Establishing a protocol with potential for standardization in quality detection of thyme origin using a colorimetric sensor array approach

Abstract

Standardization is paramount for the production and quality control of medicinal plants and their derivatives. One method of achieving standardization is through chemical fingerprinting. Among various techniques, colorimetric sensor arrays are deemed a reliable method for the quality control and diagnosis of medicinal plants. Unlike conventional methods such as DNA barcoding and spectroscopy, chemical fingerprinting offers a different analysis of the plant’s unique chemical patterns. Colorimetric sensor arrays were chosen for their cost-effectiveness, ease of use, and rapid results. The objective of this study was to devise an effective method for the classification of ten different commercial brands of thyme and to compare them with three reference original thymes using a colorimetric sensor array technique coupled with mathematical data analysis methods. The findings indicated that colorimetric sensor arrays could effectively represent noticeable color changes between different essential oil samples based on Color Difference Maps (CDMs). The subsequent application of multivariate pattern recognition methods improved the discrimination ability between the samples. Principal Component Analysis (PCA) revealed the discrimination of three classes of the samples with 93% of the total variance, highlighting the distinct separation between different thyme origins. Hierarchical Cluster Analysis (HCA) elucidated the similarity of the studied commercial samples to the reference samples of thyme essential oil, demonstrating clear grouping patterns. Furthermore, Partial Least Squares Discriminant Analysis (PLS-DA) indicated that the majority of commercial samples were as similar as the thymus vulgaris samples, with fitting and validation accuracy of 95% and 91%, respectively. This analysis effectively discriminated the commercial thyme essential oil samples, highlighting their notable similarities to specific reference samples and clarifying the alignment or deviation from different reference standards. Lastly, Linear Discriminant Analysis (LDA) confirmed previous results and differentiated the Thymus samples from zataria multiflora, with 100% fitting and 91% cross-validation accuracy. These methods together provided a comprehensive analysis, each contributing unique insights into the classification and potential standardization of thyme quality detection. Among the used sensing elements, 4 top sensor elements were identified using the factor loadings for the first three PCs of PCA analysis and discrimination ability. While this study primarily focuses on classification, it lays a strong foundation for future standardization efforts in quality control of thyme.

Graphical Abstract