Here, a smartphone-integrated paper-based colorimetric sensor array (PBCSA) was developed for distinguishing flavonoid-rich Citrus herbal products (FRCHPs): Citri reticulatae pericarpium, Aurantii fructus immaturus, A...
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Here, a smartphone-integrated paper-based colorimetric sensor array (PBCSA) was developed for distinguishing flavonoid-rich Citrus herbal products (FRCHPs): Citri reticulatae pericarpium, Aurantii fructus immaturus, Aurantii fructus, Citri grandis exocarpium, Citri reticulatae pericarpium viride, and Citri sarcodactylis fructus. based on the strategy of indicator displacement assay induced by flavonoids, a 3 x 3 PBCSA with a hydrophobic barrier was constructed using inkjet printing technology. The PBCSA can accurately distinguished different species or concentrations flavonoids, and FRCHPs, demonstrating its broad applicability. After optimization with Genetic Algorithm, the Support Vector Machine (SVM) reduced the number of PBCSA sensor units from nine to five while maintaining an accuracy of 100.00 %, significantly improving the efficiency and accuracy of detection. Furthermore, the optimized SVM was integrated into a self-developed Quick Viewer app for real-time detection, greatly enhancing its practicability. This study not only presents a novel strategy for optimizing sensorarrays but also introduces a simple, economical, and real-time approach for analyzing FRCHPs.
In order to timely discriminate wheat with different mildew rates, a Dyes/Dyes-Cu-MOF paper-based colori-metric sensorarray was designed. Using array points to capture volatile gases of wheat with different mildew ra...
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In order to timely discriminate wheat with different mildew rates, a Dyes/Dyes-Cu-MOF paper-based colori-metric sensorarray was designed. Using array points to capture volatile gases of wheat with different mildew rates, and output RGB values. The correlation between AR/AG/AB values and odor components was established. The AG values of array points 2 ' and 3 ' showed the best correlation with mildew rate, with R2 of 0.9816 and 0.9642. The AR value of 3 and the AG value of 2 correlate well with the mildew rate, with R2 of 0.9625 and 0.9502, respectively. Then, the ARGB values are subjected to pattern recognition processing, and LDA achieves 100% correct discrimination for all samples, or divides high and low mildew areas. This method provides an odor-based monitoring tool for fast, visual and nondestructive evaluation of food safety and quality through visualization of odors produced by different mildew rates.
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