## 3 Examples of Chemometrics and Vibrational Spectroscopy Applications in Seafood
### Authentication, Provenance and Traceability
The three chemometric techniques demonstrated that NIR spectroscopy can be reliably applied to discriminate between wild and farmed sea bass, achieving the same classification performance as classification methods that use chemical properties and morphometric traits [73]. Moreover, the NIR-based classification methods do not require reagents and were simpler, faster, more economical, and environmentally safer compared to traditional methods based on chemical analysis [73]. A study by [PERSON] and collaborators, utilised NIR spectroscopy to discriminate different species of freshwater fish including black carp (_Mylopharyngodon piccus_), grass carp (_Ctenopharyngodon idellus_), silver carp (_Hypophthalmichthys molitirix_), bighead carp (_Aristicththys nobilis_), common carp (_Cyprinus carpio_), crucian (_Carassius auratus_), and bream (_Parabramis pekinensis_) [74]. The authors evaluated severalchemometric techniques including PLS-DA, PCA, competitive adaptive reweighted sampling, and fast Fourier transform (FT) applied to LDA to classify and monitor the origin of the samples [74]. The authors concluded that the developed methodology was effective in distinguishing between freshwater fish species [74]. The ability of handheld instruments was evaluated to identify both fillets and patties of Atlantic cod (_Gadus morhua_) and haddock (_Melanogrammus aeglefinus_) [75; 76]. The authors of this study used LDA and SIMCA to analyse the NIR data. The models developed were able to classify 100% using LDA, while a sensitivity higher than 65% and specificity higher than 74% using SIMCA [72; 73]. The use of IR spectroscopy was also evaluated as a rapid tool to identify valuable species (e.g., red mullet (_Mullus surmuletus_) and plaice (_Pleuronetes platessa_)) substitution with cheaper varieties (e.g., Atlantic mullet (_Mugil cephalus_) and flounder (_Paralichthys dentatus_)) in combination with LDA and SIMCA [60]. The authors reported 100% correct classification when LDA was used, while a sensitivity higher than 70% and a specificity of 100% was obtained using SIMCA [60]. The ability of Fourier transform MIR (FT-MIR) was also evaluated by [PERSON] et al. to study the adulteration of Norwegian salmon with Heilongjiang salmon at local fish markets [77]. The authors reported that the Norwegian and Heilongjiang salmon samples could be identified using PLS-DA. However, the method was unable to classify the extent of adulteration between levels of 20-80% [77]. The use of NIR and hyperspectral imaging (900-1700 nm) was evaluated as a tool to automatically check fish quotas [78]. The authors assumed that different small pelagic fish species have different spectral signatures [78]. In this study, samples of Chilean silverside (_Odontesthes regia_), southern rays bream (_Brama australis_), and silver hake (_Merluciidae_) were scanned and their spectral signatures analysed using k-nearest neighbour (k-NN) and SVM [78]. The authors reported classification rates between 80% and 90% depending on the NIR region used during the development of the application [78]. Raman spectroscopy (wavelength of 532 nm) was evaluated to classify deep frozen fish fillets [79]. The spectra of the fillet samples sourced from twelve types of fish were scanned using Raman spectroscopy, and analysed using hierarchical cluster. The authors identified three groups namely fish from salmon family; the second group comprised of fish reared in fresh or brackish water; and the third group comprised of saltwater reared fish. The authors demonstrated the potential of Raman spectroscopy as a screening tool prior to routine standard industry methods for the identification of fish fillets [79].