## 4 Hyperspectral Image Analysis Methods
### Hyperspectral Target Identification  
Hyperspectral target identification intends to recognize sample substances utilizing both spectral and spatial information from the collected images. In various food and agricultural applications, it is usually needed to identify target pixels on sample surfaces. The important wavelengths determined by band selection methods can be used to develop simple algorithms (e.g., band ratio) for simple and fast detection purposes (e.g., pass-or-fail food safety inspection). The example shown in Figure 9 demonstrated that ratio fluorescence images using two selected bands can be used to detect chicken organic residues on stainless steel sheets. However, the two bands may not be sufficient to identify or differentiate the six different types of the organic residues. Whole spectra from hyperspectral images are often necessary for precise target identification. The hyperspectral pixels can be recognized based on statistical comparisons between the extracted spectra and reference spectra of interesting targets using spectral matching methods, such as spectral angle mapper and spectral information divergence [19]. After spectral matching, a 2-D rule image is usually generated,  
Figure 9: Line-scan hyperspectral fluorescence imaging for inspecting chicken residues on stainless steel sheets: (**a**) fluorescence emission spectra of organic residues and stainless steel excited by 400 nm blue LEDs; (**b**) correlation analysis for selection of two bands; and (**c**) detection of organic residues using band ratio images with wavelength pair selected by correlation analysis (F503/F666). Reproduced with permission from [18], Copyright ASABE, 2011.  
and image postprocessing procedures (e.g., thresholding, spatial filtering) are often used to create the final results for identification, classification, and mapping tasks. On the other hand, spectral signals from line-scan spatially resolved spectroscopy measurement for heterogeneous or layered samples are normally mixed. Curve resolution and mixture analysis methods can be used to segregate and resolve the spectra from different components in a mixture. For example, self-modeling mixture analysis (SMA) is an effective method for such purpose. SMA decomposes a data matrix into the outer product of pure component spectra and contributions [20]. The resolved spectra can be identified using the aforementioned spectral matching methods. An example of separating mixed spectra from layered samples based on mixture analysis for spatially offset Raman spectroscopy (SORS) measurement is shown in Figure 10. A line-scan hyperspectral Raman system working in the SORS mode (Figure 5b) was used to collect SORS data from a layered sample, which was created by placing a 1 mm thick plastic sheet cut from an original sugar container on top of a layer of granulated cane sugar (see Figure 2b) [21]. A 1024 \(\times\) 1024 spatial-spectral scattering image covering a source-to-detector distance range of 0-36 mm (on both sides of the 785 nm laser point) was acquired over a wavenumber range of 0-2815 cm\({}^{-1}\). The longest scattering distances for pure plastic and sugar appeared at 1438 and 531 cm\({}^{-1}\) (Figure 10a), which are attributed to the highest Raman peaks of plastic and sugar, respectively. Raman signals for both plastic and sugar were observed in the scattering image of the plastic-on-sugar sample. As offset distances increased for the SORS spectra (Figure 10b), the relative intensities of the 1438 cm\({}^{-1}\) plastic peaks were steadily weakened, while those of the 531 cm\({}^{-1}\) sugar peaks were gradually amplified. The Raman signals from the two layers were successfully separated using self-modeling mixture analysis on the SORS data. Pure component spectra resolved from SMA (Figure 10c,d) recovered all the major Raman features of the plastic sheet and the cane sugar. This example reveals the potential of the line-scan SORS technique coupled with the SMA method for nondestructive subsurface inspection of food safety and quality.