## 1 Introduction
This method utilizes two data points such as two Raman peaks of the same component, using the summation or multiplication of the two selected bands to improve the signal-to-noise ratio and thus generate the required Raman image. Since most studies utilizing Raman imaging for the detection of food adulteration focus on a single adulterant showing intense Raman peaks not overlapping with those of the background material, bivariate analysis has not yet been used for agro-food quality analysis. In contrast to the above methods, multivariate analysis employs full spectra and can thus be adopted when selective information is not available [12]. Although the application of this method for both qualitative and quantitative imaging has been reviewed by [1; 13], most of the provided examples correspond to pharmaceutical and biomedical domains. Only few investigations apply multivariate analysis for Raman imaging of agro-food products, probably because the unique Raman peaks of adulterants allow the application of the univariate method. However, multivariate analysis methods are highly suggestive when the nature of adulterants is unknown or when the sample is suspected to contain multiple adulterants, particularly if one wants to evade target-based conventional methods that can only deal with one kind of adulterant at a time. The above methods are particularly useful for extracting pure component spectra by means of classical least square analysis, self-modeling mixture analysis (SMA), independent component analysis, etc. Another option is the development of a spectral library of potential adulterants for a particular food product and the utilization of spectral similarity analysis to compare individual adulterant spectra with those of collected pixels. [PERSON] et al. [14] and [PERSON] et al. [15] used an SMA algorithm to extract the Raman signature of adulterants added to milk powder and create Raman chemical images of these samples, revealing a strong correlation between the predicted and SMA imaging-based adulterant contents. Very few studies compare univariate and multivariate analysis methods, particularly in the case of pharmaceutical [7] and biomedical applications [16]. To the best of our knowledge, no comparison of analysis methods for the quantitative characterization of food material Raman imaging data has yet been reported. Thus, since Raman imaging-based food quality and authenticity analysis is gaining popularity, and food samples are different (e.g., due to exhibiting higher fluorescence backgrounds) from pharmaceutical and biological materials, a comprehensive comparison of data analysis methods is urgently required. Therefore, we herein aimed to quantitatively compare the performances of univariate, bivariate, and multivariate analyses of imaging data, using these methods to generate Raman chemical images of two adulterants (Sudan-I and Congo Red dyes) and thus facilitating the visualization of their spatial distribution and quantifying their concentration. Since in this study, a single data set was practically tested, the findings of this study are legitimate for the reported dataset, and not necessarily for all Raman imaging data.