PubMed Health⌕ Search

PubMed · 15801165

Data reproducibility in fluorescence image analysis.

Abstract

Fluorescence image analysis provides quantitative data on fluorescence in situ hybridization signals (FISH), immunofluorescence labelings, Green Fluorescent Protein (GFP) expression and microarrays. It is a valuable tool for decision making in the fields of biology and medicine. The aim of this study was to evaluate the reproducibility of fluorescence intensity measurements and standardization when acquisitions are performed under various but well defined conditions. Fluorescent intensity of standard beads (Inspeck series, Molecular Probes) was repeatedly measured using an image analyzer and automated procedures. Images were acquired using several integration times and neutral filter sets. A standardization procedure was used for expressing the data in a same unit: data were multiplied by the light attenuation factor and were divided by the CCD integration times. Results show that 1) standardization is possible 2) accurate and reliable fluorescence measurements can be obtained and 3) specimens showing large differences in fluorescence intensity can be objectively compared. Moreover fluorescent test slides including fluorochrome solutions and altuglas slides were tested for shading correction and as overall test systems.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Catherine Souchier, Christine Brisson, Bernadette Batteux, Michel Robert-Nicoud, Paul-André Bryon. 2003. Data reproducibility in fluorescence image analysis.. https://doi.org/10.1007/s11022-004-2383-4

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Fermentation process tracking through enhanced spectral calibration modeling.

The FDA process analytical technology (PAT) initiative will materialize in a significant increase in the number of installations of spectroscopic instrumentation. However, to attain the greatest benefit from the data generated, there is a need for calibration procedures that extract the maximum information content. For example, in fermentation processes, the interpretation of the resulting spectra is challenging as a consequence of the large number of wavelengths recorded, the underlying correlation structure that is evident between the wavelengths and the impact of the measurement environment. Approaches to the development of calibration models have been based on the application of partial least squares (PLS) either to the full spectral signature or to a subset of wavelengths. This paper presents a new approach to calibration modeling that combines a wavelength selection procedure, spectral window selection (SWS), where windows of wavelengths are automatically selected which are subsequently used as the basis of the calibration model. However, due to the non-uniqueness of the windows selected when the algorithm is executed repeatedly, multiple models are constructed and these are then combined using stacking thereby increasing the robustness of the final calibration model. The methodology is applied to data generated during the monitoring of broth concentrations in an industrial fermentation process from on-line near-infrared (NIR) and mid-infrared (MIR) spectrometers. It is shown that the proposed calibration modeling procedure outperforms traditional calibration procedures, as well as enabling the identification of the critical regions of the spectra with regard to the fermentation process.

Calibration↗

Development of a novel nitrate-selective composite sensor based on doped polypyrrole.

The manufacture and evaluation of a novel sensor built with a composite material, highly selective to nitrate ions using doped polypyrrole as a recognition agent, are presented. When the ratio of recognition agent to graphite was optimized at 1:1, and the sensitivities found closely approached nernstian behavior. The stability times attained were less than 14 min with response times also below 20 s. Batch characterization of the sensor displayed a sensitivity of 57.1 mV/decade of nitrate ion activity (alphaNO3-) and a detection limit of 5.37 x 10(-5) M, which are comparable to those reported for commercial sensors. Evaluation of the selectivity coefficients showed high affinity to nitrate ion, superior to that of commercial sensors and others reported in the literature. The composite material gives the sensor a prolonged service life with the added capability of allowing the regeneration of its active surface. Coupling the sensor and a solid state, composite-type, reference electrode to a flow injection analysis system (FIA) permitted to achieve an effective overall assessment of the system. A nitrate determination test was conducted on real samples. A comparison of the results obtained, either with stationary measurements or with FIA, indicated that there were no significant differences from the values from manufacturer's specifications.

Calibration↗

Use of NIRS technology with a remote reflectance fibre-optic probe for predicting mineral composition (Ca, K, P, Fe, Mn, Na, Zn), protein and moisture in alfalfa.

In the present work we study the use of near-infrared spectroscopy (NIRS) technology together with a remote reflectance fibre-optic probe for the analysis of major (Ca, K, P) and minor (Fe, Mn, Na, Zn) elements, protein and moisture in alfalfa. The method allows immediate analysis of the alfalfa without prior sample treatment or destruction through direct application of the fibre-optic probe on ground samples in the case of the mineral composition and on-ground and compacted (baled) samples in the case of protein and humidity. The regression method employed was modified partial least-squares (MPLS). The calibration results obtained using samples of alfalfa allowed the determination of Ca, K, P, Fe, Mn, Na and Zn, with a standard error of prediction (SEP(C)) and a correlation coefficient (RSQ) expressed in mg/kg of alfalfa of 1.37x10(3) and 0.878 for Ca, 1.10x10(3) and 0.899 for K, 227 and 0.909 for P, 103 and 0.948 for Fe, 5.1 and 0.843 for Mn, 86.2 and 0.979 for Na, and of 1.9 and 0.853 for Zn, respectively. The SEP(C) and RSQ values (in %) for protein and moisture in ground samples were 0.548 and 0.871 and 0.150 and 0.981, respectively; while in the compacted samples they were 0.564 and 0.826 and 0.262 and 0.935, respectively. The prediction capacity of the model and the robustness of the method were checked in the external validation in alfalfa samples of unknown composition, and the results confirmed the suitability of the method.

Calibration↗