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    ๋‚˜๋…ธ๋””์Šคํฌ ๋ฐ ๋‚˜๋…ธ๋ฒ ์ง€ํด์— ๋‚ด์žฅ๋œ ํ›„๊ฐ ์ˆ˜์šฉ์ฒด์˜ ์ตœ์ ์˜ ์ƒ์‚ฐ๊ณผ ๋ƒ„์ƒˆ์˜ ํŒจํ„ด ๋ฐ ์‹œ๊ฐํ™”์— ๋Œ€ํ•œ ์‘์šฉ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€, 2022. 8. ๋ฐ•ํƒœํ˜„.G protein-coupled receptors (GPCRs) are the most intensively studied for screening drug targets. Especially, class A GPCR including olfactory receptor (OR) which accounts for about 85 % of GPCR family is more important for codifying and screening target receptors. There are about 400 kinds of ORs in human olfactory system. The interactions between ORs and odorants generate signals which are transferred to brain as combinatorial codes. Humans can discriminate more than 1 trillion olfactory stimuli with a limited number of ORs because of widespread OR-driven modulation such as inhibition and enhancement in peripheral olfactory coding. Since the sense of smell perceives the complex external world as a pattern, many studies have been conducted to mimic the response of ORs. In particular, protein-based nanobiosensor is expected as a platform to mimic the olfaction because it has advantages such as mass production, ease of reuse, and low cost. However, reconstitution of the structure of GPCRs is challenging because almost all GPCRs produced in E. coli system are expressed as inclusion bodies. For this reason, reconstitution techniques have been developed to recover the functionality of GPCRs, such as the use of detergent micelles, nanovesicles, bicelles and nanodiscs (NDs). Among these materials, NDs have been considered the most effective reconstitution material because of their stability in various environments and their functional lifetimes. In this thesis, ORs were produced in E. coli system with high productivity and reconstituted to ND or nanovesicle forms. Then the functional reconstituted ORs were applied to monitoring meat freshness/spoilage, disease diagnosis and practical colorimetric sensor. First, OR was overexpressed by coexpressing effector genes, such as djlA, the membrane-bound DnaK cochaperone, and rraA, inhibitor of the mRNA-degrading activity of E. coli RNase E. The E. coli strains coexpressing DjlA or RraA suppressed protein-induced toxicity and overexpressed the ORs. By controlling the molar ratio of OR, membrane scaffold protein, and phospholipid, ND of appropriate size were made, and high-purity ND could be purified. OR-embedded NDs showed stability to various temperature and storage time. Second, Human ORs which bind to gastric cancer and halitosis biomarkers were successfully reconstituted to ND form and purified. The NDs had various patterns to artificial saliva samples because NDs had various binding affinities to target molecules. Through principal component analysis of various patterns for artificial saliva samples, it was possible to distinguish between healthy control samples and patient samples. Third, trace amine-associated receptors (TAARs), TAAR13c and TAAR13d, were successfully overexpressed in E. coli system and reconstituted to ND form. These NDs were utilized for development of ND-based BE-nose for monitoring meat freshness. The ND-based BE-noses was successfully performed towards diverse on-site and the various real samples and could be used to monitor freshness of meat. Lastly, human OR1A2 (hOR1A2) was reconstituted into detergent micelle and it was used for development of colorimetric sensor detecting geraniol. Polydiacetylene (PDA) was used as secondary transducer for visualization of responses of OR. The structural and functional properties of the hOR1A2 were maintained when it was embedded in PDA/lipid nanovesicles. The hOR1A2 embedded in PDA/lipid nanovesicle caused a color transition from blue to purple when it reacted with geraniol, whereas there was no color transition when it reacted with other molecules. In this study, various ORs were successfully reconstituted with ND or nanovesicles. The reconstructed OR is expected to be applied to food freshness monitoring, disease diagnosis by pattern analysis, and practical colorimetric sensors.G ๋‹จ๋ฐฑ์งˆ ์—ฐ๊ฒฐ ์ˆ˜์šฉ์ฒด (GPCR)๋Š” ์•ฝ๋ฌผ ํ‘œ์  ์Šคํฌ๋ฆฌ๋‹์„ ์œ„ํ•ด ๊ฐ€์žฅ ์ง‘์ค‘์ ์œผ๋กœ ์—ฐ๊ตฌ๋˜๋Š” ๋‹จ๋ฐฑ์งˆ์ด๋‹ค. ํŠนํžˆ, GPCR ๊ณ„์—ด์˜ ์•ฝ 85%๋ฅผ ์ฐจ์ง€ํ•˜๋Š” ํ›„๊ฐ ์ˆ˜์šฉ์ฒด (OR)๋ฅผ ํฌํ•จํ•˜๋Š” ํด๋ž˜์Šค A GPCR์€ ํ‘œ์  ์ˆ˜์šฉ์ฒด๋ฅผ ์ฝ”๋“œํ™”ํ•˜๊ณ  ์Šคํฌ๋ฆฌ๋‹ํ•˜๋Š” ๋ฐ ๋” ์ค‘์š”ํ•˜๋‹ค. ์ธ๊ฐ„์˜ ํ›„๊ฐ ์‹œ์Šคํ…œ์—๋Š” ์•ฝ 400 ์ข…๋ฅ˜์˜ OR์ด ์žˆ๋‹ค. OR๊ณผ ๋ƒ„์ƒˆ ๋ฌผ์งˆ ์‚ฌ์ด์˜ ์ƒํ˜ธ ์ž‘์šฉ์€ ํŒจํ„ด์˜ ์กฐํ•ฉ์œผ๋กœ ๋‡Œ์— ์ „๋‹ฌ๋˜๋Š” ์‹ ํ˜ธ๋ฅผ ์ƒ์„ฑํ•œ๋‹ค. ์ธ๊ฐ„์€ ๋ง์ดˆ ํ›„๊ฐ ์ฝ”๋”ฉ์˜ ์–ต์ œ ๋ฐ ํ–ฅ์ƒ๊ณผ ๊ฐ™์€ ๊ด‘๋ฒ”์œ„ํ•œ OR์— ์˜ํ•œ ์กฐ์ ˆ๋กœ ์ธํ•ด ์ œํ•œ๋œ ์ˆ˜์˜ OR๋กœ๋„ 1์กฐ๊ฐœ ์ด์ƒ์˜ ํ›„๊ฐ ์‹ ํ˜ธ๋ฅผ ๊ตฌ๋ณ„ํ•  ์ˆ˜ ์žˆ๋‹ค. ํ›„๊ฐ์€ ๋ณต์žกํ•œ ์™ธ๋ถ€ ์„ธ๊ณ„๋ฅผ ํŒจํ„ด์œผ๋กœ ์ธ์‹ํ•˜๊ธฐ ๋•Œ๋ฌธ์— OR์˜ ๋ฐ˜์‘์„ ๋ชจ๋ฐฉํ•˜๊ธฐ ์œ„ํ•ด ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€ ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ํŠนํžˆ, ๋‹จ๋ฐฑ์งˆ ๊ธฐ๋ฐ˜ ๋‚˜๋…ธ๋ฐ”์ด์˜ค์„ผ์„œ๋Š” ์–‘์‚ฐ์„ฑ, ์žฌ์‚ฌ์šฉ ์šฉ์ด์„ฑ, ์ €๋น„์šฉ ๋“ฑ์˜ ์žฅ์ ์ด ์žˆ์–ด ํ›„๊ฐ์„ ๋ชจ๋ฐฉํ•˜๋Š” ํ”Œ๋žซํผ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ GPCR ๊ตฌ์กฐ์˜ ์žฌ๊ตฌ์„ฑ์€ ๋Œ€์žฅ๊ท  ์‹œ์Šคํ…œ์—์„œ ์ƒ์„ฑ๋˜๋Š” ๊ฑฐ์˜ ๋ชจ๋“  GPCR์ด ๋ด‰์ž…์ฒด๋กœ ๋ฐœํ˜„๋˜๊ธฐ ๋•Œ๋ฌธ์— ์–ด๋ ค์šด ์ผ์ด๋‹ค. ์ด๋Ÿฌํ•œ ์ด์œ ๋กœ ์„ธ์ œ ๋ฏธ์…€, ๋‚˜๋…ธ๋ฒ ์ง€ํด, ๋ฐ”์ด์…€ ๋ฐ ๋‚˜๋…ธ๋””์Šคํฌ (ND)์™€ ๊ฐ™์€ GPCR์˜ ๊ธฐ๋Šฅ์„ ๋ณต๊ตฌํ•˜๊ธฐ ์œ„ํ•œ ์žฌ๊ตฌ์„ฑ ๊ธฐ์ˆ ์ด ๊ฐœ๋ฐœ๋˜์—ˆ๋‹ค. ์ด๋“ค ๋ฌผ์งˆ ์ค‘ ND๋Š” ๋‹ค์–‘ํ•œ ํ™˜๊ฒฝ์—์„œ์˜ ์•ˆ์ •์„ฑ๊ณผ ๊ธฐ๋Šฅ์  ์ˆ˜๋ช… ๋•Œ๋ฌธ์— ๊ฐ€์žฅ ํšจ๊ณผ์ ์ธ ์žฌ๊ตฌ์„ฑ ๋ฌผ์งˆ๋กœ ์—ฌ๊ฒจ์ ธ ์™”๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋Œ€์žฅ๊ท  ์‹œ์Šคํ…œ์—์„œ ๋†’์€ ์ƒ์‚ฐ์„ฑ์œผ๋กœ OR์„ ์ƒ์‚ฐํ•˜์—ฌ ๋‚˜๋…ธ๋””์Šคํฌ ๋˜๋Š” ๋‚˜๋…ธ๋ฒ ์ง€ํด ํ˜•ํƒœ๋กœ ๊ตฌ์กฐ๋ฅผ ์žฌ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฐ ๋‹ค์Œ ๊ธฐ๋Šฅ์ ์œผ๋กœ ์žฌ๊ตฌ์„ฑ๋œ OR์„ ์œก๋ฅ˜ ์‹ ์„ ๋„/๋ถ€ํŒจ ๋ชจ๋‹ˆํ„ฐ๋ง, ์งˆ๋ณ‘ ์ง„๋‹จ ๋ฐ ์‹ค์šฉ์ ์ธ ๋น„์ƒ‰ ์„ผ์„œ์— ์ ์šฉํ–ˆ๋‹ค. ์ฒซ์งธ, OR์€ djlA, ๋ง‰ ๊ฒฐํ•ฉ DnaK cochaperone ๋ฐ rraA, E. coli RNase E์˜ mRNA ๋ถ„ํ•ด ํ™œ์„ฑ ์–ต์ œ์ œ์™€ ๊ฐ™์€ ์ดํŽ™ํ„ฐ ์œ ์ „์ž๋ฅผ ๊ณต๋™ ๋ฐœํ˜„ํ•จ์œผ๋กœ์จ ๊ณผ๋ฐœํ˜„๋˜์—ˆ๋‹ค. DjlA ๋˜๋Š” RraA๋ฅผ ๊ณต๋™ ๋ฐœํ˜„ํ•˜๋Š” ๋Œ€์žฅ๊ท  ๊ท ์ฃผ๋Š” ๋‹จ๋ฐฑ์งˆ ๋ฐœํ˜„์— ์˜ํ•œ ๋…์„ฑ์„ ์–ต์ œํ•˜๊ณ  ํ›„๊ฐ ์ˆ˜์šฉ์ฒด๋ฅผ ๊ณผ๋ฐœํ˜„ํ–ˆ์Šต๋‹ˆ๋‹ค. ํ›„๊ฐ ์ˆ˜์šฉ์ฒด, ๋ง‰ ์ง€์ง€์ฒด ๋‹จ๋ฐฑ์งˆ, ์ธ์ง€์งˆ์˜ ๋ชฐ๋น„๋ฅผ ์กฐ์ ˆํ•˜์—ฌ ์ ์ ˆํ•œ ํฌ๊ธฐ์˜ ND๋ฅผ ๋งŒ๋“ค๊ณ  ๊ณ ์ˆœ๋„ ND๋ฅผ ์ •์ œํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ํ›„๊ฐ ์ˆ˜์šฉ์ฒด๊ฐ€ ๋‚ด์žฅ๋œ ND๋Š” ๋‹ค์–‘ํ•œ ์˜จ๋„ ๋ฐ ๋ณด๊ด€ ์‹œ๊ฐ„์— ๋Œ€ํ•ด ์•ˆ์ •์„ฑ์„ ๋ณด์˜€๋‹ค. ๋‘˜์งธ, ์œ„์•” ๋ฐ ๊ตฌ์ทจ ๋ฐ”์ด์˜ค๋งˆ์ปค์— ๊ฒฐํ•ฉํ•˜๋Š” ์ธ๊ฐ„ OR์€ ND ํ˜•ํƒœ๋กœ ์„ฑ๊ณต์ ์œผ๋กœ ์žฌ๊ตฌ์„ฑ๋˜๊ณ  ์ •์ œ๋˜์—ˆ๋‹ค. ND๋Š” ํ‘œ์  ๋ถ„์ž์— ๋Œ€ํ•œ ๋‹ค์–‘ํ•œ ๊ฒฐํ•ฉ ์นœํ™”์„ฑ์„ ๊ฐ€์กŒ๊ธฐ ๋•Œ๋ฌธ์— ์ธ๊ณต ํƒ€์•ก ์ƒ˜ํ”Œ์— ๋Œ€ํ•œ ๋‹ค์–‘ํ•œ ํŒจํ„ด์„ ๊ฐ€์กŒ๋‹ค. ์ธ๊ณต ํƒ€์•ก ์ƒ˜ํ”Œ์— ๋Œ€ํ•œ ๋‹ค์–‘ํ•œ ํŒจํ„ด์˜ ์ฃผ์„ฑ๋ถ„ ๋ถ„์„์„ ํ†ตํ•ด ๊ฑด๊ฐ•ํ•œ ๋Œ€์กฐ๊ตฐ ์ƒ˜ํ”Œ๊ณผ ํ™˜์ž ์ƒ˜ํ”Œ์„ ๊ตฌ๋ณ„ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์…‹์งธ, ๋ฏธ๋Ÿ‰ ์•„๋ฏผ ๊ด€๋ จ ์ˆ˜์šฉ์ฒด (TAAR), TAAR13c ๋ฐ TAAR13d๊ฐ€ ๋Œ€์žฅ๊ท  ์‹œ์Šคํ…œ์—์„œ ์„ฑ๊ณต์ ์œผ๋กœ ๊ณผ๋ฐœํ˜„๋˜์—ˆ๊ณ  ND ํ˜•ํƒœ๋กœ ์žฌ๊ตฌ์„ฑ๋˜์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ND๋Š” ์œก๋ฅ˜ ์‹ ์„ ๋„๋ฅผ ๋ชจ๋‹ˆํ„ฐ๋งํ•˜๊ธฐ ์œ„ํ•œ ND ๊ธฐ๋ฐ˜ ์ „์ž ์ฝ”์˜ ๊ฐœ๋ฐœ์— ํ™œ์šฉ๋˜์—ˆ๋‹ค. ND ๊ธฐ๋ฐ˜ ์ „์ž ์ฝ”๋Š” ๋‹ค์–‘ํ•œ ํ˜„์žฅ ๋ฐ ์‹ค์ œ ์ƒ˜ํ”Œ์— ์„ฑ๊ณต์ ์œผ๋กœ ์ž‘๋™๋˜์—ˆ์œผ๋ฉฐ ์œก๋ฅ˜์˜ ์‹ ์„ ๋„๋ฅผ ๋ชจ๋‹ˆํ„ฐ๋งํ•˜๋Š” ๋ฐ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ธ๊ฐ„ OR1A2 (hOR1A2)๋ฅผ ์„ธ์ œ ๋ฏธ์…€๋กœ ์žฌ๊ตฌ์„ฑํ•˜์—ฌ ์ œ๋ผ๋‹ˆ์˜ฌ์„ ๊ฐ์ง€ํ•˜๋Š” ๋น„์ƒ‰ ์„ผ์„œ ๊ฐœ๋ฐœ์— ํ™œ์šฉํ•˜์˜€๋‹ค. ํด๋ฆฌ๋‹ค์ด์•„์„ธํ‹ธ๋ Œ (PDA)์€ ํ›„๊ฐ ์ˆ˜์šฉ์ฒด์˜ ๋ฐ˜์‘์„ ์‹œ๊ฐํ™”ํ•˜๊ธฐ ์œ„ํ•œ 2์ฐจ ๋ณ€ํ™˜๊ธฐ๋กœ ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. hOR1A2์˜ ๊ตฌ์กฐ์  ๋ฐ ๊ธฐ๋Šฅ์  ํŠน์„ฑ์€ PDA/์ง€์งˆ ๋‚˜๋…ธ๋ฒ ์ง€ํด์— ๋‚ด์žฅ๋˜์—ˆ์„ ๋•Œ ์œ ์ง€๋˜์—ˆ๋‹ค. PDA/์ง€์งˆ ๋‚˜๋…ธ๋ฒ ์ง€ํด์— ๋‚ด์žฅ๋œ hOR1A2๊ฐ€ geraniol๊ณผ ๋ฐ˜์‘ํ•  ๋•Œ ํŒŒ๋ž€์ƒ‰์—์„œ ๋ณด๋ผ์ƒ‰์œผ๋กœ ์ƒ‰์ƒ ์ „์ด๋ฅผ ์ผ์œผํ‚จ ๋ฐ˜๋ฉด ๋‹ค๋ฅธ ๋ถ„์ž์™€ ๋ฐ˜์‘ํ•  ๋•Œ๋Š” ์ƒ‰์ƒ ์ „์ด๊ฐ€ ์—†์—ˆ์Šต๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ๋‹ค์–‘ํ•œ OR์ด ND ๋˜๋Š” ๋‚˜๋…ธ๋ฒ ์ง€ํด๋กœ ์„ฑ๊ณต์ ์œผ๋กœ ์žฌ๊ตฌ์„ฑ๋˜์—ˆ๋‹ค. ์žฌ๊ตฌ์„ฑ๋œ OR์€ ์‹ํ’ˆ ์‹ ์„ ๋„ ๋ชจ๋‹ˆํ„ฐ๋ง, ํŒจํ„ด ๋ถ„์„์— ์˜ํ•œ ์งˆ๋ณ‘ ์ง„๋‹จ ๋ฐ ์‹ค์šฉ์ ์ธ ๋น„์ƒ‰ ์„ผ์„œ์— ์ ์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๊ฐ€ ๋œ๋‹ค.Chapter 1 Research background and objective 15 Chapter 2 Literature review 20 2.1 Olfaction 22 2.1.1 Olfactory system 21 2.1.2 Olfactory receptors 25 2.1.3 Patterns of odorants 29 2.2 Nanobiosensor 32 2.2.1 Nanobiosensor system 32 2.2.2 Components for the nanobiosensor 35 2.2.3 Nanobiosensor detecting smell 37 2.3 Biomarkers in disease and food spoilage 44 2.3.1 Gastric cancer 44 2.3.2 Halitosis 47 2.3.3 Meat spoilage 49 2.4 Expression of GPCR in E. coli system 50 Chapter 3 Experimental procedures 52 3.1 Materials 53 3.2 Gene Cloning . 