Ruth H. Krywicki, PhD, MS

ASQ-CSSBB

Minitab Insights Conference

Philadelphia, PA

September 12-13, 2016

Brewing beer—it’s more than just water, barley, hops and yeast!

Brewery lab operations and challenges

Where the journey started

How an internal standard works

FMEA and Gage R&R

Check Beers, Check Standards and Control Charts

Just look at the picture!

Lab Results How lab results and process capability can help the

Brewer to optimize his process

2

Historical records indicate beer has been brewed for over 7,000 years; found in hieroglyphics and stelae on pyramid walls

Many styles of beer are brewed around the world by small craft breweries up to the mega-size breweries

Big beer is big business…..

Wall Street Journal July 21, 2016/B1….“AB InBev’s $108 billion takeover of SABMiller gets U.S. approval………”

A large scale brewery operates 24/7 and expects to brew and package millions of cases of beer that are consistently high in quality with predictable flavor. Brewery may have multiple wort streams and many brands which adds complexity

Voice Of the Customer “VOC” drives the business! Customer expects great taste EVERY time 3

4

Large scale brewery Quality Lab may process as many as 35,000 tests in a year—operates 24/7—lean staffing

Test results provide critical information to the Brewer Quality, flavor, process characteristics, etc. and help ensure

yeast health and ultimately great beer every time

Sophisticated instrumentation used for monitoring PM schedules and recalibrations—challenge to figure out

root cause for out of spec results

How can test results be assured? Is the problem because the process is out of spec? Is it

analyst error? Is it the instrument itself?

5

HPLC-High Performance Liquid

Chromatograph

Head-Space Gas Chromatograph

Atomic Absorption Spectrometer

UV/Vis Spectrometer

Anton-Paar Alcolyzer

Instruments are used around the clock by

several different technicians. On a PM

schedule , but instrument can have problems before next

service is due.

Recalibrated if a problem was suspected. Didn’t always fix issue.

6

Initial use of Minitab started by doing an FMEA(Failure Modes Effects Analysis)

A Quality Gate in the process also was the process bottle-neck—the Vicinal Di-Ketone (VDK) check

Brewer could not move the fermenter to the “fast cool” step [to halt fermentation] until the VDK was at an acceptable level; lab would check several times before the final result was below limit

The Brewer wanted to be able to “project” when the fermenter would be ready based on a predictable drop in VDK

Results from the lab were “all over the place”—instead of predictably dropping with time, rechecks might be too high or too low.

Brewer wanted to save time—time is $$$$

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VDK is an acronym for Vicinal Di Ketone—better known as 2,3 butanedione or “diacetyl”

It occurs in fermentation along with another vicinal di-ketone known as 2,3 pentanedione

At low levels, diacetyl contributes a slipperiness to the feel of the beer in the mouth. As diacetyl levels increase, it imparts a buttery or butterscotch flavor

In some styles of beer (e.g. in most beers produced in the British Isles, such as India Pale Ale), the amount of diacetyl is part of the brand design. Consumer expects a certain flavor profile

VDK increases during fermentation. Once fermentation is finished, the yeast will re-absorb the diacetyl back into the cell but not if yeast is stressed

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A sample from the fermenter is spun down, and the top phase [supernatant] is carefully removed; a portion is incubated with sulfuric acid to force the conversion of the VDK precursors to diacetyl and pentanedione. Many steps, different people/1 test

The compounds of interest are volatile and when heated, the space above the liquid becomes saturated

The “headspace” (gas phase) is then tested

If we add a known amount of something similarly volatile, [hexanedione], we can quantify how much VDK is present

Hexanedione is called an “internal standard”

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W3

1.0

W2

0.4

W1

0.2

• 3 different concentrations of analyte of interest (W1, W2, W3)

• Add same amount (10µl) of “internal standard” hexane-dione to each vial

• Measure; generate 3-point curve [straight line]

• Add 10µl internal standard to the sample; measure peak height

• Interpolate concentration

10 µl 10 µl 10 µl

Sample

10 µl

Standards for 3 point curve

W3

W1 W2

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FMEA—Failure Modes Effects Analysis evaluates every step in a process (usually complex) and assesses the frequency, severity, and ability to detect the failure—value from 1-10 is assigned

Each value is multiplied to yield RPN [Risk Priority Number]; higher the RPN, the greater the impact to the process

Team determined there were 34 potential failures

Top hitters included operator not flushing sample tap long enough, and anything that affected the delivery of 10 µl of the hexanedione internal standard. (Six main causes that impacted consistent delivery of 10 µl of internal standard.)

