Electron Spin Resonance or electron paramagnetic resonance spectroscopy is a technique for examining materials with unpaired electrons. ESR spectroscopy can provide valuable information about the structure and bonding of paramagnetic species. By analyzing the splitting patterns observed in the ESR spectrum, researchers can gain insights into the coordination environment and symmetry of these species.
This technique has proven to be a powerful tool in various fields, including food science, biochemistry, and materials science. Moreover in diagnosis of several diseases as it detects free radicals that are generated by chemical or biological systems by observing the spectrum of a spin adduct.
In theory, ESR detects paramagnetic centers (such as radicals) that may or may not be caused by radiation. Resonance absorption of an applied microwave energy occurs when an intense external magnetic field creates a difference between the energy levels of the electron spins, ms = +1/2 and ms = -1/2. The ESR spectra are depicted as the first derivative of the absorption in proportion to the applied magnetic field. Contrary to the food’s shelf life, radiation-induced paramagnetic species can endure for a long time in a food sample’s rigid and dehydrated components.
Identifying Food Additives
Various types of food colorants are chemical substances added to food matrices in food technology to maintain or improve the food product’s sensory qualities, which can be impacted or lost during processing or storage, as well as to maintain the desired color appearance. Regulatory agencies such as the Food and Drug Administration (FDA) in the USA and the EFSA in Europe need a series of toxicity testing before allowing the use of food colorants in the manufacturing of food products. Therefore, it is imperative to examine their existence and quantity.
Some examples of commonly used food additives are:
Chromotrope: A monoazide, also known as acid red 14 (AR14), is a bright orange-red powder. It efficiently stains fatty substances and is also used to dye food and medicines. Its distinctive color and high solubility in water make it a popular choice for visualizing and quantifying various chemical reactions. Furthermore, its stability and compatibility with different solvents allow for versatile use in both qualitative and quantitative analysis.
Eryoglaucine: Tiny hydrophilic blue dye called , sometimes referred to as Brilliant Blue FCF. It is very soluble in water but insoluble in vegetable oils. This dye is commonly used in the food industry to enhance the visual appeal of various products such as beverages, candies, and baked goods.
Riboflavin: In its purified, solid form, it is a water-soluble yellow-orange crystalline powder. In addition to its function as a vitamin, it is used as a food coloring agent.
Through the measurement of these colorants’ distinct electron spin characteristics, ESR can offer important insights about their concentration and their effects on food safety. The non-destructive technique is a perfect tool for verification of safety standard compliance since it permits precise analysis without modifying the sample. Furthermore, by keeping an eye on the stability of production processes, ESR can assist firms in optimizing their operations.
Principal Components Analysis (PCA)
An ordination approach called principal components analysis (PCA) is mostly used to show patterns in multivariate data. It seeks to investigate correlations between dependent variables and show the relative positions of data points in fewer dimensions while preserving as much information as possible.
Utilizing PCA in the electron spin resonance method is especially helpful in minimizing the number of dimensions in complex spectra and pinpointing the primary causes of the observed fluctuations. This helps distinguish between various samples or situations and enables a better understanding of the underlying causes driving the data.
HSA KIT
ESR Analysis by HSA GmbH intends to improve quality control procedures and guarantee the authenticity of products in diverse industries by successfully re-identifying chemicals through their ESR-Spectra. It enables firms to rapidly and effectively check the authenticity of their products, preventing counterfeiting and fostering consumer confidence by precisely detecting the distinct fingerprint of each substance in a mixture. Additionally, by delivering accurate data for adherence to industry norms and laws, this technology helps simplify regulatory processes.