54 3.3 Expression 54 3.3.1 Expression of olfactory receptors in E. coli. 54 3.3.2 Expression of membrane scaffold protein in E. coli 56 3.3.3 Expression of olfactory receptors in HEK-293T cell 57 3.4 Purification. 57 3.4.1 Purification of olfactory receptors. 57 3.4.2 Purification of membrane scaffold protein 58 3.5 Functional reconstitution of olfactory receptors 59 3.5.1 Nanodisc 59 3.5.2 Detergent micelle. 60 3.5.3 Polydiacetylene/Lipid nanovesicle 61 3.6 Characterization . 61 3.6.1 Nano-glo dual luciferase assay 61 3.6.2 SDS-PAGE analysis 62 3.6.3 Dynamic light scattering 63 3.6.4 Circular dichroism . 63 3.6.5 Tryptophan fluorescence quenching assay 63 3.7 Immobilization of olfactory receptor-embedded nanodisc on graphene 64 Chapter 4 Enhancement of olfactory receptor production in E. coli system and characterization of olfactory receptor-embedded nanodiscs. 66 4.1 Introduction 67 4.2 Expression and purification of olfactory receptor in E. coli system 69 4.3 Purification and size analysis of olfactory receptor-embedded nanodiscs. 73 4.4 Stability of immobilized olfactory receptor-embedded nanodiscs. 77 4.5 Conclusions 82 Chapter 5 Development of nanodisc-based bioelectronic nose using trace amine-associated receptors for monitoring meat freshness/spoilage . 84 5.1 Introduction 85 5.2 Characterization of TAARs 87 5.3 Performance of nanodisc-based bioelectronic nose in the liquid phase. 91 5.4 Gas sensing performance of bioelectronic nose and its application to meat spoilage 97 5.5 Measurements of real samples using TAAR nanodisc-based bioelectronic nose 99 5.6 Conclusions 104 Chapter 6 Pattern analysis for gastric cancer biomarkers using human olfactory receptor-embedded nanodiscs . 105 6.1 Introduction 106 6.2 Affinities of human olfactory receptor-embedded nanodiscs to gastric cancer biomarkers 108 6.3 Patterns for gastric cancer biomarkers in artificial saliva 112 6.4 Principal component analysis for artificial saliva samples 115 6.5 Conclusions 117 Chapter 7 Pattern analysis for halitosis biomarkers in artificial saliva using olfactory receptor-embedded nanodiscs 118 7.1 Introduction 119 7.2 Characterization of olfactory receptor-embedded nanodiscs. 120 7.3 Patterns for halitosis biomarkers in artificial saliva. 122 7.4 Principal component analysis for artificial saliva samples 125 7.5 Conclusions 127 Chapter 8 Visual detection of geraniol using human olfactory receptor embedded in polydiacetylene/lipid nanovesicle 128 8.1 Introduction 129 8.2 Functionality of hOR1A2 embedded in detergent micelle and PDA/lipid nanovesicle. 130 8.3 Structural assay of hOR1A2 embedded in detergent micelle and PDA/lipid nanovesicle. 133 8.4 Size analysis and morphology of hOR1A2 embedded in PDA/lipid nanovesicle. 135 8.5 Photoluminescence intensity of hOR1A2 embedded in PDA/lipid nanovesicle. 137 8.6 Conclusions 141 Chapter 9 Overall discussion and further suggestions. 142 Bibliography . 147 ๊ตญ๋ฌธ์ดˆ๋ก 157๋ฐ•

