This study (open-access author’s link available until February 14, with thanks to Michele Suman for sharing) reports the development and validation of a non-targeted classification method for authenticity of dried oregano leaves by atmospheric pressure matrix-assisted laser desorption ionization mass spectrometry (AP-MALDI-MS).
The model was trained on 23 authentic oregano samples (sourced from a reputable company with full supply chain traceability - originated from Italy, France, Turkey, or Albania, harvested between 2019 and 2022) along with five pure adulterants (dried leaves of savory (Satureja montana), myrtle (Lagerstroemia indica), sumac leaves (Rhus coriaria), strawberry tree (Arbutus unedo), and olive tree (Olea europaea)), plus sixteen adulterated oregano samples, intentionally mixed with the above mentioned adulterants at ranges between 5 % and 60 %.
The most abundant signals were characterized by collision induced dissociation and library search, the spectral data were submitted to statistical analysis. A basal inquiry of the data by partial least squared discriminant analysis (PLS-DA) was carried out for the simple assessment of the discrimination capabilities of the ± AP-MALDI-MS signatures. The researchers then constructed two distinct random forest (RF) classifiers using the positive and negative most informative ions teased out by recursive feature elimination from the training sets. The aforementioned most significant variables (m/z values) were also merged by mid-level data fusion and used to build a third RF classifier.
They report that the cross-validations of the three RF classifiers achieved good outcomes as demonstrated by the satisfactory values of overall accuracy (84.9 %, 92.1 %, and 92.8 %, respectively). The three RF classifiers were tested on the hold-out data, which revealed reliable classifier performances (accuracy 80.1 %, 87.0 %, and 85.4 %).
Photo by 360floralflaves on Unsplash
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