## 1 Introduction  
Raman spectroscopy coupled with chemical imaging finds numerous applications in the food and pharmaceutical industries, combining both spectral and spatial information and thus allowing the simultaneous identification and localization of chemical species. Since the chemical properties and distribution of species usually influence the quality of both food and pharmaceutical samples, the above technique can be used to generate chemical maps showing the distributions of certain parameters of interest [1]. This unique feature of Raman imaging has made it a popular method of pharmaceutical quality analysis from the initial step of preparing API-excipient solid dispersions (e.g., by powder blending) through the manufacturing process until the final product fabrication step [2,3]. Although Raman imaging has also been used for food quality analysis, its full-scale potential for food quality and authenticity evaluation as well as the selection of appropriate processing methods for imaging data are yet to be optimized. The applications of Raman imaging for agro-food product evaluation are reviewed in reference [1,4]. Up to now, Raman imaging has been performed using confocal imaging systems that collect a large number of spectra at the desired sample positions, which is known as the point-scan method. Recently, macro-scale, line-scan Raman imaging for high-throughput screening, pioneered by [PERSON] et al. [5], has been established and used for a range of applications related to agro-food quality analysis, in particular determining the authenticity of powdered foods. The above method uses a several-centimeters-wide laser line to illuminate the sample and a relatively large charge-couple device (CCD) detector to generate the corresponding Raman chemical images. Since the laser line can illuminate a large sample area, and the scattered Raman signal can be mapped by the CCD, one sample dimension can be scanned at a time, allowing faster analysis. As the hyperspectral imaging technique acquires spectral information from each pixel unit, it can yield detailed sample information on the scale of a single pixel [6]. The numeric format of line-scan Raman imaging data corresponds to a 3D hypercube in which intensity values are functions of two spatial dimensions and one spectral dimension. Since macro-scale Raman imaging requires the effective extraction of information from this hypercube, the analysis of collected data is very important for evaluating the spatial distribution of a given constituent. The choice of an appropriate data analysis method allows the visualization of sample biochemical constituents separated into particular image regions, with methods for preprocessed Raman hypercube analysis broadly classified into univariate, bivariate, and multivariate methods. The simplest and most convenient approach is the univariate method, which considers only one Raman peak and thus presents information on only one characteristic functional group. Since Raman spectra comprise numerous peaks, the intensity of a single band can be utilized to map the component of interest by plotting unique frequencies as functions of spatial position and spectral intensity. However, the key requirement of univariate imaging is the precise spectral characterization of the sample (i.e., adulterant materials) prior to imaging, as the uniquely assignable wavenumbers should be known in advance [7]. In food authenticity analysis, the unique peaks of potential adulterants are either known or determined experimentally and thus allow single-band imaging of the target chemical, as exemplified by the successful application of Raman mapping in chocolate analysis [8], detection of melamine in milk powder [9; 10], and benzoyl peroxide in wheat flour [11]. When the target chemical (adulterant) does not show a relatively high-intensity band or this band partially overlaps with the Raman peaks of other components, imaging can be performed using bivariate analysis.