    Development of a Portable Electronic Nose System for the Detection and Classification of Fruity Odors

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    In this study, we have developed a prototype of a portable electronic nose (E-Nose) comprising a sensor array of eight commercially available sensors, a data acquisition interface PCB, and a microprocessor. Verification software was developed to verify system functions. Experimental results indicate that the proposed system prototype is able to identify the fragrance of three fruits, namely lemon, banana, and litchi

    Mimicking the human olfactory system: a portable e-ยญmucosa

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    The study of electronic noses has been an active area of research for over 25 years. Commercial instruments have been successfully deployed within niche application areas, for example, the food, beverage and pharmaceutical industries. However, these instruments are still inferior to their human counterparts and have not achieved mainstream success. Humans can distinguish and identify many thousands of different aromas, even at very low concentration levels, with relative ease. The human olfactory system is extremely sophisticated, which allows it to out-ยญperform artificial instruments. Though limited, artificial instruments can provide a lower cost option to specific problems and can be an alternative to the use of organoleptic panels. Most existing commercial electronic nose (e-ยญnose) instruments are expensive, bulky, desktop units, requiring a PC to operate. In addition, these instruments usually require a trained operator to gather and analyse the data. Motivated to improve the performance, size and cost of e-ยญnose instruments, this research aims to extract biological principles from the mammalian olfactory system to aid the implementation of a portable e-ยญnose instrument. This study has focused on several features of the biological system that may provide the key to its superior performance. Specifically, the large number of different olfactory receptors and the diversity of these receptors; the nasal chromatograph effect; stereo olfaction; sniff rate and odour conditioning. Based on these features, a novel, portable, cost effective instrument, called the Portable e-ยญMucosa (PeM), has been designed, implemented and tested. The main components of the PeM are three sensor arrays each containing 200 carbon black composite chemoresistive sensors (totalling 600 sensors with 24 different tunings) mimicking the large number of olfactory receptors and two gas chromatographic columns (coated with non-ยญpolar and polar compounds to maximise the discrimination) emulating the โ€œnasal chromatographโ€ effect of the human mucus. A preconcentrator based on thermal desorption is also included as an odour collection system to further improve the instrument. The PeM provides USB and Multimedia Memory Card support for easy communication