Fourier transform infrared spectroscopy, a powerful tool to monitor biopharmaceuticals production

A Escherichia coli é o microorganismo mais usado como hospedeiro para a produção de produtos recombinantes, tais como plasmídeos usados para terapia génica e vacinação de ADN. Desta forma, torna-se importante compreender as relações metabólicas complexas e a bioprodução de plasmídeo, que ocorre em ambientes de cultura dinâmicos, a fim de controlar e optimizar o desempenho do sistema de expressão recombinante. O objectivo principal deste trabalho consiste em avaliar a potencialidade da espectroscopia FT-IR para monitorizar e caracterizar a produção do plasmídeo pVAX-LacZ em culturas recombinantes de E. coli, nomeadamente para extrair informação relacionada com as variáveis críticas (biomassa, plasmídeo, fontes de carbono e acetato) e informação metabólica da célula hospedeira E. coli. Para tal, culturas de E. coli com diferentes concentrações de glucose e glicerol e diferentes estratégias de cultivo (batch e fed-batch) foram monitorizadas por espectroscopia de infravermelho perto (NIR) e de infravermelho médio (MIR). Tanto a espectroscopia NIR com a MIR permitiram extrair informação sobre as variáveis críticas do bioprocesso, através da construção de modelos de regressão por mínimos quadrados parciais, que resultaram em elevados coeficientes de regressão e baixos erros de previsão. A abordagem NIR apresenta a vantagem de aquisição em tempo real das variáveis do bioprocesso, já a abordagem MIR permite a leitura simultânea de centenas de amostras de várias culturas ao mesmo tempo através do uso multi-microplacas, sendo muito vantajosa nos casos de micro-bioreactores usados para optimização. Para além disso, como os espectros MIR apresentam mais informação do que os espectros NIR, uma vez que representam os modos de vibração fundamentais das biomoléculas, enquanto que os espectros NIR representam sobreposições e combinações de vibrações, os dados espectrais MIR também permitiram a aquisição de informação bioquímica ao longo das culturas de E. coli a partir da análise das componentes principais (PCA) bem como do estudo das características bioquímicas, tais como as reservas de glicogénio e os níveis de transcrição aparente. Portanto, a espectroscopia FT-IR apresenta assim características relevantes para a compreensão e monitorização do processo de produção de culturas recombinantes, sendo, de acordo com Quality-by-Design e Process Analytical Technology, muito importante para fins de controlo e optimização.Escherichia coli is the most used microorganism as host for the production of recombinant products, such as plasmids used for gene therapy and DNA vaccination. Therefore, it is important to understand the complex metabolic relationships and the plasmid bioproduction process occurring in dynamic culture environments, in order to control and optimize the performance of the recombinant expression system. The main goal of this work is to evaluate the potential of Fourier Transform Infrared (FT-IR) spectroscopy to monitor and characterize recombinant E. coli cultures producing the plasmid model pVAX-LacZ, namely to extract information concerning the critical variables (biomass, plasmid, carbon sources and the by-product acetate) and metabolic information regarding the host E. coli. To achieve that cultures of E. coli conducted with different mixture of glucose and glycerol and different cultivation strategies (batch and fed-batch) were monitored in-situ by a fiber optic probe in near- infrared (NIR) and of the cell pellets in at-line in high-throughput mode by mid-infrared (MIR) spectroscopy. Both NIR and MIR spectroscopy setup enabled to extract information regarding the critical variables of the bioprocess by the implementation of partial least square regression models that result in high regression coefficients and low prediction errors. The NIR setup presents the advantage of acquiring in real time the knowledge of the bioprocess variables, where the at-line measurements with the MIR setup presents more advantageous in cases of micro-bioreactors used in optimization protocols, enabling the simultaneously information acquisition of hundreds samples by using multi-microplates. Furthermore, as the MIR spectra presents more information than the NIR spectra, since it represents the fundamental vibration modes of biomolecules while the NIR spectra represents overtones and combinations of vibrations, the MIR data also enabled to acquire biochemical information along the E. coli cultures as pointed out in an principal component analysis and by the estimation of biochemical features as glycogen reserves and apparent transcriptional levels. Therefore, FT-IR spectroscopy presents relevant features towards the understanding and monitoring of the production process of recombinant cultures for control and optimization purposes, in according to the Quality-by-Design and the Process Analytical Technology

