• 생태와환경
• /
• 제41권2호
• /
• pp.188-197
• /
• 2008

본 연구는 피라미(Zacco platypus)를 대상으로 어류 조직별 총수은 함량을 규명하고, 서식지 특성 및 화학적 수질조건에 따른 생태 건강성을 평가하기 위한 사례연구로서 수행되었다. 2007년 6월$\sim$10월에 갑천을 대상으로 어류조사를 실시하였고 간, 신장, 아가미, 척추 및 근육 등 피라미의 5개 조직을 적출하여 직접수은분석기(DMA-80, US EPA Method 7473)을 이용하여 총수은 함량을 분석하였다. 전체 조직들의 평균 총수은 농도는 상.하류 각각 $67.2\;{\mu}g\;kg^{-1}$ 및 $20.7\;{\mu}g\;kg^{-1}$로 나타나 맑고 깨끗한 상류지역이 하수종말처리장의 영향을 받고 있는 하류지역에 비해 3배 이상 높게 나타났다. BOD, COD 및 영양염류(TN, TP)에 근거한 화학적 수질평가에서는 상류보다 하류에서 심각한 질적저하가 발생하였다. 어류를 이용한 다변수 모델인 생물통합지수(IBI)는 평균 32 "양호$\sim$보통상태"로 나타났고, S2는 42 "최적$\sim$양호상태"로 최고치를 보인 반면 S5에서는 최저치인 22 "보통$\sim$악화상태"로 나타나 지점별 변이를 보이고 있었다. 정성적 서식지평가지수(QHEI)의 경우 평균 142로서 "양호상태"로 나타났지만 지점별 변이(범위 67$\sim$181)가 크게 나타났다. 종합적으로 IBI및 QHEI를 통한 생태 건강성 평가에서는 상류지역이 양호하게 나타난 반면 어류 조직 내 수은 생물 농축도는 상반된 결과를 보였다. 따라서 총체적인 수환경 건강성 평가를 위해서는 다양한 변수를 이용한 평가기법이 필요할 것으로 사료되었다.

• 한국근적외분광분석학회:학술대회논문집
• /
• 한국근적외분광분석학회 2001년도 NIR-2001
• /
• pp.1031-1031
• /
• 2001

The mammary gland is made up of remarkably sensitive tissue, which has the capability of producing a large volume of secretion, milk, under normal or healthy conditions. When bacteria enter the gland and establish an infection (mastitis), inflammation is initiated accompanied by an influx of white cells from the blood stream, by altered secretory function, and changes in the volume and composition of secretion. Cell numbers in milk are closely associated with inflammation and udder health. These somatic cell counts (SCC) are accepted as the international standard measurement of milk quality in dairy and for mastitis diagnosis. NIR Spectra of unhomogenized composite milk samples from 14 cows (healthy and mastitic), 7days after parturition and during the next 30 days of lactation were measured. Different multivariate analysis techniques were used to diagnose the disease at very early stage and determine how the spectral properties of milk vary with its composition and animal health. PLS model for prediction of somatic cell count (SCC) based on NIR milk spectra was made. The best accuracy of determination for the 1100-2500nm range was found using smoothed absorbance data and 10 PLS factors. The standard error of prediction for independent validation set of samples was 0.382, correlation coefficient 0.854 and the variation coefficient 7.63%. It has been found that SCC determination by NIR milk spectra was indirect and based on the related changes in milk composition. From the spectral changes, we learned that when mastitis occurred, the most significant factors that simultaneously influenced milk spectra were alteration of milk proteins and changes in ionic concentration of milk. It was consistent with the results we obtained further when applied 2DCOS. Two-dimensional correlation analysis of NIR milk spectra was done to assess the changes in milk composition, which occur when somatic cell count (SCC) levels vary. The synchronous correlation map revealed that when SCC increases, protein levels increase while water and lactose levels decrease. Results from the analysis of the asynchronous plot indicated that changes in water and fat absorptions occur before other milk components. In addition, the technique was used to assess the changes in milk during a period when SCC levels do not vary appreciably. Results indicated that milk components are in equilibrium and no appreciable change in a given component was seen with respect to another. This was found in both healthy and mastitic animals. However, milk components were found to vary with SCC content regardless of the range considered. This important finding demonstrates that 2-D correlation analysis may be used to track even subtle changes in milk composition in individual cows. To find out the right threshold for SCC when used for mastitis diagnosis at cow level, classification of milk samples was performed using soft independent modeling of class analogy (SIMCA) and different spectral data pretreatment. Two levels of SCC - 200 000 cells/$m\ell$ and 300 000 cells/$m\ell$, respectively, were set up and compared as thresholds to discriminate between healthy and mastitic cows. The best detection accuracy was found with 200 000 cells/$m\ell$ as threshold for mastitis and smoothed absorbance data: - 98% of the milk samples in the calibration set and 87% of the samples in the independent test set were correctly classified. When the spectral information was studied it was found that the successful mastitis diagnosis was based on reviling the spectral changes related to the corresponding changes in milk composition. NIRS combined with different ways of spectral data ruining can provide faster and nondestructive alternative to current methods for mastitis diagnosis and a new inside into disease understanding at molecular level.