with a PC. The instrument weighs 700g and, with dimensions of 110 x 210 x 110 mm, is slightly larger than the commercial Cyranose 320 (produced by Smiths Detection). This novel instrument generates โ€˜spatio-ยญtemporalโ€™ data and when coupled with an appropriate pattern recognition algorithm, has shown an enhanced ability to discriminate between odours. The instrument successfully discriminates between simple odours (ethanol, ethyl acetate and acetone) and more complex odours (lavender, ylang ylang, cinnamon and lemon grass essential oils). This system can perhaps be seen as a foundation for a new generation of e-noses

    Differential Detection of Potentially Hazardous Fusarium Species in Wheat Grains by an Electronic Nose

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    Fungal infestation on wheat is an increasingly grave nutritional problem in many countries worldwide. Fusarium species are especially harmful pathogens due to their toxic metabolites. In this work we studied volatile compounds released by F. cerealis, F. graminearum, F. culmorum and F. redolens using SPME-GC/MS. By using an electronic nose we were able to differentiate between infected and non-infected wheat grains in the post-harvest chain. Our electronic nose was capable of distinguishing between four wheat Fusaria species with an accuracy higher than 80%

    Volatile diagnostic techniques for ventilator associated pneumonia

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    Ventilator associated pneumonia (VAP) is a significant challenge for the Intensive Care doctors worldwide. It is both difficult to diagnose accurately and quickly and to treat effectively once the diagnosis has been established. Current diagnostic microbiological methods of diagnosis can take up to 48 hours to yield results. Early diagnosis and treatment remain the best way of improving outcome for patients with VAP. In this study we look at novel diagnostic techniques for VAP. Electronic nose (Enose) technology was used to identify to identify the presence of microorganisms in bronchoalveolar lavage (BAL) fluid samples taken from the respiratory tracts of ventilated patients. The results were compared with standard microbiological culture and sensitivities. The Enose was able to discriminate 83% of samples into growth or no growth groups on samples grown in the lab. When the technique was employed to samples taken directly from patients the accuracy fell to 68.2%. This suggests that patient related factors may be reducing the accuracy of the Enose classification. The use of antimicrobial drugs prior to patient sampling is likely to have played a major role. The second part of this study used Gas Chromatography-Mass Spectrometry (GC-MS) analysis of patientโ€™s breath in an attempt to identify patients with VAP. Breath samples were taken at the same time as the bronchoalveolar lavage samples described above. The use of this technique did show