Chemometrics software is a powerful tool for analyzing complex data sets from various fields of chemistry, biology and engineering. It can help researchers and practitioners to obtain meaningful information, optimize processes and solve problems. In this blog post, we present some of the features and benefits of the chemometrics software and show how it can be used for various applications.
Introduction: What is chemometric software?
Chemometric software packages are indispensable tools for analytical chemists who need to process large amounts of multivariate data precisely. These software packages utilize various techniques such as linear discriminant analysis, multivariate data analysis and quantitative analysis to provide comprehensive solutions for the analysis of unknown samples, classification models and the detection of critical material attributes in raw materials. In this article, we will explore the various applications and benefits of chemometric software in different industries, including agriculture, chemicals and product manufacturing, and finally present our own solution.
Examples of concrete applications of chemometric data models are…
- Prediction of quality characteristics of soy products: A chemometric model can be developed to predict the quality characteristics of soy products, such as protein content, oil content and moisture content. This model could be used by food manufacturers to accurately measure these critical components in their products.
- Detection of impurities in medicinal products: A chemometric model can be developed to detect traces of impurities in pharmaceuticals. This model could be used by the pharmaceutical industry to ensure that their products comply with safety standards and regulations.
- Identification of off-flavors in beverages: A chemometric model can be developed to identify off-flavors in beverages. This model could be used by beverage manufacturers to reduce or eliminate off-flavors in their products and thus improve customer satisfaction.
- Identification of plastic polymers: A chemometric model has been developed to accurately identify different types of plastic polymers used in the automotive and consumer goods industries. This model can be used to ensure that the correct polymer is used for a particular application.
- Coating thickness measurement: A chemometric model can be used to measure the thickness of thin films and coatings used in the electronics and photovoltaic industries. This model can be used to ensure that the layers are within the required tolerances.
- Estimation of the vegetation index: A chemometric model was developed to estimate vegetation indices from satellite images. This model can be used for remote sensing applications as well as for monitoring agricultural crops and land use patterns.
- Classification of feed ingredients: A chemometric model can be used to classify feed ingredients in animal feed. This model can be used to precisely identify the different types of ingredients in a particular feed mixture.
- Metrology: A chemometric model could be developed for metrological applications such as surface profiling, dimensional measurements and 3D scanning. This model can be used to ensure accurate measurement and control of critical components in manufacturing.
A graphical user interface is an essential part of chemometric software packages as it provides users with an intuitive and user-friendly interface. These interfaces allow users to easily navigate the software, analyze and visualize complex data sets and optimize their workflows. In addition, these interfaces allow analysts to pre-process their data, select the spectral range and perform multivariate analyses with minimal effort.
Different analytical methods have different spectral ranges, noise ratios and standard errors, and chemometric software can effectively analyze data regardless of the source method. These software tools can analyze spectra such as derivative spectra, library spectra, mass spectra, NIR spectra, visible spectra and absorption spectra.
In summary, chemometric software packages are powerful tools that can assist in the development of complex models for data processing, multivariate data analysis and the identification of critical material properties. With an intuitive graphical user interface and the flexibility to work with different analytical methods, these tools make chemometric analysis accessible to a wider range of users.
What are typical applications for NIR spectroscopy?
NIR spectroscopy is a powerful instrument that is used in various fields and industries for the non-invasive and non-destructive analysis of samples. The sectors in which NIR spectroscopy is typically used for analysis include agriculture, pharmaceuticals and the food and beverage industry.
However, the application of NIR spectroscopy is not limited to these industries. It is also used in the plastics industry, particularly for recycling and process control. The technology offers considerable advantages for these applications as it is fast and can provide precise information on the chemical composition of raw materials.
One of the main advantages of NIR spectroscopy is its ability to quickly provide information on critical material properties such as the chemical composition of raw materials. This makes it an indispensable tool for industries where understanding the composition of materials is crucial.
NIR spectroscopy is often used during inspection to determine product strength in the pharmaceutical industry. By analyzing the NIR spectra of the product, it is possible to determine its strength and consistency, for example. It is also used to analyze the components of agricultural products such as corn chaff samples. It is used in the food and beverage industry to monitor fermentation processes.
NIR spectrometers are single-pixel cameras in the NIR wavelength range. With the right technology, they can be built very compactly. Many NIR spectrometers are built without optics, i.e. the measurements must be carried out by contact.
