Implementation of improved fat standardization using statistical process control

Degree project work

Author: Lovisa Bjenning Supervisor: Christian Magnusson (Arla Foods) and Boel Lindegård (Lnu) Examiner: Maria Bergström Term: VT19 Subject: Chemistry Level: First cycle Course code: 2KE01E

Abstract The aim of this project was to apply statistical process control (SPC) and measure the variation of fat content in milk in order to improve the standardization, so that the fat content does not change more than 0.03 percentage units from target. Recommendations of how to adjust the standardization should also be developed. The standardization takes place together with pasteurization in one of the three pasteurizers. Thereafter, the milk goes to a common product tank with all the pasteurizers. Samples from the three pasteurizers and the product tank were collected and analyzed on MilkoScan FT2 and the fat content was plotted into Shewhart and cumulative sum (CUSUM) charts. Sampling on the pasteurizers from startup showed that samples should be taken after about 20 minutes, because then the variation is in general smaller. The data from the product tank showed a smaller variation than the pasteurizers. Because the milk from all the pasteurizers is transported into one product tank, it is impossible to know which pasteurizer that is out of control and need to be adjusted. Therefore, the conclusion is that samples should be taken after the pasteurizer and plotted into Shewhart and CUSUM charts. Action limits were achieved from the Shewhart and CUSUM charts, respectively. These are the limits that should be used to determine when adjustments of the pasteurizers are needed, and not the brand limits that are considerably wider. If the measurements fall outside the second limit in the Shewhart chart (three times the standard deviation) or outside the limits (H) in the CUSUM chart, the standardization before the pasteurizer in question should be considered. It is not known if using SPC will improve the fat content to be within 0.03 percent units from target, because the recommendation has not been applied in the process yet, but it going to be that soon.

Svensk sammanfattning Syftet med detta arbete var att implementera statistisk processtyrning och mäta variationen i mjölkens fetthalt för att kunna förbättra standardiseringen. Detta skulle medföra att fetthalten inte förändras mer än 0,03 procentenheter från målet. Rekommendationer om hur standardiseringen bör justeras av fetthalten i mjölk ska också utvecklas. Standardiseringen sker tillsammans med pastöriseringen i en av de tre pastörerna. Därefter går mjölken till en gemensam produkttank för pastörerna. Prover från de tre pastörerna samt produkttank samlades in om analyserades på MilkoScan FT2 och fetthalten plottades in i Shewhart och Kumulativsumma (CUSUM) diagram. Provtagning från produktstart på pastörerna visade att prover skulle tas efter 20 minuter, eftersom variationerna då är mindre. Data från produkttank visade en lägre variation än hos de tre pastörerna. Eftersom mjölken från pastörerna transporteras till en produkttank är det omöjligt att veta vilken av

de tre pastörerna som är utom kontroll och bör justeras. Därför är slutsatsen att proverna ska tas efter pastörerna och plottas in i Shewhart och CUSUM diagram. Åtgärdsgränser togs fram för både Shewhart och CUSUM diagram. Dessa är gränserna som ska användas för att bestämma när justeringar bör göras på pastörerna och inte varumärkesgränserna som är avsevärt bredare. Om ett mätvärde faller utanför den andra gränsen i Shewhart diagrammet eller utanför gränserna (H) i CUSUM diagrammet bör standardiseringen innan pastören i fråga justeras. Det är ännu oklart om standardiseringen kommer förbättras eftersom rekommendationerna inte har applicerats i processen än. Men kommer att göra det i den närmsta framtiden.

Key words Milk, Whole milk, Semi-skimmed milk, Fat content, Statistical process control, MilkoScan FT2

Acknowledgments I gratefully thank my supervisor Christian Magnusson (QEHS Site Manager) at Jönköping dairy, Arla Foods, for giving me the opportunity to work on this project and for the help and guidance. I would also want to say thanks to the co-workers at Arla for being helpful and always answer my questions. I would also like to thank my supervisor Boel Lindegård, PhD, Senior Lecturer, Department for Chemistry and Biomedical Science, Linneaeus University, Sweden for all support and helpful advice along the way.

