Great news and less great news

The Great News

Increased measurement accuracy by over 100 times

You heard right – 100 times! Our old detector was actually pretty capable – we could see even very small changes in absorbance like ECS (electrochemical shift) measurements which the vast majority of other handheld fluorometers don’t even attempt to measure. But the new detector is insanely better. We’ll probably devote a whole post to it later, but suffice it to say it’s going to open up a lot of new measurement possibilities.

Results from our first outdoor field tests

I went out to the greenhouse and took measurements of green, yellow, and red leaves (the red ones were poinsettias) from the top of the tree and the bottom of each plant. The goal was to see how our measurements varied based on location in the canopy and leaf color. We took a wider variety of measurements than listed below (including temperature, CO2, relative humidity, and some other absorbance measures), but these are the interesting results:

Greenhouse at MSU
Photosystem I is active
Photosystem I activity is basically binary even though I’m showing a scale on the y-axis here. If there is a value then it’s active, if it’s zero then it’s not active.
Phi2 is a measure of how much light hitting the plant surface is being used by photosynthesis
Phi2 is a measure of how much light hitting the plant surface is being used by photosynthesis
Just showing that the light meter works :)
Just showing that the light meter works 🙂
SPAD value is a measure of chlorophyll content, and relates to nitrogen content in leaves. For many crops, it can indicate when additional fertilization is necessary.
SPAD value is a measure of chlorophyll content, and relates to nitrogen content in leaves. For many crops, it can indicate when additional fertilization is necessary. This is basically a normalized measure of ‘greenness’.

So – quick recap of results:

1. Photosystem I is active in red and green leaves, and not active in red leaves. This makes sense, red leaves have no photosynthetic activity because they have no chlorophyll.

2. Photosynthetic efficiency is higher in the bottom of the canopy and higher for green leaves, and zero for red leaves (again – no chlorophyll), with a normal health green leaf achieving efficiency of ~.85 which again is in line with what we’d expect.

3. Light intensity is higher the the top of the canopy (as expected).

4. And finally SPAD values are highest for green leaves, about half for yellow, and near zero for red. The values themselves are a little higher than I would expect in absolute terms (usually a healthy plant gets a SPAD value of 60 – 80), but the relative values for each color make perfect sense.

So the takeaway here is the protocols we tested were a success! We have a lot more direct comparisons to other devices, like the LiCOR and handheld fluorometers in the future, but we know we’re on the right track.

New web-based analysis tool

Sebastian has been working hard to make a really cool web-based tool for analyzing PhotosynQ data via the website. We found a few example projects doing really neat things, specifically Data Explorer and Plot.ly, but Sebastian found it easier to build his own version, taking elements from these and others, and really hone in on exactly what we will need. You can quickly and easily sort your data in most any way, and then plot either the raw values, or plot computed values (like Fs, Fm, Phi2, etc.) against one another to look for interesting relationships. You can quickly add new series’ and reset the graph. You can even view the sorted data on a map, to see the GPS locations of each of the measurements. Basically, it’s awesome – here’s some screenshots:

You can compare any two variables on the x and y axis, or you can just do a histogram on a single variable to see the distribution in the population you’ve selected
You can vew averaged raw data from any protocol included in the experiment. This is a standard Phi2 fluorescence protocol (the spikes aren’t supposed to be there – ignore them!)
You can select any two variables and compare them on the X and Y axis. This makes it easy to identify relationships between variables, and between populations.

Soon we’ll be putting this up on github along with the rest of the website, we’ll let you know once it’s up.

The less great news – delaying start of beta

When Dave and Robert saw the significant improvements the signal quality on the new detector, we decided to delay the beta rollout in order to incorporate this into the initial units. It was a hard decision, since we had an effective, functioning design but we felt it was the right thing to do. There’s two big reasons:

1) The new design should be capable of detecting more interesting stuff. Since many of our beta testers are plant scientists, we felt that they would appreciate that additional capability up front and not have to pay another 100 bucks later to get the quality they know they’ll want.

2) The detector actually addresses some issues we had with the old design. Specifically, we had little spikes in the response when applying a bright saturating light (you can see them in the raw fluorescence data in the above screenshot) – this always has to be removed in post-processing the data. Also, in the old design the detector could be completely saturated with light causing the response to become meaningless. As a result, you had to adjust the brightness of the LED more frequently to ensure that any given measurement didn’t saturate the detector. This requires a lot of end-user adjustment which may confuse or turn off users, especially beginners. The new design addresses this problem.

So, expect the beta units to ship a little later than expected. I don’t want to put another date on it and miss that, but shouldn’t delay us much and as soon as we have another achievable date we’ll let folks know.

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