Identification and discrimination of herbicide residues using a conducting polymer electronic nose
The identification of herbicide residues on crop foliage is necessary to make crop-management decisions for weed pest control and to monitor pesticide residue levels on food crops. Electronic-nose (e-nose) methods were tested as a cheaper, alternative means of discriminating between herbicide residue types (compared with conventional chromatography methods), by detection of headspace volatiles released from inert surfaces. Detection methods were developed for a conducting polymer (CP)-type electronic nose device, the Aromascan A32S, to identify and discriminate among eight herbicide types from five different herbicide chemical classes including: chlorophenoxy acids, cyclohexenones, dinitroanilines, organoarsenics, and phosphonoglycines. A herbicide-specific aroma signature library was developed from known herbicide residues. The A32S e-nose effectively distinguished between eight different herbicide residues, correctly identifying them at frequencies ranging from 81-98%. The distribution of aroma class components, based on artificial neural net (ANN) training and analysis, indicated the percentage membership of aroma classes shared by herbicide types. Principal component analysis (PCA) provided indications of the relatedness of herbicide types based on sensor array response patterns (aroma profiles) of individual herbicides. PCA generated precise statistical values (quality factors of significance) as numerical indications of chemical relatedness between herbicides based on pairwise comparisons of headspace volatiles from individual herbicide types. The potential applications and advantages of e-nose methods (over current chromatography methods) for the detection and identification of herbicide residues on crop surfaces in agronomic fields are discussed.