# Detection of Azo Dyes in Curry Powder Using a 1064-nm Dispersive Point-Scan Raman System
## 3 Results and Discussion
### Simultaneous Detection of Sudan-I and Metanil Yellow in Curry Powder  
Sudan-I and metanil yellow (5% each, \(w/w\)) were mixed together with curry powder to investigate the methodology for identification of multiple chemical contaminants in a food powder. Raman spectra of the mixture sample consisted of mixed spectra of each component. For identification of Sudan-I and metanil yellow in the mixture, the Raman spectra of individual components must be identified. One of the methods for identification of individual components from the matrix of mixture spectra is to decompose the mixed spectral matrix to extract the spectra of individual components and then identify the components. Self-modeling mixture analysis (SMA) was used to extract pure component spectra and score vectors. The number of pure components is pre-defined for SMA computation. For an unknown number of components in a mixture sample, the number of pure components is  
Figure 8: Raman spectra of detected metanil yellow pixels at five concentrations. Figure 9: Relationship between actual metanil yellow concentration in samples and the percentage of detected pixels in 1147 cm\({}^{-1}\) binary images. overestimated, and the SMA result is visually analyzed to determine the actual number of pure components in the mixture sample. Although three known components, Sudan-I, metanil yellow, and curry powder were mixed together, overestimating the number of components (six components) produced favorable SMA results. The mixed spectra of the mixture sample were decomposed into six component spectra and six corresponding scores, of which two were identified as the pure component spectra of Sudan-I and metanil yellow. Due to the low spectral intensity of curry powder, the pure component spectrum of curry powder was not resolved. The remaining four spectra and scores were residual noise and disregarded from further analysis. The pure component spectra of Sudan-I and metanil yellow obtained by SMA is shown in Figure 10a. After SMA, the component corresponding to the extracted pure component spectrum and contributions was identified using the spectral information divergence (SID) method. Each pure component spectra extracted by SMA was compared and computed against the reference spectra of Sudan-I and metanil yellow in the spectral library to obtain SID values. The pure component spectra with the smallest SID values were assigned the identification of the component. The pure component spectra identified as Sudan-I and metanil yellow in Figure 10a are well matched with their corresponding reference spectra in Figure 2. Almost all spectral peaks of Sudan-I and metanil yellow are resolved in pure component spectra demonstrating that SMA and SID methods can be used to decompose the mixture spectra and identify the components in the mixture sample. Figure 10: Results of self-modeling mixture analysis of components. (**a**) Resolved spectra of Sudan-I and metanil yellow; (**b**) corresponding contribution images. The corresponding score factors of each component obtained by SMA were unfolded back to obtain a two-dimensional contribution image (100 \(\times\) 100) of Sudan-I and metanil yellow as shown in Figure 10b. The high intensity pixels represent the component particles in the contribution image. The contribution images were used to create a Raman chemical image of the corresponding component. The pixel-by-pixel analysis method was performed to select the pixel intensity threshold value for each Raman chemical image. The threshold value of 2150 was selected for the Raman chemical image of Sudan-I and 3800 for metanil yellow. The intensity threshold value was applied to obtain binary detection images of Sudan-I and metanil yellow. All the pixels below the threshold value were converted into background pixels, and remaining pixels represented the component's pixels. The binary detection images of Sudan-I and metanil yellow were compared with the single band images at 1593 cm\({}^{-1}\) and 1437 cm\({}^{-1}\), respectively, to ensure none of the pixels were misclassified.