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A new concept of flow analysis, internal standard-amplitude modulated multiplexed flow analysis, is proposed. A proof of concept was verified by applying it to the determination of ferrous ion (Fe 2+ ) by 1,10-phenanthroline (o-Phen) spectrophotometry. The flow rates of sample solutions containing Methylene Blue (MB) as an internal standard substance were sinusoidally varied at different frequencies. The solutions were merged with a color reagent (o-Phen) solution, while the total flow rate was held constant. Downstream, analytical signals were monitored at the maximum absorption wavelengths of Fe 2+ -o-Phen complex and of MB (510 and 644 nm, respectively). The signals were respectively analyzed by fast Fourier transform. The concentrations of the analytes in respective samples were simultaneously determined from the amplitudes of the corresponding wave components. The precision, linearity of the calibration curve, limit of detection and robustness against deliberate fluctuation in flow rate were greatly improved by introducing the internal standard method. Good recoveries of around 100% were obtained for Fe 2+ spiked into real water samples.

Plant extract is a mixture of diverse phytochemicals, and considered as an important resource for drug discovery. However, large-scale exploration of the bioactive extracts has been hindered by various obstacles until now. In this research, we have introduced and evaluated a new computational screening strategy that classifies bioactive compounds and plants in semantic space generated by word embedding algorithm. The classifier showed good performance in binary (presence/absence of bioactivity) classification for both compounds and plant genera. Furthermore, the strategy led to the discovery of antimicrobial activity of essential oils fromLindera trilobaandCinnamomum sieboldiiagainstStaphylococcus aureus. The results of this study indicate that machine-learning classification in semantic space can be a highly efficient approach for exploring bioactive plant extracts.