## 3 Examples of Chemometrics and Vibrational Spectroscopy Applications in Seafood
### Freshness, Spoilage and Storage  
[PERSON] and colleagues [80] investigated the potential of VIS and NIR hyperspectral imaging as a rapid and non-invasive method to assess the freshness of prawns. The authors evaluated both unfrozen and frozen samples (280 prawns total) and reported the rapid non-invasive classification of unfrozen and frozen prawn samples with an average correct prediction rate of 98.33% and 95%, respectively [80]. The authors noted that their study should encourage greater research efforts in regard to the on-line application of hyperspectral imaging for classification and prediction of seafood products. However, they also acknowledged that as the number of samples analysed in the study was low and only a limited number of storage periods were investigated, that additional samples possessing a more varied range of freshness levels should be considered to improve the accuracy and reliability of the models in future studies. The authors also recommended that more advanced algorithms should be developed to determine the relationship between freshness and hyperspectral datasets [80]. [PERSON] and co-workers [49] reported the rapid and non-destructive assessment of silver chub (_Macrhyopsis storeiana_) freshness using FT-NIR spectroscopy. The authors reported that the eye-ball of the fish was the best position to locate the NIR fibre optic in order to collect the spectra [49]. The main explanation of this by the authors was related to the structure of the fish-eyes and the direct relationship that exist between eye characteristics with the overall freshness of the fish during cold storage [49]. The authors reported that the combination of spectral analysis combined with chemometrics successfully identified the freshness of samples with a correlation coefficient of 95.59% [49]. Similarly, [PERSON] and co-workers [81] determined the freshness of big head carp (_Hypophthalmichthys nobilis_) with NIR spectroscopy combined with different regression methods. The authors reported the prediction of chemical parameters directly associated with freshness using NIR spectroscopy such as pH, total volatile basic nitrogen (TVN), and thiobarbituric acid (TBARS) [81]. The prediction models reported yield a coefficient prediction of 0.945, 0.932 and 0.954 and root mean square error of prediction of 0.081, 2.099 and 0.107 for pH, TVN and TBARS, respectively [81]. In another study, [PERSON] and collaborators [82] reported the use of FT-MIR to identify and monitor spoilage of salmon samples stored in three different environments, in real time. The homogenised salmon samples were analysed using an attenuated total reflectance (ATR) cell where PCA was used to identify the regions in the MIR region associated with the spoilage process [82]. The researchers also used LDA to analyse the MIR spectral data in combination with sensory to better quantify sample freshness [82]. The use of hyperspectral imaging to monitor freshness in cod fillets (freeze storage, thawing, vacuum packed) was reported [83]. The authors concluded that the freezing history could be predicted on frozen and thawed samples [83]. The combination of VIS and NIR hyperspectral imaging was evaluated to differentiate between frozen/ thawed samples of halibut (_Psetta maxima_) [84]. The classification rates reported by the authors were above 97% for the prediction samples [84]. However, the classification rates were lower when the different freezing rates were considered. The authors concluded that VIS and NIR hyperspectral imaging was able to differentiate between fresh and frozen/ thawed samples [85]. A recent article provides more details about the current state-of-the-art application of hyperspectral imaging to monitor freshness, safety and storage conditions of fish [86]. Raman spectroscopy was evaluated as a means to identify and classify the species of fish as well as to determine their freshness (e.g., freezing vs. thawing) by [PERSON] et al. [87].