Table of contents 1 Svensk sammanfattning Fel! Bokmärket är inte definierat.

2 Introduction 1 2.1 The composition of milk 1 2.2 Separation 1 2.3 Homogenization 2 2.4 Standardization 2

2.4.1 Tetra Alfast 2 2.5 Pasteurization 3 2.6 MilkoScan FT2 3

2.6.1 AnalyticTrust 4 2.7 The milk process at Arla Foods in Jönköping 4 2.8 Statistical process control 5

2.8.1 Shewhart chart 6 2.8.2 Cumulative sum 6

2.9 The present situation 6

3 Aim and Objective 8

4 Method 8 4.1 Sampling 8

4.1.1 Sampling from pasteurizers 9 4.1.2 Data from product tank 9

4.2 MilkoScan FT2 9 4.3 Cumulative sum 9

5 Results and Discussion 10 5.1 Pasteurizers 10

5.1.1 Results of pasteurizers 10 5.1.2 Discussion of pasteurizers 13

5.2 Product tank 13 5.2.1 Results of product tank 13 5.2.2 Discussion of product tank 15

5.3 MilkoScan FT2 15 5.3.1 Results of MilkoScan FT2 15 5.3.2 Discussion of MilkoScan FT2 15

5.4 Cumulative sum 16 5.4.1 Results of cumulative sum 16 5.4.2 Discussion of cumulative sum 17

5.5 Conclusion 17

References 19

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1 Introduction 1.1 The composition of milk The composition of milk varies with factors as different breed of cows or even individual cows, environment and animal nutritional status. Cow milk has an average composition of 87 % water, 4.7 % lactose, 3.2 % protein, 3.6 % fat, 0.8 % minerals and 0.1 % vitamins (1). Milk protein consists of caseins that represent 80 % and whey protein that represent 20 %. Both are high quality in the view of amino acid composition, digestibility and bioavailability. Milk contains several essential amino acids and is therefore an important protein source for humans. The caseins can easily form complex, also called casein micelles. About 95 % of all casein is collected in casein micelles (1). Casein micelles is a colloidal suspension and that is the reason milk is white. This complex contains submicelle, protruding peptide chain, calcium phosphate (Ca3(-PO4)2) and casein. The calcium phosphate links the submicelles together and give the complex a porous structure. Protruding peptide chains constitute the surface of the micelles, which contribute to the hydrophobic interactions in the micelles, and give a negative charge (2). Milk has a very complex fat content, where triacylglycerols is representing the largest volume of the lipids containing 96-98 % of the total. In addition to triglycerides, milk fat also consists of di- and monoacylglycerols, free fatty acids, sterols, carotenoids and vitamins (A, D, E and K). Milk is a fat-in-water emulsion, it means that the fat in milk remain as small globules in the milk serum. The globules are 2-6 µm in diameter and surrounded by membrane material from the cell membrane, it is about 15 billion globules per ml. The fat is the largest and lightest particle in milk. For this reason, fat rise to the surface if the milk is left in a vessel for a longer time, the cream and skim milk will separate from each other (2, 3).

1.2 Separation Separation of fat and protein can be done using gravity or centrifugal force. If gravity is used it is called sedimentation and is mostly used naturally in the environment for example when sand sinks to the bottom in water. In milk process, centrifugal devices are useful for the fat separation. A vessel is filled with liquid and start to spin, this generates a centrifugal force. It creates centrifugal acceleration which is not constant like gravity. The acceleration is affected by the distance from the axis of rotation and speed of rotation (4). The centrifugal separator comprises mainly of a bowl body and a hood which is held together with a lock ring. In the bowl, a disc stack is placed which the milk goes through from the distributor. A centrifugal force is moving milk components outwards respective inwards in the separation channels

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depending on the density. The cream with fat globules has a lower density than skim milk and will move inwards to the inner part of the bowl. Contaminants in milk have a high density, therefore the particles will locomote outwards and accumulate in the sediment space. In precise shots, the particles are discharged from the separator. For this reason, factors like pressure and flow change, which affect the fat content for a short period. Thereafter, the cream goes through an axial outlet. The skim milk moves outwards and is collected outside the disc stack and goes through a channel to a skim milk outlet (4, 5).