In summary, NIR spectroscopy offers a wide range of applications in industries such as agriculture, pharmaceuticals, food and beverages and plastics. Due to its non-invasive and non-destructive analysis capabilities, it is particularly useful for determining the chemical composition of raw materials and for control purposes, e.g. in the pharmaceutical industry. It can also be used to analyze the components of agricultural products and to monitor fermentation processes in the food and beverage industry.
What is hyperspectral imaging?
Hyperspectral imaging (HSI) is a powerful analytical technique that enables the identification and characterization of a sample’s composition and structure by analyzing its spectrum across many narrow wavelength bands. In hyperspectral imaging, light is passed through or reflected from a sample and the resulting spectrum is recorded with a suitable detector. This spectrum contains information about the chemical composition of the sample, which can be used to create a detailed picture of its structure and properties.
Hyperspectral imaging has a wide range of applications in chemometrics. One of the most important applications is the analysis of agricultural products, where it is used to determine the quality, composition and nutritional value of plants such as fruit and vegetables. Hyperspectral imaging can also be used to check the quality of finished products such as pharmaceuticals, cosmetics and food to identify critical material properties that could affect their performance or safety.
One of the main advantages of hyperspectral imaging is that it enables non-invasive and non-destructive analysis of a wide range of samples, including solids, liquids and gases. This makes them ideal for use in industries where the integrity of samples is crucial, such as the pharmaceutical, food and cosmetics industries. Hyperspectral imaging can also be used to analyze samples in situ without the need for sample preparation or manipulation, saving time and reducing analysis costs.
HSI cameras are available in the visible, near-infrared and mid-infrared wavelength ranges. They are equipped with either 320 or 640 pixels and operate at a frequency of approx. 300 Hz to capture spectra over the entire range. These cameras can be used continuously and are suitable for inline applications. Optics and lighting should be selected carefully, as HSI cameras measure at a distance of 20 cm to 50 cm.
In summary, hyperspectral imaging is a valuable tool for chemometricians as it allows the composition and structure of a sample to be analyzed across many wavelength bands. Their ability to analyze a wide range of sample types non-invasively and non-destructively makes them useful for a variety of applications, such as agricultural product analysis, quality control and identification of critical material properties.
What is the chemotmetric software used for?
The data from NIR spectrometers contain a lot of information, and this is even more true for the data from HSI cameras. Without chemometric software, you cannot perform data analysis for the classification and quantitative analysis of unknown samples. This software enables analytical chemists to develop multivariate models, such as multivariate calibration models and classification models. The software has a user-friendly graphical interface that facilitates the pre-processing of spectral data in a wide range of spectral ranges and noise ratios.
The most important criteria for finding the best chemometrics software for your application
If you work in the field of analytical chemistry, you already know how important it is to have the right software tools to help you analyze and interpret your data accurately. Chemometric software is a powerful tool that can help you process and analyze large amounts of data to identify patterns, classify samples and create models. However, with so many chemometric software packages available on the market, choosing the right software can be a difficult task. In this article, we will give you some insights into the most important criteria for finding the best chemometrics software for your application.
Your experience
Your level of experience with chemometrics software can influence your choice. If you are familiar with programming languages such as Python, you should use them to analyze chemometric data. However, if you are not familiar with programming, you should use software with a graphical user interface (GUI), which is more user-friendly and easier to operate.
Required functions: PCA, LDA, PLS and more?
Functions: The functions offered by the software can also influence your choice. Some software is better suited to specific types of analysis, such as multivariate data analysis, while others offer a wider range of features that can be used for a variety of applications.
Offline analysis or inline processing?
Before proceeding, you should determine whether the data models are required exclusively for offline analysis or whether an inline HSI camera is to be used with the models later. In the latter case, industrial-grade software is required that can communicate with the intended camera and execute the models in real time.
The following section contains information on the various steps in the development of data models, including pre-processing and frequently used filters.
Data pre-processing and model development
Data pre-processing and model development are crucial steps in chemometric software analysis. Data pre-processing involves cleaning, noise reduction and conversion of raw data into a format suitable for modeling, while model development involves training models with the pre-processed data to make predictions or classify new data. These two steps are essential to ensure accurate analysis and interpretation of data sets in various applications such as quality control, inspection of agricultural products and non-invasive analysis of samples. In the following, data preprocessing and model development are discussed in detail to understand their importance and the key factors to consider when implementing them in chemometric software packages.
