Foodstuff authentication from spectral data: Toward a species-independent discrimination between fresh and frozen–thawed fish samples Matteo Ottavian a , Luca Fasolato b , Pierantonio Facco a,⇑ , Massimiliano Barolo a a CAPE-Lab – Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova PD, Italy b Department of Biomedicine and Food Science, University of Padova, Viale dell’Università 16, 35020 Legnaro PD, Italy article info Article history: Received 5 April 2013 Received in revised form 4 June 2013 Accepted 8 July 2013 Available online 16 July 2013 Keywords: Near-infrared spectroscopy Foodstuff authentication PLS-DA Orthogonal PLS abstract The substitution of fresh fish with frozen–thawed fish is a typical fraud that can damage consumers for several reasons. In fact, not only the quality of thawed meat can be negatively affected during freezing, but also safety issues can arise, as thawed meat is more susceptible to microbial growth. Though several strategies have been proposed for fresh fish authentication, their classification ability is strongly affected by the fish species being considered. In this paper, we propose three different strategies based on latent variable modeling techniques in order to develop a multi-species classifier of the fresh/frozen–thawed status of fish samples using near-infrared spectra. Whereas the first two strategies model the information related to the species and to the fish together (either jointly or sequentially), the third strategy aims at explicitly separating them to improve the classification performance. The proposed strategies were validated over a database of more than 1200 samples of several different species, with near-infrared spectra collected with two different instruments. The overall classification accuracies ranged between 80% and 91%, according to the strategy and the instrument used. We believe that this study can contribute to the development of a species-independent approach to foodstuff classification. Ó 2013 Elsevier Ltd. All rights reserved. 1. Introduction The substitution of fresh fish with frozen–thawed fish is a typ- ical fraud that not only damages consumers from an economical point of view, but can also cause safety issues (Pavlov, 2007). In fact, although freezing is one of the most widely used methods to extend the shelf life of seafood, it can affect the overall organoleptic properties of the product, and thawed meat is char- acterized by a higher susceptibility to microbial growth. Further- more, fish authentication is important for correct product labeling (Martinez et al., 2003), as promoted by recent regulatory actions (Uddin, 2010; European Parliament Legislative Resolution, 2011). Several methods have been proposed for the identification of the fresh/frozen–thawed substitution fraud (e.g., eye lens evalua- tion, measurements of dielectric properties, erythrocytes lysis, hematocrit evaluation, muscles histology, enzymatic methods, etc.; Uddin, 2010). The classification ability of the majority of these systems is strongly affected by the species under investigation, the integrity of the product (whole fish or fillet) or by its shelf life (Ud- din, 2010). For example, the use of methods based on changes in dielectric properties, while being accurate on intact fish, provides poor results when applied on fillets (Duflos et al., 2002). Enzymatic assays were found to be useful in fillets, but not applicable to all species (Duflos et al., 2002). Recently, Bozzetta et al. (2012) pro- posed muscles histology as a simple method for the evaluation of the fresh/frozen–thawed status. Despite the good classification re- sults obtained on a wide range of species (more than 35 different species), the method requires time for sample processing (e.g., fix- ation and coloration) and the use of several reagents. As an alternative to the abovementioned techniques, more ra- pid analytical technologies have been developed. Among them (Nott et al., 1999; Karoui et al., 2006; Vidac ˇek et al., 2008; Fernán- dez-Segovia et al., 2012; Leduc et al., 2012), near-infrared spectros- copy (NIRS) has been suggested by the promising results obtained on some species (Uddin, 2010; Sivertsen et al., 2011; Fasolato et al., 2012; Zhu et al., 2012; Kimiya et al., 2013; Ottavian et al., 2013). NIRS is a well consolidated analytical technology and plenty of applications can be found in the field of seafood authentication (Cozzolino and Murray, 2012). To the authors’ knowledge, there are currently no NIRS application to multi-species databases, i.e. so far the fresh/frozen–thawed authentication problem has been solved only analyzing single species separately. 0260-8774/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jfoodeng.2013.07.005 ⇑ Corresponding author. Tel.: +39 0498275470. E-mail address: pierantonio.facco@unipd.it (P. Facco). Journal of Food Engineering 119 (2013) 765–775 Contents lists available at SciVerse ScienceDirect Journal of Food Engineering journal homepage: www.elsevier.com/locate/jfoodeng