Intelligent Remote Monitoring and Management system for Type1 Diabetes

The work presented in this thesis focuses on developing a telemedicine system for better management of type1 diabetes in children and teenagers. The research and development of the system is motivated by the inadequate communication in the current system of management of the disease, which results in non-compliance of patients following the regimen. This non-compliance generally results in uncontrolled blood glucose levels, which can result in hypoglycaemia, hyperglycaemia and later life health complications. This further results in an increase in health care costs. In this context, the thesis presents a novel end-to-end, low cost telemedicine system, WithCare+, developed in close collaboration between the University of Sheffield (Electronics & Electrical Engineering) and Sheffield Children’s Hospital. The system was developed to address the challenges of implementing modern telemedicine in type 1 diabetic care with particular relevance to National Health Service children’s clinics in the United Kingdom, by adopting a holistic care driven approach (involving all stakeholders) based on specific key enabler technologies such as low cost and reconfigurable design. However, one of the major issues with current telemedicine system is non-compliance of the patients due to invasive procedure of the glucose measurement which could be clearly addressed by non-invasive method of glucose measurement. Hence, the thesis also makes a contribution towards non-invasive glucose measurement using Near Infrared spectroscopy in terms of addressing the calibration challenge; two methods are proposed to improve the calibration of the Near Infrared instrument. The first method combines locally weighted regression and partial least square regression and the second method combines digital band pass filtering with support vector regression. The efficacy of the proposed methods is validated in experiments carried out in a non-controlled environment and the results obtained demonstrate that the proposed methods improved the performance of the calibration model in comparison to traditional calibration techniques such as Principal Component Regression and Partial Least Squares regression

Classification before regression for improving the accuracy of glucose quantification using absorption spectroscopy

This work contributes to the improvement of glucose quantification using near-infrared (NIR), mid-infrared (MIR), and combination of NIR and MIR absorbance spectroscopy by classifying the spectral data prior to the application of regression models. Both manual and automated classification are presented based on three homogeneous classes defined following the clinical definition of the glycaemic ranges (hypoglycaemia, euglycaemia, and hyperglycaemia). For the manual classification, partial least squares and principal component regressions are applied to each class separately and shown to lead to improved quantification results compared to when applying the same regression models for the whole dataset. For the automatic classification, linear discriminant analysis coupled with principal component analysis is deployed, and regressions are applied to each class separately. The results obtained are shown to outperform those of regressions for the entire dataset

Monitoring biological wastewater treatment processes: Recent advances in spectroscopy applications

Biological processes based on aerobic and anaerobic technologies have been continuously developed to wastewater treatment and are currently routinely employed to reduce the contaminants discharge levels in the environment. However, most methodologies commonly applied for monitoring key parameters are labor intensive, time-consuming and just provide a snapshot of the process. Thus, spectroscopy applications in biological processes are, nowadays, considered a rapid and effective alternative technology for real-time monitoring though still lacking implementation in full-scale plants. In this review, the application of spectroscopic techniques to aerobic and anaerobic systems is addressed focusing on UV--Vis, infrared, and fluorescence spectroscopy. Furthermore, chemometric techniques, valuable tools to extract the relevant data, are also referred. To that effect, a detailed analysis is performed for aerobic and anaerobic systems to summarize the findings that have been obtained since 2000. Future prospects for the application of spectroscopic techniques in biological wastewater treatment processes are further discussed.The authors thank the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, COMPETE 2020 (POCI-01-0145-FEDER-006684) and the project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte. The authors also acknowledge the financial support to Daniela P. Mesquita and Cristina Quintelas through the postdoctoral Grants (SFRH/BPD/82558/2011 and SFRH/BPD/101338/2014) provided by FCT - Portugal.info:eu-repo/semantics/publishedVersio

Nonlinear multiple regression methods for spectroscopic analysis: application to NIR calibration

