Advances in Electronic-Nose Technologies Developed for Biomedical Applications

The research and development of new electronic-nose applications in the biomedical field has accelerated at a phenomenal rate over the past 25 years. Many innovative e-nose technologies have provided solutions and applications to a wide variety of complex biomedical and healthcare problems. The purposes of this review are to present a comprehensive analysis of past and recent biomedical research findings and developments of electronic-nose sensor technologies, and to identify current and future potential e-nose applications that will continue to advance the effectiveness and efficiency of biomedical treatments and healthcare services for many years. An abundance of electronic-nose applications has been developed for a variety of healthcare sectors including diagnostics, immunology, pathology, patient recovery, pharmacology, physical therapy, physiology, preventative medicine, remote healthcare, and wound and graft healing. Specific biomedical e-nose applications range from uses in biochemical testing, blood-compatibility evaluations, disease diagnoses, and drug delivery to monitoring of metabolic levels, organ dysfunctions, and patient conditions through telemedicine. This paper summarizes the major electronic-nose technologies developed for healthcare and biomedical applications since the late 1980s when electronic aroma detection technologies were first recognized to be potentially useful in providing effective solutions to problems in the healthcare industry

Advanced nanoporous material–based QCM devices: A new horizon of interfacial mass sensing technology

Mass interfacial processes have been considered as one of the crucial factors supporting fundamental research. Due to the low cost and conceptual simplicity, significant advancements have been achieved in the development of methodologies based on piezoelectric devices for in situ determination of mass changes on the surfaces of deposited materials under various conditions. The introduction of nanomaterials for designing sensors and monitoring systems becomes essential to create advanced detection systems for selective sensing of toxic analytes for environmental remediation. The integration of materials with predesignated nanostructures into sensor devices, such as surface acoustic wave (SAW), quartz crystal microbalance (QCM), and QCM with dissipation (QCM-D) monitoring, has led to an immense progress in the sensing applications of toxic target analytes at the nanogram range. Here, an overview is introduced of recent advancement in the fabrication of piezoelectric devices for the interfacial mass sensing of targeted chemical vapors and ions through combination with nanoporous materials including mesoporous materials carbon-based nanomaterials, metal–organic frameworks (MOFs), MOF-derived nanoporous carbons, Prussian blue (PB) and its analogues (PBA), zeolites and related materials. Challenges and future prospect are also summarized by the advanced QCM technique associated with properties of nanostructured materials

