The potential of spectral and hyperspectral-imaging techniques for bacterial detection in food: A case study on lactic acid bacteria
2016
Foca, Giorgia | Ferrari, Carlotta | Ulrici, Alessandro | Sciutto, Giorgia | Prati, Silvia | Morandi, Stefano | Brasca, Milena | Lavermicocca, Paola | Lanteri, Silvia | Oliveri, Paolo
Official methods for the detection of bacteria are based on culture techniques. These methods have limitations such as time consumption, cost, detection limits and the impossibility to analyse a large number of samples. For these reasons, the development of rapid, low-cost and non-destructive analytical methods is a task of growing interest.In the present study, the capability of spectral and hyperspectral techniques to detect bacterial surface contamination was investigated preliminarily on gel cultures, and subsequently on sliced cooked ham. In more detail, two species of lactic acid bacteria (LAB) were considered, namely Lactobacillus curvatus and Lactobacillus sakei, both of which are responsible for common alterations in sliced cooked ham.Three techniques were investigated, with different equipment, respectively: a macroscopic hyperspectral scanner operating in the NIR (10,470–5880cm⁻¹) region, a FT-NIR spectrophotometer equipped with a transmission arm as the sampling tool, working in the 12,500–5800cm⁻¹ region, and a FT-MIR microscopy operating in the 4000–675cm⁻¹ region.Multivariate exploratory data analysis, in particular principal component analysis (PCA), was applied in order to extract useful information from original data and from hyperspectrograms. The results obtained demonstrate that the spectroscopic and imaging techniques investigated can represent an effective and sensitive tool to detect surface bacterial contamination in samples and, in particular, to recognise species to which bacteria belong.
Afficher plus [+] Moins [-]Mots clés AGROVOC
Informations bibliographiques
Cette notice bibliographique a été fournie par National Agricultural Library
Découvrez la collection de ce fournisseur de données dans AGRIS