1.3 Homogenization Principally, homogenization resolve fat globules into smaller ones that makes it hard for the fat to regenerate and coalesce. A combination of turbulence and cavitation makes it possible to homogenize milk (6). Cavitation is when the pressure changes and rise above the vapor pressure and condense the vapor. The result of this is high local pressure and the fat globules collapse (7). The turbulence is creating eddies with high velocity which collides with a fat globule in the same size. This results in that the fat globules break up in smaller pieces. The cavitation on the other hand is based on that steam bubbles disrupt the fat globules. The high pressure that occur makes the homogenization effective. The temperatures and pressure that is commonly used during homogenization is 55-80 °C and 10-25 MPa (100-250 × 105 Pa). If the milk is colder than 55 °C the homogenization gets ineffective. It is a fast process and takes only 10-15 microseconds. A homogenizer consists of a high-pressure pump and a homogenization device. The high-pressure pump is run by an electric motor and boost the pressure of the milk from 300 kPa to 10-25 MPa (6).

1.4 Standardization Standardization is used to get the desired fat and protein content. The principle for standardization of fat is that cream from the separator is added to skim milk with a certain amount to achieve the correct fat content. A direct in-line standardization is common in modern milk processing and is usually combined with the separation. The skim milk and cream are separated from each other and thereafter, the skim milk is added to the process until the optimal fat content is reached. To control the fat content valves, flow and density meters and a computerized control loop is used. That makes it possible to measure variations in fat content of incoming milk, throughput and pre-heating temperature (4).

1.4.1 Tetra Alfast Tetra Alfast is a standardization unit that makes it possible to have an automatic in-line standardization. A flow transmitter measures the total volume and a density transmitter measure the density and calculate the fat content of the cream. A second flow transmitter measures the total volume of

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skim milk. Those send signals to a computer that calculates the fat content relationship which in turn send signals to a flow regulation vent, that control the adding of cream to standardized skim milk (8).

1.5 Pasteurization Pasteurization is a heat treatment of milk that eliminate micro-organisms without affecting the physical and chemical quality of the milk. The time and temperature can be used in different combinations. The combination is very important for the intensity of heat treatment and for the lethal effect on bacteria. But if a high temperature is used during a long time, the quality of milk can be affected. Proteins in milk are heat sensitive and can be denatured. It can also change the taste to a cooked or burnt flavor. HTST pasteurization means high temperature short time. When HTST treatment is used for milk is it common to heat it up to 72-75 °C for 15-20 seconds, after that the milk is cooled down (9).

1.6 MilkoScan FT2 Infrared (IR)-spectroscopy is an analytic method to characterize and identify chemical and bonding structures. IR are divided into near- (0,8-2,5 µm), mid- (2,5-15 µm) and far-IR (15-100 µm) regions. Near- and mid-IR are mostly used for quantitative and qualitative analysis of food. Mid-IR use two different spectrometers, dispersive or Fourier transform (FT) instruments. The radiation in Fourier transform infrared (FTIR) instruments is not dispersed, all wavelengths arrive to the sample simultaneously. Therefore, a mathematical treatment called an FT is used to transform the results to an IR spectrum. In FT instruments an interferometer is used instead of a monochromator. In a Michelson interferometer (Figure 1), an IR source send radiation through a beam splitter that divides the beam of radiation into two identical beams. One of the beams reflects off the moving mirror and the other beam reflects off the fixed mirror. Later, the two beams are recombined by the mirrors that reflect back the split beams. The moving mirror vary in pathlength which results in a phase difference between the two beams. The phase difference causes the beams to interfere with each other, either constructively or destructively. A sample is placed in front of the detector and the recombined beam goes through it. The molecules in the sample absorb light of a characteristic frequency. The light intensity is reduced because of the molecules in the sample that absorbs the light. Thereafter the recombined beam reaches the detector. The radiation that reach the detector varies in intensity because of the beams interfere. The energy pattern as a function of the optical path difference is called an interferogram. The interferogram shows the intensity versus pathlength and is converted by Fourier transformation to an IR spectrum that shows absorbance versus frequency. The absorbance is proportional to the concentration (10).