Elektronik burun verilerinin yapay zeka tabanlı algoritmalarla sınıflandırılması

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Günümüzde elektronik burun teknolojisi, yiyecek kalitesinin belirlenmesi, sağlık, savunma sanayi ve çevre gibi birçok farklı alanda başarıyla kullanılmaktadır. Adı geçen bu alanlarda genellikle koku verisinin sınıflandırıldığı görülmektedir. Gaz Sensörleri yardımıyla elde edilen koku verisinin sınıflandırılmasında birçok yöntem kullanılmakla birlikte literatürde özellikle yapay sinir ağlarına (YSA) sıklıkla rastlanmaktadır. YSA'da geleneksel olarak kullanılan geri yayılım algoritmasının (YSA-BP) bilindiği üzere, lokal minimuma takılma ve eğitim verisini ezberleme gibi zayıf yönleri bulunmaktadır. Bu tez kapsamında bu zayıf yönleri aşmak için, gaz sensörlerinden elde edilen koku verisinin sınıflandırılmasında YSA'nın eğitim kısmı yapay arı koloni (YSA-ABC) ve genetik algoritma (YSA-GA) ile optimize edilmiştir. Geliştirilen yazılım sayesinde, veri seti okutulup, YSA tasarlanıp ve eğitim modeli seçilerek algoritma çalıştırılabilmektedir. YSA'nin eğitilmesindeki geleneksel yöntemde (BP) karesel ortalama hata (MSE) baz alınırken, geliştirilen yazılım sayesinde, YSA-ABC ve YSA-GA eğitiminde, istenirse MSE, ortalama mutlak hata (MAE) veya R2 kullanılabilir. YSA-ABC'nin eğitilmesinde MAE'nin kullanılması MSE'ye göre daha başarılı sonuçlar verdiği görülmüştür. Bu yöntemler kullanılarak 4 farklı çalışma yapılmıştır. Bu 4 çalışmada eğitim hata değerleri olarak YSA-BP E-06, YSA-GA E-03 ile E-18 ve YSA-ABC ise E-16 düzeylerinde başarım göstermişlerdir. Test verisindeki başarımları ise YSA-BP E-06, YSA-GA E-03 ile E-09 ve YSA-ABC E-08 ile E-16 düzeylerinde başarım göstermişlerdir. Bu sonuçlar YSA-ABC'nin 4 çalışmanın 3'ünde diğer iki eğitim modeline göre daha başarılı eğitim ve test sonucu ürettiğini YSA-GA'nın ise sadece 1 çalışmada başarılı olduğunu göstermiştir. Eğitim süreleri karşılaştırıldığında, bütün çalışmalarda en hızlı eğitim modelinin saniyeler içerisinde tamamlanan YSA-BP olduğu daha sonra dakikalar düzeyinde süren YSA-ABC geldiği ve en yavaş modelin ise saatler süren YSA-GA olduğu görülmüştür. Eğitim modellerinin, eğitim süresi ve test verilerinde gösterdikleri başarı bir arada düşünüldüğünde, YSA-ABC'nin koku verisinin sınıflandırılmasında kullanımının daha uygun olacağı sonucuna varılmıştır.Today, electronic nose technology is successfully used in a wide range of areas such as food quality, health system, defense industry, and in environment. Usually, it is required to classify odour data during the use of e-nose technology in these fields. There are many methods used in classification of gas sensor data. Artificial neural networks (ANN) are especially come across in numerous studies as a classification method. Back propagation algorithm (ANN-BP) which is traditionally used in ANN, is known to get stuck in local minima and overfit the training data. In this thesis, during the classification of gas sensor data, artificial bee colony (ANN-ABC) and genetic algorithm (ANN-GA) are used to optimize ANN training in order to overcome these weaknesses of ANN-BP. A software is developed to run the algorithm after dataset is given to the network, ANN is designed and training method is determined. Mean squared error (MSE) is traditionally used as the only performance measure in ANN training (with BP). However, in the software developed here, mean absolute error (MAE) and R2 are also measured during ANN-ABC and ANN-GA training. It is observed that using MAE in ANN-ABC training as a performance measure gives more successful results compared to MSE use. Four different studies are conducted using this method. In these studies, ANN-BP had training error values in the level of E-06, while ANN-GA had E-03 and E-18, and ANN-ABC achieved E-16 level. The success levels of the networks in the test data were E-06 for ANN-BP, E-03 and E-09 for ANN-GA, and E-08 and E-16 for ANN-ABC. These results showed that, ANN-ABC produced more satisfactory training and test results in three of the four studies, compared to other two training methods. ANN-GA is found to be the most successful in only one of the studies. In terms of training time, ANN-BP is seen to be the fastest in all of the studies with a completion time in seconds it is followed by ANN-ABC with a minutes-level completion time, while ANN-GA is observed to be the slowest by lasting for hours. When training time and performance in test data measures are both considered simultaneously, it can be concluded that ANN-ABC is more suitable to be used in classification of odour data

QCM sensor arrays for monitoring volatile plant emanations via molecularly imprinted polymers

