Authentication and quality assessment of meat products by fourier-transform infrared (FTIR) Spectroscopy

These days, food safety is getting more attention than in the recent past due to consumer awareness, regulations, and industrial competition to offer best quality products. Meat and meat products are very valuable but highly perishable. There is a need for reliable assessment techniques to ensure the safety and quality of these products throughout their shelf life. Classical analytical methods have been replaced with alternative, rapid, simple, and noninvasive methods to enhance productivity and profitability in the meat supply chain. Fourier-transform infrared (FTIR) spectroscopy has become a valuable analytical technique for structural or functional studies related to foods as a rapid, nondestructive, cost-efficient, and sensitive physicochemical fingerprinting method. This technique is readily applicable for routine quality control or industrial applications with a high degree of confidence. FTIR spectroscopy coupled with chemometrics has drawn attention to quality control, safety assessment, and authentication purposes in the meat and meat products domain. This review covers fundamental knowledge on FTIR spectroscopy coupled with chemometric techniques, as well as major applications of this robust method in meat science and technology for adulteration detection, monitoring biochemical and microbiological spoilage and shelf life, determining changes in chemical components such as proteins and lipids

Raman Microspectroscopy and Fluorescence Lifetime Imaging Microscopy based Data-Driven Tissue Discrimination and Diagnostics

The ultimate objective of any new interventional or surgical techniques is to achieve a balance of minimal invasiveness, optimal efficacy, and rapid treatment duration, all while minimizing the risk of complications. A pivotal component in the management of any disease is the distinction between the impacted target structures and the neighboring healthy tissue throughout all medical interventions, encompassing surgical procedures. Such differentiation is paramount in reducing harm to healthy tissue and augmenting the efficacy of the treatment. Within the current therapeutic procedures, innovations in the field of preoperative and postoperative diagnostics contribute to the improved differentiation of benign and malignant tissue structures. On the one hand, sophisticated imaging techniques support an improved surgical decision-making, while on the other hand, histopathological examination methods enable a precise classification of the tissue after surgery. In contrast, intraoperative tissue differentiation has been based on the time-consuming gold standard of frozen section diagnostics for many years. However, by incorporating the supplementary data provided by advanced imaging sensors during surgery, and integrating it with cutting-edge machine learning methodologies, it is feasible to augment the quality of information utilized for tissue differentiation, thereby increasing the precision of the overall process. Another important task of modern medicine is the patient-specific treatment of cancer, as it has been found that different patients do not respond in the same way to drug treatment due to developed resistance mechanisms. This thesis aimed to establish Raman microspectroscopy (RMS) as marker-independent, and non-destructive technique to monitor fibrotic and epigenetic modifications in malign and benign human tissue. Additionally, the potential of RMS and fluorescence lifetime imaging microscopy (FLIM) was evaluated to non-invasively monitor the drug efficacy on patient-derived organoids from cancer patients. Towards this aim, collagen type I (COL I) structures of formalin-fixed paraffin-embedded (FFPE) tissue sections of various fibrotic diseases were compared to respective control tissue sections. Incorporating Raman measurements into the experimental protocol, we conducted routine histological and immunofluorescence (IF) staining techniques to showcase the superior efficacy of RMS when paired with spectral deconvolution. This technique offers a time- and cost-efficient alternative to conventional procedures. For the differentiation of pathological tissue, the identification of epigenetic modes of action is a promising and potentially successful approach. In alignment with IF imaging of the most abundant epigenetic modification of 5-methylcytosine (5mC), Raman spectra of cell nuclei were evaluated using multivariate data analysis. Compared to invasive staining methods, non-invasive RMS showed promising results for the differentiation of pathological tissue changes in cardiac fibrosis and endometriosis. In addition, RMS and FLIM have been used on several bladder and colon cancer organoids to evaluate their potential to monitor patient-specific responses to drug treatment. The results showed both that organoids are generally suitable as a screening platform for drug treatments and that Raman and FLIM have the potential to assess drug sensitivity. This work highlights the potential of RMS for future applications in ex vivo tissue discrimination of fibrotic diseases and identification of epigenetic changes. In addition, this work demonstrates the proof of principle that RMS and FLIM are suitable for monitoring patient-specific responses to medications on organoid models

Computational analysis of microbial flow cytometry data

Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. We place this overview in the context of a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research

Optimisation of machine learning methods for cancer detection using vibrational spectroscopy

