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研究生:許育銘
研究生(外文):Xu, Yu-Ming
論文名稱:多通道串聯壓系統於測試樣品中含菌數之快速檢測
論文名稱(外文):Application of Multi-channel Series Piezoelectric Quartz Crystal System in Rapid Detection of Total Counts in Samples
指導教授:張憲彰
指導教授(外文):Zhang, Xian-Zhang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:醫學工程研究所
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:1998
畢業學年度:86
語文別:中文
論文頁數:90
中文關鍵詞:串聯式壓電石英晶體系阻抗匹配類神經綱路生菌數檢測串聯式壓電石英晶體系統類神經網路醫學工程
外文關鍵詞:Series Piezoelectric Quartz Crystal SystemImpedance MatchNeural NetworkViable CountSeries piezoelectric quartz crystal systemImpedance matchNeural networkViable count
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傳統微生物檢測在臨床醫學的應用上仍有許多待改進的空間,如:(1)需
花費大量的時間於菌體的培養,所以檢測時間較長(約需24-48小時);(2)
若樣品中菌數較高,則通常需先做十倍系列稀釋;(3)培養結果需要人工
計數,此計數過程需要花費許多時間與人力;(4)難以達到連續性的監測
,無法掌握時效,對於一些因微生物感染之急症如菌血症、菌尿症甚至食
物中毒等,無法滿足臨床上的檢測時效需求。利用電導度計之電導檢測法
來進行微生物的快速檢測,雖然目前已有成熟的技術與設備,但因受限其
系統設備昂貴,難以普遍至中小醫療檢驗單位。在本研究中我們自行研製
了多通道串聯式壓電石英晶體檢測系統,其輸出頻率對電導度變化,具有
良好的的解析靈敏度(0.15 Hz/(S),適合進行微觀的電導監測,並應用於
微生物濃度之快速檢測上。以提供別於電導度計測量法與阻抗分析法之檢
測工具,來縮短微生物之檢測時間,並提供另一高靈敏度電導檢測之工具
。另外,多通道串聯壓電石英晶體檢測系統對於不同初始電導度之待檢測
物,可經由適當的阻抗匹配來達成系統最佳頻率響應靈敏度;且本研究中
為評估阻抗匹配之最佳頻率靈敏度,更應用了類神經網路來模擬頻率響應
之全域函數最佳靈敏度,在電導度變化從1.6~2.3mS,匹配500 (電阻時,
可得最佳頻率響應變化130 Hz。本研究應用於食品衛生安全的分析如生乳
總生菌數檢測,在SPYE broth培養液進行檢測,可得高靈敏度與可靠度之
檢測結果,且生乳菌數為107與105 cells/ml時,檢測時間分別為3.5及5
小時左右,比傳統檢測24~48小時縮短大量檢測時間;另外本系統亦可應
用於臨床醫學微生物E. coli感染急症如菌尿症(細菌濃度從5×108~2.6
×101 cells/ml,檢測時間為40~343 min, Mueller Hinton broth)、菌
血症(細菌濃度從8×106~2×102cells/ml,檢測時間為172~502 min,
Nutrient broth) 。 從此系統應用於食品乃至於臨床醫學微生物檢測
之快速性而言,顯示將易於發展為方便且普及化的檢測利器 。
For the traditional detection of bacterial concentration, there
are somedisadvantages needed to be improved as following:First,
the detecting bacteria need to be cultured on the nutrient agar
orbroth for growing and metabolizing, so the detection time is
much longer(ca. 24 ~ 48 hr). Second, if the bacterial
concentration of samples are toohigh to count, then the original
bacterial concentration must to be dilutedby ten-fold dilution
method. Third, the culture results of bacteria needmanpower to
concern, and the process will waste more time and manpower.
Finally, the traditional method is not suitable to monitor the
cultureprocesses continuously and save the detection time.
Therefore, look like thediseases of bacterial infection, such as
bacteriuria and bacteremia, even thefood poisoning, etc. The
traditional methods can not shorten the detectiontime
effectively. Although there are mature techniques and equipment
forbacterial detection, most of current systems are too
expensive for commonly use.In this study, we had constructed a
multi-channel series piezoelectric quartzcrystal system. This
system can be applied to monitor the small change ofconductivity
by its frequency response, and it also has good sensitivity(the
best sensitivity of frequency is 0.15 Hz/(S). Here, we use the
detectionsystem to distinguish conductivity method and impedance
method for shorteningthe detection time of bacterial
concentration, and afford another high sensitivitytool for
conductivity detection. By this way, due to different initial
conductivity of samples, the multi-channelpiezoelectric quartz
crystal system can achieve the optimal frequency sensitivityby
proper impedance match. Therefore, the algorithm " Neural
Network " is adoptedto find out the optimal frequency
sensitivity. For the conductivity change from1.6 to 2.3 mS, the
system matched a resistance about 500 ( can achieve the
bestfrequency sensitivity 130 Hz.The optimal system is applied
for food, such as fresh milk. It expresses a highsensitivity and
reliability which samples cultured in the SPYE broth. The
frequencydetection time (FDT) is 3.5 and 5 hr respected to 107
and 105 cells/ml of milk\bacterial counts individually, the
final results are shorter than traditionalmethods. The system
also can be used for the bacteriuria (bacterial
concentrationfrom 5×108~2.6×101 cells/ml, the detection time
from 40~343 min, MuellerHinton broth) and bacteremia ( bacterial
concentration from 8×106~2×102 cells/ml, the detection time
from 172~502min, Nutrient broth).The MMSPQC system is easy to
develop a convenient and efficient analysis tool,especially for
food and clinical medical detection of microorganisms.
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