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specific characteristics of the used qRT-PCR-
analysis and the used qRT-PCR-KIT and have to
adapt to them each time. This adaption/calibration
can only be executed by a manual process. This fact
prevents the usage of the qRT-PCR-analysis in an
automated industrial environment or at the
customer`s side as an easy-to-use analysis product.
Therefore, the weakness of parameter based noise
removal methods and the research of the usage of
methods without parameters for noise removal are
the focus of this article.
This article presents a new approach for an
automatic pre-processing method for common qRT-
PCR-analysis. The objective is to achieve a pre-
processing method without the required intervention
of experts. If a pre-processing without parameters is
possible, it enables an automatic qRT-PCR-analysis
with common analysis methods. Such a solution has
been established under the project ProDiap (Bremer
Institut für Produktion und Logistik, 2010).The
solution is presented in chapter 4.
In this article, the problem and its solution,
which lies in an approach for the development of an
automated pre-processing method for a qRT-PCR-
analysis are presented. In the next section, the
common noise removal methods are discussed. After
this, the approach will be described and finally, the
evaluation by using the demonstrator will be shown.
2 STATE OF THE ART WITH
REGARD TO
PRE-PROCESSING
The influence of the noise within the analysis
process implies the need for pre-processing. As
minimal information, a qRT-PCR measurement
contains a set of tuples, which represent a time-
stamp and a measured fluorescence. The magnitude
of the fluorescence correlates with the concentration
of the qRT-PCR-analysis result product. The
influence on this correlation is declared in this
article as noise.
The interpretation of a measurement result is
based on the mapping of a measurement result in a
Cartesian coordinate system. The qualitative and the
quantitative analyses are grounded on such a curve.
In figure 1, an example curve is shown, which
represents a positive result of a qRT-PCR-analysis.
It is characterized by four phases (Wong and
Medrano, 2005).
In such a case, a specific micro-organism would
be detected.
Figure 1: Four phases of qRT-PCR curve.
The quality of a curve decreases with the
decreasing quality of these four phases. In real
measurements, the noise changes the expression of
the four phases of the curve. This complicates the
classification of such curves compared to the
classification of noise-free curves significantly. The
effects on the curve are described below with regard
to different noise types.
2.1 Noise
Generally, in test results, two different types of noise
can occur (Wilhelm, 2003). As pointed out by
Wilhelm (2003) and Larinonov, Krause and Miller
(2005) a background noise is caused by properties of
materials and other external influences. Here,
Larinonov et al. (2005) a correlation between the
qRT-PCR systems and the occurrence of
background noise is mentioned. The expression of
noise ranges from a constant shift to a linear increase
of noise over cycles (Larinonov et al., 2005). The
second type of noise is defined as signal trend. The
possible causes of the occurrence of this are not yet
resolved. According to Wilhelm` s (2003) opinion,
the product accumulation is no reason for the signal
trend. The signal trend can influence the curve of a
test result in two ways. It can increase or decrease
the measured fluorescence of a test result. Without
additional information, an expert can not know the
resulting expression.
The effects of background noise and signal trend
for a repeated application of a sample analysis may
lead to varying results. In general, a background
noise always occurs, which leads to a measurable
fluorescence from the first cycle, although the
fluorescence in the linear ground phase would have
the value zero. The influence of noise is shown in
figure 2 and 3 by way of example.
The curve in figure 3 is representative for a test
result which would be classified as positive and
containing just a little noise. Figure 2 shows a
complement curve, which should be evaluated as
positive, although stronger effects of noise have
occurred.
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