DNA MELTING CURVE ANALYSIS

Abstract: A homogeneous method of detection of target DNA, such as sequences distinctive of particular bacteria, is taught. A melting curve is derived and a computer controlled analysis is carried out in which a predictive model is used based on a teaching regimen with known DNA.

Description: TITLE DNA MELTING CURVE ANALYSIS FIELD OF THE INVENTION This invention relates to the field of molecular biology and more particularly to DNA-based diagnostic protocols. BACKGROUND OF THE INVENTION There are many methods available to detect the presence of double-stranded DNA (dsDNA) in an optically complex solution. Typically, these involve separation of the nucleic acid products of an assay, such as a DNA amplification reaction, from the starting matrix. Separation methods include analytical centrifugation, equilibrium dialysis, melting temperature profiles, and polyacrylamide gel electrophoresis and the like. Such methodology is complex, expensive and time consuming. Alternative methods which permit identification of dsDNA in a solution or suspension of the initial matrix without separation are also known. These methods are termed "homogeneous detection" methods. For example, European Patent 0 487 218 teaches a method for detecting a dsDNA target nucleic acid amplification product in a sample by contacting or reacting the dsDNA with a reagent having a detectable property that changes upon contact or reaction with the dsDNA and detecting that change. Fluorescent pigments, especially intercalating fluorescent pigments, are taught. Canadian Patent Application 2,067,909, Higuchi discloses a DNA binding agent which provides a detectable signal when bound to dsDNA, which signal is distinguishable from the signal when it is unbound. Higuchi employs an intercalating fluorescent dye (ethidium bromide) for labeling during the amplification and subsequent homogeneous detection of the dsDNA. Additionally, Yamamoto et al. in U.S. Patent 5,670,315 teach measurement of fluorescence during Polymerase Chain Reaction (PCR) using a specified dye (a pyrilium) as a quantitative indicator of PCR product. PCR is the well-known method in which extremely small amounts of genetic material are rapidly amplified as set forth in U.S. Patent 4,683,195 by Mullis et al. and U.S. Patent 4,683,202 by Mullis. The above cited methods are useful for the detection and quantitation of amplified target DNA. However, each lack the ability to distinguish between dsDNA amplified target and other dsDNA in the sample. Further, some of these methods can not adequately distinguish single stranded DNA (ssDNA) from dsDNA. All of the methods use purified, homogeneous DNA sample in the stated amplification reactions, and some methods use variable-temperature analyses with sensitive DNA-intercalating dyes. However, none of the methods, using variable-temperature analyses with sensitive DNA-intercalating agents, analyze the pattern of fluorescence signal in the manner described herein, namely monitoring and interpreting the change in fluorescence while slowly raising the temperature of the DNA sample to a denaturing level. By so doing, the analysis of the patterns generated can be used to distinguish specific target dsDNA from other nucleic acid material, for example an internal positive control DNA fragment. Accordingly, there is a need for a homogeneous detection method capable of distinguishing dsDNA target amplification product from other nucleic acid material that is not a target of the assay and to do so in a complex mixture. Surprisingly and unexpectedly, it was found that one can distinguish specific-target dsDNA product from non-target nucleic acid material by using a DNA intercalating agent and analyzing the change in the fluorescence signal generated while slowly raising the temperature of the sample to a denaturing level. Data generated during the melting curve analysis can be used to predict the presence of a specific-target dsDNA by comparing the results to a model or models generated ("trained") in analyses done on samples of control dsDNAs. Applicants have made the unexpected and surprising discovery that using divergent modeling techniques for the analysis of melting curve data and comparing the model's predictions improves the quality of data obtained from the assay. This method can definitively detect the presence of a specific dsDNA fragment in the presence of other non- target amplified material. Further, the method of the invention is applicable to the detection of a specific target dsDNA regardless of its source. In practice, the dsDNA usually is derived by a primer-directed amplification protocol. Applicants have discovered that the information needed to identify specific dsDNA exists in melting curves, and can be extracted despite i) extraneous information derived from a starting matrix (such as occurs in food samples) ii) amplified background material and iii) interfering enzymatic reactions such as those that cause smears in gel electrophoresis. Further, it was found that appropriate, predictive, modeling techniques can be applied to data derived from these curves and used to overcome these difficulties and be probative of the presence of target DNA. Therefore, an important aspect of this invention is to provide a rapid, inexpensive, easily-carried-out, homogeneous test for identifying i) a target DNA, or ii) an organism by identifying a specific piece of DNA target unique to that organism. The test provides improved reliability, particularly in the presence of a starting matrix that ordinarily interferes with DNA-based identification assays. A further aspect provides a more definitive result than temperature dependent fluorescence (TDF). TDF is described in World Patents WO 97/46712 and 46714 by Wittwer et al. or endpoint fluorescence measurements. A still further aspect of this invention is that it equals or exceeds the sensitivity of gel detection methods. SUMMARY OF THE INVENTION This invention pertains to the use of computer-processed melting curve analysis to achieve target DNA strand detection. More particularly, this invention concerns a computational method for homogeneous detection of a specific target DNA sequence in a solution of unknown sequences including any starting matrix comprising: i. providing a solution containing a specific DNA sequence at an effective concentration; ii. adding an effective amount of reagents sufficient to amplify a specific DNA sequence including an intercalating dye that emits a significant fluorescent signal when bound to double stranded DNA and an insignificant signal in the presence of single stranded DNA; iii. applying successive serial treatments to amplify the specific target DNA in the reaction; iv. subjecting the solution of step ii to a single temperature cycle rising from an annealing condition to a melted condition while exciting fluorescence and monitoring the excited fluorescence at selected intervals to produce a series of analog output signals representative of the melting curve of DNA in the solution; v. inputting the output signals to a computer programmed to analyze the curve in digital form, smoothing and transforming the data of the curve to form a spectrum and analyzing the spectrum to determine a probative predictive values therein based on values previously determined by training the computer program with known DNA sequences; and vi. identifying the unknown sequence by a predictive value in excess of a selected threshold values. BRIEF DESCRIPTION OF THE DRAWINGS Figures la-c show, as a function of temperature, the progression from a) smooth melting curve data, to b) the transformed (i.e., -d(logF)/dT) data, and c) the sub-sampled data for a DNA fragment amplified from Listeria monocytogenes. Figure 2 plots the sub-sampled, transformed -d(logF)/dT fluorescence data generated by raising the temperature of multiple samples to a denaturing level. The plot is a composite of many experiments, and includes both Salmonella spiked (added bacterial DNA) and unspiked (no added bacterial DNA) samples presented in Example 1. Figure 3 plots the sub-sampled, transformed -d(logF)/dT fluorescence data generated by raising the temperature of multiple samples to a denaturing level. The plot is a composite of many experiments, and includes both Salmonella spiked and unspiked samples presented in Example 2. Figure 4 plots the sub-sampled, transformed -d(logF)/dT fluorescence data generated by raising the temperature of multiple samples to a denaturing level. The plot is a composite of many experiments, and includes both Escherichia coli spiked and unspiked samples presented in Example 1. Figure 5 plots the sub-sampled, transformed -d(logF)/dT fluorescence data generated by raising the temperature of multiple samples to a denaturing level. The plot is a composite of many experiments, and includes both Listeria monocytogenes spiked and unspiked samples presented in Example 1. Figure 6 plots the sub-sampled, transformed -d(logF)/dT fluorescence data generated by raising the temperature of a sample to a denaturing level. The plot includes typical product "smears" that were generated in Examples 1 and 2. A smear is defined as a nonspecific amplified DNA product having a wide range of fragment sizes. This range is typically from 100 base pairs to greater than 2,000 base pairs. Figure 7 is a flow sheet for the data analysis methods of the invention. It is believed that the information rich areas defined by InfoEvolve could be modeled using Neural Networks, and conversely the sub-sampled data could be modeled with InfoEvolve, as indicated by the arrows connecting the respective boxes. However, this practice is not explicitly demonstrated in this application. Figure 8 shows typical raw melting curve data as a function of temperature. Figure 9 shows a typical transformed melting curve spectrum. Figure 10 is a visualization of cells with two features and two output states. Figure 11 is an information map for the smear versus non-smear Salmonella example of InfoEvolve. Figure 12 is an information map showing the information rich regions in the temperature spectrum for distinguishing positive versus negative samples in the second Salmonella example of InfoEvolve. Figure 13 is a plot of the L. mono melting curve of Example 4. Figure 14 is a plot of the melting curve of the positive control of Example 4. Figure 15 is a plot of the melting curve of the solution with both positive control and L. mono in Example 4. BRIEF DESCRIPTION OF THE SEQUENCE LISTINGS SEQ ID NO:l is the synthetic oligonucleotide sequence for a primer specific for amplifying a section of the Salmonella genome. SEQ ID NO:2 is the synthetic oligonucleotide sequence for a primer specific for amplifying a section of the Salmonella genome. SEQ ID NO: 3 is the synthetic oligonucleotide sequence for a primer specific for amplifying a section of the SV-40 genome. SEQ ID NO:4 is the synthetic oligonucleotide sequence for a primer specific for amplifying a section of the SV-40 genome. SEQ ID NO: 5 is the synthetic oligonucleotide sequence for a primer specific for amplifying a section of the Escherichia coli 0157:H7 genome. SEQ ID NO:6 is the synthetic oligonucleotide sequence for a primer specific for amplifying a section of the Escherichia coli 0157:H7 genome. SEQ ID NO:7 is the synthetic oligonucleotide sequence for a primer specific for amplifying a section of the Listeria monocytogenes genome. SEQ ID NO: 8 is the synthetic oligonucleotide sequence for a primer specific for amplifying a section of the Listeria monocytogenes genome. SEQ ID NO: 9 is the synthetic oligonucleotide sequence for a primer specific for amplifying a section of the genus Listeria genome. SEQ ID NO: 10 is the synthetic oligonucleotide sequence for a primer specific for amplifying a section of the genus Listeria genome. These primers are used in amplification of strain specific DNA fragments. All are included as part of the Qualicon BAX® for Screening kits, manufactured by Qualicon Inc., Wilmington, DE, that are useful in detecting genus Listeria (product number 17710603), Salmonella (product number 17710604), Listeria monocytogenes (product number 17710605), Escherichia coli 0157:H7 (product number 17710606), and the SV-40 fragment is a control reaction. DESCRIPTION OF THE INVENTION This invention pertains to the use of computerized melting curve analysis to achieve target DNA strand