chemometrics
Sign in to saveChemometrics is the science of extracting information from chemical systems by data-driven means. Chemometrics is inherently interdisciplinary, using methods frequently employed in core data-analytic disciplines such as multivariate statistics, applied mathematics, and computer science, in order to address problems in chemistry, biochemistry, medicine, biology and chemical engineering. In this way, it mirrors other interdisciplinary fields, such as psychometrics and econometrics.
Research
19,970 papers- Chemometrics-powered spectroscopic techniques for the measurement of food-derived phenolics and vitamins in foods: A review.Food chemistry · 2025
- Application and Progress of Chemometrics in Voltammetric Biosensing.Biosensors · 2022
- Overview of cocaine identification by vibrational spectroscopy and chemometrics.Forensic science international · 2023
- The Application of Chemometrics in Metabolomic and Lipidomic Analysis Data Presentation for Halal Authentication of Meat Products.Molecules (Basel, Switzerland) · 2022
- Recent advances of vibrational spectroscopy and chemometrics for forensic biological analysis.The Analyst · 2021
via PubMed
~15 min read
Encyclopedic overview
11 sectionsContents
- Background
- Origins
- Techniques
- Multivariate calibration
- Classification, pattern recognition, clustering
- Multivariate curve resolution
- Other techniques
- Chemometrics and Food Science
- References
- Further reading
- External links
Chemometrics is the science of extracting information from chemical systems by data-driven means. Chemometrics is inherently interdisciplinary, using methods frequently employed in core data-analytic disciplines such as multivariate statistics, applied mathematics, and computer science, in order to address problems in chemistry, biochemistry, medicine, biology and chemical engineering. In this way, it mirrors other interdisciplinary fields, such as psychometrics and econometrics.
==Background== Chemometrics is applied to solve both descriptive and predictive problems in experimental natural sciences, especially in chemistry. In descriptive applications, properties of chemical systems are modeled with the intent of learning the underlying relationships and structure of the system (i.e., model understanding and identification). In predictive applications, properties of chemical systems are modeled with the intent of predicting new properties or behavior of interest. In both cases, the datasets can be small but are often large and complex, involving hundreds to thousands of variables, and hundreds to thousands of cases or observations.
Excerpted from Wikipedia’s “chemometrics” article, available under the CC BY-SA 4.0 licence.