Zur Überwachung flüchtiger organischer Verbindungen (VOC volatile organic component) wurde ein Sensorarray bestehend aus mehrkanaligen Schwingquarzen (MQCM multichannel quartz crystal microbalance) entwickelt. Als selektive Schicht für den jeweiligen Analyten kamen molekular geprägte Polymerbeschichtungen zum Einsatz. Diese Sensor Arrays wurden vorerst zur kontinuierlichen online Überwachung und selektiven Quantifizierung von Terpenen eingesetzt, die von Arten der Familie Lamiaceae, wie beispielsweise Pfefferminze (Mentha x piperinta) und Basilikum (Ocimum Basilicum) freigesetzt werden. Dabei erzielten die Sensoren bei der Bestimmung des Frischegrades bemerkenswerte Reproduzierbarkeit der Emanationsmuster. Diese waren vergleichbar zu GC-MS Messungen, mit einem Detektionslimit unter 70 ppm. Zusätzlich können diese Muster mit Erkennungsmethoden wie zum Beispiel der Hauptkomponentenanalyse (PCA principal component analyses), der PLS (partial least squares) und mittels künstlichen neuronalen Netzwerken (ANN artifical neuronal networks) untersucht werden. Des Weiteren wurde eine elektronische Nase mit einer molekular geprägten, biomimetischen Polymerschicht (MIP molecular imprinted polymer) entwickelt, die das Terpenemissionsmuster von frischen und getrockneten Kräutern charakterisiert. Hierzu wurden Rosmarin (Rosmarinus Officinalis L.), Basilikum (Ocimum Basilicum) und Salbei (Salvia Officinalis) eingesetzt. Die dazu notwendigen Optimierungsparamater der elektronischen Nase sind: Schichtdicken, Sensitivität der Analyte <20 ppm, Selektivität bei einer Konzentration von 50 ppm der Terpene und Reproduzierbarkeit. Die reversiblen Sensorantworten sind in einem Konzentrationsbereich von <20 ppm bis 200 ppm linear. Die Isomere α- und β-Pinen sind signifikant unterscheidbar. Die Unterscheidbarkeit zwischen frischen und getrockneten Kräutern konnte durch die entsprechenden Messeffekte (20-1200 Hz) der elektronischen Nasen realisiert werden. Die erhaltenen Daten wurden zur Mustererkennung mittels PCA und ANN analysiert und durch Ergebnisse der GC-MS Messungen, welche einen ähnlichen Trend darstellen, validiert. Die Haltbarkeitsdauer der Kräuter konnte durch die Emanation der flüchtigen organischen Bestandteile über einen Zeitraum von mehreren Tagen bestimmt werden. Die Detektionslimits sind besser als 20 ppm und erlauben die Überwachung der Lagerung über mehrere Tage. Zusätzlich wurde eine MIP Screening Methode zur chemischen Bestimmung von Ethlyacetat entwickelt. Dazu wurden sechs MIPs mit unterschiedlichen Monomer¬zusammensetzungen aus VP, PS und DVB hergestellt und getestet. Als das am besten geeignete Sensormaterial für Ethylacetat erwies sich das Polymer mit einer Monomer¬zusam¬mensetzung von VP:PS:DVB 1:2:7 etabliert. Damit konnten Sensitivitäten und Selektivitäten über einen weiten Konzentrationsbereich von 25-3000 ppm für Ethylacetat erreicht werden. Die Querselektivität dieses MIP zwischen 250 und 750 ppm zu 1-Propanol erwies sich als sehr gering (≤ 1 Hz). Schlussendlich wurde ein Sensor Array mit vier Elektroden pro Substrat konstruiert. Dessen Herstellung wurde durch die Optimierung der Elektrodengröße, deren Geometrie und dem Kalibrieren der Heizwendel bestimmt. Diese neu entwickelte Strategie wurde zur massensensitiven real-time Bestimmung und Unterscheidung von Terpenen, welche von Thymian freigesetzt werden, eingesetzt. Die Muster der freigesetzten Terpene, welche mittels vier-Elektroden QCM Arrays erhalten wurden, sind mit den GC-MS Daten vergleichbar. Somit können derartige QCM Sensorarrays in der Praxis zur sensitiven und selektiven Bestimmung einer Vielzahl von biologischen Analyten im mikro- und makro-Bereich, wie beispielsweise die DNA Bestimmung, die Überwachung von VOCs von Pflanzen, bei der Kompostierung oder bei Abbauprozessen, die Qualitätskontrolle, die Haltbarkeit und Frische von Lebensmittel und in zahlreichen Industriebereichen, in unterschiedlichen Phasen zum Einsatz kommen.Arrays of chemical sensors derived from 10 MHz piezoelectric multichannel quartz crystal microbalance (MQCM) have been developed for selective monitoring of volatile organic compounds. Molecularly imprinted artificial recognition membranes have been used for imprinting the analytes of interest. At first the designed sensor array was used for continuous online surveillance and selective quantification of terpenes emanated from species of Lamiaceae family, i.e., peppermint (Mentha × piperita)and basil (Ocimum Basilicum). In terms of monitoring freshness, appreciable reproducibility in emanation patterns comparable to GC-MS analysis was attained with a limit of detection below 70 ppm. Hence, its competency to be explored jointly with pattern recognition tools, i.e., PCA, PLS and ANN. Furthermore, an e-nose with MIP coated biomimetic sensitive layers for comparative study of emanating terpenes of fresh and dried: rosemary (Rosmarinus Officinalis L.), basil (Ocimum Basilicum) and sage (Salvia Officinalis) was made. The optimal e-nose parameters: layer heights, sensitivity <20 ppm of analytes, selectivity at 50 ppm of terpenes, repeatability and reproducibility were systematically achieved. Linearity in reversible responses over a concentration range of ≤ 20-200 ppm has been observed. Isomers, α-pinene and β-pinene can be significantly differentiated by the sensor system. Sensitive and selective properties of e-nose for sensor effects (20–1,200 Hz) have been established which distinguish fresh herbs from dried. The sensor data was analyzed for pattern recognition via PCA and ANN and corroborated with GC-MS results which revealed a similar trend. Moreover, the limit of detection to ≤ 20 ppm and shelf-life of herbs to few days have been examined via designed e-nose. In addition, an ethyl acetate MIP screening strategy has been successfully developed for its chemical sensing. Six MIPs with different monomer compositions of VP, PS and DVB were prepared and tested. Polymer B with monomers ratio (VP: PS: DVB, 1:2:7) was observed as most favorable sensing material for ethyl acetate. Sensitivity and selectivity from a broad range of concentration 25-3000ppm of ethyl acetate was achieved. Cross selectivity of this MIP at 250-750 ppm against 1-propanol was observed to be quite low, i.e., ≤ 1Hz. Finally a tetra-electrode QCM sensor array has been designed. Its fabrication was done through optimizing electrodes size, geometry and with calibration of heating coil. This novel strategy was used for real-time differential mass sensing of terpenes emitted from thyme plant. Patterns of emanating terpenes observed from tetra-electrode QCM array and GC-MS were comparable. Such QCM sensors arrays in practice can explore sensitive and selective concerts for a variety of analytes in different phase’s ranging from bio- micro to macromolecules, e.g., in DNA sensing, monitoring VOCs of plants, composting and degradation process, estimating quality , shelf-life and freshness of food products and in various industries