detection as stated below: a computational method for homogeneous detection of a specific target DNA sequence in a solution of unknown sequences including any starting matrix comprising: i. providing a solution containing a specific DNA sequence at an effective concentration; ii. adding an effective amount of reagents sufficient to amplify a specific DNA sequence including an intercalating dye that emits a significant fluorescent signal when bound to double stranded DNA and an insignificant signal in the presence of single stranded DNA; iii. applying successive serial treatments to amplify the specific target DNA in a reaction; iv. subjecting the solution of step ii to a single temperature cycle rising from an annealing condition to a melted condition while exciting fluorescence and monitoring the excited fluorescence at selected intervals to produce a series of analog output signals representative of the melting curve of DNA in the solution; v. inputting the output signals to a computer programmed to analyze the curve in digital form, smoothing and transforming the data of said curve to form a spectrum and analyzing the spectrum to determine a probative predictive model therefrom based on values previously established by training the computer program with known DNA sequences; and vi. identifying the unknown sequence by a predictive value in excess of a selected threshold value. By "significant FIU" it is meant any Fluorescence in Arbitrary Units (FIU) signal that is at least one standard deviation above the mean of a reagent blank FIU value. By "insignificant FIU" it is meant any FIU signal that is less than the mean of a reagent blank FIU value. By "smoothing" it is meant removing the random scatter of data points throughout the overall trend. By "training" it is meant determining the weights or parameters of the model which give the best match of the observed data. By "predictive value" it is meant a number that is generated by the computational method of the invention, that has a value of between zero and one. Wherein zero is defined as having no correlation to a trained positive value, and one is defined as having a 100% correlation to a trained positive value. By "threshold value" it is meant an arbitrary value used to compare with the predictive value. If the predictive value is greater than the threshold value the sample is considered positive, i.e., contains target DNA. The closer the threshold value is to one, the more confident you are in calling the sample positive. It is preferred that the threshold value is greater than 0.4, preferably greater than 0.5, more preferably greater than 0.7, and most preferably greater than 0.9. By "effective amount" it is meant the minimal-to-optimal amount of reagent necessary to produce or detect a product, when used in concert with other defined reagents in protocols outlined in the invention. By "effective concentration" it is meant the minimum-to-optimum amount of DNA in a solution that can be amplified by the method of the invention to form a detectable product. The "selected length" of said product is determined by the sequence of the target DNA and the selection of primers that will amplify a specific DNA fragment when the protocol outlined in the invention is followed. The effective concentration of DNA will be affected by components introduced by the tested sample. By "starting matrix" it is meant all constituents contained in a sample solution including, DNA, buffer components, food components, and other additives. The preferred method uses any well characterized amplification protocol, including but not limited to, PCR, strand displacement, ligase chain reaction (LCR), and the particularly preferred method of nucleic acid sequence based amplification (NASBA) with PCR (Food Microbiology Fundamentals and Frontiers, 1997, M. E. Doyle, L. R. Beuchat, and T. J. Montville, ASM Publication, pp. 723-724). Intercalating dyes such as ethidium bromide, YoPro®, and SYBR® Green (manufactured by Molecular Probes) are useful in practicing the technique. YoPro® (Quinolinium, 4-[(3-methyl-2(3H)-benzoxazolylidene)- methyl]-l-[3-(trimethylammonio)propyl]-,diiodide) and SYBR® Green (Molecular Probes, Inc., Eugene, OR) are capable of intercalating into the DNA molecule with a binding constant of less than 5x10 5 (molar 1 ). The mathematical basis for determining the nature of the melting curve may involve the determination of information rich regions which may be done either by visual isolation of significant features or, preferably, by algorithmic creation of an information map. The smoothing step is best performed by a Savitzky-Golay algorithm. Further, the data establishing a melting curve for definitive comparison is analyzed by one or more of several divergent modeling procedures. These include neural net analysis and "InfoEvolve" analysis. Where less precise results are adequate (i.e., lesser than 80% accuracy), multiple linear regression (MLR), partial least squares regression (PLSR), and logistic regression (LR) can be used to establish a predictive model. If more than one mathematical protocol is employed to model the same test data, and this is preferred, the results are polled to improve performance of predictive accuracy. In addition, it is possible to determine the presence of a "smear" by first examining regions of the curve away from peak-containing regions and detecting any base line shifts. A "smear" is a test in which an excess of amplified genetic matter is present producing multiple bands in electrophoresis indicating a non-predictive test. The predictive method can reproducibly identify samples generating smears, thereby allowing for the removal of these samples from further analysis or consideration. Further, the method of the invention may be done in the presence of a positive control. It has been found that computer analysis of melting curves using the modeling techniques disclosed herein provides a more definitive result than TDF or endpoint fluorescence measurements. Moreover, this method of melting curve analysis is equally or more sensitive than the electrophoretic gel detection methods of the prior art. The invention comprises subjecting a sample having an adequate number of copies of a target DNA sequence (such as derived from a nucleic acid amplification technique) to a single temperature cycle passing from the annealed state of the target DNA to the denatured state and obtaining temperature and fluorescence data. In practice this data is obtained from a thermal cycler with fluorescence detection means such as the ABI 7700 available from The Perkin-Elmer Corporation. The data is exported and processed with various transformations in preparation for analysis. The processed data is then fed into a predictive modeling system, or systems, which have been trained using melting curve data derived from known examples tested under pristine conditions. The procedure preferred is to (a) interpolate the data to get evenly spaced temperature data points (T), (b) take the log of fluorescence (F), and (c) smooth the results. The Savitzky-Golay algorithm, known to those skilled in the art of mathematical and statistical analysis, is preferred for smoothing. The negative derivative, -d(logF)/dT is calculated and the data is preferably reduced to 11 to 13 data points using a target specific range between 70°C and 95°C with a preferred spacing of one degree Celsius. Figures la, b, and c show this progression using data obtained from Listeria monocytogenes. In Figure la, the smoothed fluorescence in arbitrary units (FIU) is shown. In Figure lb, there is seen the plot of the derivative and Figure lc shows the transformed data. Data from target DNAs are best reduced to a lower resolution by reduction to a manageable number of data points. Examples of the best number of data points for specific target DNAs are described in Table I along with the temperature range spanned. TABLE I ORGANISM TEMPERATURE RANGE NUMBER OF DATA POINTS Salmonella 82°C-93°C 12 E. coli 82°C-92°C 11 L. mono 75°C- 7°C 13 Results The analysis of the fluorescent signal generated during running melting curves on the samples indicated that there were three distinct patterns associated with the samples. There were unique patterns for samples that contained L. monocytogenes specific DNA fragment, the positive control DNA fragment (based on Salmonella) and one that contained both products (Figures 2-5). Since there are unique melting curve patterns associated with these samples, algorithms can be generated that will enable one to correctly predict the content of an unknown sample. The process of the present invention may be used to detect the presence of a wide variety of target nucleic acids. Generally, these include, but are not limited to, DNA derived from microorganisms such as bacteria, yeast and fungi, as well as viruses, insects, plants, animals and humans. Of particular interest are pathogenic microorganisms known to contaminate food. These include but are not limited to E. coli 017:H57. Salmonella, Listeria monocytogenes, genus Listeria, and Campylobacter which, despite the attention paid to preventive measures, sicken and kill significant numbers of people each year making rapid and reliable identification highly important. In addition, this process can detect whether a sample contains multiple-sized DNA amplification products that are defined as a smear. DETAILS OF ANALYTICAL PROTOCOLS Three approaches were employed to derive algorithms for programming a computer to analyze the data. These are neural networks, InfoEvolve, and Logistic Regression. The neural network and InfoEvolve approach are preferred for practicing the invention. As is plain to those skilled in the art, other modeling techniques could well be used. Further, the method includes the use of hybrid models which indeed are preferred. Therein a smoothed- data set is analyzed by two or more methods and the results are polled with or without weighting to minimize both data bias and model bias. The approaches are outlined below: DESCRIPTION OF NEURAL NETS Neural nets methodology has become wide spread. It proceeds from an attempt to duplicate the process by which the human brain functions. The particular approach used in the instant application is described in U.S. Patents 4,773,024; 5,046,020 and 5,119,468, all the contents of which are incorporated herein by reference. DESCRIPTION OF NEURAL NETWORKS An artificial neural network, or a parallel distributed processing network, is a computational circuit which is implemented as a multi-layered arrangement of interconnected processing elements. In a typical arrangement of a parallel distributed network, known as the back propagation network, the network architecture contains at least three layers of processing elements or units: a first, or "input", layer; a second, intermediate or "hidden", layer; and a third, or "output" layer. There can also be additional hidden layers. The layers are typically fully connected with weights (i.e., one from each input unit to each hidden unit, one from each hidden unit to each output unit), and the output of each unit in the hidden and output layers is given by the weighted sum of the connections coming from the prior layer, followed by a nonlinear transfer function like the sigmoid or hyperbolic tangent. This network architecture is discussed in Rumelhart and McClelland (1986). The weights of the network are obtained with respect to a training data set by an iterative procedure of changing the connection weights in order to minimize the sum of squares of predicted outputs versus the observed outputs. Methods of training the network include the Delta Rule (Rumelhart and McClelland, 1986), the Levenberg-Marquardt method, conjugate gradient, quasi-Newton methods (Hertz et al, 1991), and the stiff backpropagation method (Filkin, U.S. Patent 5,046,020). Once the weights are determined from a training data set, a quick feedforward computation gives output predictions for new data samples. DETAILED DESCRIPTION OF THE NEURAL NETS METHODOLOGY FOR MELTING CURVE DATA ANALYSIS The objective of the data analysis method is to take an observed melting curve, with counting rate of photons (F) as a function of temperature (T), and to input that information into a computer. The computer is programmed to make a prediction of whether or not the corresponding sample was contaminated with a specific DNA of a