Discrimination of phthalate species using a simple phage-based colorimetric sensor in conjunction with hierarchical support vector machine

Seol, Daun; Jang, Daeil; Oh, Jin Woo; Cha, Kwang-Yul; Chung, Hoeil

doi:10.1016/j.envres.2018.12.030

Cited by 7 publications

(1 citation statement)

References 12 publications

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“…Combined color changes could be more component-descriptive and advantageous for improving the discrimination of samples. In parallel, the introduction of different amino acid residues into the M13 bacteriophage can be readily accomplishable by genetic engineering [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Use of Multiple Bacteriophage-Based Structural Color Sensors to Improve Accuracy for Discrimination of Geographical Origins of Agricultural Products

Seol

Jang

Cha

et al. 2021

Sensors

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A single M13 bacteriophage color sensor was previously utilized for discriminating the geographical origins of agricultural products (garlic, onion, and perilla). The resulting discrimination accuracy was acceptable, ranging from 88.6% to 94.0%. To improve the accuracy further, the use of three separate M13 bacteriophage color sensors containing different amino acid residues providing unique individual color changes (Wild sensor: glutamic acid (E)-glycine (G)-aspartic acid (D), WHW sensor: tryptophan (W)-histidine (H)-tryptophan (W), 4E sensor: four repeating glutamic acids (E)) was proposed. This study was driven by the possibility of enhancing sample discrimination by combining mutually characteristic and complimentary RGB signals obtained from each color sensor, which resulted from dissimilar interactions of sample odors with the employed color sensors. When each color sensor was used individually, the discrimination accuracy based on support vector machine (SVM) ranged from 91.8–94.0%, 88.6–90.3%, and 89.8–92.1% for garlic, onion, and perilla samples, respectively. Accuracy improved to 98.0%, 97.5%, and 97.1%, respectively, by integrating all of the RGB signals acquired from the three color sensors. Therefore, the proposed strategy was effective for improving sample discriminability. To further examine the dissimilar responses of each color sensor to odor molecules, typical odor components in the samples (allyl disulfide, allyl methyl disulfide, and perillaldehyde) were measured using each color sensor, and differences in RGB signals were analyzed.

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