  Surface enhanced Raman spectroscopy, is a technique of fundamental importance
to analytical science and technology where the amplified Raman spectrum of
analytes is used for chemical fingerprinting. Here, we showcase an engineered
hierarchical substrate in which the plasmonically active regions are restricted
to a micron scale, 2D hexagonal pattern. The Raman signal enhancement of any
analyte uniformly coating the substrate is consequently bears a high registry
with the 2D pattern. This spatially segregated enhancement allows optical
imaging of the 2D pattern solely using the Raman scattered photons from the
analyte. While pattern brightness and contrast determine analyte identification
and detection sensitivity, the spectrally selective contrast allows for tuning
specificity. Conceptual proof of the technique is demonstrated via the
acquisition of Raman images with rhodamine and fluorescein and finally applied
to detect glucose in 40 mM concentration. The large area imaging and the
inherent requirement of spatial uniformity for positive detection implemented
using a machine learning based automated pattern recognition protocol increases
the statistical confidence of analyte detection. This simultaneously multisite
signal detection sacrifices continuous spectral information at the cost of
speed, reproducibility and human error via automation of detection of the
hyperspectral imaging technique presented here.