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Figure 1. Schematic diagram of a Michelson interferometer and associated electronics used in a FTIR instrument (10).

FOSS MilkoScan FT2 uses Mid-IR and have a costume-built FTIR unit. This instrument is dedicated to analyzing liquid and semi-solid dairy products. MilkoScan makes it possible to measure parameters among others fat, protein, lactose, citric acid and total solids content (11).

1.6.1 AnalyticTrust software AnalyticTrust is a software used to control the analytical instrument, Foss Milkoscan FT2. A control sample is measured every day, the data is put into the software and a chart is built. The software calculates standard deviation based on a control sample with a fat content of 3.5 %. The purpose with the control sample is to follow the performance of the instrument. The control samples limit value is based on statistic and is approximately 0.006 %. If the standard deviation is too high, or if the measurements are too high or low, the instrument has to be recalibrated. It is important to use a control sample with the same fat content during a long period. Therefor is ultra-high-temperature milk used that has a long-lasting durability. Calibration samples are used to ensure that the fat, protein and dry matter content correspond to the reference sample that is analyzed using a reference method at an accredited laboratory. A range of fat contents is used to obtain a good calibration curve, 0.1 %, 0.5 %, 1.5 %, 3 % and 3.5 %. The limit value for the control samples are approximately 0.04 %.

1.7 The milk process at Arla Foods in Jönköping The milk production (Figure 2) starts with that raw milk with a fat content around 4 % is transported to the dairy factory from the farms. In the milk reception the raw milk is going through a sieve before it is stored in a silo. Thereafter it is pre heated to 50 °C and transported to a centrifugal separator with a rotation of 1400 revolutions per minute. The heated raw milk is separated to skim milk and cream with a fat content of 0.05 % and 40 % respectively (12). The milk is transported from the separator through a flow transmitter that makes it possible to measure the total volume of the

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milk. Following, the milk is going through a density transmitter to measure the fat content. A motor pressure port has a function of adjust and give the optimum flow. A partition amount of cream is added to the skim milk which is given the fat content 12%. The excess cream is transported to other processes with other products. The milk with 12 % fat is homogenized and vitamin supplement is added to it. Homogenization occurs at 65-70 °C and 130 × 105 Pa with a two-stage device. Thereafter standardization takes place, additional skim milk is added to the milk with 12 % fat to the wanted fat content (3.00 for whole milk and 1.50 for semi-skimmed milk). After the addition of skim milk, the milk is pasteurized in one of the three pasteurizers (P1, P2 or P201). The milk is cooled down with ice water in a plate heat exchanger until the milk reaches 6 °C. The cooled milk goes to a product-tank and is thereafter packaged and placed on load carriers. The packaged product is stored in a cooler storage while waiting for distribution (12).

Figure 2. A simplified flow chart of milk production at Arla Foods in Jönköping, from raw milk to distribution (12).

1.8 Statistical process control A process can be analyzed using statistical process control (SPC) to visual real-time changes. SPC makes it possible to distinguish between random and systematic causes of variation. The random variation has to be accepted because it will always be present in the process, these variations give a normal distribution (13, 14).

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1.8.1 Shewhart chart Shewhart chart is a control chart that is a useful tool of SPC. The data from samplings is plotted into a chart with control lines that mark the central line as target or average, two upper and two lower limits. These limits are based on standard deviation. The first limit is two times standard deviation above and below the mean value and the second limit is three times the standard deviation above and below the mean value. When the process is stable 99.73 % of all measurements should be within the second limit. If the measurements fall outside the first limits, two times standard deviation, it is a warning. If the process is not in control the measurements will fall outside the second limit, three times the standard deviation, and something has to be adjusted in the process (14, 15).