differences between the breath samples of patients who did not have any microbiological growth from their BAL samples and those that did. Leave one out cross validation of a PC fed LDA model showed 84% correct classification between healthy volunteers, no growth and growth groups. Finally, we evaluated the Breathotron, which is a breath analysis device designed and built at Cranfield Health. It allows for analysis of breath samples using a single sensor system as opposed to a sensor array employed in traditional Enose devices. This allows it to be more portable and cheaper to build. The Breathotron also allows collection of breath onto sorbent cartridges for GC-MS analysis. Its single sensor did not allow for accurate discrimination between samples.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Implementation of Digital Technologies on Beverage Fermentation

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    In the food and beverage industries, implementing novel methods using digital technologies such as artificial intelligence (AI), sensors, robotics, computer vision, machine learning (ML), and sensory analysis using augmented reality (AR) has become critical to maintaining and increasing the productsโ€™ quality traits and international competitiveness, especially within the past five years. Fermented beverages have been one of the most researched industries to implement these technologies to assess product composition and improve production processes and product quality. This Special Issue (SI) is focused on the latest research on the application of digital technologies on beverage fermentation monitoring and the improvement of processing performance, product quality and sensory acceptability

    Flight Test and Evaluation of a Low-Cost, Compact, and Reconfigurable Airborne Data Acquisition System Based on Commercial Off-The-Shelf Hardware

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    Digitization of physical parameters for the display and recording by computers is the essential aspect of any airborne data acquisition system. The objective of this thesis was to develop a data acquisition system for General Aviation research and certification flight testing based on a low-cost Commercial Off-The- Shelf (COTS) hardware, in particular, a common glass cockpit system for experimental aircraft. A kneeboard computer was used to monitor data communications between the various devices of the Grand Rapids Technology (GRT) Electronic Flight Information System (EFIS). The monitored data was then displayed for use in-flight, and recorded aboard the aircraft for post-flight data reduction. The developed system and software was tested in simulation on virtual and actual hardware, on an Extra 300 in ground testing, and in flight. An in flight air-data calibration and several common stability and control certification test points were flown to evaluate and demonstrate the usefulness of the