Antioxidant and DPPH-Scavenging Activities of Compounds and Ethanolic Extract of the Leaf and Twigs of Caesalpinia bonduc L. Roxb.

Antioxidant effects of ethanolic extract of Caesalpinia bonduc and its isolated bioactive compounds were evaluated in vitro. The compounds included two new cassanediterpenes, 1α,7α-diacetoxy-5α,6β-dihydroxyl-cass-14(15)-epoxy-16,12-olide (1)and 12α-ethoxyl-1α,14β-diacetoxy-2α,5α-dihydroxyl cass-13(15)-en-16,12-olide(2); and others, bonducellin (3), 7,4’-dihydroxy-3,11-dehydrohomoisoflavanone (4), daucosterol (5), luteolin (6), quercetin-3-methyl ether (7) and kaempferol-3-O-α-L-rhamnopyranosyl-(1Ç2)-β-D-xylopyranoside (8). The antioxidant properties of the extract and compounds were assessed by the measurement of the total phenolic content, ascorbic acid content, total antioxidant capacity and 1-1-diphenyl-2-picryl hydrazyl (DPPH) and hydrogen peroxide radicals scavenging activities.Compounds 3, 6, 7 and ethanolic extract had DPPH scavenging activities with IC50 values of 186, 75, 17 and 102 μg/ml respectively when compared to vitamin C with 15 μg/ml. On the other hand, no significant results were obtained for hydrogen peroxide radical. In addition, compound 7 has the highest phenolic content of 0.81±0.01 mg/ml of gallic acid equivalent while compound 8 showed the highest total antioxidant capacity with 254.31±3.54 and 199.82±2.78 μg/ml gallic and ascorbic acid equivalent respectively. Compound 4 and ethanolic extract showed a high ascorbic acid content of 2.26±0.01 and 6.78±0.03 mg/ml respectively.The results obtained showed the antioxidant activity of the ethanolic extract of C. bonduc and deduced that this activity was mediated by its isolated bioactive compounds