1.8.2 Cumulative sum Cumulative sum (CUSUM) is using a more advanced technique than a Shewhart chart. The purpose of cumulative sum is to compile samples that is taken at given times to get a mean for the process. A CUSUM chart illustrates the accumulation of the data from the current and previous samples. Because of this, CUSUM is more effective than a Shewhart chart when small variations are of interest. When High CUSUM is calibrated, an equation is used (Equation 1). This gives the upper line. The Low CUSUM is calibrated with another equation (Equation 2). When calculating, the sample value (Xi), a target (T), and two constants (K and H) is needed. In the equations K is half of the standard deviation and is usually called a reference value. The constant H is the upper and lower limit and are based on the standard deviation times 4.5. This is because of when 4,5 times the standard deviation is used, it is sure that such measurements are outside the normal distribution which indicate that adjustments need to be done (16).

𝐻𝑖𝑔ℎ𝐶𝑈𝑆𝑈𝑀* = 𝑋* − (𝑇 + 𝐾) + 𝐻𝑖𝑔ℎ𝐶𝑈𝑆𝑈𝑀*34(1)𝐿𝑜𝑤𝐶𝑈𝑆𝑈𝑀* = (𝑇 − 𝐾) − 𝑋* + 𝐿𝑜𝑤𝐶𝑈𝑆𝑈𝑀*34 (2)

1.9 The present situation Arla Food in Jönköping has three pasteurizers; P1, P2 and P201, with different capacity. P1 and P2 are usually used for conventional milk and P201 is used for organic milk. Fat sampling in pasteurizers takes place at startups or at suspicion that the fat content is too low or high. Sampling takes place in the product tank on starting production to notice any fat and protein variation but also, when the tanks are refilled. This results in about two to three samples each day per product. The milk has a limit value for fat content, it is acceptable that it varies ± 0.2 percentage units from the optimal fat content (2.8-3.2 % for whole milk and 1.30-1.70 % for semi-skimmed

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milk). But the vision is to try to obtain a fat content with the target content (3.00 % and 1.50 %). The diagrams below (Figures 3 and 4) is sampled from whole milk (3.0 % fat content) and semi-skimmed milk (1.5 % fat content) after packaging. This is how the fat content in milk has varied during the last year. The mean value for fat content in whole milk was calculated to 3.13 % and with a standard deviation of 0.054 %. For semi-skimmed milk the mean value for fat content was 1.53 % with a standard derivation of 0.048 %. However, a desire is to always strive for the target fat content.

Figure 3. The figure shows how the fat content vary over time in whole milk with a fat content of 3.0 % with the limits at 2.8 % and 3.2 %, from January 1st of 2018 to April the 2nd of 2019.

Figure 4. The figure shows how the fat content vary over time in semi-skimmed milk with a fat content of 1.5 % with the limits at 1.3 % and 1.7 %, from January 1st of 2018 to April the 2nd of 2019.

2,752,802,852,902,953,003,053,103,153,203,25

02.0

1.20

1819

.01.

2018

05.0

2.20

1820

.02.

2018

08.0

3.20

1827

.03.

2018

13.0

4.20

1830

.04.

2018

18.0

5.20

1806

.06.

2018

22.0

6.20

1810

.07.

2018

01.0

8.20

1817

.08.

2018

03.0

9.20

1821

.09.

2018

09.1

0.20

1825

.10.

2018

13.1

1.20

1830

.11.

2018

17.1

2.20

1806

.01.

2019

23.0

1.20

1910

.02.

2019

26.0

2.20

1913

.03.

2019

01.0

4.20

19

Fat c

onte

nt (%

)

Date

1,251,301,351,401,451,501,551,601,651,701,751,80

02.0

1.20

1817

.01.

2018

31.0

1.20

1814

.02.

2018

01.0

3.20

1815

.03.

2018

02.0

4.20

1817

.04.

2018

03.0

5.20

1820

.05.

2018

03.0

6.20

1819

.06.

2018

30.0

7.20

1824

.08.

2018

11.0

9.20

1826

.09.

2018

10.1

0.20

1828

.10.

2018

12.1

1.20

1828

.11.

