Browsing by Author "Matousek, Pavel"

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    Application of Spatial Offset Raman Spectroscopy (SORS) and machine learning for sugar syrup adulteration detection in UK Honey
    (MDPI , 2024-07-31) Shehata, Mennatullah; Dodd, Sophie; Mosca, Sara; Matousek, Pavel; Parmar, Bhavna; Kevei, Zoltan; Anastasiadi, Maria
    Honey authentication is a complex process which traditionally requires costly and time-consuming analytical techniques not readily available to the producers. This study aimed to develop non-invasive sensor methods coupled with a multivariate data analysis to detect the type and percentage of exogenous sugar adulteration in UK honeys. Through-container spatial offset Raman spectroscopy (SORS) was employed on 17 different types of natural honeys produced in the UK over a season. These samples were then spiked with rice and sugar beet syrups at the levels of 10%, 20%, 30%, and 50% w/w. The data acquired were used to construct prediction models for 14 types of honey with similar Raman fingerprints using different algorithms, namely PLS-DA, XGBoost, and Random Forest, with the aim to detect the level of adulteration per type of sugar syrup. The best-performing algorithm for classification was Random Forest, with only 1% of the pure honeys misclassified as adulterated and <3.5% of adulterated honey samples misclassified as pure. Random Forest was further employed to create a classification model which successfully classified samples according to the type of adulterant (rice or sugar beet) and the adulteration level. In addition, SORS spectra were collected from 27 samples of heather honey (24 Calluna vulgaris and 3 Erica cinerea) produced in the UK and corresponding subsamples spiked with high fructose sugar cane syrup, and an exploratory data analysis with PCA and a classification with Random Forest were performed, both showing clear separation between the pure and adulterated samples at medium (40%) and high (60%) adulteration levels and a 90% success at low adulteration levels (20%). The results of this study demonstrate the potential of SORS in combination with machine learning to be applied for the authentication of honey samples and the detection of exogenous sugars in the form of sugar syrups. A major advantage of the SORS technique is that it is a rapid, non-invasive method deployable in the field with potential application at all stages of the supply chain.
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    Study of calcification formation and disease diagnostics utilising advanced vibrational spectroscopy
    (Cranfield University, 2012-10) Kerssens, Marleen Maartje; Stone, Nicholas; Matousek, Pavel; Rogers, Keith
    The accurate and safe diagnosis of breast cancer is a significant societal issue, with annual disease incidence of 48,000 women and around 370 men in the UK. Early diagnosis of the disease allows more conservative treatments and better patient outcomes. Microcalcifications in breast tissue are an important indicator for breast cancers, and often the only sign of their presence. Several studies have suggested that the type of calcification formed may act as a marker for malignancy and its presence may be of biological significance. In this work, breast calcifications are studied with FTIR, synchrotron FTIR, ATR FTIR, and Raman mapping to explore their disease specific composition. From a comparison between vibrational spectroscopy and routine staining procedures it becomes clear that calcium builds up prior to calcification formation. Raman and FTIR indicate the same size for calcifications and are in agreement with routine staining techniques. From the synchrotron FTIR measurements it can be proven that amide is present in the centre of the calcifications and the intensity of the bands depends on the pathology. Special attention is paid to the type of carbonate substitution in the calcifications relating to different pathology grades. In contrast to mammography, Raman spectroscopy has the capability to distinguish calcifications based on their chemical composition. The ultimate goal is to turn the acquired knowledge from the mapping studies into a clinical tool based on deep Raman spectroscopy. Deep Raman techniques have a considerable potential to reduce large numbers of normal biopsies, reduce the time delay between screening and diagnosis and therefore diminish patient anxiety. In order to achieve this, a deep Raman system is designed and after evaluation of its performance tested on buried calcification standards in porcine soft tissue and human mammary tissue. It is shown that, when the calcification is probed through tissue, the strong 960 cm-1 phosphate band can be used as a pseudo marker for carbonate substitution which is related to the pathology of the surrounding tissue. Furthermore, the first study in which human breast calcifications are measured in bulk tissue with a thickness of several millimetres to centimetres is presented. To date, measurements have been performed at 41 specimens with a thickness up to 25 mm. Measurements could be performed through skin and blue dye. The proposed deep Raman technique is promising for probing of calcifications through tissue but will need refinement before being adopted in hospitals.