pathogen. Figure 7 describes the data analysis methods of the invention. Begin with the raw Melting Curve data: the change in fluorescence F(t) (where F = fluorescence, and t = time) is measured at a set of temperatures T(t) as in block A. For an example of melting curve for a Salmonella containing sample, see Figure 8. First, do preprocessing on the data to produce a Melting Curve Spectrum as referenced in Figure 7 block B. Since the temperatures T(t) output by the thermal cycler (Perkin Elmer 7700) are not necessarily equally spaced, use linear interpolation to produce a sequence of equally- spaced temperatures T(t') and the corresponding fluorescence F(t'). In the preferred implementation, the equal temperature step is 0.1 degrees centigrade. The equally spaced observations of fluorescence are next transformed to produce a signal that looks like a spectrum. A spectrum is a positive-definite curve with one or more spectral peaks. Transformations commonly applied to random variables in the statistics community include exponential, logarithmic, power-law, etc. (Velleman and Velleman, 1988). With regard to spectra, transformations often include smoothing, differentiation, normalization, reflectance to absorbance, and multiplicative scatter correction, etc. (Camo AS, 1996). In the preferred implementation, the transformation takes the logarithm (log) of the fluorescence, followed by taking the negative of the first derivative: Spectrum = -d log(F)/dT For subsequent analysis, the pre-processing step between blocks A and B should also reduce the fluctuations or noise in the measurements. This is done by averaging, integrating, or smoothing the signal log(F(t')). In a preferred implementation, both the smoothing and the derivative are performed using the Savitsky-Golay smoothing algorithm (PLS Toolbox for Matlab, EigenvectorResearch, Inc.), with a smoothing interval of 2.5 degrees Celsius. The Melting Curve Spectrum (block B) corresponding to the raw Melting Curve Data in Figure 8 is shown in Figure 9. Note that the spectrum is smooth, positive, and with well- distinguished peaks. The Melting Curve Spectrum (block B) is then analyzed by two parallel methods, as shown in Figure 7. In the left alternative, block C, the spectrum is either averaged or subsampled to produce a spectrum with fewer points. This is done because empirical models are generally more robust when trained on fewer features. The distance in temperature is increased from 0.1 °C to 1.0°C either by averaging 10 successive data points or by using each tenth data point (sub-sampling). The sub-sampled Melting Curve Spectrum in block C, consisting of between 10 and 14 points, can then be used with either a neural network model (block E) or an InfoEvolve model (block F). In the right branch of Figure 7, block D, the Melting Curve Spectrum can be used with an InfoEvolve model to select a set of information-rich Temperatures. (See the discussion of InfoEvolve following). The Melting Curve Spectrum corresponding to these Temperatures, the number of which can be selected by the analyst, can then be used either with a neural network model (block E) or an InfoEvolve Model (block F). Given the Melting Curve Spectrum, either subsampled (block C) or with a small number of information-rich Temperatures selected, a model is made to determine the classification of the sample as spiked or not spiked. A spiked Salmonella sample is shown in Figure 9. There are many possible empirical models which can be used to determine the classification, including multiple linear regression, partial least squares (PLS), soft iterative modeling of class analogy (SIMCA), logistic regression, feedforward neural networks, as well as InfoEvolve. The first four methods are linear methods and are described in PLS Toolbox for Matlab, EigenvectorResearch, Inc. Neural networks and InfoEvolve are nonlinear methods. All six of these methods were investigated. For Melting Curve Analysis, there were some shifts in peak location from sample to sample, so the nonlinear methods give better classification accuracy than the linear methods. However, the strengths and weaknesses of each method were distinctly different. This means that neural net inaccuracies were not fully overlapping with InfoEvolve inaccuracies. Similarly, each of the linear methods showed some non-overlapping inaccuracies. Therefore, by employing multiple methods simultaneously, it is possible to increase the overall accuracy of the analysis. Before discussing the use of modeling in this particular application, it would be well to review the background. ANALYSIS OF SPECTRAL DATA In the field of chemometrics, the analysis of spectra to relate them to properties of the chemicals that produced them is common. For one-dimensional spectra, the method most used for predicting a real-values output is Partial Least Squares (PLS) (Camo AS, 1996, Eigenvector Research, 1998). For predicting a classification output, PLS is used, as well as soft inferential modeling of class analogy (SIMCA). The linear method practiced in this invention was logistic regression, but it is believed that any one, or group, of the previously mentioned linear methods could be employed in the approach described. It is further believed that combinations of linear methods could improve the accuracy of the analysis when performed with a single method. These methods, which produce a linear relationship between inputs and outputs, are closely related to the dimensionality reduction technique of principal component analysis [PC A]. Neural networks sometimes are also used to analyze spectra, usually with a smaller number of subsampled or smoothed values rather than at full resolution (Owens, 1998). APPLICATION OF NEURAL NETWORKS TO MELTING CURVE ANALYSIS The neural network used (block E) in the preferred implementation is a feedforward multi-layer perceptron (Rumelhart and McClelland, 1986), with one input unit for each of the 10 to 14 subsampled points on the Melting Curve Spectrum (-d log(F)/dT), a number, M, of hidden units, and a single output unit with zero target corresponding to a non-spike sample and one to a spiked sample. In the preferred implementation, the network is trained using stiff backpropagation (Filkin, U.S. Patent 5,046,020), with the DuPont proprietary software package called "The Neural Webkit" (van Stekelenborg, 1997). The input elements are scaled together, which is appropriate for a spectrum. The number of hidden units, M, is chosen as that number which gives the best classification accuracy on a specified test data set. Typically M = 2 or 3 for the pathogens in this study. There are cases in which two (or more) simultaneous analyses are conducted in the same reaction (see Example 7). In these reactions the amplified DNA from a target organism (Target) is detected simultaneously with the amplified product from an internal positive control DNA (INPC). The Target and INPC fragments are produced from the same substrate pool, so their peak heights in the Melting Curve Spectrum are related. Neural networks can model two related simultaneous outputs like Target and INPC. The temperature range of the Melting Curve Spectrum must encompass both peak temperatures for Target and INPC. The neural network can also be trained using the data on information rich regions generated by InfoEvolve (block D). This gives a second neural network model. It can also be trained on multiple data sets, with inputs either from block C or block D, to give several parallel neural network models, each capable of making a prediction of the not spiked versus, spiked classification. DESCRIPTION OF INFOEVOLVE The second preferred predictive model, InfoEvolve, block F of Figure 7, is a methodology that applies the principle of evolution to information theory. It is the subject of the provisional U.S. Application No. 60/131,804, filed on April 30, 1999, and commonly owned and filed of even date with the instant application, the contents of which are incorporated herein by reference. The method is nonlinear, robust and inherently parallel in architecture, making it suitable for the modeling of complex, multi-dimensional processes. In addition, it simultaneously produces solutions to the inverse problem of data or process control as a natural consequence. An important benefit is the identification of the most information rich inputs, or combination of inputs, for the modeling task at hand. This aids in developing optimal strategies for decision making. For example, in DNA identification, the ability to locate the most information rich portion of a melting curve temperature spectrum permits development of the most robust predictive empirical model. A related data visualization tool using concepts derived from image analysis is part also of the conception. This enables easy visualization of information rich zones in a multi-dimensional data space leading to methods for identifying the most information rich pathway from a starting point in a process space to a targeted ending point. A fundamental postulate of the approach is that there is an underlying order in many apparently disordered systems and, by evolving the optimum (most information rich) representation of the data set, the underlying structure can be revealed. The method uses both local and global information measures in characterizing the information content of multi-dimensional feature spaces. Empirical results show that local information dominates the predictive capability of the model. Thus the method is a globally-influenced, local technique in contrast to many other methods that use global optimizations over the entire data set as the major mechanism by which to converge to a solution. The InfoEvolve modeling/discovery framework can be broadly divided into two stages. The first stage represents the process of "Feature Identification" (going from box B to D in Figure 7) where the most information rich combinations of input features for a particular modeling problem are evolved. The second stage represents the process of "Model Evolution" (box D to F in Figure 7), where the best predictive model is evolved using the information rich features discovered in the first stage. In the Feature identification stage, a potentially large list of input features, many of which might be irrelevant to the modeling problem at hand, is provided. A genetic framework is used to evolve the most information rich subset of input features so that the dimensionality of the modeling problem is reduced. This is very important in order to develop more robust predictive models. We start initially with a family of randomly selected feature combinations. The training data set is projected into a quantified subspace corresponding to each feature combination. Each subspace may be considered as a hyper volume consisting of discrete hyper cells. During the data projection process, many of these cells in the volume are populated with individual data points which project into them. After all the data has been projected into a subspace, the information content of the subspace can be measured using entropic measures derived from information theory (Shannon, 1997). The local information content of a cell can be described by the relative distribution of output states present in that cell. For example, if all the data points which project into a cell represent one output state, that cell is considered to be relatively information rich. Conversely, if multiple output states are represented in the population of data points within a given cell, that cell is considered to be relatively information poor. Figure 10 illustrates both information rich and information poor cells within a representative feature subspace. In the figure the round dots represent output-state 1 and the square dots represent output-state 2. We can define a measure known as the local entropy to measure the information content of each cell within a feature subspace based on the distribution of output states within the cell (Shanon, 1997; Hayashi. et al, 1991; Hayashi et al 1992). The global entropy of a subspace can be calculated as a number weighted sum of local cell entropies over all the cells comprising the subspace volume. We note that the method of quantizing the feature subspaces is critical in determining the local entropy. In the ultimate limit of too fine a quantization resolution, each cell could have either one or zero data points