Is pre-processing always neccessary?
[Not specific for Raman spectroscopy]

There is a trend in some areas of machine learning to aim at end-to-end learning, that is, devise machine learning algorithms that take raw input data and gives the desired output (e.g. classification label, regression values). This way, end-to-end learning avoids introducing expert knowledge or preprocessing, usually relying only on huge amounts of data. This has been specially true in the last years in the field of deep learning, where images are taken with no preprocessing, audio is not filter-banked, and games are played by receiving only raw pixels.

So it is certainly possible to skip data preprocessing completely.

We should nevertheless take into account that most of the successful end-to-end machine learning approaches impose constraints that enable the learning to exploit the very aspects of the input data that were previously defined by experts. An example for this are Convolutional Networks, that impose a feature locality constraint that enables the learning to identify features, which could have been pre-defined as gabor filters instead.

BUT, in more traditional data science, feature engineering is a key part of the process of devising ML systems and data exploration and expert knowledge are normally used to identify the kind of preprocessing to be applied to data.