Use of NAD(P)H Fluorescence Measurement for On-Line Monitoring of Metabolic State of Azohydromonas australica in Poly(3-hydroxybutyrate) Production

2012 ◽  
Vol 169 (3) ◽  
pp. 821-831 ◽  
Author(s):  
Geeta Gahlawat ◽  
Ashok K. Srivastava
2011 ◽  
Vol 64 (3) ◽  
pp. 747-753 ◽  
Author(s):  
M. Oshiki ◽  
H. Satoh ◽  
T. Mino

The present study was conducted (1) to develop a rapid quantification method of polyhydroxyalkanoates (PHA) concentration in activated sludge by Nile blue A staining and fluorescence measurement and (2) to perform on-line monitoring of PHA concentrations in activated sludge. Activated sludge samples collected from laboratory scale sequencing batch reactors and full-scale wastewater treatment plants were stained with Nile blue A and their fluorescence intensities were determined. There was a high correlation (R2 > 0.97) between the fluorescence intensities of Nile blue A and PHA concentrations in activated sludge determined by gas chromatography. The Nile blue A staining and fluorescence measurement method allows us to determine PHA concentrations in activated sludge within only five minutes and up to 96 samples can be measured at once by using microplate reader. On-line monitoring of PHA concentrations in activated sludge was achieved by using a fluorometer equipped with a flow cell and the time point at which PHA concentration in activated sludge reached the maximum level could be identified. In addition, we examined the influence of pH, floc size and co-existing chemicals in activated sludge suspension on the fluorescence intensities of Nile blue A.


2004 ◽  
Vol 37 (3) ◽  
pp. 397-402
Author(s):  
P. Hrnčiřik ◽  
J. Vovsík ◽  
J. Náhlík

2010 ◽  
Vol 139-141 ◽  
pp. 2550-2555 ◽  
Author(s):  
Jian Wei Ji ◽  
Ming Hu Xu ◽  
Zheng Ming Li

Chlorophyll fluorescence (CF) has become a powerful tool in plant photosynthesis research and stress detection, considered a rapid, highly sensitive and non-invasive probe of photosynthetic activity. With the rapid development of computer technology and sensor technology for weak signal, recently much attention has been paid to chlorophyll fluorescence on-line monitoring for estimation of plant photosynthesis. This paper analyzes the fundamental principle of chlorophyll fluorescence on-line monitoring technology, introduces a new measurement and control system for chlorophyll fluorescence, which bases on MINIPAM, using light-emitting diode (LED) excitation by means of measuring the fluorescence parameters, and introduces application of active fluorescence measurement and passive fluorescence measurement in the study of the biological information monitoring in plant photosynthesis. Finally, the future development trends, the prospect and the difficulties of chlorophyll fluorescence on-line monitoring technology are discussed.


2000 ◽  
Vol 54 (3) ◽  
pp. 438-444 ◽  
Author(s):  
D. Baunsgaard ◽  
L. Munck ◽  
L. Nørgaard

It has been shown that fluorescence spectroscopy of sugar in aqueous solution carries important quality and process information related to beet sugar factories, which is accessible by multivariate analyses. A method for measuring crystalline sugar directly on-line in the process should be advantageous. In this paper we compare the solution measurement technique with two methods of fluorescence measurement on solid sugar. Surprisingly, it was possible to measure fluorescence through the sugar crystals by using the same transmission techniques with 90° detection as with the sugar solutions. This method was compared with a 45° front-surface reflection method. Sugar samples from six different sugar factories were examined. The spectral responses were reasonable, but they were influenced by the heterogeneous sample composition and the sample geometry. It was possible with the two methods to separate sugar samples according to factory with the use of principal component analysis (PCA). Seasonal time trends were found in weekly samples from the same factory. Partial least-squares regression (PLS) was used to predict quality parameters, where color (range: 6–41), ash (range: 0.003–0.018), and α-amino-N (range: 0.28–5.07) could be modeled with errors of 2.3–2.6, 0.0015–0.0016, and 0.40–0.42, respectively. Model errors for similar solution data have been determined to 2.4, 0.0012, and 0.266, respectively.


2001 ◽  
Vol 75 (3) ◽  
pp. 345-354 ◽  
Author(s):  
Christoph Herwig ◽  
Ian Marison ◽  
Urs von Stockar

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