in them, resulting in artificially high information content. Conversely, too coarse a quantization may result in artificially lowering the information content by including too many output states within each cell. Part of the InfoEvolve framework thus deals with the issue of identifying optimum quantization levels for evolving the best predictive models. The global entropy of each subspace in our original random pool of subspaces is then used as a fitness function to drive an evolutionary process to evolve the most information rich subspaces. Standard genetic algorithms have been adapted to perform this task (Holland, 1975: Goldberg, 1989; Mitchell, 1997). The final pool of information rich input feature combinations can be used directly as inputs to the Model Evolution phase of InfoEvolve. Alternatively, this pool can be analyzed statistically to isolate the most frequently occurring inputs by creating a histogram of input feature frequencies derived from the final pool. We refer to this histogram as an Information Map. Once a subset of input features have been isolated from the Information Map (see Figure 11 and 12), an exhaustive search over all sub dimensions of this subset can be performed to determine an optimum modeling architecture involving this feature subset. For example, if we select 12 input features from the Information Map, we can test all one dimensional projections of these 12 features against a test set to determine a test error rate. We describe how the test error rate is measured for a given subset of feature subspaces in the following paragraph. We can repeat this process by considering all two dimensional, three dimensional and higher dimensional projections respectively for modeling accuracy against the test set. Once an optimum dimensionality has been determined, all the feature subspace combinations consistent with the optimum dimensional projection can be computed and this set of subspaces can be used as inputs into the Model Evolution phase. In the second phase of InfoEvolve, the pool of feature subspaces determined via the methods described above is used as inputs to evolve the optimum subset of feature subspaces which results in the minimum test error. A genetic framework is also used in this step, but the fitness function which drives the second evolutionary stage is based on the predicted error in the test data set used to develop the empirical model. In this case, we start with a randomly selected pool of subsets of feature subspaces derived from the input pool of feature subspaces. For a given subset of feature subspaces , the training data is projected into each feature subspace as described before and a global entropy measure is calculated for each subspace in the subset of interest. Each test data point is then projected into each feature subspace, and an entropically weighted probability vector is calculated over all the subspaces present in the subset to predict the likelihood of that test point being in each possible output state. An output state for the test point is then predicted by selecting the state which has the highest probability value. This process is repeated for all the test data points, and an overall test error rate is calculated based on the difference between the actual and predicted output states. This test error is used as the fitness function in the second evolutionary stage. The test error is calculated for each subset of feature subspaces in our original random pool, and is used to drive an evolutionary process which results in evolving the subset of feature subspaces which results in the minimum error. This final subset of feature subspaces derived from information rich features or feature combinations constitutes the empirical model evolved by InfoEvolve. New data points can be projected into this subset of feature subspaces and probability vectors can be calculated as described above in order to predict an output state. HIERARCHICAL DNA IDENTIFICATION STRATEGY AND HYBRID MODELING We can develop a powerful DNA fragment identification method by using the two modeling schemes outlined above back-to-back in a sequential fashion. The first level of identification is to separate non-smear from smeared samples. This is followed by identifying the specific DNA fragment of interest for the non smeared samples. In practice, this hierarchical method has proven to be more accurate than using a single 3 state model with positives, negatives and other nucleic acid material representing the possible output categories. HYBRID MODELING Multiple models, trained on the same data can be used for empirical modeling. The use of such models, termed "a committee of experts" in the neural network community increased significantly during the 1990s (e.g., Bishop, 1995). The radial basis function (RBF) network can, in fact, be thought of as a committee of experts where each model is one radial basis function. Usually the different models in the committee are closely related, like the RBF and feedforward neural networks with different numbers of hidden layers. It is known also that improved prediction accuracy can be obtained by combining the results of very different models. This is illustrated in Figure 7 by the parallel used, and preferred, of neural networks and InfoEvolve. A final combination stage (block G), in which each model "votes" on the classification, uses information from both models. This is termed "polling". In the case of neural networks and InfoEvolve, the predicted classification has associated with it an implicit statement of certainty. Both models produce a continuous-valued output prediction which is between zero (not spiked) and one (spiked). A value of 0.99 would indicated a prediction of "spiked" with high confidence, while a value of 0.55 has low confidence. Enhanced accuracy is obtained in block G by using different kinds of models (neural networks, InfoEvolve) and also by using several models of each type, trained on different selections of the training data. The present invention is further defined in the following Examples. It should be understood that these Examples, while indicating preferred embodiments of the invention, are given by way of illustration only. From the above discussion and these Examples, one skilled in the art can ascertain the essential characteristics of this invention, and without departing from the spirit and scope thereof, can make various changes and modifications of the invention to adapt it to various usages and conditions. EXAMPLE 1 EVALUATION OF HOMOGENEOUS DETECTION PCR FRAGMENTS USING MELTING CURVE ANALYSIS IN SPIKED FOOD SAMPLES Preparation of spiked food samples The foods and bacterial strains used in this study were selected for the following reasons. Ground beef and ground turkey were selected because these foods are commonly tested, have a high level of animal commensals, high fat content or have non uniform physical properties. Soft cheese was chosen for high fat content, high level of soft cheese organisms and are mechanically challenging. Mozzarella cheese contains high background of hard cheese organisms. Milk was included in the testing panel because of the high lactic bacteria background and it is optically dense with strong light scattering properties. Cocoa was tested because this food contains high levels of polyphenolic compounds (PCR inhibitors) and is optically dense with strong light absorbing properties. Finally coleslaw was chosen because of the low pH associated with this food. The bacterial strains that were chosen are either common contaminants of the foods being tested or represent different ribotypes of the BAX® target organisms. TESTED FOOD MATRIX Food Salmonella E. coli L. monocytogenes Ground Beef X Ground Turkey X Cole Slaw X Milk X X Cocoa X X Mozzarella Cheese X Soft Cheese All foods were pre-enriched according the procedure described in the Bacterial Analytical Manual (BAM), published 1995, by the Association of Analytical Chemists. Following the prescribed enrichment, samples were spiked with target organisms to a level of 10 8 /ml, 10 5 /ml, 10 4 /ml or were left unspiked. For samples spiked with Salmonella or Listeria monocytgenes the overnight enrichment was diluted 1:10 in Brain Heart Infusion (BHI/Difco) (1 ml enrichment + 9 ml BHI) and then incubated at 37°C for 3 hours. Salmonella Sample Preparation: In a 2 ml screw cap tube, 5 microliters (ul) of the incubated diluted enrichment were added to 200 ul of the lysis reagent (5 ul BAX® lysis buffer and 62.5 ul BAX® Protease) containing a 1 : 10,000 dilution of the DNA intercalating dye SYBR® Green (Molecular Probes). The tubes were incubated at 37°C for 20 minutes followed by 95°C for 10 minutes. Fifty microliters of this crude bacterial lysate was used to hydrate the one BAX® Salmonella sample tablet that was contained in each of the PCR tubes of the type used with the Perkin Elmer 7700 Sequence Detector instrument. The tubes were capped and thermal cycled according to the following protocol in a Perkin Elmer 9600 thermal cycler: 94°C 2.0 minutes 1 cycle 94°C 15 seconds 35 cycles 72°C 3.0 minutes 72°C 7 minutes 1 cycle 4°C "forever"* *The 9600 instrument requires the operator to specify a holding time or to indicate "forever". Listeria monocvtosenes Sample Preparation: In a 2 ml screw cap tube, five (5) microliters of the incubated diluted enrichment was added to 200 ul of the lysis reagent (5 ml BAX® lysis buffer and 62.5 ul BAX® Protease) containing a 1 :10,000 dilution of the DNA intercalating dye SYBR® Green (Molecular Probes). The tubes were incubated at 55°C for 60 minutes followed by 95°C for 10 minutes. Fifty (50) microliters of this crude bacterial lysate was used to hydrate one BAX® Listeria monocytogenes sample tablet that were contained in PCR tubes used with the Perkin Elmer 7700 Sequence Detector instrument. The tubes were capped and thermal cycled according to the following protocol in a Perkin Elmer 9600 thermal cycler: 94°C 2.0 minutes 1 cycle 94°C 15 seconds 38 cycles 70°C 3.0 minutes 72°C 7 minutes 4°C "forever" E. coli Sample Preparation: In a 2 ml screw cap tube, three (3) microliters of the overnight enrichment was added to 400 ul of the lysis reagent (5 ml BAX® lysis buffer + 62.5 ul BAX® Protease) containing a 1 :25,000 dilution of the DNA intercalating dye SYBR® Green (Molecular Probes). The tubes were incubated at 37°C for 20 minutes followed by 95°C for 10 minutes. Fifty (50) microliters of this crude bacterial lysate was used to hydrate one BAX® E. coli sample that were contained in PCR tubes used with the Perkin Elmer 7700 Sequence Detector instrument. The tubes were capped and thermal cycled according to the following protocol in a Perkin Elmer 9600 thermal cycler: 94°C 2.0 minutes 1 cycle 94°C 15 seconds 35 cycles 70°C 2.0 minutes 72°C 7 minutes 1 cycle 4°C "forever" Post Amplification (Thermal Cycling') Analysis: Following the amplification, melting curves were generated on the Perkin Elmer 7700 DNA Sequence Detector by running the following conditions: Plate Type: Single Reporter Instrument: 7700 Sequence Detection System Run: Real Time Dye Layer: FAM Sample type: Unknown Sample volume: 50 ul Running Conditions: 70°C 2 minutes 1 cycle No data collection 68°C 10 seconds 98 cycles Collect data Auto increment +0.3 °C/cy cle 25°C "forever" The multicomponent data was exported from the instrument and was used in the analysis. The production of the specific DNA fragment was verified by adding 15 ul of BAX® Loading Dye to the amplified sample. A 15 ul aliquot was then loaded into a well of a 2% agarose gel containing ethidium bromide. The gel was run at 180 volts for 30 minutes. The specific product was then visualized using UV transillumination. Data Analysis: The raw fluorescence data was imported into Microsoft Excel for processing. For each well, the fluorescence data was normalized by subtracting the lowest fluorescence value from the rest of the data points. From this stage divergent approaches were used for modeling the data. 1. Melting Curve The normalized data is then smoothed with a Savitzky-Golay smoothing algorithm. The negative derivative was taken of the logarithm of the fluorescence with respect to temperature (-dlog(F)/dT) and plotted, -dlog(F)/dT (y-axis) vs. Temp (x-axis). Melting temperature ranges for all three target PCR products were established. All samples that had a melting curve peak in the temperature range for a specific target was considered positive and if no peak was found in that range the sample was called negative. The calls were made independent of peak height. Once the thresholds and temperature ranges were established, the data was processed through an Excel macro that automatically determined whether the sample was positive or negative. Once processed, the data was then compared to the BAX™ gel result. Method agreement was determined by adding those samples that gave the same result and dividing by the total number of samples. 