Chemometric Differentiation of Sole and Plaice Fish Fillets Using Three Near-Infrared Instruments

Abstract

Fish species substitution is one of the most common forms of fraud all over the world, as fish identification can be very challenging for both consumers and experienced inspectors in the case of fish sold as fillets. The difficulties in distinguishing among different species may generate a "grey area" in which mislabelling can occur. Thus, the development of fast and reliable tools able to detect such frauds in the field is of crucial importance. In this study, we focused on the distinction between two flatfish species largely available on the market, namely the Guinean sole (Synaptura cadenati) and European plaice (Pleuronectes platessa), which are very similar looking. Fifty fillets of each species were analysed using three near-infrared (NIR) instruments: the handheld SCiO (Consumer Physics), the portable MicroNIR (VIAVI), and the benchtop MPA (Bruker). PLS-DA classification models were built using the spectral datasets, and all three instruments provided very good results, showing high accuracy: 94.1% for the SCiO and MicroNIR portable instruments, and 90.1% for the MPA benchtop spectrometer. The good classification results of the approach combining NIR spectroscopy, and simple chemometric classification methods suggest great applicability directly in the context of real-world marketplaces, as well as in official control plans.

Keywords: European plaice; Guinean sole; NIR; chemometrics; fish fillets; food fraud.

Figure 1

Fillets of (a) Guinean sole (Synaptura cadenati) and (b) European plaice (Pleuronectes platessa).

Figure 2

Visual representation of the datasets of the study: (a–c) the raw data, (d–f) the SNV-pre-processed data and (g–i) the SNV and mean centred (MC) data.

Figure 3

Most relevant PCA scores plots (a–c) in terms of SO-PL class separation, and the corresponding loadings plots (d–f). The three columns refer to, respectively, the (a) SCiO, (b) MicroNIR, and (c) MPA datasets.

Figure 4

Variable importance in projection (VIP) scores of the three PLS-DA classification models: (a) SCiO results, (b) MicroNIR results, and (c) MPA results. The traditional VIP threshold = 1 is represented in red.