Chemometrics has been applied to analyse near-infrared (NIR) spectra for decades. Linear regression methods such as partial least squares (PLS) regression and principal component regression (PCR) are simple and widely used solutions for spectroscopic calibration. My dissertation connects spectroscopic calibration with nonlinear machine learning techniques. It explores the feasibility of applying nonlinear methods for NIR calibration. Investigated nonlinear regression methods include least squares support vec- tor machine (LS-SVM), Gaussian process regression (GPR), Bayesian hierarchical mixture of linear regressions (HMLR) and convolutional neural networks (CNN). Our study focuses on the discussion of various design choices, interpretation of nonlinear models and providing novel recommendations and insights for the con- struction nonlinear regression models for NIR data. Performances of investigated nonlinear methods were benchmarked against traditional methods on multiple real-world NIR datasets. The datasets have differ- ent sizes (varying from 400 samples to 7000 samples) and are from various sources. Hypothesis tests on separate, independent test sets indicated that nonlinear methods give significant improvements in most practical NIR calibrations

Metabolic profiling on 2D NMR TOCSY spectra using machine learning

Due to the dynamicity of biological cells, the role of metabolic profiling in discovering biological fingerprints of diseases, and their evolution, as well as the cellular pathway of different biological or chemical stimuli is most significant. Two-dimensional nuclear magnetic resonance (2D NMR) is one of the fundamental and strong analytical instruments for metabolic profiling. Though, total correlation spectroscopy (2D NMR 1H -1H TOCSY) can be used to improve spectral overlap of 1D NMR, strong peak shift, signal overlap, spectral crowding and matrix effects in complex biological mixtures are extremely challenging in 2D NMR analysis. In this work, we introduce an automated metabolic deconvolution and assignment based on the deconvolution of 2D TOCSY of real breast cancer tissue, in addition to different differentiation pathways of adipose tissue-derived human Mesenchymal Stem cells. A major alternative to the common approaches in NMR based machine learning where images of the spectra are used as an input, our metabolic assignment is based only on the vertical and horizontal frequencies of metabolites in the 1H-1H TOCSY. One- and multi-class Kernel null foley–Sammon transform, support vector machines, polynomial classifier kernel density estimation, and support vector data description classifiers were tested in semi-supervised learning and novelty detection settings. The classifiers’ performance was evaluated by comparing the conventional human-based methodology and automatic assignments under different initial training sizes settings. The results of our novel metabolic profiling methods demonstrate its suitability, robustness, and speed in automated nontargeted NMR metabolic analysis

Development and application of Nuclear Magnetic Resonance spectroscopy and chemometric methods for the analysis of the metabolome of Saccharomyces cerevisiae under different growing conditions