system. Special consideration was paid to work flow prior to, during, and after the flight with the overall goal of reducing the time required for data reduction. The output of this research work includes software for decoding data files logged on one common low-cost EFIS, software for monitoring, displaying, and recording EFIS data on a kneeboard computer in-flight, and tools for managing and viewing data files after the flight. From this research work, it is concluded that commercially available EFIS systems do in fact provide a core data set which is useful in flight research and flight test certification programs. The 15 Hz sampling rate of the GRT system was more than sufficient for all the test points evaluated as a part of this research. The cost of the tested hardware was less than $10,000 at current pricing (2009). The resultant system is compact, adds little weight to a test aircraft, has few interfaces to aircraft systems, and allows for future growth and the incorporation of new sensor types and interfaces. The addition of a flight test air-data boom with angle of attack and sideslip vanes and control position and force sensors would create a very complete data acquisition package without the expense of purpose designed hardware

    A computer-based medical record system and personal digital assistants to assess and follow patients with respiratory tract infections visiting a rural Kenyan health centre

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    BACKGROUND: Clinical research can be facilitated by the use of informatics tools. We used an existing electronic medical record (EMR) system and personal data assistants (PDAs) to assess the characteristics and outcomes of patients with acute respiratory illnesses (ARIs) visiting a Kenyan rural health center. METHODS: We modified the existing EMR to include details on patients with ARIs. The EMR database was then used to identify patients with ARIs who were prospectively followed up by a research assistant who rode a bicycle to patients' homes and entered data into a PDA. RESULTS: A total of 2986 clinic visits for 2009 adult patients with respiratory infections were registered in the database between August 2002 and January 2005; 433 patients were selected for outcome assessments. These patients were followed up in the villages and assessed at 7 and 30 days later. Complete follow-up data were obtained on 381 patients (88%) and merged with data from the enrollment visit's electronic medical records and subsequent health center visits to assess duration of illness and complications. Symptoms improved at 7 and 30 days, but a substantial minority of patients had persistent symptoms. Eleven percent of patients sought additional care for their respiratory infection. CONCLUSION: EMRs and PDA are useful tools for performing prospective clinical research in resource constrained developing countries
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