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

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.

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

Here, the analytical potential of an M13 bacteriophage-based color sensor for discrimination of 4 phthalates with similar molecular structures (bis-(2-ethylhexyl)-phthalate (BEHP), dibutyl phthalate (DBP), diethyl phthalate (DEP), and benzyl-butyl-phthalate (BBP)) was investigated. The pattern and magnitude of the RGB color changes were different depending on the functional groups present in the phthalate structures. For example, BEHP possessing a long alkyl chain resulted in a minute color change, while the variation of color was substantially large when BBP containing an additional benzene ring was measured. Since a tryptophan-histidine-tryptophan residue possessing indole and imidazole was present on the self-assembled phages, the π-π interaction of benzene in BBP with the sensor surface produced a considerably greater color change. To evaluate the multi-modally varying color signals due to diverse interactions of the phthalates with the sensor and to discriminate them, support vector machine (SVM), which can construct a boundary hyperplane among complexly scattered sample groups, was used. In addition, hierarchical SVM (H-SVM) was adopted to deal with multi-class discrimination. The use of H-SVM improved the discrimination accuracy up to 90.1%, compared to 87.1% using SVM. The demonstrated color sensor is versatile and can be potentially adopted as an on-site screening tool. Strategies to improve the accuracy further for real applications are also discussed.

A weighted twin support vector machine as a potential discriminant analysis tool and evaluation of its performance for near-infrared spectroscopic discrimination of the geographical origins of diverse agricultural products.

A weighted twin support vector machine (wTWSVM) was proposed as a potential discriminant analysis tool and its utility was evaluated for near-infrared (NIR) spectroscopic identification of the geographical origins of 12 different agricultural products including black soybean and garlic. In the wTWSVM, weights were applied on each variable in the sample spectra to highlight detailed NIR spectral features and the optimal weights to minimize the discrimination error were iteratively searched. Then, the weighted spectra were employed to determine the samples' geographical origins using a TWSVM adopting two non-parallel hyperplanes for the discrimination. For the performance evaluation, SVM, TWSVM, and wTWSVM were separately used for the two-group discriminations and their accuracies were comparatively analyzed. When the SVM and TWSVM accuracies were compared, the improvements by using the TWSVM were significant (95% confidence level) for 10 out of the 12 products. Moreover, the accuracy improvements with the wTWSVM against SVM were significant for all the 12 products. In the case of the TWSVM-wTWSVM accuracy comparison, the improvements by the wTWSVM were also significant for 10 products, thereby demonstrating superior discrimination performance of wTWSVM. Based on the overall results, the wTWSVM could be a potential chemometric tool for discriminant analysis and expandable to other areas such as spectroscopy-based biomedical disease diagnosis and forensic analysis.

Alternative selection of Raman or LIBS spectral information in hierarchical discrimination of raw sapphires according to geographical origin for accuracy improvement.

Both Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS) were cooperatively utilized to improve the geographical origin identification of raw sapphires from five different countries (Mozambique, Laos, Australia, Rwanda, and Congo). A hierarchical support vector machine (H-SVM) was used for multi-group identification. Initially, accuracy improved to 87.5% using merged Raman-LIBS data compared to those of using only Raman (82.8%) or LIBS (71.9%) information. This improvement was attributed to incorporating two complimentary spectroscopic datasets that provided molecular vibrational and elemental information. However, merging both spectroscopic datasets is may not be the best choice since it would make distinct and sample-descriptive information in one spectroscopic dataset less recognized for analysis by the inclusion of less characteristic information in another spectroscopic dataset; using only Raman or LIBS information at each discrimination stage would be more effective. When Raman information was utilized during the first three discrimination stages followed by LIBS data during the fourth (last) discrimination stage in H-SVM, the accuracy improved to 90.6%. The proper selection of molecular vibrational or elemental sample information at different discrimination stages is attributed to this improvement.

Cluster Analysis of Inhalant Allergens in South Korea: A Computational Model of Allergic Sensitization.