Early cancer detection drastically improves the chances of cure and therefore methods are required, which allow early detection and screening in a fast, reliable and inexpensive manner. A prospective method, featuring all these characteristics, is vibrational spectroscopy. In order to take the next step towards the development of this technology into a clinical diagnostic tool, classification and imaging methods for an automated diagnosis based on spectral data are required. For this study, Raman spectra, derived from axillary lymph node tissue from breast cancer patients, were used to develop a diagnostic model. For this purpose different classification methods were investigated. A support vector machine (SVM) proved to be the best choice of classification method since it classified 100% of the unseen test set correctly. The resulting diagnostic models were thoroughly tested for their robustness to the spectral corruptions that would be expected to occur during routine clinical analysis. It showed that sufficient robustness is provided for a future diagnostic routine application. SVMs demonstrated to be a powerful classifier for Raman data and due to that they were also investigated for infrared spectroscopic data. Since it was found that a single SVM was not capable of reliably predicting breast cancer pathology based on tissue calcifications measured by infrared micro-spectroscopy a SVM ensemble system was implemented. The resulting multi-class SVM ensemble predicted the pathology of the unseen test set with an accuracy of 88.9%, in comparison a single SVM assessed with the same unseen test set achieved 66.7% accuracy. In addition, the ensemble system was extended for analysing complete infrared maps obtained from breast tissue specimens. The resulting imaging method successfully detected and staged calcification in infrared maps. Furthermore, this imaging approach revealed new insights into the calcification process in malignant development, which was not previously well understood.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Advancing Vibrational Spectroscopy for Cellular and Sub Cellular Analysis: Raman Spectroscopy as an in Vitro Chemotherapeutic Screeening and Assessment Protocol

Vibrational spectroscopy, including Raman micro-spectroscopy, has attracted considerable attention over the last few years, as a powerful, non-invasive tool for clinical applications, especially in cancer diagnosis, in vivo and ex vivo. As a molecular fingerprinting technique with optical resolution, Raman micro-spectroscopy is able to monitor biochemical processes, drug uptake, efficacy and mode of action and mechanisms of interaction of chemotherapeutic drugs at a subcellular level. In vitro applications may be more strategically achievable, and can help guide drug design and discovery, and eventually evaluate To this end, different lung cell lines were used and Raman micro-spectroscopy was coupled to valuable other techniques such as Confocal Laser Scanning Fluorescence Microscopy, Flow Cytometry and Atomic Force Microscopy, in order to explore itssignatures of drug resistance, towards potential applications in personalised therapy and as a companion diagnostic tool. However, to evaluate accurately the potential of Raman micro-spectroscopy for such applications, it is essential to optimise measurement and data processing protocols associated with subcellular analysis in order to extract all valuable spectroscopic information and to demonstrate the ability of this technique to distinguish not only between normal and cancer cells but also between cancer cell lines before exploring its potential as a chemotherapeutic screening and assessment protocol using commercially available chemotherapeutic agents. To be considered as an in vitro companion diagnostics technique to screen for personalised therapies, Raman micro-spectroscopy should be able to monitor subcellular interaction with chemotherapeutic drugs and to characterise cellular resistance. To this end, different lung cell lines were used and Raman micro-spectroscopy was coupled to valuable other techniques such as Confocal Laser Scanning Fluorescence Microscopy, Flow Cytometry and Atomic Force Microscopy, in order to explore its potential to elucidate drug pathways, chemical binding signature, mechanisms of action and efficacy and physiological cellular responses to the drug exposure. As chemotherapeutic agents, Doxorubicin and Actinomycin D, both anthracyclines widely used in clinics especially for lung cancer were employed as pilot molecules. Multivariate data analysis, consisting of Principal Component Analysis, Linear discriminant and Partial Least Square Regression analysis were employed to deeply investigate the spectral features related to drug effects and cellular responses. Investigations demonstrate the ability of Raman micro-spectroscopy not only to track the subcellular accumulation of the drug as function of time but also to identify its mechanism of action, the subsequent cellular response and to differentiate cellular resistance. Moreover, despite the fact that different cell lines show different chemotherapeutic resistance, the chemical binding signature appears to be identical from anti-cancer drugs which belongs to same chemotherapeutic group with implications of different mechanisms of action function of time and dose. In human lung cancer cell lines which show different cytotoxic sensitivities to the drugs, different spectroscoic response profiles to the drugs are observed, which can be potentially linked to cellular defence mechanisms, such as the expression of anti-apoptotic proteins, and DNA repair

Biosensors: 10th Anniversary Feature Papers

Biosensors are analytical devices used for the detection of a chemical substance, or analyte, which combines a biological component with a physicochemical detector. Detection and quantification are based on the measurement of the biological interactions. The biological element of a biosensor may consist of tissues, microorganisms, organelles, cell receptors, enzymes, antibodies and nucleic acids. These devices have been shown to have a wide range of applications in a vast array of fields of research, including environmental monitoring, food analysis, drug detection and health and clinical assessment, and even security and safety. The current Special Issue, “Biosensors: 10th Anniversary Feature Papers”, addresses the existing knowledge gaps and aids the advancement of biosensing applications, in the form of six peer-reviewed research and review papers, detailing the most recent and innovative developments of biosensors