Sampling and Chemometrics
Sampling and chemometrics cover how representative samples are obtained and prepared and how analytical data are calibrated, validated, and interpreted statistically.
Definition
Sampling and chemometrics is the branch of analytical chemistry concerned with obtaining representative samples, preparing them, and applying statistical and mathematical methods to calibrate, validate, and interpret chemical measurements.
Scope
This area covers the parts of the analytical process surrounding the measurement itself: obtaining a representative sample and preparing it for analysis, calibrating instruments and validating methods, and the statistical and chemometric treatment of the resulting data. It treats sampling error, sample preparation and extraction, the figures of merit of a method, calibration and standard-addition strategies, error and uncertainty, and multivariate data analysis.
Sub-topics
Core questions
- How is a representative sample obtained, and how does sampling contribute to total error?
- How are instruments calibrated and methods validated for accuracy and reliability?
- How are random and systematic errors and measurement uncertainty quantified?
- How do multivariate and chemometric methods extract information from complex data?
Key theories
- Error, calibration, and figures of merit
- Analytical results carry random and systematic errors that are characterized statistically; calibration relates instrument response to concentration, and figures of merit such as accuracy, precision, sensitivity, detection limit, and linear range define a method's performance and underpin its validation.
- Multivariate chemometrics
- Chemometric methods such as principal component analysis and multivariate calibration extract chemical information from many simultaneous measurements, modelling spectra or other high-dimensional data to classify samples and predict concentrations beyond what single measurements allow.
Mechanisms
A reliable analytical result requires that the whole process be controlled, not just the instrument reading. A representative sample is taken using a sound sampling plan, then prepared—dissolved, extracted, or cleaned up—to a form the method can measure. The instrument is calibrated, often with external standards or standard additions, and the method is validated against figures of merit. Statistical treatment quantifies random error and uncertainty and tests for bias, while chemometric models interpret multivariate data.
Clinical relevance
These principles govern data quality across all of analytical practice: clinical-laboratory quality control and method validation, environmental monitoring where sampling can dominate uncertainty, regulatory and pharmaceutical validation, and the chemometric interpretation of spectroscopic and chromatographic data in many fields.
History
Statistical treatment of analytical data grew with the broader 20th-century development of statistics and quality control. Chemometrics emerged as a distinct discipline in the 1970s, with Svante Wold coining the term and Bruce Kowalski among its founders, as inexpensive computing made multivariate analysis of instrumental data practical.
Key figures
- Svante Wold
- Bruce Kowalski
- W. Edwards Deming
Related topics
Seminal works
- miller2018
- skoog2014fac
- harris2020
Frequently asked questions
- Why is sampling considered part of analysis?
- A measurement is only as good as the sample it is made on; if the sample does not represent the whole material, even a perfect measurement gives a misleading result, so sampling error often dominates the overall uncertainty.
- What is chemometrics?
- Chemometrics is the use of statistical and mathematical methods to design experiments and extract chemical information from data, especially multivariate methods that interpret many measurements at once, such as full spectra, to classify samples or predict concentrations.