## 2 Recent Application of Hyperspectral in Food Authentication  
### Animal Proteins (Meat and Meat Products)  
In order to gain consumer faith in the food industry, it is essential that the accurate labelling of meat products be consistently ensured [9]. Verification of correct meat labelling is a critical aspect of good industry practice and important to producers, retailers, and consumers [9]. The distinction between fresh, and frozen and thawed, and matured, and matured frozen-thawed beef meat samples using hyperspectral imaging (500-1100 nm) and CIELAB measurements was reported [9]. Accordingto the authors, they were successfully able to distinguish beef into fresh and frozen-thawed and fresh and matured samples based upon CIELAB between beef met samples. However, superior classification rates were reported using HSI [9]. It is well known that the NIR spectra contain abundant data and heterospectral two-dimensional correlation (H2D-CS) analysis can be considered as a new tool to interpret these kinds of data [10]. The use of H2D-CS was reported to correlate the NIR HSI data with MIR spectra in order to select wavebands for developing screening models to monitor the oxidative damage of pork myofibrils during storage by freezing [10]. The HSI images were acquired at frozen state without thawing and the oxidative damage of myofibrils and calibration models that were developed on a partial least squares (PLS) regression platform [10]. The authors indicated that the PLS models based on H2D-CS identified specific wavebands to predict oxidative damage (coefficient of regression (R\({}^{2}\)) of 0.896 and root mean square error in prediction (RMSEP) of 0.177 nmol/mg protein) [10]. NIR HSI has also been used to discriminate between pork, poultry, and fish species in processed animal protein meals [11]. The results showed combining spectral and appearance characteristics in a single classification tree presented better classification results for the samples that were used in the study (92% correct classification) than when using the PLS discriminant analysis (DA) spectral model (83% correct) [11]. A challenge with hyperspectral applications can be the voluminous numerical data associated with the approach. This can be overcome by analysing the outputs using new statistical approaches, such as machine-learning algorithms [12]. It is difficult to identify an optimum pattern recognition or suitable machine learning approach for a given analytical platform, as it involves a comparative analysis of various algorithms for the most accurate predictions [12]. Recently, a web-based application to automate the procedure of identifying the best machine learning method for comparing data from several analytical techniques was reported. It was able to predict the counts of microorganisms that were responsible of meat spoilage independently of the packaging [12]. The authors evaluated up to seven regression methods including least squares (LS) regression, stepwise linear regression (STR), PLS regression, principal component regression (PCR), support vector regression, random forest, and K-nearest neighbours [12]. The application is called \"_MeatReg_\" and tested minced samples of beef that were stored under aerobic and modified atmosphere packaging [12]. The authors put forward recommendations on suitable analytical platforms that were able to predict each type of bacteria and which machine learning methods to use in each case [12]. Hyperspectral image was used to discriminate between Chinese sausages from different commercial standards (e.g., top-class, first-class, and second-class) [13]. The NIR hyperspectral band information of sausage applied the successive projection algorithm (SPA) to extract the characteristic bands, and then respectively established grading model of PLS regression and SPA-MLR (characteristic band). The decision coefficient of the SPA-MLR model that is based on NIR was 0.93, and the correct rate of classification was 100% [13].