2. Modeling of the Data Starting with the normalized data, the data is interpolated to a 0.1 °C resolution using a cubic spline interpolating function. The logarithm of the interpolated data is then taken and smoothed with a Savitzky-Golay smoothing algorithm over 2.5 degrees. The negative derivative is taken of the log fluorescence with respect to temperature (-d(log F)/dT) and parsed at a 1.0°C interval using the data ranges below: Salmonella 82.0°C to 93.0°C (12 data points) E. coli 82.0°C to 92.0°C (11 data points) L. mono 75.0°C to 92.0°C (13 data points) These data points are sections of the melting curve which have proven to contain the most information about the sample. The goal of modeling the data is to differentiate a positive sample from a blank reaction based on the melting curve data. For modeling the system, the positive samples are assigned a classification of ' 1 ', while the blank reactions were assigned a classification of '0'. The combined data set is then randomly split into a training set and a test set using an approximate 75:25 ratio. The training set is used to train a neural network after which the test set is used to verify the trained model. Once the model has been trained, any data generated using the same reaction and melting curve conditions should be able to be processed with the model, to obtain a predicted result. Modeling involves minimizing the errors in the classification of the training and test samples. Three modeling approaches were used to analyze the data: Neural Networks, InfoEvolve, and Logistic Regression. Salmonella Results: 149 Salmonella samples were produced during these experiments. The data set consisted of 119 positive samples (samples spiked with Salmonella) and 30 blank reactions (unspiked samples). All of the samples which were spiked with Salmonella were positive based on the agarose gel results. All, but one of the thirty blank reactions were negative on the gel. This one sample showed no Salmonella melting curve pattern. By examining the remaining melting curves, there was a 100% agreement between the melting curve pattern and the spiked/unspiked status of the samples. Using the 'spiked'/'unspiked' status of a sample as a classification (as opposed to gel result), a training set and test set were created. The training set consisted of 110 samples, while the test set consisted of 39 samples. Each set contained a proportional distribution of spiked and unspiked samples. Based on the training data set, each of the modeling techniques were able to classify all of the test samples correctly. Method % Agreement to Spike Neural Network 39/39 = 100% InfoEvolve 39/39 = 100% Logistic Regression 39/39 = 100%) E. coli Results: 58 E. coli samples were produced during these experiments. The data set consisted of 48 positive samples (samples spiked with E. coli) and 10 blank reactions (unspiked samples). All but one of the samples spiked with E. coli were positive based on the agarose gel results. The spiked sample which didn't have a band on the gel shows a characteristic E. coli melting curve, yet the pattern is within the noise of the system. All ten blank reactions were negative on the gel. A visual inspection of the melting curves reveals a 97% agreement between the melting curve pattern and the spiked/unspiked status of the samples. The two discrepant samples both produced bands on the gel, but the melting curve patterns were not distinguishable from the noise. Using the 'spiked'/'unspiked' status of a sample as a classification, a training set and test set were created. The training set consisted of 45 samples, while the test set consisted of 13 samples. Each set contained a proportional distribution of spiked and unspiked samples. Based on the training data set, both the Neural Network and InfoEvolve showed a correct classification of 12 of the 13 samples. Both errors were caused by one of the samples which was spiked and positive on the gel, but produced no detectable melting curve. Logistic regression produced a perfect classification of the test samples. Method %Agreement to Spike Neural Network 12/13 =92.3% InfoEvolve 12/13 =92.3% Logistic Regression 13/13 =100.0% L. mono Results: The data set analyzed consisted of 96 positive samples (samples spiked with L. mono) and 24 blank reactions (unspiked samples). Of the samples which were spiked with L. mono, 78 were positive based on the agarose gel results. Of the 18 spiked samples which were not detected by the gel method, 14 occurred in chocolate and 4 occurred in coleslaw. All of the spiked samples had characteristic L. mono melting curve patterns and were detected as positives by the method of the invention. The 24 blank reactions were all negative on the gel, and were negative on analysis of the melting curve patterns. Using the 'spiked'/'unspiked' status of a sample as a classification, a training set and test set were created. The training set consisted of 89 samples, while the test set consisted of 31 samples. Each set contained a proportional distribution of spiked and unspiked samples. Based on the training data set, the Neural Network and InfoEvolve models were able to classify all of the test samples correctly. The logistic regression model failed on one sample. Method % Agreement to Spike Neural Network 31/31 =100% InfoEvolve 31/31 =100% Logistic Regression 30/31 =96.7% By using a committee of experts in the above example whereby a positive result would be reported if any one of the three models predicted a spiked sample the overall predictive performance of the test would be improved. Using such an approach would have resulted in a 100% agreement of the predicted result to the spike level for all targets that were tested. EXAMPLE 2 EVALUATION OF THE HOMOGENEOUS DETECTION PCR FRAGMENTS USING MELTING CURVE ANALYSIS IN PROBLEM FOOD SAMPLES Preparation of spiked food samples: This study evaluated foods that are known to inhibit PCR. The evaluation tested the ability of the addition of bovine serum albumin (BSA) to the reaction to overcome the inhibitory effect of the inhibitory foods. In addition, the homogeneous detection of PCR product using melting curve analysis was compared to standard gel electrophoresis with ethidium bromide staining. Foods were purchased from local grocery stores and were stored at 4°C. Thirty different foods were pre-enriched according the BAM procedure. Following the prescribed enrichment, samples were spiked with Salmonella newport or were left unspiked, see Table III. The enrichments were then diluted 1 : 10 in BHI (Difco) and then incubated at 37°C for 3 hours. TABLE III Pre-enrichment Food:Broth Inoculation Food Broth Dilution Levels Almonds LB 1:10 0, 10 4 /mL, 10 5 /mL Liquid Egg TSB 1 :10 0, 10 4 /mL, 10 5 /mL Red Wheat Bran LB 1 :10 0, 10 4 /mL, 10 5 /mL Peanut Butter LB 1 :10 0, 10 4 /mL, lO mL Walnuts LB 1 :10 0, 10 4 /mL, 10 5 /mL Ground Coffee LB 1:10 0, 10 7 /mL Instant Coffee LB 1 :10 0, 10 7 /mL Instant Tea LB 1:10 0, 10 7 /mL Thyme TSB 1:10 10 7 /mL Chocolate Ice Cream Non fat dry milk 1:10 10 7 /mL Basil TSB 1:10 10 7 /mL Hot Chocolate Mix Non fat dry milk 1:10 10 7 /mL Pre-enrichment Food:Broth Inoculation Food Broth Dilution Levels Oregano TSB 1 :100 10 7 /mL Pastry Nut Mix LB 1 :10 10 7 /mL All Spice TSB 1:100 10 7 /mL Rosemary TSB 1 :10 10 7 /mL Cinnamon TSB 1 :100 10 7 /mL Wheatbran LB 1 :10 10 7 /mL Carnation, Hot Cocoa Mix Non fat dry milk 1:10 0, 10 7 /mL Nestle' s Cocoa Non fat dry milk 1 :10 0, 10 7 /mL Oreo Crumbs Non fat dry milk 1 :10 0, 10 7 /mL Swiss Mocha Cafe Non fat dry milk 1 :10 0, 10 7 /mL Nestle Chocolate Liquor Non fat dry milk 1 :10 0, 10 7 /mL Milk Chocolate Non fat dry milk 1 :10 0, 10 7 /mL Hershey's Cocoa Non fat dry milk 1 :10 0, 10 7 /mL Dark Cocoa Non fat dry milk 1 :10 0, 10 7 /mL Viennese Chocolate Cafe Non fat dry milk 1 :10 0, 10 7 /mL Walnut Whip Non fat dry milk 1 :10 0, 10 7 /mL Nestle' s milk chocolate crumbs Non fat dry milk 1 :10 0, 10 7 /mL Polyvinvlpolypvrrolidone (PVPP) treatment: A 500 ul ∑ iliquot ofthe j jrowback sample was added to a tube containing a 50 mg tablet of PVPP (Qualicon, Inc.). The tube was vortexed and the PVPP was allowed to settle for 15 minutes. The resultant supernatant was then used in the lysis procedure. Salmonella Sample Preparation: In a 2 ml screw cap tube, five (5) microliters of the enrichment or PVPP treated sample was added to 200 ul of the lysis reagent (5 ml BAX® lysis buffer and 62.5 ul BAX® Protease) containing a 1 : 10,000 dilution of the DNA intercalating dye SYBR® Green (Molecular Probes). The tubes were incubated at 37°C for 20 minutes followed by 95°C for 10 minutes. Following the 95°C incubation, 50 ul of a 4 mg/ml BSA solution was added to the lysate. This was done for both PVPP treated and untreated samples. As a control, some samples were left untreated. Fifty (50) microliters of this crude bacterial lysate was used to hydrate one BAX® Salmonella sample tablet that were contained in PCR tubes used with the Perkin Elmer 7700 Sequence Detector instrument. The tubes were capped and thermal cycled according to the following protocol in a Perkin Elmer 9600 thermal cycler: 94°C 2.0 minutes 1 cycle 94°C 15 seconds 35 cycles 72°C 3.0 minutes 72°C 7 minutes 1 cycle 4°C "forever" Post Amplification Analysis: Following ; the amplification, melting curves were generated the Perkin Elmer 7700 DNA Sequence Detector by running the following conditions: Plate Type: Single Reporter Instrument: 7700 Sequence Detection System Run: Real Time Dye Layer: FAM Sample type: Unknown Sample volume: 50ul Running Conditions: 70°C 2 minutes 1 cycle No data collection 68°C 10 seconds 98 cycles Collect data Auto increment +0.3°C/cycle 25°C "forever" The multicomponent data was exported from the instrument and was used in the analysis. The production of the specific DNA fragment was verified by adding 15 ul of BAX® Loading Dye to the amplified sample. A 15 ul was aliquot was then loaded into a well of a 2% agarose gel containing ethidium bromide. The gel was run at 180 volts for 30 minutes. The specific product was then visualized using UV transillumination. Data Analysis: The raw fluorescence data was imported into Microsoft Excel for processing. For each well, the fluorescence data was normalized by subtracting the lowest fluorescence value from the rest of the data points. From this stage divergent approaches were used for visualizing the data and making predictions from the data. 1. Visualizing the Data The normalized data was then smoothed with a Savitzky-Golay smoothing algorithm. The negative derivative is taken of the smoothed fluorescence with respect to temperature (-dlog(F)/dT) and plotted, -dlog(F)/dT (y-axis) vs. Temperature (x-axis). 