title: Identification of oil contaminants on polymer coated beverage cans using fluorescence spectroscopy.
abstract: In a recent publication we introduced a novel method for detection of impurities on beverage can surfaces using Raman spectroscopy. While investigating the technique, limitations emerged due to the low sensitivity of Raman scatter. This is a particular problem with the largest contributor of impurities in beverage cans: lubricants employed in the manufacturing process. This paper presents an alternative approach, using the more sensitive technique of fluorescence spectroscopy to tackle the same problem. Measurements using fluorescence spectroscopy were conducted indirectly in a cuvette as well as directly on a can surface with the aid of fiber optics. The chemometrics methods of linear discriminant analysis (LDA) and principal components analysis (PCA) were used to classify acquired spectra as belonging to one of the 16 investigated lubricants. Fiberoptic scans revealed that a successful detection and recognition of lubricant is possible down to a volume of 0.5 microL deposited on the can surface. Contaminant detection was possible at even lower levels down to 0.01 microL, but reliable identification proved difficult at such low volumes. Indirect measurements of lubricants dissolved in cyclohexane yielded the lowest detection limits between 1-0.1 ppb.
authors.affiliation: Cranfield Centre for Analytical Science, Cranfield University, Bedfordshire, MK43 4DT, UK.
authors.forename: Michael
authors.initials: M
authors.lastname: Malecha
authors.forename: Conrad
authors.initials: C
authors.lastname: Bessant
authors.forename: Selwayan
authors.initials: S
authors.lastname: Saini
mesh_terms: D001628:Beverages / Q000032:analysis*; D016002:Discriminant Analysis; D005506:Food Contamination; D007219:Industrial Oils / Q000032:analysis*; D008153:Lubrication; D025341:Principal Component Analysis; D011786:Quality Control; D012015:Reference Standards; D012680:Sensitivity and Specificity; D013050:Spectrometry, Fluorescence / Q000379:methods* / Q000706:statistics & numerical data