LeoZ wrote:doesnt pyrolysis occur in high (above 800F) temps and in the absence of oxygen? i suppose it could be argued that while roasting there is some sort of boundry layer around the beans that prevents oxygen from getting too close to the surface, but i cant see how, if at all, this small amount of a reaction would affect the outcome of the bean, except at the infamous 3rd crack, which could correlate to carbonization.

pyrolysis :
n. Decomposition or transformation of a compound caused by heat.

InterFIRE definition:

Pyrolysis

A chemical process that occurs when heating in the absence of oxygen causes the chemical decomposition of a compound into its component substances. Pyrolysis often precedes combustion and the gases given off by it are the materials that burn in the flame. Pyrolysis takes place just below the char layer of the material (i.e., wood).

This is an overly restrictive definition. The chemical decomposition process takes place with or without oxygen present. I'm not sure what is meant here by "component substances." Once a molecular bond is broken, what remains is a different substance. Butane is not a "component" of octadecane, but a butane ion may be produced when octadecane pyrolyzes. Heat causes large molecules to break into smaller ones as a result of the breaking of bonds between atoms. If oxygen is present, combustion might occur, assuming there is an energy source that will heat the vapors to their ignition temperature. Further, pyrolysis usually takes place in virgin material in front of the char layer, as well as under it. Finally, stating that pyrolysis "often" precedes combustion fails to discuss the state of the fuel. While liquid fuels can vaporize and burn, pyrolysis (almost) always occurs in the combustion of typical solid fuels. The only exceptions are metals and substances like pool chemicals.

Accurate definition

The chemical decomposition of a compound into one or more other substances by heat alone; pyrolysis often precedes combustion.

cpl593h wrote:Interesting. The difference in solubility between light and dark might not be that big of an influence as I thought it would be. Nevertheless, the fine grind + low dose/shallow puck formula is maximizing extraction of "balancing" flavor compounds that are present in lighter and darker coffees in similar amounts, but more need to be extracted to compensate for the acidity of lighter roasted coffee.

dsc wrote:Is this the end of the "pucks with shower screen impression" era?

In the dark roast, bitterness rose faster than sweetness as one reduced the dose. In a very dark roast, the sweetness of the caramels is reduced, and the amount of distillate bitters increased. It could be that very dark coffees need to be overdosed and underextracted, since there's so few light compounds left. Fingerstrike dosing, which leads to low extractions, was probably introduced on the West Coast, by Peets and early Seattle roasters. They roasted very dark in those days (except for Starbucks, everyone there is gradually lightening it up), and perhaps in that context, the technique was optimal.

My object is always the same. If you don't like a shot of espresso; do you know how to change it? I don't care how you change it; only that you know what options are available, and which directions are most promising. Maybe someday we'll see no more posts of someone making a shot that didn't taste right, and asking which new machine they need to buy.

Right now, I'm doing 4 different coffees roasted to 430F (a scootch lighter than Illy, about like Metropolis's Redline): Kenya Mamuto, Rwanda Musasa, Bolivia Cenaproc, and Yrgacheffe Idido. They are all working fairly well on balancing sweetness versus acids and bright-bitters at around 13 grams in an E61 style basket. I slowed the Cenaproc down before the first crack to get a lot of bright-bitter almond, and it needs a very cool shot temperature. The Mamuto likes it hot. Surprisingly, for the Musasa and Idido, the bright-bitters dominate the acids, so I'm going fairly cool.

This was also true of Terroir's Northern Roast, as Peter reported, which also requires fairly cool shot temps after one down doses to get more of the florals and less of the woody and rooty flavors and balance everything out properly.

So roughly speaking, I'm following this overall diagnostic model for lighter roasts:
-- First Balance Sweetness versus Sour and Bright Bitter: dose less for sweet, more for sour and bright-bitter
-- Then balance Sour versus Bright Bitter: cooler for more sour, hotter for more bright-bitter.

The final version is up; mostly unchanged except for endnotes, spiffed up html, and a reiteration that low dose shots are not weaker (i.e. less coffee solids in the cup) than high dose ones if pulled properly.