2. Predictions From the Data Starting with the normalized data, the data is interpolated to a 0.1 °C resolution using a cubic spline interpolating function. The logarithm of the interpolated data is then taken and then smoothed with a Savitzky-Golay smoothing algorithm over 2.5 degrees. The negative derivative is taken of the log fluorescence with respect to temperature (-d(log F)/dT) and parsed at a 1.0°C interval using the data range for Salmonella: 82.0°C to 93.0°C (12 data points). The models used for the prediction of the samples are those created using the original 149 Salmonella data points previously mentioned. For method comparison, the three modeling approaches: Neural Network, InfoEvolve, and logistic regression, were used to make predictions from the data. Results: Of the 309 data samples produced during these experiments, 204 were spiked with Salmonella and 105 samples were 'blank' reactions. Of the 204 spiked samples, 143 samples were positive on an agarose gel and 61 were negative on the gel. The negative samples can be attributed to the inhibition of PCR or inadequate gel or PCR sensitivity. Of the 105 'blank' reactions, 95 were negative on the gel, and 10 were positive on the gel. The positive samples can be attributed to natural food contamination (e.g., liquid egg samples) or technical errors. The following Table IV summarizes the results of the three modeling methods. The output of each of the modeling methods is a number between one and zero. A T represents a 'spiked' prediction while a '0' represents an 'unspiked' prediction. The closer the number is to zero or one, the more confidence can be placed in the prediction. Any prediction higher than the threshold of 0.5 is considered positive. The number for each of the methods below shows the number of samples which agreed with the expected prediction. TABLE IV 1 These samples were spiked, but were negative on the gel. Because homogeneous detection is more sensitive than gel detection, it is possible to detect a positive sample with homogeneous detection and not with a gel- based method. When calculating percent agreement, all samples in this category are assumed to be correct. ^The 'Expected Prediction' column displays a one or a zero based on the spike status and gel result. This number is what the model would be expected to predict based on the training samples. ^The 'Number of Samples' column displays the number of samples which fall into a particular spike/gel category. There a number of ways a committee of experts approach could be used to analyze the above data. If there is a desire to minimize the percent of false negatives, as in the case in testing for foodborne pathogens, a positive result would be reported if any one of the models used predicted a spiked sample. If this rule was applied to the data in this example the false negative based on gel results would be less than 0.7%. Whereas the false negative rate for any one model would have been 4.5%, 3.9%), and 5.8%) for neural networks, InfoEvolve and logistic regression respectively. The downside of using the any-one-call rule is that the rate of false positives would have significantly increased. EXAMPLE 3 EVALUATION OF THE HOMOGENEOUS DETECTION FOR THE DISCRIMINATION OF NON-SPECIFIC PCR PRODUCT FROM SPECIFIC PCR PRODUCT The PCR process can generate non specific DNA products in the absence or presence of the target DNA. The mechanism that causes the formation of these products is not well understood. In order to have an acceptable detection performance, the method should be able to discriminate specific target DNA fragments from non-specific DNA products such as primer dimers and multiple sized fragments. Generation of non specific PCR products An overnight culture of E. coli O157:H7 was prepared by transferring one colony from an agar plate and placing it into 10 mL of BHI (Difco Laboratories) broth. The culture was incubated overnight (22 ± 2 hrs) at 35°C. In a 2-ml screw cap tube 3 ul of the overnight culture was added to 400 ul BAX® Lysis Reagent (5 ml BAX® Lysis Buffer + 62.5 ul BAX® Protease). This was done in triplicate. The tubes were placed in a 37°C water bath for 20 minutes, followed by a 95°C water bath for 10 minutes. After lysis, the samples were cooled on ice. For each lysate, two BAX® E coli Sample tablets contained in PCR tubes were hydrated with 50 ul of the lysate. The tubes were capped and thermal cycled on a Perkin-Elmer 9600 Thermal Cycler using the following conditions: 94°C 2.0 minutes 1 cycle 94°C 15 seconds 35 cycles 70°C 2.0 minutes 72°C 7 minutes 1 cycle 4°C "forever" Following the amplification, the production of the specific E coli fragment was verified by adding 15 ul of BAX® Loading Dye to one sample from each lysate. A 15 ul aliquot was then loaded into a well of a 2% agarose gel containing ethidium bromide. The gel was run at 180 volts for 30 minutes. The specific product then visualized using UV transillumination. In order to generate non-specific PCR products, 10 ul of the E. coli amplified product and 5 ul of a DNA Mass Ladder solution (BAX® DNA Mass Ladder tablet hydrated with 200 ul of BAX® Lysis Buffer) were added to 10 ml BAX® Lysis Buffer containing a 1 : 10,000 dilution of SYBR® Green. This solution (50 ul) was used to hydrate eight BAX® Salmonella sample tablets that were contained in Perkin Elmer optical PCR tubes. The tubes were capped and thermal cycled on a Perkin Elmer 9600 Thermal Cycler using the following conditions: 94°C 2.0 minutes 1 cycle 94°C 15 seconds 35 cycles 72°C 3.0 minutes 72°C 7 minutes 1 cycle 4°C "forever" Additional amplification reactions that contained non specific products were generated from environmental bacterial and Salmonella isolates. The isolates were incubated in 10 ml of BHI at 37°C for 24-48 hours. In a 2-ml screw cap tube 5 ul of the overnight culture was added to 200 ul BAX® Lysis Reagent (5 ml BAX® Lysis Buffer and 62.5 ul BAX® Protease) containing a 1 : 10,000 dilution of SYBR® Green. The tubes were placed in a 37°C water bath for 20 minutes, followed by a 95°C water bath for 10 minutes. For each lysate, a BAX® Salmonella Sample tablet contained in a PCR tube was hydrated with 50 ul of the lysate. The tubes were capped and thermal cycled on a Perkin-Elmer 9600 Thermal Cycler using the following conditions: 94°C 2.0 minutes 1 cycle 94°C 15 seconds 35 cycles 72°C 3.0 minutes 72°C 7 minutes 1 cycle 4°C "forever" Post Amplification Analysis: Following the amplification process all samples were analyzed by DNA melting curve analysis and agarose gel electrophoresis. Melting curves were generated on the Perkin Elmer 7700 DNA Sequence Detector by running the following conditions: Plate Type: Single Reporter Instrument: 7700 Sequence Detection System Run: Real Time Dye Layer: FAM Sample type: Unknown Sample volume: 50ul Running Conditions: 70°C 2 minutes 1 cycle No data collection 68°C 10 seconds 98 cycles Collect data Auto increment +0.3°C/cycle 25°C Forever The multicomponent data was exported from the instrument and was used in the analysis. Data Processing and Modeling The raw fluorescence data was imported into Microsoft Excel for processing. For each well, the fluorescence data was normalized by subtracting the lowest fluorescence value from the rest of the data points. From this stage divergent approaches are used for visualizing the data and modeling the data. 1. Visualizing the Data The normalized data was then smoothed with a Savitzky-Golay smoothing algorithm. The negative derivative was taken of the fluorescence with respect to temperature (-dlog(F)/dT) and plotted, -dlog(F)/dT (y-axis) vs. Temp (x-axis). 2. Modeling the Data Starting with the normalized data, the data was interpolated to a 0.1 °C resolution using a cubic spline interpolating function. The logarithm of the interpolated data was then taken. This data was then smoothed with a Savitzky-Golay smoothing algorithm over 2.5 degrees. The negative derivative was taken of the log fluorescence with respect to temperature (-d(log F)/dT). The derivative data was then parsed at a 1.0°C interval from 82.0°C to 93.0°C (12 data points). These twelve points are a section of the melting curve which has proven to contain the most information about the sample. The modeling process utilizes the 12 data points described above. The goal of modeling the data is to differentiate smears from non-smears (in this case Salmonella and blank samples). For this reason, the data was combined with Salmonella and blank samples. The smear samples were assigned a classification of ' 1', while the non-smear samples were assigned a classification of '0'. The combined data set was then randomly split into a training set and a test set using an approximate 75:25 ratio. The training set was used to train a neural network. The test set was used to verify the effectiveness of the trained model. The intent of the modeling process was to minimize errors in the training and test sets. Results A total of thirty smear samples were generated. Eight of the thirty samples were generated artificially by the previously mentioned protocol. The remaining 22 smears occurred naturally in various experiments and consisted of 8 environmental samples, 12 Salmonella samples, and 2 unspiked reactions. A model was generated which was based on 149 non-smeared Salmonella samples (Salmonella spiked samples and blank reactions) combined with 30 of the smear samples. The samples were randomly distributed into a training (n=131) and test set (n=48). A neural network model with 2 hidden layers was created which was able to classify all the test samples correctly. The adjusted r 2 value for the model was 0.999. EXAMPLE 4 THE DETECTION OF MULTIPLE DNA FRAGMENTS IN A SINGLE TUBE USING MELTING CURVE ANALYSIS It would be advantageous to have the ability to be able to homogeneously distinguish multiple DNA fragments in a single reaction tube. This would allow the incorporation of an amplification control reaction in the same tube as the test reaction. In addition, it could allow for the amplification and detection of a multiplex reaction where a sample is being screened for two or more specific DNA targets. This example shows the feasibility of using melting curve analysis for the homogeneous detection of multiple DNA fragments in a single tube. A 1 :10,000 dilution of SYBR® Green was made in BAX® lysis buffer (Qualicon, Inc.). To an aliquot of the diluted dye solution was added a control plasmid to yield a level of about 10 3 -10 5 copies/reaction. In addition, amplifying primers to the control plasmid were added to a final concentration of 0.1 uM. L. monocytogenes DNA was added to both diluted dye solutions, with and without plasmid control reagents, to yield reactions containing 2, 0.2, 0.02 or 0 ng DNA/reaction. These solutions were then used to hydrate a BAX ® for Screening/ . monocytogenes sample tablet that was contained in a PCR reaction tube. The tube was capped and thermal cycled under the following conditions: 94°C 2.0 minutes 1 cycle 94°C 15 seconds 38 cycles 70°C 3.0 minutes 72°C 7 minutes 4°C forever Post Amplification Analysis: Following the amplification, melting curves were generated on the Perkin Elmer 7700 DNA Sequence Detector by running the following conditions: Plate Type: Single Reporter Instrument: 7700 Sequence Detection System Run: Real Time Dye Layer: FAM Sample type: Unknown Sample volume: 50ul Running Conditions: 70°C 2 minutes 1 cycle No data collection 68°C 10 seconds 98 cycles Collect data Auto increment and 0.3°C/cycle 25°C "forever" The multicomponent data was exported from the instrument and was used in the analysis. Following melting curve analysis, the production of the specific DNA fragment was verified by adding 15 ul of BAX® Loading Dye to the amplified sample. A 15 ul was aliquot was then loaded into a well of a 2% agarose gel containing ethidium bromide. The gel was run at 180 volts for 30 minutes. The specific product was then visualized using UV transillumination. Data Processing and Modeling The raw fluorescence data was imported into Microsoft Excel for processing. For each well, the fluorescence data was normalized by subtracting the lowest fluorescence value from the rest of the data points. The normalized data was then smoothed with a Savitzky- Golay smoothing algorithm. The negative derivative was taken of the fluorescence with respect to temperature (-dF/dT) and plotted, -dF/dT (y-axis) vs. Temp (x-axis). The plots were visually inspected to determine the absence or presence of the specific DNA targets. These results were compared to the results obtained from agarose gel detection. Results The melting curve profiles for the Listeria monocytogenes amplification product and the positive control amplification product were produced according to the instant method (Figures 13 and 14). The melting curve profile of the combined products was also produced (Figure 15). There was a clear difference between the melting curve profiles of the Listeria monocytogenes and the positive control product. The profile difference between the two products implied that a trained model would be capable of identifying a Listeria amplification product from a positive control when amplified in a single tube. EXAMPLE 5 SENSITIVITY COMPARISON BETWEEN GEL BASE DETECTION AND MELTING CURVE ANALYSIS Previous examples have shown that melting curve analysis can be used to distinguish between specific amplified target dsDNA from other nucleic acid material. The experiment described below was conducted to compare the sensitivity of a gel based detection system to melting curve analysis for the