[eng] Nuclear Magnetic Resonance (NMR) spectroscopy is able to produce by a single direct measurement a very high amount of chemical information. However, this information is not always easy to interpret. In fact, the complexity of the NMR spectral data analysis is proportional to the number of compounds present simultaneously in the analyzed sample, as resonances from different compounds overlap. One of the most extreme situations can be found for NMR spectra of samples from metabolomics studies, from which approximately fifty compounds can be detected in a single measurement. In the study of the chemical processes involving metabolites (metabolomics), the most commonly used NMR spectra are the one-dimensional proton (1D 1H) NMR spectra, since they are relatively fast to acquire and proton sensitivity is the highest. The 1H-13C Heteronuclear Single Quantum Coherence (HSQC) NMR spectra are also frequently used in metabolomics for an improved structural characterization of the detected metabolites. In this Thesis, we have developed different data analysis strategies of 1H NMR and 1H-13C HSQC NMR metabolomics datasets. The investigated NMR spectra were acquired from extracts of Saccharomyces cerevisiae cells previously exposed to different environmental perturbations. The aim of these studies was to better understand the different metabolic processes that regulate the yeast metabolism acclimation to different growing conditions. From the study of these NMR metabolomics experiments, we designed new strategies and protocols for the analysis of these datasets that include the steps of data import, data pre-treatment, resonance assignment and metabolite quantification. Moreover, different chemometric methods were applied for the identification of the possible biomarkers that define the metabolic states of yeast cells and to extract the main metabolic profiles that describe the observed changes in the metabolome. Furthermore, two chemometric strategies were proposed for the untargeted analysis of 1H NMR and 1H-13C HSQC NMR, respectively. For the study of 1H NMR spectra of metabolomics samples, the application of the Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) chemometric method allowed the satisfactory resolution of the individual 1H NMR spectra and concentrations of the different metabolites. On the other hand, the investigation of metabolomics datasets by 1H-13C HSQC NMR revealed that most of the data values in these NMR spectra are only descriptive of noise, hampering their chemometric data analysis. In this context, a new strategy to filter the variables relative to noise, named ‘Variables of Interest’ (or VOI) is proposed. After the application of this procedure, we observed that the analysis of the noise-filtered 1H-13C HSQC NMR spectra produced similar results to the corresponding analysis of 1H NMR spectra. Due to the existence of the second dimension in the 1H-13C HSQC NMR spectra, resonances are less overlapped and they could be integrated without using deconvolution approaches. For all these reasons, and linked to the fact that more chemical information is contained in the 1H-13C HSQC NMR spectra than in the 1H NMR spectra, the analysis of noise-filtered 1H-13C HSQC NMR spectra allow a more accurate characterization of the metabolomic system, in a reduced amount of time in comparison to the analysis of the corresponding 1H NMR spectra.[cat] L'espectroscòpia de ressonància magnètica nuclear (RMN) és capaç de generar mitjançant una mesura simple i directa una gran quantitat d'informació química. Tanmateix, aquesta informació no sempre és fàcil d'interpretar. De fet, la complexitat de l'anàlisi espectral és proporcional al nombre de compostos presents en la mostra analitzada, ja que les ressonàncies dels diferents compostos es troben superposades. Una de les situacions més extremes la podem trobar en el cas dels espectres de RMN de mostres obtingudes en estudis de metabolòmica, en les que es poden arribar a detectar al voltant d’una cinquantena de compostos en una sola mesura. En l'estudi dels processos químics relacionats amb els metabòlits (metabolòmica), els espectres de RMN més utilitzats són els espectres monodimensionals de protó (1D 1H), ja que són relativament ràpids d'adquirir i la sensibilitat del protó és la més alta. És també corrent utilitzar en estudis de metabolòmica els espectres de RMN bidimensionals 1H-13C heteronuclears de coherència quàntica única (2D 1H-13C HSQC), els quals permeten obtenir una millor caracterització estructural dels metabòlits detectats. En aquesta Tesi, s’han desenvolupat diferents estratègies d'anàlisi d’espectres de RMN de 1H i de 1H-13C HSQC de mostres de metabolòmica. Els espectres de RMN van ser adquirits d’extractes de llevat Saccharomyces cerevisiae que prèviament havia estat exposat a diferents pertorbacions mediambientals. L’objectiu d’aquests estudis ha estat millorar la comprensió dels diferents processos metabòlics que regulen l'aclimatació de les cèl·lules de llevat a diferents condicions de creixement. A partir d’aquests estudis de metabolòmica realitzats, es van dissenyar noves estratègies i protocols d'anàlisi de dades de RMN que inclouen la seva importació, el seu preprocessament, l'assignació de les ressonàncies i la seva integració. A més, es van aplicar diferents mètodes quimiomètrics que van permetre identificar els biomarcadors de l’estat metabòlic de les cèl·lules del llevat i extreure els principals perfils metabòlics que descriuen els canvis en el seu metabolisme. Es van proposar a més, dues estratègies quimiomètriques per a l’anàlisi no dirigida d’espectres de RMN de 1H i de 1H-13C HSQC, respectivament. En el cas dels estudis d’espectres de RMN de 1H, l'aplicació del mètode de resolució multivariant de corbes per mínims quadrats alternats (MCR-ALS) va permetre resoldre satisfactòriament les concentracions i els espectres individuals dels diferents metabòlits. D’altra banda, la investigació de l’estructura de les dades dels espectres de RMN de 1H-13C HSQC va revelar que la majoria dels valors espectrals són descriptius del soroll, cosa que dificulta la seva anàlisi. En aquest context, s’ha desenvolupat una nova estratègia per filtrar les variables descriptives del soroll, anomenada selecció de les variables d'interès (Variables of Interest, VOI). Després d’aplicar aquest procediment, es va observar que l'anàlisi dels espectres 1H-13C HSQC filtrats produeix resultats similars als obtinguts amb els espectres corresponents de 1H. Degut a l’existència de la segona dimensió en els espectres de 1H-13C HSQC, les ressonàncies estan menys solapades i es poden integrar sense fer servir estratègies basades en la seva deconvolució. Degut a tot això i al fet que els espectres de 1H-13C HSQC contenen més informació química que els de 1H, l’anàlisi dels espectres de 1H-13C HSQC filtrats amb aquest procediment permet una caracterització del sistema metabolòmic més acurada i amb temps d’anàlisis més curts, en comparació a l’anàlisi dels espectres de 1H corresponents