Data (pre)processing
Data processing is a crucial step in chemometric software that involves various techniques to prepare the data for accurate analysis and interpretation. Accurate data analysis is essential for the development of reliable models that assist in the identification of unknown samples and raw materials, classification models and multivariate calibration models. The process of data processing usually begins with the pre-processing of the data, including the import of spectral data, formatting and cleansing.
Normalization is a common technique used in chemometric software to correct data for variations in their original intensity levels due to variations in experimental conditions, such as absorbance. Scaling is another method of adapting the data to a standard form for comparison. When scaling, the data is usually divided by the standard deviation or the mean value in order to obtain a ratio that facilitates data comparison.
Baseline correction is another method for eliminating systematic errors that can affect the accuracy of the data. When background noise and systematic errors are removed using this technique, accurate data analysis and evaluation is guaranteed. Noise suppression is another technique for improving the signal-to-noise ratio of the data.
The detection of outliers is another important aspect of data processing in chemometric software. Outliers can significantly affect the accuracy of the results obtained, and their detection and elimination are necessary to obtain reliable results. Outliers are usually identified by means of multivariate statistical analysis. Once outliers are identified, they can be removed from the data set, enabling the creation of more accurate and reliable models.
To summarize, data processing is a crucial step in chemometric software that contributes significantly to accurate data analysis and interpretation. Normalization, scaling, baseline correction and noise reduction are some common data processing techniques, while the detection and removal of outliers helps to generate reliable results. Accurate models and analytical methods developed through the use of reliable data processing techniques facilitate, among other things, the analysis of agricultural products and non-invasive and non-destructive testing of product strength.
Development of models
This section presents several important filters for the development of models.
Multivariate data analysis
Multivariate data analysis (MVA) is a powerful tool used in chemometric software packages for the simultaneous evaluation of different measurements. MVA allows the analytical chemist to evaluate the interaction of components within a sample, which can lead to more accurate predictive models and classifications. This is achieved using various techniques and models, including principal component analysis (PCA) and regression analysis.
PCA is a technique frequently used in MVA to determine correlations between different variables. To do this, the original data is converted into a new coordinate system in which the first few principal components explain the majority of the data variability. These principal components can then be used to identify patterns, detect outliers and create more robust models.
Regression analysis is another MVA technique that enables the creation of multivariate prediction models. By analyzing the relationship between one or more independent variables and a dependent variable, regression analysis can estimate the effects of these variables on the dependent variable.
Various types of measurements can be used in MVA, including near-infrared (NIR) spectra, mass spectra and absorption spectra. NIR spectra are a non-destructive and non-invasive analysis technique that provides information about the composition of a sample. Mass spectra measure the masses and relative abundances of ions in a sample, while absorption spectra measure the amount of light absorbed by a sample at different wavelengths.
In summary, MVA is a powerful analytical technique that can be used to evaluate the interactions between different components in a sample. By using models and techniques such as principal component analysis and regression analysis, as well as measurement techniques such as NIR spectra and mass spectra, MVA enables accurate predictions and classifications in various areas, including agricultural products, where it can be used to check product strength.
What is PCA?
PCA (Principal Component Analysis) is a statistical method that is often used in chemometric software packages for the analysis and visualization of multivariate data. It aims to transform a set of potentially correlated input variables into a smaller, uncorrelated set of variables called principal components. These principal components represent the main sources of variation in the original data and can reveal hidden structures, patterns and relationships between variables.
PCA is based on linear algebra and eigenvalue analysis and can process large and complex data sets. It is often used in chemometrics to analyze and interpret spectroscopic, chromatographic and other analytical data, especially for the quantitative analysis and classification of unknown samples and raw materials. PCA can be used to create classification models and multivariate calibration models that can be used to predict the properties of new samples with high accuracy.
PCA has several advantages, including the ability to reduce the dimensionality of the data, highlight the most important sources of variation, and remove noise and redundancy. PCA can simplify data visualization and help identify outliers, trends and clusters. However, PCA has some limitations, including sensitivity to outliers, susceptibility to overfitting and the assumption of linearity. In addition, the interpretation of PCA results can be a challenge, especially for non-experts.
Alternative methods to PCA include partial least squares (PLS) regression, discriminant analysis and factor analysis. PLS regression is suitable for cases in which there is a strong correlation between the input and output variables. Discriminant analysis is useful for classifying samples into predefined groups or classes. Factor analysis is used when it is hypothesized that the variables are associated with a smaller number of latent factors that cannot be observed directly.
