Automatic text processing
A vector space model for automatic indexing
Communications of the ACM
Managing Gigabytes: Compressing and Indexing Documents and Images
Managing Gigabytes: Compressing and Indexing Documents and Images
Improving the similarity search of tandem mass spectra using metric access methods
Proceedings of the Third International Conference on SImilarity Search and APplications
Proceedings of the Third International Conference on SImilarity Search and APplications
| Hi-index | 0.00 |
In this paper, we propose a technique for automatic element detection from Laser Induced Breakdown Spectroscopy (LIBS) spectra. The presented approach uses a technique derived from information retrieval and, more specifically, from the Vector Space Model, to compute the similarity between spectra of elements and samples. These spectra, obtained by LIBS methods, can be represented as sequences of peaks of light emissions of specific wavelengths and intensities. In text retrieval, vectors are built using terms of the vocabulary and weight assessing the relevance of terms in documents or queries. In our case, peaks play the role of terms, elements that of documents, and samples that of queries. We will discuss how to define vectors, weights, and similarity between spectra. Experiments prove the validity of the method.