Development and application of Nuclear Magnetic Resonance spectroscopy and chemometric methods for the analysis of the metabolome of Saccharomyces cerevisiae under different growing conditions

[eng] Nuclear Magnetic Resonance (NMR) spectroscopy is able to produce by a single direct measurement a very high amount of chemical information. However, this information is not always easy to interpret. In fact, the complexity of the NMR spectral data analysis is proportional to the number of compounds present simultaneously in the analyzed sample, as resonances from different compounds overlap. One of the most extreme situations can be found for NMR spectra of samples from metabolomics studies, from which approximately fifty compounds can be detected in a single measurement. In the study of the chemical processes involving metabolites (metabolomics), the most commonly used NMR spectra are the one-dimensional proton (1D 1H) NMR spectra, since they are relatively fast to acquire and proton sensitivity is the highest. The 1H-13C Heteronuclear Single Quantum Coherence (HSQC) NMR spectra are also frequently used in metabolomics for an improved structural characterization of the detected metabolites. In this Thesis, we have developed different data analysis strategies of 1H NMR and 1H-13C HSQC NMR metabolomics datasets. The investigated NMR spectra were acquired from extracts of Saccharomyces cerevisiae cells previously exposed to different environmental perturbations. The aim of these studies was to better understand the different metabolic processes that regulate the yeast metabolism acclimation to different growing conditions. From the study of these NMR metabolomics experiments, we designed new strategies and protocols for the analysis of these datasets that include the steps of data import, data pre-treatment, resonance assignment and metabolite quantification. Moreover, different chemometric methods were applied for the identification of the possible biomarkers that define the metabolic states of yeast cells and to extract the main metabolic profiles that describe the observed changes in the metabolome. Furthermore, two chemometric strategies were proposed for the untargeted analysis of 1H NMR and 1H-13C HSQC NMR, respectively. For the study of 1H NMR spectra of metabolomics samples, the application of the Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) chemometric method allowed the satisfactory resolution of the individual 1H NMR spectra and concentrations of the different metabolites. On the other hand, the investigation of metabolomics datasets by 1H-13C HSQC NMR revealed that most of the data values in these NMR spectra are only descriptive of noise, hampering their chemometric data analysis. In this context, a new strategy to filter the variables relative to noise, named ‘Variables of Interest’ (or VOI) is proposed. After the application of this procedure, we observed that the analysis of the noise-filtered 1H-13C HSQC NMR spectra produced similar results to the corresponding analysis of 1H NMR spectra. Due to the existence of the second dimension in the 1H-13C HSQC NMR spectra, resonances are less overlapped and they could be integrated without using deconvolution approaches. For all these reasons, and linked to the fact that more chemical information is contained in the 1H-13C HSQC NMR spectra than in the 1H NMR spectra, the analysis of noise-filtered 1H-13C HSQC NMR spectra allow a more accurate characterization of the metabolomic system, in a reduced amount of time in comparison to the analysis of the corresponding 1H NMR spectra.[cat] L'espectroscòpia de ressonància magnètica nuclear (RMN) és capaç de generar mitjançant una mesura simple i directa una gran quantitat d'informació química. Tanmateix, aquesta informació no sempre és fàcil d'interpretar. De fet, la complexitat de l'anàlisi espectral és proporcional al nombre de compostos presents en la mostra analitzada, ja que les ressonàncies dels diferents compostos es troben superposades. Una de les situacions més extremes la podem trobar en el cas dels espectres de RMN de mostres obtingudes en estudis de metabolòmica, en les que es poden arribar a detectar al voltant d’una cinquantena de compostos en una sola mesura. En l'estudi dels processos químics relacionats amb els metabòlits (metabolòmica), els espectres de RMN més utilitzats són els espectres monodimensionals de protó (1D 1H), ja que són relativament ràpids d'adquirir i la sensibilitat del protó és la més alta. És també corrent utilitzar en estudis de metabolòmica els espectres de RMN bidimensionals 1H-13C heteronuclears de coherència quàntica única (2D 1H-13C HSQC), els quals permeten obtenir una millor caracterització estructural dels metabòlits detectats. En aquesta Tesi, s’han desenvolupat diferents estratègies d'anàlisi d’espectres de RMN de 1H i de 1H-13C HSQC de mostres de metabolòmica. Els espectres de RMN van ser adquirits d’extractes de llevat Saccharomyces cerevisiae que prèviament havia estat exposat a diferents pertorbacions mediambientals. L’objectiu d’aquests estudis ha estat millorar la comprensió dels diferents processos metabòlics que regulen l'aclimatació de les cèl·lules de llevat a diferents condicions de creixement. A partir d’aquests estudis de metabolòmica realitzats, es van dissenyar noves estratègies i protocols d'anàlisi de dades de RMN que inclouen la seva importació, el seu preprocessament, l'assignació de les ressonàncies i la seva integració. A més, es van aplicar diferents mètodes quimiomètrics que van permetre identificar els biomarcadors de l’estat metabòlic de les cèl·lules del llevat i extreure els principals perfils metabòlics que descriuen els canvis en el seu metabolisme. Es van proposar a més, dues estratègies quimiomètriques per a l’anàlisi no dirigida d’espectres de RMN de 1H i de 1H-13C HSQC, respectivament. En el cas dels estudis d’espectres de RMN de 1H, l'aplicació del mètode de resolució multivariant de corbes per mínims quadrats alternats (MCR-ALS) va permetre resoldre satisfactòriament les concentracions i els espectres individuals dels diferents metabòlits. D’altra banda, la investigació de l’estructura de les dades dels espectres de RMN de 1H-13C HSQC va revelar que la majoria dels valors espectrals són descriptius del soroll, cosa que dificulta la seva anàlisi. En aquest context, s’ha desenvolupat una nova estratègia per filtrar les variables descriptives del soroll, anomenada selecció de les variables d'interès (Variables of Interest, VOI). Després d’aplicar aquest procediment, es va observar que l'anàlisi dels espectres 1H-13C HSQC filtrats produeix resultats similars als obtinguts amb els espectres corresponents de 1H. Degut a l’existència de la segona dimensió en els espectres de 1H-13C HSQC, les ressonàncies estan menys solapades i es poden integrar sense fer servir estratègies basades en la seva deconvolució. Degut a tot això i al fet que els espectres de 1H-13C HSQC contenen més informació química que els de 1H, l’anàlisi dels espectres de 1H-13C HSQC filtrats amb aquest procediment permet una caracterització del sistema metabolòmic més acurada i amb temps d’anàlisis més curts, en comparació a l’anàlisi dels espectres de 1H corresponents