OBJECTIVES: Sensitization to specific inhalant allergens is a major risk factor for the development of atopic diseases, which impose a major socioeconomic burden and significantly diminish quality of life. However, patterns of inhalant allergic sensitization have yet to be precisely described. Therefore, to enhance the understanding of aeroallergens, we performed a cluster analysis of inhalant allergic sensitization using a computational model. METHODS: Skin prick data were collected from 7,504 individuals. A positive skin prick response was defined as an allergen-to-histamine wheal ratio ≥1. To identify the clustering of inhalant allergic sensitization, we performed computational analysis using the four-parameter unified-Richards model. RESULTS: Hierarchical cluster analysis grouped inhalant allergens into three clusters based on the Davies-Bouldin index (0.528): cluster 1 (Dermatophagoides pteronyssinus and Dermatophagoides farinae), cluster 2 (mugwort, cockroach, oak, birch, cat, and dog), and cluster 3 (Alternaria tenus, ragweed, Candida albicans, Kentucky grass, and meadow grass). Computational modeling revealed that each allergen cluster had a different trajectory over the lifespan. Cluster 1 showed a high level (>50%) of sensitization at an early age (before 19 years), followed by a sharp decrease in sensitization. Cluster 2 showed a moderate level (10%-20%) of sensitization before 29 years of age, followed by a steady decrease in sensitization. However, cluster 3 revealed a low level (<10%) of sensitization at all ages. CONCLUSION: Computational modeling suggests that allergic sensitization consists of three clusters with distinct patterns at different ages. The results of this study will be helpful to allergists in managing patients with atopic diseases.

Incorporation of two-dimensional correlation analysis into discriminant analysis as a potential tool for improving discrimination accuracy: Near-infrared spectroscopic discrimination of adulterated olive oils.

A strategy of combining temperature-induced spectral variation and two-dimensional correlation (2D-COS) analysis as a potential tool to improve accuracy of sample discrimination is suggested. The potential application of this method was evaluated using near-infrared (NIR) spectroscopic discrimination of adulterated olive oils. Rather than utilizing static spectral information at a certain temperature, dynamic spectral features induced by an external perturbation such as temperature change would be more informative for sample discrimination, and 2D-COS analysis was a reliable choice to characterize temperature-induced spectral variation. For evaluation, NIR spectra of 9 pure olive oils and 90 olive oils adulterated with canola, soybean, and corn oils (adulteration rate: 5%) were collected at four different temperatures (20, 27, 34, 41 °C). In constant-temperature measurements, the scores of pure and adulterated samples obtained by principal component analysis (PCA) were considerably overlapped. When 2D-COS analysis was performed using temperature-varied (20-41 °C) spectra and the resulting power spectra from 2D synchronous correlation spectra were used for PCA, identification of the two groups was noticeably enhanced and subsequent k-nearest neighbor (k-NN)-based discrimination accuracy substantially improved to 86.4%. While, the accuracies resulted in the constant-temperature measurements ranged only from 50.9 to 55.8%. The dynamic temperature-induced spectral variation of the samples effectively featured by 2D-COS analysis was ultimately more informative and allowed improvement in accuracy.

Feasibility of using a bacteriophage-based structural color sensor for screening the geographical origins of agricultural products.

An M13 bacteriophage-based color sensor, which can change its structural color upon interaction with a gaseous molecule, was evaluated as a screening tool for the discrimination of the geographical origins of three different agricultural products (garlic, onion, and perilla). Exposure of the color sensor to sample odors induced the self-assembled M13 bacteriophage bundles to swell by the interaction of amino acid residues (repeating units of four glutamates) on the bacteriophage with the odor components, resulting in a change in the structural color of the sensor. When the sensor was exposed to the odors of garlic and onion samples, the RGB color changes were considerable because of the strong interactions of the odor components such as disulfides with the glutamate residues on the sensor. Although the patterns of the color variations were generally similar between the domestic and imported samples, some degrees of dissimilarities in their intensities were also observed. Although the magnitude of color change decreased for perilla, the color change patterns between the two groups were somewhat different. With the acquired RGB data, a support vector machine was employed to distinguish the domestic and imported samples, and the resulting accuracies in the measurements of garlic, onion, and perilla samples were 94.1, 88.7, and 91.6%, respectively. The differences in the concentrations of the odor components between both groups and/or the presence of specific components exclusively in the odor of one group allowed the color sensor-based discrimination. The demonstrated color sensor was thus shown to be a potentially versatile and simple as an on-site screening tool. Strategies able to further improve the sensor performance were also discussed.