Raman spectroscopy (RS) is a spectroscopic method which indirectly measures the
vibrational states within samples. This information on vibrational states can be utilized as …

Artificial intelligence‐based methods such as chemometrics, machine learning, and deep
learning are promising tools that lead to a clearer and better understanding of data. Only …

Since the discovery of the inelastic scattering of light, ie, the so-called Raman effect, 1
Raman spectroscopy has become an attractive tool in a high number of research fields …

Raman spectroscopy has emerged as a non-invasive and versatile diagnostic technique
due to its ability to provide molecule-specific information with ultrahigh sensitivity at near …

Pesticide detection is of tremendous importance in agriculture, and Raman
spectroscopy/Surface-Enhanced Raman Scattering (SERS) has proven extremely effective …

Surface-enhanced Raman spectroscopy (SERS), providing near-single-molecule-level
fingerprint information, is a powerful tool for the trace analysis of a target in a complicated …

Resource-efficient production of value-added products from lignocellulosic waste is an
important requisite for sustainable development. Since constituent separation of …

The precise classification of coal and gangue is a crucial link for effective sorting and
efficient utilization. However, there are some shortcomings in traditional methods, such as …

In this study, we developed a deep convolution neural network (DCNN) model for predicting
the optical properties of carbon dots (CDs), including spectral properties and fluorescence …

Background and objective With the rapid development of data science methods like deep
learning, these methods have already been used into the field of healthcare and medicine …