These CNL spectra were used to build the true positive (TP) and true negative (TN) probability distributions that were required for the Bayesian CNL model. To calculate these, the CNL spectra were converted to binary vectors for a CNL range of 0 to 1000 Da with 0.001 Da steps. The TP binary vector, for each chemical, contains ones for CNLs found in the spectra and zeros for the remaining bits while the TN binary vector contains ones for the CNLs that were not found in a spectrum (Figure ). The CNL masses that were larger than the precursor ion were set to zero for the TN binary vector. The TP and TN CNL occurrence distributions were calculated by summing the binary vectors obtained for all 4205 CNL spectra and adding 1 to each CNL bin to avoid obtaining probabilities equal to 0. Finally, the TP and TN probability distributions were obtained by dividing each CNL occurrence by the total number of TP and TN CNL occurrences, respectively. These calculated TP and TN CNL probabilities were used as the conditional probability (i.e., P (B|A)) in the Bayes theorem (Equation ). Additionally, to be able to calculate the TP or TN probability of a precursor ion having a specific CNL (i.e., posterior probability or P (A|B )), a flat prior (P (A)) is assumed and, since, the marginal probability (P (B )) is a constant normalizing factor, the Bayes theorem can be reduced to an equation 2, meaning that the TP and TN probability given a certain CNL is proportional to the probability of a CNL given that it is a TP or TN.