![]() This can be achieved simply by querying a given database containing knowledge of a given subject. Following confirmation of result repeatability, the next step is to check if this result has a precedent. First, the experiment must be repeatable to be valid and exclude experimental and measurement noise. ![]() To achieve this, we proposed a framework for assessing novelty and newness of the experimental results ( Figure 6). Further details on chemometrics and algorithms that enable exploration of chemical space are found elsewhere [ Finally, each of the analyses ends with data interpretation. If the produced outcomes are relevant, the next steps incorporate validation to ensure high quality conclusions are formed. Support vector machine, along with partial least-square discriminant analysis, are probably the most well-known supervised approaches that allow samples to be classified into distinctive groups based on relevant information. Probably one of the most well-known unsupervised approaches is principal component analysis (see Glossary), which allows summarizing large data sets into several components that capture most of the information. This process is followed by statistical modeling, which is divided into supervised and unsupervised approaches. The next step is data preprocessing, which covers a variety of procedures depending on the type of data analyzed (e.g., peak detection, input of missing data, and/or normalization). The process begins with data that may be of various formats that depend upon the experiment type and/or posed question. Figure 2 presents a standard chemometrics workflow for processing data.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |