Monthly Archives: May 2011

My short-list for summer drinking choices

I believe I have found a few beers that I will be enjoying considerable amounts of over the coming months. As I’ve mentioned before, much of the beer I look for now is what I consider to be drinkable and clean. While I can appreciate full-flavored big beers with lots of hops and/or alcohol, most of the time I like to reach for refreshing, lightly flavored beers with no appreciable flavor defects. I’ve mentioned here before that I like Heineken (out of a can/keg only!) for that reason. A couple of other beers that fall in this category for me are Sierra Nevada’s Summerfest pilsner and even Pyramid’s Curveball kolsch/blond. But these are seasonals and you can’t get them year-round.

My recent additions to this group both come from Redhook: Pilsner and Wit. As I understand it, the pilsner is the same as the Rope Swing summer seasonal that Redhook released within the last couple years, and now it’s been upgraded to year-round availability and I’m happy about that. It’s a good beer with a nice balance between malt and hop influences, and it’s quite easy to drink. Few, if any, defects stand out. One thing I with it had a tiny bit more of is some noble hop aroma. Something spicy, with eugenol and beta-caryophyllene. Even so, it’s a fine beer. Anyway, the Wit is what replaced the Rope Swing / Pilsner as the summer seasonal this year, and it’s another good beer. It’s unfiltered and is brewed with various spices consistent with the Belgian wit style: coriander, orange peel and some ginger (as it cleverly jokes about on the label). The citrus notes definitely stand out, and some ginger does peak out from the flavor now and again, but it is not significant. Bready/yeasty flavors are also present, but not overpowering. I tasted this up against Blue Moon recently and while I found the initial aroma of Blue Moon to be stronger and more pleasant, the body was considerably lighter and the after a few drinks the more prominent spices of the Blue Moon got to be too much. I enjoyed the subtly and balance of the Wit much more, and the higher body made the beer taste more robust as well.

Now Redhook isn’t a brewery that I’d consider “innovative” or “exciting”, but one thing they do relatively well is make clean and drinkable beers. The kind of beers that you can eat with most any meal and it will compliment the food well without distracting you. A number of them are what I would consider “session beers”, even though they aren’t necessarily lagers like most Sessions are. These two beers definitely fit that bill.

So give these guys a try. If you’re looking for warm-weather summertime activity beers, I don’t think that you’ll be disappointed in these.

[note: eugenol and beta-caryophyllene are compounds commonly found in many hop varieties, and they have spicy-type aromas. eugenol tends to have a cinnamon/clove-like aroma (somewhat different than the belgian beer type of clove) while caryophyllene can smell floral, carrot-like, and woody]

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This is not a post

Yeesh, 15 days since my last post…

I’ve been covering for vacationing co-workers, installing various upgrades to our lab’s infrastructure, attending meetings, shipping training standards out, and creating various Powerpoint presentations to educate certain members of our workforce. I’ve kind of been a “Jack of All Trades” around here recently since I’m somewhat able to do a number of different things that need to be done in various departments. It’s fun, but I feel like I’m running to stand still…

I hope to post again soon.

Overall Difference Tests

Let’s change tack a little bit here and discuss a specific set of sensory tests: overall difference tests.

Deciding whether two samples of beer are different is not as easy as it may seem. Everyone perceives their senses slightly different than others, so what one person may find to be a noticeable difference may only be detected by few, if any, others. Various types of bias may also lead people to find differences that don’t exist. On top of this, the need for accuracy in your data means that you often need more than just a few people to be able to truly say whether there is a difference. So, as with all laboratory procedures, there are standardized methods and tests that are used when searching for differences in food and beverage systems. Even under the heading of “overall difference tests” there are a number of different tests that can be used, each with their own pros and cons.

Overall difference tests are used to find whether there is any detectable difference between two samples. Where exactly that difference originates is not necessarily part of the goal of the difference test, although you can usually pull out some hints to help guide your progress. This type of test differs from the more specific “attribute difference test” which seeks to determine whether a difference exists on the basis of one specific aspect of the sample, whether it is the color, the bitterness, the phenolic aroma, etc. I’ll discuss attribute difference tests later, but before we move on to the tests themselves, a word about error first.

In statistical tests such as these, there are essentially two types of error: α-error, and β-error. α-error is a numerical representation of the risk you are willing to accept for the possibility of finding a false-positive, or finding a difference when one doesn’t exist.  β-error is the same type of numerical representation, but it signifies the risk you accept for possibly finding a false negative, or missing a real difference that exists between the samples. In practical situations, you must balance which risk you want to minimize over the other, since minimizing both requires many more panelists and samples than most production environments can accommodate. For overall difference tests it is usually the alpha that is minimized, while the β-risk is allowed to be large to keep the number of assessors reasonable. The default value for α is usually 0.05 meaning you, as the administrator, are accepting the possibility that there is a 1/20 chance that the results will indicate a difference when one doesn’t actually exist. An α of 0.05 isn’t required by any means, but it usually offers a good balance between risk-management and panel size.

What follows is a breakdown of a few of the more commonly used types of tests that can be used to find an overall difference between test samples.

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