Biomolecular structure elucidation is one of the major techniques for studying the basic processes of life. These processes get modulated, hindered or altered due to various causes like diseases, which is why biomolecular analysis and imaging play an important role in diagnosis, treatment prognosis and monitoring. Vibrational spectroscopy (IR and Raman), which is a molecular bond specific technique, can assist the researcher in chemical structure interpretation. Based on the combination with microscopy, vibrational microspectroscopy is currently emerging as an important tool for biomedical research, with a spatial resolution at the cellular and sub-cellular level. These techniques offer various advantages, enabling label-free, biomolecular fingerprinting in the native state. However, the complexity involved in deciphering the required information from a spectrum hampered their entry into the clinic. Today with the advent of automated algorithms, vibrational microspectroscopy excels in the field of spectropathology. However, researchers should be aware of how quantification based on absolute band intensities may be affected by instrumental parameters, sample thickness, water content, substrate backgrounds and other possible artefacts. In this review these practical issues and their effects on the quantification of biomolecules will be discussed in detail. In many cases ratiometric analysis can help to circumvent these problems and enable the quantitative study of biological samples, including ratiometric imaging in 1D, 2D and 3D. We provide an extensive overview from the recent scientific literature on IR and Raman band ratios used for studying biological systems and for disease diagnosis and treatment prognosis.

Rapid, sensitive and label‐free methods to probe bacterial growth irrespective of the culture conditions can shed light on the mechanisms by which bacteria adapt to different environmental stimuli. Raman spectroscopy can rapidly and continuously monitor the growth of bacteria under varied conditions. In this study, the growth of Escherichia coli in Luria broth (nutrient rich conditions) and minimal media with either glucose or glycerol as carbon source (nutrient limiting conditions) is profiled using Raman spectroscopy. Moreover, the study also gives insights into the altered bacterial biochemistry upon exposure to low‐ (25°C) and high‐temperature (45°C) stress. Raman spectral measurement was performed on bulk bacteria cultured under laboratory conditions. A detailed analysis of the spectra as a function of bacterial growth reveals changes in Raman band intensities/area of biomolecules such as DNA, proteins and lipids. We also report five novel ratiometric markers (I830/I810, I1126/I1100, I1340/I1440, I1207/I1240 and I1580/I1440) that can identify the phase of growth, independent of the culture condition. Unsupervised multivariate methods like Principal Component Analysis also corroborate the aforementioned markers of growth. Altogether, our findings highlight the potential of Raman spectroscopy in yielding universal biochemical signatures that may be indicative of stress and aging in a growth milieu.

<p class="p1">Vibrational spectroscopic techniques have advantages over conventional microbiological approaches towards identification &amp; detection of pathogens. Since unique spectral fingerprint is obtained, one can identify very closely related bacteria using such methods. In this study Raman microspectroscopy in combination with chemometric method has been used to classify four strains of <em>E</em>. <em>coli </em>(two pathogenic &amp; two non-pathogenic). Different multivariate approaches such as hierarchical cluster analysis, principal component analysis &amp; linear discriminant analysis were explored to obtain efficient classification of the Raman signals obtained from the four strains of <em>E.coli</em>. It was observed that multivariate analysis was able to classify the bacteria at strain level. Linear discrimination analysis using PC scores (PC-LDA) was found to give very good result with as high as 100% accuracy. This hybrid technique (Raman spectroscopy &amp; multivariate analysis) has tremendous potential to be developed as a tool for bacterial identification.<span class="Apple-converted-space"> </span></p>

Genomic deoxyribounucleic acid (DNA) extracted from Brucella and Bacillus genera including Bacillus anthracis was investigated for the first time using Raman spectroscopy coupled with deep learning technique. Since DNA sequence is unique and independent of growth phases of bacteria, Raman spectroscopy can be a potential molecular diagnostic tool to identify different pathogens. Additionally, pure cellular components such as DNA provide pure Raman spectra and are not corrupted by spectral features from other cell components which is usually the case in whole organism detection. In this work, 15 DNA samples (two from Brucella genus and 13 from Bacillus genus) were studied. Raman signatures revealed unique features for Brucella and Bacillus genus bacteria. We propose an artificial intelligence (AI) based method, convolutional neural network (CNN) to discriminate all 15 DNA samples. The results reveal that Bacillus anthracis has distinct Raman DNA signatures compared to Bacillus cereus and Bacillus thuringiensis and could be discriminated from the latter two using principal component analysis (PCA), hierarchical cluster analysis (HCA), principal component-linear discriminant analysis (PC-LDA). In addition to these multivariate analysis techniques, we show that using convolutional neural network (CNN) architecture all 15 DNA samples could be discriminated with 100% accuracy.

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