2018

17.1

2.20

1808

.01.

2019

23.0

1.20

1905

.02.

2019

25.0

2.20

1913

.03.

2019

28.0

3.20

19

Fat c

onte

nt (%

)

Date

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This data was collected after packaging and due to this, other sources of error may occur than if the sample was taken in connection with pasteurizing. One source of error that can affect the results, is the mix phase that occurs between two products. The reason of that is because the process does not stop between the products, it switches product, and the result can be an incorrect fat content for a short period. Another difficulty is that the milk is coming from different pasteurizers to the same product tank and all the pasteurizers have to be adjusted separately. This means that it is impossible to know which pasteurizer that is incorrect when measuring the final product. During the year a seasonal variation of fat content occur. But the fat content can also vary because of cleaning the tanks. In some situations, the milk shows a fat content that is too high or low, but no reason is found. Every day, control milk is analyzed at MilkoScan FT2. The control milk is ordered to Arla Foods and has the same fat content every day. Because of this, MilkoScan can be investigated if the control milk does accord with the true value. If not, the instrument has to be recalibrated (see 1.6.1). Standardization is adjusted over time and not batch wise. Milk from pasteurizers fill up the product tank, at the same time the product tank empties the milk to packaging. This means that the milk that leaves the pasteurizer has to have the right fat content.

2 Aim and Objective The aim of the project is to apply statistical process control and to measure the variation of fat content in milk. The objective is to improve the standardization so that the fat content does not change more than 0.03 percentage units from target. Additionally, recommendations of how the standardization of fat content in milk should be adjusted will be developed. In the present situation, standardization is not adjusted batch wise, it is adjusted over time. This result in that the fat content in milk can be a little too low or high (but never below or above the brand limit). Through improved sampling, adjustments of fat content could be made more often which in turn would mean a more uniform product with less variation.

3 Method 3.1 Sampling Sample of milk were taken both from pasteurizers and product tank to evaluate where and when in the process the sampling should be performed.

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3.1.1 Sampling from pasteurizers Samples were taken by filling plastic sample cans with milk from a sample tap after the pasteurizers right before the milk is transported to the product tank. Samples were taken from the three pasteurizers and the two different milk types, whole milk and semi-skimmed milk. This results in six different sample series to investigate when the pasteurizers give a steady result, which is when the standard deviation is as small as possible. When the production started to process the milk, one sample was taken every minute for 30 minutes from a sample tap after pasteurization. After 30 minutes a sample was taken every fifth minute for another 15 minutes (if possible).

3.1.1.1 Analysis All the samples were analyzed with MilkoScan FT2 to get the fat content for each sample. The data were put into tables and diagrams and thereafter investigated to locate when the pasteurizers were constant. The standard deviation was calculated for each pasteurizers and milk type.

3.1.2 Data from product tank In the process, samples were taken from the product tanks of milk before the packaging. The samples were analyzed in MilkoScan FT2 to receive the fat content. The data from the last month were analyzed and put into tables and Shewhart charts.

3.2 MilkoScan FT2 Data from Mars to May were analyzed to see the variation in control samples. The standard deviation was calculated to find the random error for Foss MilkoScan FT2. The standard deviation from MilkoScan FT2 can be added to the standard deviation from the product tank (Equation 3) to calculate the total standard deviation (SD). The standard deviation from MilkoScan FT2 (SDMilkoScan FT2) and the standard deviation from product tank (SDProduct tank) was needed in this calculation.

𝑆𝐷 = :𝑆𝐷;*<=>?@ABCDEE + 𝑆𝐷FG>HI@JJAB=E (3)

3.3 Cumulative sum Data from the product tank was used to plot a CUSUM chart. Two equations were used (Equation 1-2) and the High and Low CUSUM was calculated along with the upper and lower limits. The standard deviation that was used was the one that was calculated in equation 3 above.

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4 Results and Discussion 4.1 Pasteurizers

4.1.1 Results of pasteurizers Samples were taken from pasteurizer P1, P2 and P201. Milk samples were taken on semi-skimmed milk and whole milk on pasteurizer P1 and P201. On pasteurizer P2 only semi-skimmed milk was taken. The results were plotted into charts (Figure 5-9).