Evaluation of intranasal particulate strategies to enhance the delivery of anti-seizure therapeutics to the brain

Epilepsy is a common and serious neurological disorder to which a high proportion of patients continue to be considered “drug-resistant” despite the availability of a host of anti-seizure drugs. Investigation into new treatment strategies is therefore of great importance, one such strategy being the use of the nose to deliver drugs directly to the brain with the help of pharmaceutical formulation to overcome the physical challenges presented by this route. The overall aim of this thesis was to establish and apply a seizure model to the investigation of two types of particulate intranasal delivery systems; microparticles and cubosomes. Chapter One introduces the topic of intranasal delivery of anti-seizure drugs, covering the link between the nose and seizures, pathways from the nose to the brain, current rudimentary formulations in clinical use, animal seizure models and their proposed application in studying intranasal treatments, and a critical discussion of relevant pre-clinical studies in the literature. Upon this, Chapter Two begins by validating a seizure model based on the Maximal Electroshock Seizure Threshold (MEST) test with the intention of using it to detect the effects of intranasally administered therapeutics. The design attempts to address previously scarcely acknowledged issues of sensitivity in the MEST model and confounding by anaesthetics which are currently necessary to reliably and ethically perform intranasal administration to the olfactory region in rats. The results show that the model was able to clearly detect a change in seizure threshold after administration of the positive control, intravenous phenytoin, which was supported by therapeutic brain and plasma concentrations of the drug as determined using an internally developed Liquid Chromatography Mass Spectrometry (LC-MS) assay. Importantly, this effect was able to be detected despite the use of the inhaled anaesthetic, isoflurane, to briefly sedate the animals 60 minutes prior to stimulation. In Chapter Three, the seizure model is applied to the evaluation of tamarind seed polysaccharide (TSP) microparticles as a proposed intranasal delivery system for the pharmacokinetically troublesome anti-seizure drug phenytoin. In this first pharmacodynamic study, to the author’s knowledge, of a dry powder mucoadhesive microparticle formulation for seizure treatment, the model identified a peak anti-seizure effect time of 120 minutes after administration, which coincided with peak brain concentrations and supported its utilisation in intranasal delivery screening. Furthermore, the complementary demonstration of a histologically intact nasal epithelium and simultaneous measurement of phenytoin’s major metabolite, 5-(4-Hydroxyphenyl)-5-phenylhydantoin (4-HPPH) in brain tissue and plasma, supported the hypothesis of a direct intranasal delivery to the brain and the suitability of the microparticles for further trials. In Chapter Four, the seizure model is applied to explore a potential new type of anti-seizure therapeutic, the endogenous endocannabinoid-like molecule, oleoylethanolamide (OEA), which has not yet had an effect on seizures documented. A cubosome dispersion was selected as the delivery vehicle, presenting one of the few pharmacodynamic in vivo studies conducted with this class of formulation to date. Given the unknown effects of oleoylethanolamide, it was firstly administered intravenously as a control, but no effect on seizure threshold was evident. Considering the complex nature of the hydrolysis-susceptible oleoylethanolamide and the self-assembling cubosome dispersion, complementary in vivo pharmacokinetic studies (utilising an internally developed LC-MS assay) and in vitro structural stability studies (utilising Small-angle X-ray Scattering (SAXS)) were conducted to further explore confounding factors. Despite presenting with complexities of their own, they overall supported the lack of pharmacodynamic effect seen after systemic administration. Intranasal studies were conducted in an attempt to bypass the challenges of systemic administration, but also demonstrated no measurable change in seizure threshold. Histological studies to determine a safe dose uncovered a toxicity of cubosomes to the nasal epithelium at the highest dose, independent of lipid type, which has not yet been described in any in vivo liquid crystalline nanoparticle studies to date and should be considered in future related work. In summary, this thesis presents a tailored seizure model for screening intranasal delivery systems, a practical template for studying these systems in vivo, and a pre-clinical evaluation of two such systems. Notwithstanding the discussed limitations, it concludes that dry-powder mucoadhesive microparticles appear to be a promising platform for future study of intranasal anti-seizure drug delivery, while cubosomes and oleoylethanolamide may be better suited to other applications until a more thorough in vivo exploration of their respective fields exists