Minitab Gage R&R of the instrument along with control charting the internal standard helped further pin point opportunities

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One analyst in particular seemed to have a problem with sample range and the mean was higher as well.

10 µl of internal standard should yield the same value every time

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W3W2W1

30000

25000

20000

15000

10000

5000

Standard

He

igh

t B

uta

ne

Individual Value Plot of Height Butane

The GC can be reasonably

eliminated as a source of the

variation by looking at the

individual peak heights value

plots—i.e. recovery off the

column of the standards made

for the 3-point curve. (One bad

prep—outlier.)

The 3 standards (W1, W2, W3)

were then injected into the GC as

“unknowns”

Peak heights were graphed by

Minitab for individual value plots

of the runs

W3W2W1

40000

30000

20000

10000

0

Standard

He

igh

t P

en

tan

e

Individual Value Plot of Height Pentane

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W3W2W1

15000

14000

13000

12000

11000

Standard

He

igh

t H

exa

ne

Individual Value Plot of Height Hexane

Look at the individual value plot of

the hexanedione internal standard

peak heights

Notice how they are all over the

place? Variation within each

standard should be consistent

When looking at a control chart of the

data you can see that there is quite a bit

of data spread

This implies that the introduction of the

internal standard is where much of the

test error is incorporated—for the W1,

W2, & W3 standards as well as the

process samples.

11-A pr10-A pr9-A pr8-A pr7-A pr6-A pr5-A pr5-A pr4-A pr4-A pr3-A pr1-A pr

16500

15000

13500

12000

Date

In

div

idu

al

Va

lue

_X=13408

UC L=15967

LC L=10849

11-A pr10-A pr9-A pr8-A pr7-A pr6-A pr5-A pr5-A pr4-A pr4-A pr3-A pr1-A pr

3000

2000

1000

0

Date

Mo

vin

g R

an

ge

__MR=962

UC L=3144

LC L=0

1

I-MR Chart of Height Hexane

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4/18/20104/13/20104/9/20104/4/20103/30/20103/25/20103/18/20103/13/20103/7/20103/1/2010

16000

14000

12000

10000

8000

Sample Pull T ime

In

div

idu

al

Va

lue

_X=10494

UC L=12292

LC L=8695

4/18/20104/13/20104/9/20104/4/20103/30/20103/25/20103/18/20103/13/20103/7/20103/1/2010

4000

3000

2000

1000

0

Sample Pull T ime

Mo

vin

g R

an

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__MR=676

UC L=2210

LC L=0

1111111

1

111

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11111111

1

1

1

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I-MR Chart of IS Ht Counts

Control limits established after instrument was recalibrated. Data points from each test were added and trends observed to figure out where the problem was

Graph of individual values of internal standard height counts shows three distinct populations of data

The first shift was caused by using an old vial of Hexanedione that deteriorated [over-punctured]. It is likely the second shift was caused by the same thing. Note the dip. Loss of internal standard affects the interpolation of results; false high VDK results for samples tested

Downward shift caused by degradation

Prior to using

Minitab, the lab

would have re-

calibrated the

instrument and

assumed

calibration was

the problem

Recalibration may

take 8-10 hours

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16000

14000

12000

10000

Ind

ivid

ual V

alu

e

_X=11237

UCL=12804

LCL=9670

4500

3000

1500

0

Mo

vin

g R

ang

e

__MR=589

UCL=1925

LCL=0

1

11

1

1

1

1

1

Internal Standard HeightOutliers removed from natural control limit calculations

Data points omitted from the calculations: 1, 43, 64, 66

Further investigation showed that there was poor control over the number of internal standard vials being used. Since the internal standard affected ALL results, this was critical. New program put in place along with a better size syringe for delivery of the internal standard. Control charting showed big improvement. Spurious outliers investigated quickly.

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Typical limit ranges before investigation were ~10,000; Reduced to <1,000

FMEA is used for complicated or multi-step processes where it’s not easy to figure out what is contributing to the problem

Gage R&R used if good indication that problem appears to be related to people/instrument interaction

Lab did Gage R&R [w/o FMEA] on an instrument used to measure an analyte from a new process introduced to the brewery

Instrument already in use to measure something else

Master Brewer felt certain the problem was instrument related—

not process related

No historical performance for the new process; no analyte “trends”

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• Gage study involved 3 operators [lab techs], 7 different sample source-types, n=3 replicates

• Could clearly see that variation was in source samples—not the lab techs or the instrument

• Further investigation showed that the Brewer could adjust the process to reduce the variability in the analyte concentration

• Later introduced a control chart to monitor analyte trends 18

Lab implemented a “Check Standard” program for Atomic Absorption Spectrometer [“AA”]

“AA” measures metals—several of which are critical

components of yeast vitality

Poor yeast health creates off-flavors in beer that are not part

of brand design; also results in slower fermentation

Check standard is something purchased that has a Certificate

of Analysis

Control-charting a Check Standard ensures the AA is

operating correctly—but more importantly reveals trends that

might not otherwise be recognized—when burner head is

dirty, or lamp is going out

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10/29/

12 17

: 04

10/0

3/12

01:19

09/12/

12 11

: 36

08/0

9/12

05:26

07/16/

12 02

: 09

06/2

4/12

07:27

06/05/

12 19

: 27

05/2

2/12

01:46

05/06/

12 09

: 48

04/1

8/12

16:55

04/02/

12 01

: 05

120

110

100

90

Sample Pull Time

Ind

ivid

ual V

alu

e

_X=101.46

UCL=110.25

LCL=92.66

10/2

9/12

17:0

4

10/0

3/12

01:19

09/1

2/12

11:3

6

08/0

9/12

05:26

07/1

6/12

02: 0

9

06/2

4/12

07:27

06/0

5/12

19: 2

7

05/2

2/12

01:46

05/06/

12 09

: 48

04/1

8/12

16:55

04/02/

12 01

: 05

20

15

10

5

0

Sample Pull Time

Mo

vin

g R

ang

e

__MR=3.31

UCL=10.80

LCL=0

11111

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11111

111111111111111111

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1

Purchased Metals Check Standard

Supplier guaranteed 100 +/- 10 units of metal. Prior to using Minitab to control chart instrument performance, the lab assumed that everything worked ok if a check of the standard fell somewhere between 90 and 110 units. Example above shows multiple data points at lower control limit. Investigation found the lamp was going bad. A new one had to be ordered—took some time. Result—kept source lamps in inventory and reacted to shifts without having to wait for critical supplies to be delivered. 20

“Check Beer” program implemented to observe performance of alcolyzers

Check beer is a sample from production—same matrix, same prep conditions as routine samples.

Alcolyzer [Anton Paar] measures alcohol, real-extract, calories, and specific gravity of beer and ensures packaged product meets label claim requirements

Used to ensure in-line analysis for making up tanks was correct

OEM indicates instrument repeatability is +/-0.02 % w/w alcohol (“ROH”)

Lab was responsible for 4 instruments—2 in the Quality Lab, and 2 in the Brewing Lab (remote location)

As long as all four instruments operated with min-max range of 0.03 from each other on the same check beer, they were considered to be operating correctly

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Before Control-Chart Program

After Implementing Control Charts

Limits set +/- 2 sigma for each instrument; Lab react to shifts.

