Research

Interactive effects of drought severity and simulated herbivory on tea (Camellia sinensis) volatile and non-volatile metabolites

The importance of insect herbivore density to induced metabolite blends in tea plants (Camellia sinensis) and implications for tea quality.

[poster] Interactive effects of drought severity and herbivory on tea (Camellia sinensis) volatile and non-volatile metabolites.

Can pests rescue tea quality from climate change?

Last Fieldwork Season in China

I’m currently in Hangzhou, China at the Tea Research Institute(TRI) for my fourth and last time. It’s bitter sweet (like my favorite teas ;-) ) since I’m both glad to be nearing the end of my PhD, and sad to say goodbye to all the friends I’ve made and a city I’ve really grown to enjoy living in. Fieldwork This final summer, I’ve been focusing on a few experiments having to do with leafhoppers and their effects on tea chemistry (see the project page for more info).

Retrieve chemical retention indices from NIST with {webchem}!

My PhD has involved learning a lot more than I expected about analytical chemistry, and as I’ve been learning, I’ve been trying my best to make my life easier by writing R functions to help me out. Some of those functions have found a loving home in the webchem package, part of rOpenSci. Papers that use gas chromatography to separate and measure chemicals often include a table of the compounds they found along with experimental retention indices and literature retention indices.

Combined Effects of Drought and Herbivory on Tea Metabolism

Importing data from a LI-COR photosynthesis meter into R

The LI-6400XT is a portable device used to measure photosynthesis in plant leaves. As you take measurements by pressing a button on the device, they are recorded into memory. In order to keep track of which measurments go with which plants (or experimental treatments), there is an “add remark” option where you can enter sample information before taking measurements. When the data are exported, you get a series of .

Quantifying leafhopper damage with automated supervised classification

As part of my fieldwork in China, I collected harvested tea leaves that were damaged by the tea green leafhopper. I want to quantify the amount of leafhopper damage for each harvest. I was able to find several solutions for quantifying holes in leaves or even damage to leaf margins, but typical leafhopper damage is just tiny brown spots on the undersides of leaves. I did find some tutorials on using ImageJ to analyze diseased area on leaves, but found that the leafhopper damage spots were too small and too similar in color to undamaged leaves for these tools to work reliably and be automated.