Paper spray mass spectrometry and PLS-DA improved by variable selection for the forensic discrimination of beers

• Published in: Analytica chimica acta Vol. 940; pp. 104 - 112
• Main Authors: Pereira, Hebert Vinicius, Amador, Victória Silva, Sena, Marcelo Martins, Augusti, Rodinei, Piccin, Evandro
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 12.10.2016
• Subjects: Ambient ionization; Beer; Beer - analysis; Food and beverage analysis; Forensic analysis; Mass spectrometry; Mass Spectrometry - methods; Paper spray; Partial least squares discriminant analysis; United States; Forensic analysis; Paper spray; Partial least squares discriminant analysis; Food and beverage analysis; Ambient ionization; Mass spectrometry; Beer
• ISSN: 0003-2670; 1873-4324; 1873-4324
• DOI: 10.1016/j.aca.2016.08.002

Paper spray mass spectrometry (PS-MS) combined with partial least squares discriminant analysis (PLS-DA) was applied for the first time in a forensic context to a fast and effective differentiation of beers. Eight different brands of American standard lager beers produced by four different breweries (141 samples from 55 batches) were studied with the aim at performing a differentiation according to their market prices. The three leader brands in the Brazilian beer market, which have been subject to fraud, were modeled as the higher-price class, while the five brands most used for counterfeiting were modeled as the lower-price class. Parameters affecting the paper spray ionization were examined and optimized. The best MS signal stability and intensity was obtained while using the positive ion mode, with PS(+) mass spectra characterized by intense pairs of signals corresponding to sodium and potassium adducts of malto-oligosaccharides. Discrimination was not apparent neither by using visual inspection nor principal component analysis (PCA). However, supervised classification models provided high rates of sensitivity and specificity. A PLS-DA model using full scan mass spectra were improved by variable selection with ordered predictors selection (OPS), providing 100% of reliability rate and reducing the number of variables from 1701 to 60. This model was interpreted by detecting fifteen variables as the most significant VIP (variable importance in projection) scores, which were therefore considered diagnostic ions for this type of beer counterfeit. [Display omitted] •For the first time a supervised classification multivariate method was used for PS-MS.•Direct analysis by PS-MS combined with PLS-DA was used for fast beer differentiation.•Differentiation was studied in a forensic context for beers of a single type (lagers).•Variable selection was utilised for improving the PLS-DA model.•OPS (ordered predictors selection) provided a method with 100% of reliability rate.