Visualizing high dimension VOC timeseries

Understanding the contribution of litter volatile organic compounds during wet-dry cycles offers a way to infer decomposition processes from high frequency repeat monitoring. From high dimension data (in time and in number of compounds), we developed an algorithm to detrend individual time series and fit to models of wetting response, thus automating the classification of 304 VOCs.

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