Machine learning techniques for chemical and type analysis of ocean oil samples via handheld spectrophotometer device

Abstract

We designed and constructed a handheld, sturdy fluorescence spectrometry device for identifying samples from ocean oil spills. Two large training databases of autofluorescence spectra from raw oil samples (538 samples/1614 spectra and 767 samples/2301 spectra) were cross validated using support vector machine (SVM) to identify oil type and SARA (saturate, aromatic, resin, and asphaltene) contents. The device's performance was then validated on an independent set of 79 ocean oil samples, which were added to and then collected from ocean water during outdoor exposure to hot, humid weather to represent an actual oil spill. It successfully classified oil types with 92%–100% sensitivity and specificity and F1 scores of 85.7–100%. Further classification of light fuel oils into marine gas oil (MGO)-like and Bunker A (BA)-like categories was successful with the training set (raw oil samples), while less successful with the independent validation set (ocean oil samples). SARA content classification models performed well in training for the saturate (80.8% accuracy) and asphaltene (90.7%) contents. The developed training model was validated using ocean oil samples, and the resulting accuracies were 62.0% (saturate) and 93.7% (asphaltene). These results indicate the difficulties in classifying volatile light fuel oils with a low molecular weight that have experienced weathering effects, while high molecular weight compounds and general oil type can be analyzed.