Infrared Spectroscopy of Serum Samples for Disease Diagnostics

The fundamental vibrational modes of biological constituents in the tissues and the complex body fluids coincide with optical frequencies in the infrared region. Therefore, spatially resolved molecular compositions and interaction information within the biological materials can be extracted non-destructively using IR radiation without the use of labels or probes. However, the feasibility of this technique to elucidate constituent molecular compositions and interactions within the diagnostic mediums is not well explored. This study demonstrates an application of infrared (IR) spectroscopy of sera for monitoring inflammatory bowel diseases (IBD) and various cancers. Using samples from experimental mice and human patients, the power of IR spectroscopy in structural studies of proteins and other complex band contours are explored to find spectral signatures. Two experimental models of IBD; interleukin 10 knockouts (IL10-/-) and Dextran Sodium Sulfate (DSS) induced mouse shows diagnostic accuracy with 80-100% sensitivity and specificity values. Importantly, the findings of human IBD patients’ serum also show promising results resembling our proofs-of-concept investigations of mouse models. Maximum values of sensitivity and specificity are 100% and 86%, respectively, in human samples. Similarly, in cancer studies, the EL4 mouse model of non-Hodgkin lymphoma (NHL) and a B16 mouse model of the subcutaneous melanoma are used to extract a snapshot of tumor-associated alteration in the serum. The study of both cancer-bearing mouse models in wild types (WT) and their corresponding control types emphasizes the diagnostic potential of this approach as a screening technique for the NHL and melanoma skin cancer. The breast cancer (BC) -associated protein conformational alteration in the serum samples shows the sensitivity and the specificity of identifying spectral signatures were both 90%. All in all, IR spectroscopy of serum samples accompanied by spectral analysis technique shows some promising results for disease diagnostics. The brief outlook of the fundamentals of the infrared detection technique and their applicability for the development of portable spectroscopy is also provided