data by means of principal components analysis (PCA) revealed a complex situation were an overlapped distribution of samples in the score plot was visualized instead of two groups of separated samples. Models using K-nearest neighbors (KNN) and Soft Independent Modeling of Class Analogy (SIMCA) showed poor discrimination, specially using SIMCA where a small distance between classes was obtained and no satisfactory results in the classification of the external validation samples were achieved. Good results were obtained when Mahalanobis discriminant analysis (DA) and support vector machines (SVM) were used obtaining 2 (false positives) and 0 samples misclassified in the external validation set, respectively. No false negatives were found using these techniques" |
| Keywords: | "Adult Discriminant Analysis Electronic Nose Female Humans Male Mass Spectrometry/methods Neoplasms/metabolism Pattern Recognition, Automated Principal Component Analysis Saliva/*chemistry Support Vector Machine Temperature Volatile Organic Compounds/*anal;" |
| Notes: | "MedlinePerez Anton, Ana Del Nogal Sanchez, Miguel Crisolino Pozas, Angel Pedro Perez Pavon, Jose Luis Moreno Cordero, Bernardo eng Netherlands 2016/09/04 Talanta. 2016 Nov 1; 160:21-27. doi: 10.1016/j.talanta.2016.06.061. Epub 2016 Jun 29" |