Figure 5. The figure shows how the fat content vary in semi-skimmed milk for 45 minutes from start of product from pasteurizer 1 with the target as a line.

Figure 6. The figure shows how the fat content vary in whole milk for 45 minutes from start of product from pasteurizer 1 with the target as a line.

1,3

1,35

1,4

1,45

1,5

1,55

1,6

1,65

1,7

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45

Fat c

onte

nt (%

)

Time (minutes)

2,8

2,85

2,9

2,95

3

3,05

3,1

3,15

3,2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45

Fat c

onte

nt (%

)

Time (minutes)

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Figure 7. The figure shows how the fat content vary in semi-skimmed milk for 40 minutes from start of product from pasteurizer 201 with the target as a line.

Figure 8. The figure shows how the fat content vary whole milk for 40 minutes from start of product from pasteurizer 201 with the target as a line.

1,3

1,35

1,4

1,45

1,5

1,55

1,6

1,65

1,7

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Fat c

onte

nt (%

)

Time (minutes)

2,8

2,85

2,9

2,95

3

3,05

3,1

3,15

3,2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Fat c

onte

nt (%

)

Time (minutes)

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Figure 9. The figure shows how the fat content vary in semi-skimmed milk for 45 minutes from start of product from pasteurizer 2 with the target as a line. The variation was decided by calculating the standard deviation from data of sample from pasteurizers (Table 1). The standard deviation was calculated from 0 to 45 minutes, 0 to 11 minutes, 12 to 23 minutes and 24 to 45 minutes to get 11 measurements in every period. Outliers was not included in the calculations. Table 1. The table shows the standard deviation of the sample from the pasteurizer of semi-skimmed milk and whole milk.

Pasteurizer Product

Standard deviation from 0

to 45 minutes (%)

Standard deviation from 0

to 11 minutes (%) (n=11)

Standard deviation from

12 to 22 minutes (%) (n=11)

Standard deviation from

23 to 45 minutes (%) (n=11)

P1 Semi-skimmed milk 0,035 0,017

0,033 0,012

P1 Whole milk 0,031 0,041 0,035 0,022

P201 Semi-skimmed milk 0,035 0,010 0,009 0,014

P201 Whole milk 0,022 0,033 0,025 0,008

P2 Semi-skimmed milk 0,023 0,234 0,016 0,014

1,3

1,35

1,4

1,45

1,5

1,55

1,6

1,65

1,7

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45

Fat c

onte

nt (%

)

Time (minutes)

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4.1.2 Discussion of pasteurizers In the result for the pasteurizers, some outliers are detected in Figures 5,7 and 9. This could be a result of the cleaning shots in the separator. The separator is shooting a cleaning-shot during a short time, resulting in a small volume of incorrect fat content. These measurements were not included in the calculations of standard deviation. The fat content got more constant in the period 23 to 45 minutes. The variation for all the samples, except one is lower at 23 to 45 minutes than at 0 to 22 minutes. From the operator’s experiences, it takes at about 20 minutes until the process is constant. All things considered; the sampling should be performed not earlier than after about 20 minutes. After 30 minutes, the samples were taken with five minutes apart instead of one. This could affect the result of standard deviation. The results would be more certain if all the samples would be taken with the same interval during the whole period. The positive aspects are that the sample from the pasteurizer gives a fast notification of the fat content in milk. The value of the sample from the pasteurizer is a precise value for the one specific pasteurizer out of three, enabling to adjust the fat content when it is too high or low. The negative aspect is that the measurement from the pasteurizer does not show the fat content in the final product. Because the final product depends on all the pasteurizers.

4.2 Product tank

4.2.1 Results of product tank Samples were taken from product tank. Milk samples were taken on semi-skimmed milk and whole milk. The results were plotted into Shewhart charts (Figure 10 and 11). The limits ± 2S was calculated as the standard deviation twice and ± 3S was calculated as the standard deviation three times. The standard deviation was calculated from the fat content in semi-skimmed milk and whole milk of data from product tank. The standard deviation from MilkoScan FT2 was added to these standard deviations to get the limits (see equation 3).

