Spectroscopic modelling
The application of vibrational spectroscopy in process analytical technology requires skill and experience in handling chemometric data. Adequate processing and model analysis is necessary to obtain the essential information from these rich data sources. This page shows the steps that Elegent takes to derive all the information from chemometric datasets that is insightful, useful and reliable, leading to actionable results.
Measurement
Elegent can carry out chemometric data collection thanks to its availability of spectroscopic probes as well as a wealth of material characterisation techniques, which are listed in this equipment overview. Elegent can also conduct measurements on site according to the specific needs of the client.
For inline process measurements, Elegent firstly can rely on available production devices and the experience in running these devices. For probe interfacing, or any aspect that is not part of the core of the Elegent service, Elegent teams up with partners from its network that have a proven experience in obtaining clean and reliable information from process analysers.
Finally, for probe availability itself, Elegent relies on a network of vendors that can make these devices available swiftly for R&D or industrial projects.
Data preprocessing
Data preprocessing plays a crucial role in chemometrics by preparing the data for accurate and meaningful spectroscopic modelling. It involves various steps such as selecting informative and robust wavelength regions, determining appropriate smoothing parameters that enhance interpretability without removing necessary information, and choosing the right derivative and normalisation technique.
Data cleaning and dataset balancing also fall under the umbrella of data preprocessing, ensuring the quality and balance of the dataset before the modelling phase. However, navigating through the numerous options and combinations of preprocessing parameters can be a challenging task. It requires an intensive search in the hyperparameter space to find the optimal point. At Elegent, we excel in this task by combining exhaustive search techniques with our extensive knowledge of chemometric fundamentals and experience gained within the industry.
By achieving optimal data preprocessing, we lay a solid foundation for the subsequent phases of spectroscopic modelling. It sets the stage for accurate analysis and interpretation, leading to reliable insights and meaningful predictions. Trust Elegent to guide you through the intricacies of data preprocessing, ensuring that your spectroscopic modelling journey progresses smoothly and efficiently.
Data analysis
After applying the optimal preprocessing, a comprehensive chemometric data analysis serves to unlock valuable insights hidden within your spectroscopic data. Our expertise lies in leveraging powerful techniques such as dataset decomposition, outlier assessment, and visualization of spectra to extract meaningful information and drive informed decision-making.
Principal Component Analysis (PCA) is a widely used multivariate statistical technique that allows for the exploration and reduction of complex datasets. By transforming the original variables into a new set of uncorrelated variables called principal components, PCA simplifies data representation while retaining essential information. Our skilled team at Elegent applies PCA to analyze your spectroscopic data and identify patterns, correlations, and latent structures that may not be readily apparent. This enables us to uncover key factors driving the variations within your dataset and gain a deeper understanding of the underlying processes.
Identifying outliers is crucial in any data analysis process to ensure data integrity and reliability. At Elegent, we employ robust outlier assessment techniques to detect and flag anomalous data points that may arise due to measurement errors, instrumental noise, or other sources. Our rigorous assessment helps to ensure the accuracy and consistency of your dataset, allowing for more reliable and robust analyses.
Visualizing spectra is an essential step in understanding and interpreting spectroscopic data. Elegent offers state-of-the-art, as well as customised visualisations that enable exploration and analysis of your spectra with clarity and precision. These visual representations
highlight patterns, trends, and relationships within your data, leading to clear data-driven decisions and effective communication of the analysis' findings to all stakeholders.
Model selection
Data analysis continues to model selection. Choosing the right model that best fits the data is a critical step in the spectroscopic modelling process. Being well-versed in the intricacies of model selection can lead to models as a robust basis for informed decisions.
A crucial aspect of model selection is, for instance, applying the appropriate cross-validation technique. Cross-validation helps evaluate the performance of different models by assessing their predictive capabilities on unseen data. It aids in detecting overfitting or underfitting issues and provides insights into the model's generalization ability. At Elegent, we utilize various cross-validation methods, assess the best splitting technique out of the numerous options in order to ensure robust model selection and to prevent over-optimistic results.
Furthermore, different types of regression techniques may be suitable for different spectroscopic datasets. Whether it's classical least-squares regression, multivariate curve resolution, partial least squares regression, regression techniques involving regularisation or kernel transformations, our team has expertise in assessing the appropriate regression approach for your specific needs. Along with the evaluation the characteristics of your data, such as linearity, complexity, and dimensionality, Elegent can perform an assessment with different types of regression models. Our goal is to provide you with a tailored solution that maximizes the predictive power and interpretability of your spectroscopic models. Trust Elegent to navigate you through the intricate process of spectroscopic model selection, leading meaningful results for your research or industrial applications in the next step.
Model analysis and usage
The final step in chemometrics is a model output analysis that gives clear results in terms of prediction error, output reliability, additional insights and information on the application of the model.
Various visualisations of the prediction error are produced to identify patterns in function of the variation in the spectroscopic dataset, such as formulation aspects or process settings, to assess the model's performance under different application conditions. We delve deep into the model output, examining factors such as accuracy, precision, sensitivity, and specificity to evaluate the model's reliability and robustness. Our goal is to provide you with a thorough understanding of the model's strengths, limitations, and possible areas for improvement, if inquired for.
Through timely communication and exhaustive reporting of these results, you obtain actionable insights from your spectroscopic model output, enabling you to make informed decisions and optimize your processes. Whether you need to validate the model's performance, identify influential features, or uncover hidden relationships within the data, our expertise and analytical tools are at your disposal. Partner with Elegent for reliable and comprehensive spectroscopic model projects, and harness the full potential of your chemometric datasets.