To summarize, PCA is a powerful tool for data analysis in chemometric software. Like any analysis technique, it has its advantages and limitations and should be used appropriately. Researchers and practitioners need to understand the concepts and assumptions underlying PCA in order to get the most out of it and choose the most appropriate method for each specific scenario.
What is LDA?
Linear discriminant analysis (LDA) is a machine learning method that is used to classify and analyze data. It is often used in chemometric software packages to distinguish between different classes of samples or components within a sample. With LDA, the data is projected onto a vector space in which the samples can be differentiated according to their designations or classifications.
In contrast to PCA, which is a method for dimensionality reduction, LDA was developed for classification. This involves finding the linear combination of features that best separates two or more classes of data points. In chemometrics, it is used to distinguish between different components in a sample or to recognize patterns in spectral and chromatographic data.
Our chemometric software solution
Our chemometric software solution, provided by our technology partner, is a powerful tool for performing multivariate data analysis, developing classification and calibration models, and analyzing a variety of spectral data. With an easy-to-use graphical user interface, we can offer a complete package for processing and analyzing spectral data.
One of the biggest challenges for analytical chemists is the development of accurate classification models to analyze and evaluate the quantitative analysis of unknown samples and raw materials. With our solution, this challenge is easily overcome thanks to the advanced multivariate methods that enable the creation of robust classification models. The software is also capable of developing multivariate calibration models, making it a suitable software tool for testing product strength and critical material properties.
The software offers several key features for optimal performance, such as the graphical, user-friendly interface for quick and easy analysis of spectral data. It also includes pre-processing functions that can reduce the noise ratio and enable the use of derivative spectra, library spectra, mass spectra, NIR spectra, visible spectra and absorption spectra for chemometric analysis.
Embedded system, real-time processing, C++, .NET, Python API: A truly industrial solution
Our chemotetric software is also designed to process a wide range of spectral data, making it compatible with various analytical methods used in the agricultural industry. In addition, it has an API for C++, .NET and Python so that scientific researchers and method developers can integrate their existing software with it without worrying about compatibility.
One of its unique capabilities is the ability to calculate hyperspectral imaging (HSI) camera data in real time. This function is particularly useful for the non-invasive and non-destructive analysis of components in samples, such as corn chaff samples. It includes additional validation samples and sample sets, an important feature to reduce errors in analysis and improve accuracy.
Finally, our system offers various filter options, such as terrain correction and atmospheric correction. These filter options are particularly necessary when working with HSI data and spectral multi-angle images.
The complete list of currently available filters
- Classification
- Distance Classifier Filter
- Support Vector Machines Filter
- Class Mapping Filter
- Min/Max Classification Filter
- Per-Pixel Decision Graph Filter Reference
- Color
- Color Determination Filter
- Color Conversion Filter
- Similarity Based Color Map Filter
- ΔE Calculation Filter Reference
- Decomposition
- Endmember Extraction Filter
- Abundance Determination Filter
- Object Detection
- Mask-Based Object Detector Filter
- Similarity-Based Object Detector Filter
- Object Processing
- Per-Object Averaging Filter
- Per-Object Counter Filter
- Per-Object Scatter Correction Filter
- Per-Object Distance Variation Filter
- Object Reclassification Filter
- Object Region Averaging Filter
- Per-Object Statistics Filter Reference
- Per-Object Decision Graph Filter Reference
- Dimensionality Reduction
- Principal Component Analysis Filter
- Linear Discriminant Analysis Filter
- Subspace Projection Filter
- Image Processing
- Kernel Operation Filter
In summary, our software solution is a reliable and advanced chemometric software solution that provides the necessary tools and functions for performing multivariate data analysis, developing classification and calibration models and analyzing a wide range of spectral data. With its user-friendly interface, it is aimed at various scientific researchers, analytical chemists and method developers in different industries. The additional software engine runs models in real-time on embedded systems and can be remotely controlled using Python, .NET and C++.
About us - Solid Scanner
Let’s take responsibility and recycle more plastics - ask us for suitable solutions. Our portfolio includes solutions ranging from small, portable solutions to individual solutions based on hyperspectral camera systems for simple, automated identification of plastics in the sorting process and for inline process control, e.g. for homogeneity.