## 1 Introduction  
Vibrational spectroscopy offers the opportunity for revolutionary innovation in the life sciences and shows high potential as a rapid, culture-independent, cost-effective, and label-free technique for the characterization of biological samples, compared to conventional time-consuming methods, such as microbiological and molecular techniques, which usually require expensive equipment, highly trained staff, and complicated sample preparation [1, 2]. Unlike infrared spectroscopy (FT-IR) [3], Raman spectroscopy is more suited for biological investigations because it allows real-time analysis of living samples in their natural environmental conditions [4], either dispersed in aqueous media or deposited on commercial microscope glass slides. Indeed, signals from both water and glass are very weak in the Raman spectrum [5] and do not interfere with those from biocomponents. In particular, Raman spectroscopy is widely used in microbiology as a direct and noninvasive technique that requires a minimum amount of sample and avoids long preparation processes [6] (pp. 919-935). Moreover, it offers the possibility to obtain simultaneously spatial information with high resolution of many constituents at single cell level, which can be resolved in postprocessing by multivariate statistical methods [7]. The overall biochemical profile results in a characteristic spectral fingerprint that reflects the sample chemical composition, metabolic information, biological conditions, and history [8]. Recently, some promising analytical approaches have been forwarded for the rapid and specific monitoring of cell status [9; 10; 11]. However, spectroscopic technology can generate an enormous amount of data in a short time, and due to the complexity of biological samples, Raman spectra raises several interpretation difficulties. To overcome this issue, disciplines such as image processing, statistical analysis, chemometrics, machine learning, and computational intelligence techniques are needed [12; 13]. Hyperspectral imaging technology, combining conventional spectroscopy and imaging techniques, enables to acquire both spectral and spatial information from an object at the same time [14]. Molecules leave unique chemical \"fingerprints\" in the electromagnetic spectrum, which are recorded, pixel by pixel, forming the resulting image. These spectral signatures enable identification of the materials that make up a scanned object, detection of inhomogeneity, and recognition of characteristic spatial features. The Raman spectra complexity requires automated methods that can remove qualitative interpretation in spectroscopy analysis. Therefore, mathematical methods for data processing are fundamental to manage the big amount of physical and chemical information resulting from hyperspectral Raman images of the samples. In this context, it is evident how machine learning and chemometrics assumed great importance to reduce the dimensionality of the spectral data, retain essential information, and classify or quantify important areas of a sample during postprocessing [15]. Thus, these techniques for biological samples data analysis offer a great potential for the automation of control methodologies. Immediate and accurate information at different stages of the cell cycles can be obtained and processed in a very short time [16]. The potential of Raman hyperspectral chemical imaging to provide not only spectral information but also the spatial distribution of the components in sub-micron biological objects on the basis of their different Raman spectrums is demonstrated in this work. Using this technique, we investigated the cell status of a single _E. coli_ cell, which was collected in its mid-exponential growth phase to explore the possibility of identifying different phases of the cell replication cycle thanks to the high resolution and molecular specificity offered by Raman imaging.