Method for detecting and/or quantifying a target nucleic acid in a polymerase chain reaction sample, comprising: - a target nucleic acid having a nucleic acid target sequence to be amplified, - a thermostable polymerase, - deoxynucleoside triphosphates (dNTPs) - at least one oligonucleotide primer pair that is complementary to the nucleic acid target sequence to be amplified, the primers of which primer pair, under PCR conditions, hybridise in the region to be amplified of the nucleic acid target sequence, initially a plurality of amplification cycles of a polymerase chain reaction being carried out using the PCR sample, as a result of which melting curves are obtained, from which signals are generated and undergo a melting curve analysis in which - the curve points of a melting curve are read and the negative gradient is calculated for each curve point, - the melting curves are broken down into their spectral components and the frequency components contained in the signal are ascertained by means of Fourier analysis for a feature extraction, - the extracted features of a melting curve are analysed by means of machine learning methods using classifiers and are divided into classes, characterised in that, - for the division into classes, a mathematical optimisation method is applied in which determined features of the curve are initially ascertained in order to form feature vectors, a plurality of feature vectors being used independently of one another by the utilised classifiers such as a support vector machine, learning vector quantisation and random forest, and in which the utilised classifiers are weighted differently, in order to generate a population of selectable solution candidates, by means of which identification of the curves in the test batch is improved, it being possible to ascertain the occurrence and/or the amount of target nucleic acid to be detected in the sample by means of the result of the melting curve analysis obtained thereby. - a target nucleic acid having a nucleic acid target sequence to be amplified, - a thermostable polymerase, - deoxynucleoside triphosphates (dNTPs) - at least one oligonucleotide primer pair that is complementary to the nucleic acid target sequence to be amplified, the primers of which primer pair, under PCR conditions, hybridise in the region to be amplified of the nucleic acid target sequence, initially a plurality of amplification cycles of a polymerase chain reaction being carried out using the PCR sample, as a result of which melting curves are obtained, from which signals are generated and undergo a melting curve analysis in which - the curve points of a melting curve are read and the negative gradient is calculated for each curve point, - the melting curves are broken down into their spectral components and the frequency components contained in the signal are ascertained by means of Fourier analysis for a feature extraction, - the extracted features of a melting curve are analysed by means of machine learning methods using classifiers and are divided into classes, characterised in that, - for the division into classes, a mathematical optimisation method is applied in which determined features of the curve are initially ascertained in order to form feature vectors, a plurality of feature vectors being used independently of one another by the utilised classifiers such as a support vector machine, learning vector quantisation and random forest, and in which the utilised classifiers are weighted differently, in order to generate a population of selectable solution candidates, by means of which identification of the curves in the test batch is improved, it being possible to ascertain the occurrence and/or the amount of target nucleic acid to be detected in the sample by means of the result of the melting curve analysis obtained thereby. - the curve points of a melting curve are read and the negative gradient is calculated for each curve point, - the melting curves are broken down into their spectral components and the frequency components contained in the signal are ascertained by means of Fourier analysis for a feature extraction, - the extracted features of a melting curve are analysed by means of machine learning methods using classifiers and are divided into classes, characterised in that, - for the division into classes, a mathematical optimisation method is applied in which determined features of the curve are initially ascertained in order to form feature vectors, a plurality of feature vectors being used independently of one another by the utilised classifiers such as a support vector machine, learning vector quantisation and random forest, and in which the utilised classifiers are weighted differently, in order to generate a population of selectable solution candidates, by means of which identification of the curves in the test batch is improved, it being possible to ascertain the occurrence and/or the amount of target nucleic acid to be detected in the sample by means of the result of the melting curve analysis obtained thereby.