Determining performance parameters in qualitative multivariate methods using probability of detection (POD) curves. Case study: Two common milk adulterants

Abstract A strategy for determining performance parameters of two–class multivariate qualitative methods was proposed. As case study, multivariate classification methods based on mid-infrared (MIR) spectroscopy coupled with the soft independent modelling of class analogy (SIMCA) technique for detection of hydrogen peroxide and formaldehyde in milk were developed. From the outputs (positive/negative/inconclusive) of the samples, which were unadulterated and adulterated at target value, the main performance parameters were obtained. Sensitivity and specificity values for the unadulterated and adulterated classes were satisfactory. Inconclusive ratios 12% and 21%, respectively, for hydrogen peroxide and formaldehyde were obtained. To evaluate the performance parameters related to concentration, Probability of Detection (POD) curves were established, estimating the decision limit, the capacity of detection and the unreliability region. When inconclusive outputs were obtained, two additional concentration limits were defined: the decision limit with inconclusive outputs and the detection capability with inconclusive outputs. The POD curves showed that for concentrations below 3.7 g L −1 of hydrogen peroxide and close to zero of formaldehyde, the chance of giving a positive output (adulterated sample) was lower than 5%. For concentrations at or above 11.3 g L −1 of hydrogen peroxide and 10 mg L −1 of formaldehyde, the probability of giving a negative output was also lower than 5%.