Vibralytics



Chemometrics

What is Chemometrics?

Svante Wold, a revered professor of organic chemistry at Umeå University in Sweden, famously defined chemometrics as “The art of extracting chemically relevant information from data produced in chemical experiments”(1). His definition highlights the dual nature of chemometrics: an art and a science. This field blends chemistry, mathematics, and statistics to design experiments and interpret chemical data effectively.

D.L. Massart, another influential figure, described chemometrics as “a discipline that uses mathematics and statistics to design or select optimal experimental procedures, provide chemical information of maximum relevance through data analysis, and obtain knowledge about chemical systems”.(2) This approach is not just about analyzing data; it’s about optimizing the process to get the most relevant insights.

Chemometrics, or multivariate calibration, is the science that applies optimal mathematical and statistical methods to process chemical data. The need for using it arises mainly from the development of analytical instruments that provide large amounts of increasingly complex data. To obtain information from these data and achieve various objectives, this scientific field uses a wide variety of disciplines, including mathematics, statistics and programming.

The Intersection with Machine Learning

Machine Learning (ML) is often described as a set of algorithms that learn from data to create models capable of making decisions or predictions based on identified patterns. ML can be applied to diverse types of data, from images and text to numerical data sets. José Amigo, an expert in analytical chemistry, noted in 2021 that ML can be seen as a subset of chemometrics. This is because chemometrics incorporates data science tools like ML, Artificial Neural Networks, and Deep Learning, using them to analyze data derived from natural chemical systems.

In chemometrics, ML techniques are harnessed for their predictive capabilities and pattern recognition, especially in large, complex chemical datasets. However, chemometrics brings an added layer of interpretation rooted in chemical knowledge, ensuring that results align with known chemical principles.

An excellent example of a chemometric tool is the NIPALS algorithm developed for Partial Least Squares (PLS) regression by Norwegian chemist Harald Martens. Between 1980 and 1984, this algorithm became pivotal for industries needing analytical solutions that handle multicollinearity and missing data. PLS has become a cornerstone for chemometric analysis, enabling robust, reliable predictions in industries that rely on spectroscopy and complex data.

Why use NIR along with chemometrics?

NIR is an analytical technique, applicable to the determination of the chemical and physical composition of a wide range of materials. It can be used with solids, powders, liquids, solutions and gases. It allows separating a material in different classes.

The main advantages of NIR are:

  • The speed of the analysis. The results are available in a few seconds, or even continuously, which makes the technique very useful for quality control and for the analysis of samples at the time of receipt. The results can be computerized and analyzed with chemometrics.
  • NIRS is essentially free of chemicals and is environmentally friendly.
  • The instrument is completely safe and easy to use.
  • No smoke spillage or drainage is required.
  • The instrument is very stable and the spectral data are very reproducible. In fact, because of the remarkable reproducibility of spectral data, the results of the proposed methodology are often more reliable than those of the reference methods, which usually incorporate many steps, most of which are potential sources of error.
  • The ability of the method to simultaneously determine several components of a sample is an important cost-saving factor. This may allow the laboratory to reduce the testing cost.

At Vibralytics, we blend the art of chemometrics with modern data science techniques to provide clients with deep, actionable insights. Whether you’re in food quality analysis, pharmaceutical research, or chemical engineering, our expertise can help you make the most of your data, ensuring precision, reliability, and clear interpretation.
Would you like to gain insights into the workflow of chemometrics or deepen your understanding of preprocessing techniques, variable selection, exploratory methods, classification and regression algorithms, or model evaluation strategies? At Vibralytics, we would be delighted to offer you a comprehensive overview or a detailed session tailored to your needs. Contact us and discover how chemometrics can transform your data into powerful decision-making tools.

References
1) Jordi Riu, Barbara Giussani, Analytical chemistry meets art: The transformative role of chemometrics in cultural heritage preservation,Chemometrics and Intelligent Laboratory Systems,Volume 247,2024,105095,ISSN 0169-7439, https://doi.org/10.1016/j.chemolab.2024.105095
2) Oshi, P.B. Navigating with chemometrics and machine learning in chemistry. Artif Intell Rev 56, 9089–9114 (2023). https://doi.org/10.1007/s10462-023-10391-w