Abstract
Samples of 23 seafood products were obtained internationally in processing plants and subjected to controlled decomposition to produce seven discrete quality increments. A sensory expert evaluated each sample for decomposition, using a scale of 1-100. Samples were then extracted and analyzed by liquid chromatography with high-resolution mass spectrometry (LC-HRMS). Untargeted data processing was performed, and a sensory-driven Random Forest model in the R programming language for each product was created. Five samples of each quality increment were analyzed in duplicate on separate days. Scores analogous to those obtained through sensory analysis were calculated by this approach, and these were compared to the original sensory findings. Correlation values (r) were calculated from these plots and ranged from 0.971 to 0.999. The finding of decomposition state of each sample was consistent with sensory for 548 of 550 test samples (99.6%). Of the two misidentified samples, one was a false negative, and one false positive (0.2% each). One additional sample from each of the 1st, 4th, and 7th increments of each product was extracted and analyzed on a third separate day to evaluate reproducibility. The range of these triplicate calculated scores was 15 or less for all samples tested, 10 or less for 63 of the 69 triplicate tests (91%), and five or less for 41 (59%). From the models, the most predictive compounds of interest were selected, and many of these were identified using MS2 data with standard or database comparison, allowing identification of compounds indicative of decomposition in these products which have not previously been explored for this purpose.