Instrument consistently performs +/- 0.01 w/w% alcohol

Max difference does not address individual performance for bias or drift. One instrument may be off consistently by 0.03

22 “Check Beer” program developed: analysis done daily to ensure instrument performance

252219161310741

3.28

3.26

3.24

3.22

3.20

Observation

Ind

ivid

ual V

alu

e_X=3.22889

UCL=3.26878

LCL=3.18900

252219161310741

0.048

0.036

0.024

0.012

0.000

Observation

Mo

vin

g R

ang

e

__MR=0.01500

UCL=0.04901

LCL=0

4

I-MR Chart of Rslt (Numeric)

Qualification of a new Paar unit showed an interesting “saw-tooth” pattern. Before control-charting, this would not have been recognized Note that the control chart violated test 4— “14 points in a row alternating up and down” –not likely to be analyst related Investigation showed that the pressure/flow settings were not optimized on installation

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Observed lab trends can be used to create control limits for

process

Fermentation step produces lots more yeast as well as beer

Some yeast is wasted; while a portion is recovered in a “yeast brink” and re-used in a subsequent fermentation

Each recovery is called a “generation”

If yeast is too old or another factor impacts process, off-flavors and/or negative quality attributes can be created

Monitoring a few key analytes helps Brewer determine yeast health

Happy Yeast = Great Tasting Beer

Yeast Brink

Waste Yeast

Fermenter Fermenter

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05/08/10 03:1104/29/10 16:1504/19/10 11:3104/10/10 17:2103/30/10 20:5703/15/10 16:3903/08/10 15:2902/25/10 15:4801/31/10 16:1001/15/10 23:0701/02/10 04:29

20

15

10

5

Sample Pull Time

In

div

idu

al

Va

lue

_X=10.23

UCL=17.95

LCL=2.52

05/08/10 03:1104/29/10 16:1504/19/10 11:3104/10/10 17:2103/30/10 20:5703/15/10 16:3903/08/10 15:2902/25/10 15:4801/31/10 16:1001/15/10 23:0701/02/10 04:29

12

8

4

0

Sample Pull Time

Mo

vin

g R

an

ge

__MR=2.90

UCL=9.48

LCL=0

1

1

1

1

Brand B Analyte Results for 6-Brews @ EOF

Sample pull time

• Analyte is a lagging indicator of yeast health

• Investigation revealed two outlier fermenters did not get enough yeast added

• Yeast from these fermenters not re-used

• Brewer acts on control chart—not spec!

• Capability study shows process is centered well below the upper specification limit

• Generation mix and yeast health co-factors are right

• Happy yeast!!!! 25

Prior to using Minitab in the Quality Lab, technicians relied on re-calibration of equipment to solve problems which was not always effective

The journey for continuous improvement in the lab started with using an FMEA and Gage R&R help pinpoint source of variability in test results

Control charting key test results provides assurance to the Brewer that lab process is in control and that data are believable. No more “gut feel” that instruments “seem” to be ok

Trends for key analytes in process helps Brewer assess yeast health before problems start—assurance of a predictable process that produces beer consistent with brand design

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The Practical Brewer Master Brewers Association of the Americas 1999. (J. T. McCabe Editor)

The Brewers’ Handbook The Complete Guide to Brewing Beer; 1999. Ted Goldammer. ISBN: 0-9675212-0-3

How to Brew Everything You Need to Know to Brew Beer Right the First Time; 2006. John J. Palmer. ISBN-13: 978-0-937381-88-5

Diacetyl in Fermented Foods and Beverages; 2008. Takashi Inoue. ISBN: 978-1-881696-15-5

ASBC Beer-25. Diacetyl Gas Chromatographic Method: http://methods.asbcnet.org/methods/Beer-25.pdf (method to analyze VDK)

ASQ Six Sigma Tools: http://asq.org/learn-about-quality/six-sigma/tools.html

Minitab: www.minitab.com – Assistant; Help; StatGuide (available with user license)

The photographs and clip art in this presentation are from publicly available sources. The views expressed herein are strictly those of the presenter

and not MillerCoors. The identification of specific process analytes or brands have been intentionally removed.

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