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The revolution since the 1970s: Why is chemometrics a game changer in chemistry?
In the history of scientific research, the 1970s is considered a turning point, especially in the field of chemistry, where the rise of chemometrics marked a revolution. As a data-driven technology, chemometrics uses methods from multivariate statistics, applied mathematics, and computer science to solve various problems in the fields of chemistry, biochemistry, medicine, and chemical engineering.
The core of chemometrics is to extract useful information from complex data, which is particularly important in today's data-driven scientific world.
The first application of chemometrics can be roughly traced back to the 1970s, when the popularity of computers allowed scientists to use more data for analysis and research. Two pioneers, Svante Wold and Bruce Kowalski, promoted the development of chemometrics in this context and first proposed the term "stoichiometrics" in 1971. Later, the International Society for Chemometrics was established to serve as the leader in the field. laid the foundation for further development.
In descriptive applications, chemometrics helps scientists build models of chemical systems and gain insights into their internal structures. In predictive applications, it can be used to predict new properties or behaviors. These applications often require processing of large data sets, which can range from small to large and complex, containing hundreds or thousands of variables and observations.
In a sense, how chemometrics transforms large amounts of data into valuable knowledge is why it has become an integral part of chemical research.
With the development of fields such as analytical chemistry and metabolomics, chemometrics technology and methods have also continued to advance, which in turn has promoted the innovation of analytical instruments and methods. The application-driven nature of this discipline has led to the widespread use of many standardized chemometric methods in industry. Research into chemometrics is growing steadily, both in academia and industry.
Multivariable calibration is a technique frequently used in chemometrics to predict other properties based on measured properties of a chemical system. This process requires the use of a calibration or training data set containing reference values, for example in spectroscopic analysis, by developing a multivariate model to establish the relationship between the concentration of a chemical species and the corresponding spectrum. This method not only saves time and cost, but also enables accurate quantitative analysis under overlapping interference from other elements, demonstrating its advantages.
"In today's increasingly complex scientific research environment, how to effectively process and analyze data has become the core of the continuous exploration of chemometrics."
Another important application is classification and pattern recognition, which is particularly important in quality control and authenticity verification. By using multivariate classification techniques of supervised learning, chemometrics is able to build models to classify future samples. In addition, unsupervised classification techniques in chemometrics can discover underlying patterns in complex data sets, thereby helping scientists gain insights into the structure and properties of the data.
In addition to the above-mentioned technologies, experimental design and signal processing are also indispensable parts of chemometrics. In addition to signal preprocessing, model selection, verification and performance characterization are also the focus of research, which directly affect the interpretation and practicality of the final data processing results.
The development of chemometrics is not only a breakthrough at the technical level, but also provides a new perspective to view and understand the relationship between chemistry and data.
Since the 1970s, with the rapid advancement of data technology, chemometrics has gradually become a core component of chemical research. This revolution has brought us not only an increase in data processing capabilities, but also a reshaping of our ability to model and predict esoteric chemical systems. How will chemometrics continue to impact the development of scientific research and experimental methods in the future?