detection of amplified dsDNA products. Generation of Salmonella Specific PCR Product A crude cell lysate of Salmonella typhimurium was prepared by taking a five microliter aliquot of an 10 8 /ml culture and adding it to 200 ul of lysis reagent (lysis buffer and 62.5 ul of Protease) containing a 1:10,000 dilution of SYBR® Green™ (Molecular Probes, Eugene OR). The tube was capped and incubated for 37°C for 20 minutes followed by 95°C for 10 minutes. There were four replicate lysates prepared. Fifty microliters of this crude bacterial lysate was used to hydrate eight BAX® Salmonella sample tablets that were contained in PCR tubes used with the Perkin Elmer 7700 Sequence Detector instrument. In addition, fifty microliters of lysis reagent containing dye was dispensed into fifty-six PCR tubes. All tubes were capped and thermal cycled according to the following protocol in a Perkin Elmer 9600 thermal cycler: 94°C 2.0 minutes 1 cycle 94°C 15 seconds 35 cycles 72°C 3.0 minutes 72°C 7 minutes 1 cycle 4°C "forever" PCR Product Titer Following PCR, all of the fifty-six lysis reagent containing dye reactions were pooled and was used as the diluent to titer out the Salmonella specific product. A 1 :10 dilution of the Salmonella specific product was made by adding thirty microliters of the PCR product to two-hundred seventy microliters of lysis reagent diluent. A two-fold serial dilution of this material was made by mixing one hundred microliters of the 1 :10 diluted material with one hundred microliters of the lysis reagent diluent. This was repeated six times to obtain the following dilutions: 1 :10, 1 :20, 1 :40, 1 :80, 1 :160, 1 :320, 1:640 and 1 :1280. Melting curves were generated on the Perkin Elmer 7700 DNA Sequence Detector for all dilutions by running the following conditions: Plate Type: Single Reporter Instrument: 7700 Sequence Detection System Run: Real Time Dye Layer: FAM Sample type: Unknown Sample volume: 50ul Running Conditions: 70°C 2 minutes 1 cycle No data collection 68°C 10 seconds 98 cycles Collect data Auto increment +0.3°C/cycle 25°C "forever" The multicomponent data was exported from the instrument and was used in the analysis. Following melting curve analysis, the presence of the specific DNA fragment was verified by adding 15 ul of BAX® Loading Dye to the amplified sample. A 15 ul was aliquot was then loaded into a well of a 2% agarose gel containing ethidium bromide. The gel was run at 180 volts for 30 minutes. The specific product was then visualized using UV transillumination. Data Processing and Modeling The raw fluorescence data was imported into Microsoft Excel for processing. For each well, the fluorescence data was normalized by subtracting the lowest fluorescence value from the rest of the data points. The normalized data is then smoothed with a Savitzky- Golay smoothing algorithm. The negative derivative is taken of the fluorescence with respect to temperature (-dF/dT) and plotted, -dF/dT (y-axis) vs. Temp (x-axis). The plots were visual inspected to determine the absence or presence of the specific DNA targets. These results were compared to the results obtained from agarose gel detection. Results The level of sensitivity for the gel based detection was a 1 :10 dilution of the starting material whereas the level of sensitivity for melting curve analysis was 1 :40. These results indicate that melting curve analysis is approximately four times more sensitive than gel based detection. PCR Product Titer 1 :10 1 :20 1 :40 1 :80 1 :160 1:320 1 :640 1 :1280 Gel Based + . . . . . . Melting Curve + + + - - EXAMPLE 6 INTEGRATION OF A POSITIVE CONTROL PCR REACTION . A TARGET SPECIFIC PCR REACTION AND A DNA INTERCALATING DNA IN A TABLET-FORM REAGENT DELIVERY SYSTEM A tableted reagent delivery system was developed to eliminate pipetting low volumes of critical reagents. Tablets were prepared according to the procedure disclosed in U.S. Patent 5,307,640 (Fawzy, et al.), U.S. Patent 5,475,984, and W/O 97/11197, as well as in U.S. Application Number 08/929,713, filed September 15, 1997, the contents of which are incorporated herein by reference. Reagents necessary to amplify the target and positive control DNA were added to the tablet. These reagents included the fluorescent DNA intercalating dye SYBR® Green, manufactured by Molecular Probes of Eugene, Oregon, and bovine serum albumin which has been shown to overcome PCR inhibition associated with some foods. A 168 bp fragment of SV40 (simian virus) DNA was chosen as the target for the positive control reaction. SV40 was chosen because it does not exist naturally in any food or industrial microbiological setting. Salmonella Internal Positive Control Tablet Formulation This tablet contained the following components REAGENT OUANTITY/TABLET OUANTITY/LOT (15,000 tablets) Deoxynucleotides (25mM) 0.32 ul 4.8 mis Primer rc761 0.182 ul 2.73 mis Primer 35 0.205 ul 3.075 mis Primer 4185 0.799 ul 11.985 mis Primer rc4289 0.686 ul 10.29 mis SV-40 DNA (50 pg/ul) 0.2 ul 3 mis SYBR® Green Solution (1 :100) 0.5 ul 7.5 mis AmpliTaq (300 units/ul) 1.75 units 87.5 ul Surfact-Amps 0.017 ul 255 ul BSA (40 mg/ml) 0.5 ul 7.5 mis Carbowax 0.72 mg 10.80 gm Trehalose 7.3 mg 109.5 gm Preparation of Salmonella Internal Positive Control Tablel t The trehalose and carbowax were dissolved in deionized water. The solution was cooled to 4°C. The deoxy nucleotides, primers, SV40 DNA, Surfact-Amps, SYBR® Green, BSA and AmpliTaq were added to the cooled solution of trehalose and carbowax. The solution was filtered through a 5 um cartridge, and then sprayed into a liquid nitrogen chamber at a reagent spray rate of approximately 120 ml/min through a 1.1 mm tip. The frozen blend was then collected in the tray at the end of the chamber due to gravity. The frozen blend was then freeze dried in a freeze drier (such as the GT6, available from Finn Aqua of Germany). The freeze drying program consisted of primary drying at a product temperature of -40°C and a chamber pressure of 50 micron for 50 hours. Secondary drying was done at 25°C for 20 hours. The freeze dried blend was then sized through a 24 mesh screen. The sized blend was then tableted using a 3/32 tool to a final weight of 7.6 mg/tablet. Evaluation of Salmonella Internal Positive Control Tablet Preparation of spiked food samples Foods commonly tested for Salmonella contamination were used to evaluate the performance of the Salmonella Internal Positive Control tablet. These foods included: milk, American cheese, ground beef, and ground turkey. All foods were pre-enriched according to the procedure described in the Bacterial Analytical Manual (BAM), published 1995, by the Association of Analytical Chemists. Following the prescribed enrichment, samples were spiked with Salmonella to a level of 10 8 /ml, 10 5 /ml, 10 4 /ml or were left unspiked. The spiked samples were diluted 1 : 10 in Brain Heart Infusion (BHI/Difco) (1 ml enrichment + 9 ml BHI) and then incubated at 37°C for 3 hours. Lvsate Preparation: In a 2 ml screw cap tube, 5 microliters (ul) of the incubated diluted enrichment were added to 200 ul of the lysis reagent (5 ml BAX® lysis buffer and 62.5 ul BAX® Protease). The tubes were incubated at 37°C for 20 minutes followed by 95°C for 10 minutes. Fifty microliters of this crude bacterial lysate was used to hydrate one Salmonella Internal Positive Control that was contained in PCR tubes of the type used with the Perkin Elmer 7700 Sequence Detector instrument. The tubes were capped and thermal cycled according to the following protocol in a Perkin Elmer 9600 thermal cycler: 94°C 2.0 minutes 1 cycle 94°C 15 seconds 35 cycles 72°C 3.0 minutes 4°C "forever" Post Amplification (Thermal Cycling) Analysis: Following the amplification, melting curves were generated with the samples run with the Salmonella Internal Positive Control tablet. The melting curves were generated on the Perkin Elmer 7700 DNA Sequence Detector by running the following conditions: Plate Type: Single Reporter Instrument: 7700 Sequence Detection System Run: Real Time Dye Layer: FAM Sample type: Unknown Sample volume: 50ul Running Conditions: 70°C 2 minutes 1 cycle No data collection 68°C 10 seconds 98 cycles Collect data Auto increment +0.3°C/cycle 25°C "forever" The multicomponent data was exported from the instrument and was used in the analysis. The production of the specific DNA fragment was verified by adding 15 ul of BAX® Loading Dye to the amplified sample. A 15 ul aliquot was then loaded into a well of a 2% agarose gel containing ethidium bromide. The gel was run at 180 volts for 30 minutes. The specific product was then visualized using UV transillumination. Data Analysis: The raw fluorescence data was imported into Microsoft Excel for processing. For each well, the fluorescence data was normalized by subtracting the lowest fluorescence value from the rest of the data points. 1. Melting Curve The normalized data was then smoothed with a Savitzky-Golay smoothing algorithm. The negative derivative was taken of the logarithm of the fluorescence with respect to temperature (-dlog(F)/dT) and plotted, -dlog(F)/dT (y-axis) vs. Temp (x-axis). A melting temperature range for the internal positive control reaction was established. All samples that had a melting curve peak in this temperature range were considered positive and if no peak was found in that range the sample was called negative. The decision of whether a sample was positive or negative was made independent of peak height. 2. Modeling of the Internal Positive Control Data Starting with the normalized data, the data was interpolated to a 0.1 °C resolution using a cubic spline interpolating function. The logarithm of the interpolated data was then taken and smoothed with a Savitzky-Golay smoothing algorithm over 2.5 degrees. The negative derivative was taken of the log fluorescence with respect to temperature (-d(log F)/dT) and parsed at a set interval (0.5°C) using the data ranges below: Internal Positive Control 75.0°C to 82.0°C (12 data points, 0.5°C resolution) These data points are sections of the melting curve which have proven to contain the most information about the internal positive control reaction. The goal of modeling the internal positive control data was to differentiate samples in which the internal positive control reaction worked from samples in which it failed. For modeling the system, positive internal control reactions were assigned a classification of ' 1', while the negative reactions were assigned a classification of '0'. These classifications were made independently of the status of the Salmonella reaction. The combined data set was then randomly split into a training set and a test set using a 75:25 ratio. The training set was then used to train a neural network, after which the test set was used to verify the trained model. Results: 336 Salmonella samples were produced during these experiments. The data set consisted of 252 positive samples (samples spiked with Salmonella) and 84 blank reactions (unspiked samples). Of the 336 samples, 103 samples did not produce a Salmonella-specific band on the gel. This includes the 83 unspiked samples (one of the 84 unspiked sample was positive) and 20 spiked samples. All of the samples contained the chemistry necessary for the internal positive control reaction. Two Salmonella- gaύve reactions (both were spiked) failed to produce an internal positive control band on the gel. These samples were considered indeterminate because neither the Salmonella reaction nor the internal positive control reaction worked. The remainder of the samples showed either or both the Salmonella and internal positive control reactions on the gel establishing a basis for multiplexing internal controls with the Salmonella specific reaction. Using the gel status of the internal positive control reaction as a classification, a training set and test set were created from these samples. The training set consisted of 252 samples, while the test set consisted of 84 samples. Each set contained a proportional distribution of spiked and unspiked samples. Based on the training data set, the Neural Network modeling technique was able to classify all of the test samples correctly for an effective 100% agreement to the gel method. EXAMPLE 7 SIMULTANEOUS MODELING OF TARGET AND INTERNAL POSITIVE CONTROL FOR SALMONELLA In reactions having both an internal positive control DNA ('INPC') and a specific target organism DNA ('Target') there is intrinsic competition for reagents. The signals for Target and INPC are thus related in these reactions. It is common practice with neural networks to model two related outputs simultaneously, with one model having two outputs. This Example demonstrates a preferred implementation of this method, although other variations exist for multiple output reactions. Salmonella DNA was serially diluted in BAX® lysis buffer to yield concentrations of 10-0.005 ng/ml. These solutions were then used to hydrate a BAX® for Screening/Salmonella sample tablet with built-in control (Qualicon Kit catalog #17710604) that was contained in an optical PCR reaction tube. In addition, tablets were hydrated with lysis buffer only and served as a negative 'Target' control. The tubes were capped and processed on an instrument that integrates thermal cycling and the fluorescence detection of DNA fragments. This instrument is described in US Provisional Application No. 60/165,267 filed on November 12, 1999, the disclosure of which is hereby incorporated by reference. The samples were thermal cycled using the following program: 94°C 2.0 minutes 1 cycle 94°C 15 seconds 38 cycles 70°C 3.0 minutes Following the amplification, melting curves were generated on the instrument by running the following conditions: 1) a pre-melt incubation at 68.0°C for 120 seconds followed by 2) a Melting Curve from 68.0-95.0°C in 0.3°C increment The raw fluorescence data was exported from the instrument using Excel and was used in model training and testing. Following melting curve analysis, the production of the specific DNA fragment was verified by adding 15 ul of BAX® Loading Dye to the amplified sample. A 15 ul was aliquot was then loaded into a well of a 2% agarose gel containing ethidium bromide. The gel was run at 180 volts for 25 minutes. The specific product was then visualized using UN transillumination. Data Analysis: The raw fluorescence data was imported into Microsoft Excel for processing. For each well, the fluorescence data was normalized by subtracting the lowest fluorescence value from the rest of the data points. 1. Melting Curve The normalized data was then smoothed with a Savitzky-Golay smoothing algorithm. The negative derivative was taken of the logarithm of the fluorescence with respect to temperature (-dlog(F)/dT) and plotted, -dlog(F)/dT (y-axis) vs. Temp (x-axis). A melting temperature range containing both the target organism ('Target') and the internal positive control reaction ('INPC') was established. All samples that had a target melting curve peak in the appropriate temperature range were considered Target positive, and if no target peak was found in that range the sample was called Target negative. The decision of whether a sample was positive or negative was made independent of peak height. Similarly, the samples were classified as INPC positive or negative depending on whether a peak was observed in the appropriate temperature range. The variable 'IsPeak' is defined as positive if either of Target or INPC (or both) is positive and negative if neither Target nor INPC are positive. Thus 'IsPeak' is positive if there is an appropriate peak (Target or INPC) in the melting curve and is negative if there is no corresponding peak, i.e., for a null spectrum or blank sample. It was found empirically that classification accuracy increased using IsPeak as an output rather than INPC. 2. Simultaneous Modeling of the Target and IsPeak Data Starting with the normalized data, the data was interpolated to a 0.1 °C resolution using a cubic spline interpolating function. The logarithm of the interpolated data was then taken and smoothed with a Savitzky-Golay smoothing algorithm over 2.5 degrees. The negative derivative was taken of the log fluorescence with respect to temperature (-d(log F)/dT) using the data range of 72.0°C to 93.0°C. These data points are from that part of the melting curve that contains the peaks for both the Target and the INPC reaction. For modeling the system, positive Target and IsPeak cases were assigned a classification of ' 1 ', while the negative cases were assigned a classification of '0'. The model used the melting curve at the sampled points as the inputs and two outputs: Target and IsPeak. The data set was randomly split into a training set and a test set using a 75:25 ratio. The training set was then used to train a neural network, after which the test set was used to verify the trained model. Results are reported on the neural network model of the appropriate complexity (4 hidden units) that was re-trained on the whole (training plus test) data set. Results: 2304 Salmonella samples were produced during these experiments. The data set consisted of 564 positive target samples (samples with a visible Salmonella peak) and 1740 target negative samples. The value of the IsPeak variable was positive for 736 samples and negative for 1568 samples. The negative cases included 576 blank samples (without reactants). The following table summarizes the accuracy of the neural network model: Results for Target Model Negative Model Positive Actual Negative 1732 8 Actual Positive 8 556 Error Rate: 0.69% (16 of 2304) Results for IsPeak MMooddeell NNeeggaattiivvee Model Positive Model Negative Model Positive Actual Negative 1563 5 Actual Positive 5 731 Error Rate: 0.43% (10 of 2304) The trained neural network gives classification errors of less than 1% for predicting both the Target and the IsPeak variables.

Claims: CLAIMS What is claimed is: 1. A computational method for homogeneous detection of a specific target DNA sequence in a solution of unknown sequences including any starting matrix comprising: i. providing a solution containing a specific DNA sequence at an effective concentration; ii. adding an effective amount of reagents sufficient to amplify a specific DNA sequence including an intercalating dye that emits a significant fluorescent signal when bound to double stranded DNA and an insignificant signal in the presence of single stranded DNA; iii. applying successive serial treatments to amplify the specific target DNA in the reaction; iv. subjecting the solution of step ii to a single temperature cycle rising from an annealing condition to a melted condition while exciting fluorescence and monitoring the excited fluorescence at selected intervals to produce a series of analog output signals representative of the melting curve of DNA in the solution; v. inputting the output signals to a computer programmed to analyze the curve in digital form, smoothing and transforming the data of the curve to form a spectrum and analyzing the spectrum to determine a probative predictive values therein based on values previously determined by training the computer program with known DNA sequences; and vi. identifying the unknown sequence by a predictive value in excess of a selected threshold values. 2. A computational method for homogeneous detection of a target DNA sequence of an unknown target bacterium in a solution including any starting matrix comprising: i. providing a solution containing a DNA sequence of the unknown bacterium at an effective concentration: ii. adding an effective amount of reagents sufficient to amplify a specific DNA sequence including an intercalating dye that emits a significant fluorescent signal when bound to double stranded DNA and an insignificant signal in the presence of single stranded DNA; iii. applying successive serial treatments to amplify the specific target DNA in the reaction; iv. subjecting the solution of step ii to a single temperature cycle rising from an annealed condition to a melted condition while exciting fluorescence and monitoring the excited fluorescence at selected intervals to produce a series of analog output signals representative of the melting curve of said unknown; v. inputting the output signals to a computer programmed to smooth and analyze the curve in digital form, smoothing and transforming the data of the curve to form a spectrum and determining a subset of data from information rich regions therein and using the smoothed values of the curve in the regions to predict the presence of the target sequence based on smoothed values distinctive of bacterial genera and species previously determined by training the computer program with known DNA of the target bacteria; and vi. identifying the unknown bacterium by a predictive value in excess of a selected threshold value. 3. The method of Claim 2 wherein the data in said regions is transformed to the negative d(logF)/dt of the curve and smoothed in said regions and this is predictively modeled based on training with transformed and smoothed negative d(logF)/dt values of known bacteria to identify the unknown bacterium. 4. The method of Claim 2 wherein the target DNA is produced by an amplification protocol. 5. The method of Claim 4 wherein the amplification protocol is selected from the group consisting of PCR, strand displacement, LCR and NASBA. 6. The method of Claim 2 wherein the determination of information rich regions of step v is accomplished by: i. visual isolation of significant features; or ii. algorithmic creation of an information map. 7. The method of Claim 3 wherein the smoothing is performed by a Savitzky-Golay algorithm. 8. The method of Claim 2 wherein the analysis by said computer program comprises a neural net method. 9. The method of Claim 2 wherein the analysis by said computer program comprises the InfoEvolve method wherein there is evolved an optimal model from the information rich region and an evolutionary process is driven by using predictive error on the test data. 10. The method of Claims 1 or 2 wherein the computer program comprises a method selected from the group consisting of MLR, PLS and SIMCA. 11. The method of Claims 1 or 2 wherein the computer program comprises at least two of the methods of Claims 8, 9 and 10 and the results thereof are polled. 12. The method of Claims 1 or 2 wherein said solution further comprises a positive control, said control entirely independently of data from the target, wherein said control provides a visually distinctive signal in the melting curve only when a successful amplification protocol has been accomplished thereby eliminating false negative results. 13. The method of Claims 1 or 2 wherein the program first examines regions of the curve away from peak-containing regions to detect base line shifts thereby determining the presence of a smear so indicating a non-predictive test. 14. The method of Claims 1 or 2 wherein the double stranded DNA form, the selected length and effective concentration are provided by PCR. 15. The method of Claim 14 wherein reagents, including an internal positive control reactant for PCR, are provided in a single tablet. 16. The method of Claim 15 wherein the tablet comprises effective amounts of Deoxynucleotides, Primer rc761, Primer 35, Primer 4185, Primer rc4289, SV-40 DNA, AmpliTaq, Surfact-Amps, BSA, Carbowax, and Trehalose. 17. A tablet for homogeneous PCR including an internal positive control comprising effective amounts of the following components: Deoxynucleotides, Primer rc761, Primer 35, Primer 4185, Primer rc4289, SV-40 DNA, AmpliTaq, Surfact-Amps, BSA, Carbowax, and Trehalose wherein said tablet is prepared by (a) freeze drying the components to form a powder, (b) passing the powder through a sieve to obtain a desired particle size, and (c) forming a tablet from the particles. 18. The tablet of Claim 17 wherein an intercalating dye is added prior to freeze drying. 19. The tablet of Claim 17 wherein the intercalating dye is present in about 0.8-1.5 μM. 20. The method of Claim 14 wherein the double stranded DNA is about 0.062 ng/ul-lO ng/ul. 21. The method of Claim 16 wherein the tablet contains about 50-200 μM deoxynucleotides, about 0.03-0.5μM primers, about 5-20 pg/reaction SV-40, about 1.0-2.5 units/reaction AmpliTaq, about 0.03-0.05% Surfact-Amps, about 10-40 μg/reactions of BSA, about 5-10 mg/reaction of Trehalose, and about 0.5 μl of a 1 :100 dilution of the DNA intercalating dye SYBR® Green.

Inventors: HAZEL JAMES W, JENSEN MARK A, OWENS AARON J, SCHAFFER MICHAEL, TICE GEORGE JR, VAIDYANATHAN GANESH, HAZEL, JAMES, W., JENSEN, MARK, A., OWENS, AARON, J., SCHAFFER, MICHAEL, TICE, GEORGE, JR., VAIDYANATHAN, GANESH
Applicants: QUALICON INC, QUALICON, INC.
Application year: 2000
Main classification: C12Q1
IPC codes: C12N15, C12Q1, G01N33