A computer-implemented method of determining the cycle threshold (Ct) value of a growth curve for a kinetic Polymerase Chain Reaction process, the method comprising: - receiving a data set representing a Polymerase Chain reaction growth process, the data set including a plurality of data points, each data point having a pair of coordinate values (x,y), where the x-axis represents cycle number and the y-axis fluorescence intensity; - calculating a curve that fits the data set, said curve including a second degree polynomial; - determining a statistical significance value for said curve; - determining whether the significance value exceeds a threshold; and - if not, processing the data set further; and - if so, indicating that the data set does not have significant growth and/or discarding the data set, wherein processing the data set further includes determining a cycle threshold (Ct) value of the PCR data set; and wherein determining the cycle threshold (Ct) value includes: - calculating an approximation function that fits the data set by applying a regression process to determine parameters of the function and a curve approximating the data set, wherein the approximation function is a double sigmoid function of the form: a + bx + c 1 + exp − d x − e 1 + exp − ƒ x − g; and wherein calculating includes iteratively determining the parameters a, b, c, d, e, f and g of the function by applying a Levenberg-Marquardt regression process and processing includes determining the curvature at some or all points along the normalized curve; - normalizing the curve approximating the data set using the determined parameters to produce a normalized curve; and - processing the normalized curve to determine a point of maximum curvature, wherein the point of maximum curvature represents the cycle threshold (Ct) value of the PCR growth curve. - receiving a data set representing a Polymerase Chain reaction growth process, the data set including a plurality of data points, each data point having a pair of coordinate values (x,y), where the x-axis represents cycle number and the y-axis fluorescence intensity; - calculating a curve that fits the data set, said curve including a second degree polynomial; - determining a statistical significance value for said curve; - determining whether the significance value exceeds a threshold; and - if not, processing the data set further; and - if so, indicating that the data set does not have significant growth and/or discarding the data set, wherein processing the data set further includes determining a cycle threshold (Ct) value of the PCR data set; and wherein determining the cycle threshold (Ct) value includes: - calculating an approximation function that fits the data set by applying a regression process to determine parameters of the function and a curve approximating the data set, wherein the approximation function is a double sigmoid function of the form: a + bx + c 1 + exp − d x − e 1 + exp − ƒ x − g; and wherein calculating includes iteratively determining the parameters a, b, c, d, e, f and g of the function by applying a Levenberg-Marquardt regression process and processing includes determining the curvature at some or all points along the normalized curve; - normalizing the curve approximating the data set using the determined parameters to produce a normalized curve; and - processing the normalized curve to determine a point of maximum curvature, wherein the point of maximum curvature represents the cycle threshold (Ct) value of the PCR growth curve. - calculating an approximation function that fits the data set by applying a regression process to determine parameters of the function and a curve approximating the data set, wherein the approximation function is a double sigmoid function of the form: a + bx + c 1 + exp − d x − e 1 + exp − ƒ x − g; and wherein calculating includes iteratively determining the parameters a, b, c, d, e, f and g of the function by applying a Levenberg-Marquardt regression process and processing includes determining the curvature at some or all points along the normalized curve; - normalizing the curve approximating the data set using the determined parameters to produce a normalized curve; and - processing the normalized curve to determine a point of maximum curvature, wherein the point of maximum curvature represents the cycle threshold (Ct) value of the PCR growth curve.