Jim,

Thanks for this work! You have shown an amazing ability to select a few of the key critical variables and ignore the nonessentials. I look at the problem and see a million and one issues too complicated to try to make sense of. Then you produce a set of meaningful findings that we ourselves can immediately put to the test. Here I was trying to find ways to stuff more grinds into the basket. After reading the original paper, I've reduced my dose by close to 20% and am producing richer, tastier shots. I don't think that would have happened anytime soon without your paper.

espressoperson wrote:Jim,
I've reduced my dose by close to 20% and am producing richer, tastier shots. I don't think that would have happened anytime soon without your paper.

Thanks for giving it a try.

In hindsight, it's odd we weren't playing with dose a long time ago; we've certainly been varying everything else. But it's easy to develop blindspots in a routine; it took a very flukey chain of coincidences to even get me looking at dosing variations.

[This post edited several times on 2-11-07]

Hi Jim:

I still don't understand it when you say that changing the dose is the only practical way to affect solubles yield, and that changing shot time and/or shot volume is fruitless.

In my solubles yield testing, I kept the dose fairly constant and varied the volume (ie, mass) of the resulting shot. Below is a scatter graph of the data (you have the original numbers). To me, it sure looks like there's a correlation between brewing ratio (dry coffee grams/liquid espresso grams) and solubles yield. Then again, I've never taken a statistics course and I don't understand how to manage the data like you do.

As I've mentioned to you in email, I don't see a definite blonding point. Instead I see a blonding range. Therefore, there's a range of reasonable shot volumes where the shot can be terminated.

I guess the point I'm making is that keeping the dose constant and varying the volume (mass) of the shot seems like another, valid way to manipulate solubles yield.

Hi Andy,

Looks that way to me too. But ...

Flip your axes, you have them reversed (it's easier to read a relation if the cause is on X, and the effect on Y). When the points are more scattered at one end than the other (heteroskedasticy, try saying that three times), use an exponent of one variable, or a log scaled axis, to get them more evenly scattered around the trend (homoskedastic -- in stats, homo is good), in this case use a log-scale for the brew ratio,

Finally a bit of algebra: you're saying yield ~ dose/shot.weight. If you are using the same basket throughout, then if you're varying mostly dose and less the shot.weight, your posited relation is the same as mine. If the variation in brew ratio comes from mostly changed shot.weights, then the relation is different.

No statistics required, just eyballs and knowing what variables are varying.

The plot thickens. The logs of shot time and weight correlate very nicely to the logs of yield, virtually as strongly as the logs of dose and filter.area; while they don't correlate very well when using linear terms. The dose/filter.area relation is much stronger on the linear scale. Basically the two processes take place in completely different ways.

There's no easy way of combining the best models for both so one can tell how much each is really doing overall. I'll need to read up on non-linear methods before I get started, since my knowledge of these is severely out of date (non-linear methods are all brute force computations, and require gobs of computing power. Since there's more of that every month, these methods change really fast). It's a sweet problem.

My apologies for thinking out loud and 1 step at a time in these three posts. But I flashed on a really old-school way to parcel out the relative effect of dose and basket versus shot weight & time & concentration -- rank correlation. In ranks, you substitute the rank, 1st, 2nd, 3rd, etc for the actual values. This means if the relation is monotonic, it doesn't matter whether its linear, powers, exponential, or whatever. Moreover, the coefficients tell you directly what the story is (a coefficient of one means the rank of the cause is perfectly related to the rank of the effect. Anyway, here goes:
The rankings of filter.area/dose explain 52% of the yield ranks (the "estimate" column times 100); while the rankings of shot.time*shotweight/dose explain 35% of the rankings. You can ignore all the other statistical stuff, it's meaningless. This simply is a rough answer, prior to a full model, on how much dosing versus flow explains solubles yield. If the second term is not divided by dose, it drops to about 25%

I guess I was wrong in my paper about the effect being minor, it's about 70% of the effect of dose, and at least 50%.

Jim, here's another graph with the axes flipped. Although I, too, abhor heteroskedasticy, I left the axes linear for the time being.

These data points were all generated with the same basket, an LM ridged double.