HSA provides a two-way solution to the ESR analysis:
- ESR Training: Create principal component analysis (PCAs) from the spectra of known substances. This allows for the development of a database that can be used to identify and quantify unknown substances in future samples. Additionally, ESR training enables researchers to understand the relationship between spectral patterns and specific chemical properties, facilitating more accurate analysis and interpretation of results
- ESR Evaluation: Deep learning to check spectra with PCAs previously generated using the ESR Training Module. By utilizing deep learning algorithms, the ESR evaluation system can efficiently compare the spectra of unknown substances with the PCAs generated during the ESR training. This automated process enables quick and reliable identification and quantification of unknown substances, saving time and resources in the analysis process. Moreover, this advanced technology improves the accuracy of results by leveraging the understanding of spectral patterns and their correlation with specific chemical properties obtained through ESR training
ESR Training
The goal of the ESR Training Module is to create principal component analysis (PCAs) from the spectra of known substances. These PCAs can then be used to identify and characterize unknown substances based on their spectral similarities to the known substances. This allows researchers to quickly and accurately determine the composition and properties of a wide range of paramagnetic species, aiding in the development of new materials and understanding of chemical reactions.
The name of the substance and its known concentration (between 0 and 100%) must be specified after choosing particular project files. This information is crucial for accurately interpreting the ESR spectra and determining the presence of paramagnetic species in the food sample. Additionally, specifying the concentration of the substance helps in quantifying its contribution to the overall ESR signal.
The corresponding spectrum graphs show the presence and concentration of each substance, which is listed under the “Spectra” tab. These graphs provide a visual representation of the ESR spectra, allowing for easier identification and comparison of different substances present in the food sample.
Various tools, such as the following, can be used to create an intuitive user interface:
- Zoom
- Zoom Reset
- Horizontally Select
- Keep Selections
- Clear Selections
- Restore
- Save as image.
The metadata for the chosen spectra can also be viewed in the table on the left. The sample name, experimental setup and circumstances are only a few examples of the metadata. In order to ensure consistency and accuracy in their conclusions, researchers can use this data to track and compare results across time as this feature allows reproducibility. Additionally, the information may offer more context and insights into the experimental design, assisting with the interpretation of the spectra and improving our comprehension of the makeup of the food sample as a whole.
For precise and dependable analysis, mass spectrometry data must undergo normalization processes. These procedures entail modifying the mass spectrometry signals’ intensity to take into account technical variances and ensure sample comparability. Researchers can confidently compare and interpret findings from various experiments or studies by normalizing the data, which enables them to draw more reliable conclusions and gain new insights in the field of analytical chemistry.
Each PCA substances found in the project are listed in “Models” section after applying normalisation operations.
Cross validation process in PCA generation provides parameters that are essential for the analysis. These parameters are crucial in understanding the results and interpreting the PCA. It is recommended to hover over a specific PCA in order to obtain more detailed information about its characteristics and significance within the ESR training project.
The analysis tab provides a comprehensive overview of the mean calculations and scores for the selected PCA substance, allowing for a thorough evaluation of its performance. Additionally, it offers valuable insights into the overall fit and accuracy of the chosen model.
ESR Evaluation
The goal of the ESR Evaluation module is to check spectra with PCAs previously generated using the ESR Training Module. By comparing the spectral patterns of unknown substances with the reference PCAs, the ESR Evaluation module can determine if there is a match, indicating the presence of a known substance. This evaluation process helps to ensure reliable and consistent identification and quantification of substances in various samples.
Every PCA found during an ESR training program is stored in a database and serves as a reference for future evaluations. This allows for efficient and accurate comparison of unknown spectra with known substances. Parameters generated during PCA creation as a result of PCA cross-validation are crucial in determining the validity and reliability of the PCA model. By analyzing the cross-validation results, any potential biases or errors in the model can be identified and addressed, ensuring that the evaluation process remains robust and trustworthy. To see more details, hover your cursor over a PCA.
Identical to ESR Training, all the sprectrum can be seen in “Spectra” tab. In this instance, a spectrum pass icon is an addition, though. Either a red cross (fail), a green tick (pass), or an hourglass (awaiting PCA selection) could be displayed. The R2 number must be greater than 0.99 in order to pass because a value of 1 represents a better fit and is the ideal value.
The visualisation of the results is available as an Excel sheet that displays which substance resonates with the selected PCA component. It provides a clear indication of the level of fit between the given spectrum and its approximation with the PCA. If the correlation coefficient R² is less than 0.99, it indicates that there is a significant difference between the two and the spectrum will be marked as not-fitting.