## 4 Hyperspectral Image Analysis Methods
### Hyperspectral Band Selection  
Redundant information exists across tens or hundreds of bands in 3-D hyperspectral images. Spectral dimension reduction and useful information retrieval are critical to analyze hyperspectral image data for qualitative/quantitative analysis. To fit multispectral imaging solutions for online and real-time rapid inspection applications using simple detection algorithms, a small subset of important bands (usually less than 10) must be selected. In the visible and near-infrared wavelength range, typical reflectance, transmittance, and fluorescence spectra naturally exhibit broad and overlapped spectral peaks and shoulders. Hence, it is generally not straightforward to identify the important bands that are optimal for specific hyperspectral imaging applications. Mathematical optimization algorithms and multivariate analysis techniques are usually required to reduce the spectral dimension and identify the key wavelengths. Commonly used methods include principal component analysis (PCA), partial least squares (PLS), linear discriminant analysis (LDA), correlation analysis (CA), artificial neural networks (ANN), genetic algorithm (GA), sequential forward selection (SFS), etc. In general, spectra from all hyperspectral image pixels or selected regions of interest (ROIs) are extracted to form a 2-D spectral data matrix on which multivariate analysis and band selection methods can be directly conducted to determine important wavelengths. It should be noted that Raman spectra are naturally characterized by narrow and sharp spectral peaks appearing at fixed wavenumbers with easy-to-understand physical/chemical meanings. Thus the band selection for hyperspectral Raman images is usually more straightforward without using the aforementioned advanced mathematical algorithms, especially for Raman spectra after fluorescence baseline correction.  
Figure 9 shows an example of using correlation analysis to select important bands for hyperspectral fluorescence images to detect organic residues on poultry processing equipment surfaces. Fluorescence images were collected in the spectral region of 500-700 nm (42 bands in total) from chicken residues on stainless steel sheets by a line-scan hyperspectral system using high-power 400 nm blue LEDs as an excitation source [18], which is similar to the system illustrated in Figure 5a except for the 365 nm UV-A fluorescent lamps. Chicken fat, blood, four types of feces, and stainless steel exhibited different emission patterns in their representative fluorescence spectra (Figure 9a). Correlation coefficients were calculated between designated label values (i.e., \"1\" for organic residue samples and \"0\" for stainless steel sheets) and two-band ratios of the fluorescence spectra in an exhaustive way (i.e., evaluating all possible two-band combinations from the 42 wavebands). In the  
Figure 8: Line-scan hyperspectral Raman imaging measurement of a carrot slice: (**a**) color image; (**b**) original; and (**c**) fluorescence-corrected Raman images at 1513 cm\({}^{-1}\); (**d**) carotenoid color map image based on pixel intensities of the 1513 cm\({}^{-1}\) corrected image; and (**e**) original; and (**f**) fluorescence-corrected Raman spectra of the major carrot tissues. Reproduced with permission from [14], Copyright Elsevier, 2017.  
contour plot of the correlation coefficients (Figure 9b), the two-band ratio between 503 and 666 nm (F503/F666) was found to give the maximum absolute correlation value of 0.94. Two-band ratio images were calculated using the selected two bands, and binary images were then generated by applying a simple thresholding method to the F503/F666 ratio images (Figure 9c). The final binary images can be used to detect all the chicken residues on the stainless steel sheets. The selected wavelength pair can be adopted for developing a LED-based handheld fluorescence imaging device for inspecting poultry processing equipment surfaces.