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Figure 10. The figure shows a Shewhart chart of fat content in semi-skimmed milk from product tank with average and limits, ± 2S and ± 3S, that are based on standard deviation. The measurements are taken after the production starts with the product and when the tanks are refilled, which is about two to three times per day and product.

Figure 11. The figure shows a Shewhart chart of fat content in whole milk from product tank with average and limits, ± 2S and ± 3S that are based on standard deviation. The measurements are taken after the production starts with the product and when the tanks are refilled, which is about two to three times per day and product. The variation was decided by calculating the standard deviation from the data which gave a SD value of 0.013 % for medium-fat milk and for whole milk 0.035 %. The fat content is lower than the target in semi-skimmed milk and higher than the target in whole milk. This means that an adjustment has to be done to get closer to the target content. Semi-skimmed milk has to be

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adjusted downwards, and for whole milk an upward adjustment should be done. This was even more visible with the CUSUM chart (see 4.4).

4.2.2 Discussion of product tank When using the product tank sample, the positive aspect is that the fat content is representative of the final product and have a smaller variation than in the sample from the pasteurizer. The negative aspect is that it is hard to know which pasteurizer that has fallen outside the limits when adjustment is needed. Another thing is that it takes a longer time to get the measurements compared with samples from the pasteurizers. Because this step is later in the process than the pasteurizers, and a sampling cannot be done if it is too little milk in the tank. The limits from the Shewhart chart, ± 2S and ± 3S is not the same as Arla Foods brand limits, and the Shewhart limits are narrower than the brand limits. This is positive because the Shewhart limits can be used by the operator to adjust the fat content, even if it is within the brand limits. In the present situation, if a measurement is within the brand limits, the operator will not adjust the fat content because it is acceptable. Arla foods prefers that the adjustment should be to the target value, rather than the brand limits.

4.3 MilkoScan FT2

4.3.1 Results of MilkoScan FT2 The variation was decided by calculating the standard deviation from March to May. Control milk was analyzed at MilkoScan FT2, the data was collected and put into a table (Table 2). Table 2. The table shows the standard deviation of the control milk from 25th of March to 9th of May 2019.

Date Standard deviation (%) 2019-03-25 to 2019-03-28 0,0036

2019-03-28 to 2019-04-11 0,0067

2019-04-11 to 2019-04-24 0,0087

2019-04-24 to 2019-05-09 0,0062

The mean value of the standard deviation was calculated to 0,0063 % and is the random error for Foss MilkoScan FT2.

4.3.2 Discussion of MilkoScan FT2 The random error can be added to Shewhart charts and CUSUM charts to compensate the variation from MilkoScan FT2 (see Equation 3). The standard deviation from MilkoScan FT2 was found to be much smaller than the standard deviation from the milk measurement. Therefore, the addition

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has a minor impact of the total standard deviation, but it was used in all calculations in this report.

4.4 Cumulative sum

4.4.1 Results of cumulative sum From the product tank data was CUSUM calculated and plotted into charts that shows a high and low CUSUM and with an upper and lower limit. The chart will show an increase in high CUSUM when the fat content is above target, and a decrease in low CUSUM when the fat content is below target (Figure 12-13). In the chart the target is the 0-value.

Figure 12. This shows a CUSUM chart of semi-skimmed milk from product tank with the target as 0, upper and lower limit (H) based on the date. When the measurements are intersect with the limits, a adjustment should be done and to continue the CUSUM chart is pointless. If the deviation is the same, a trend upwards or downwards is received.

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Figure 13. This shows a CUSUM chart of whole milk from product tank with the target as 0, upper and lower limit (H) based on the date. When the measurements are intersect with the limits, a adjustment should be done and to continue the CUSUM chart is pointless. If the deviation is the same, a trend upwards or downwards is received.

4.4.2 Discussion of cumulative sum In the process, the CUSUM chart will be easy to use to be sure that the pasteurizer is correctly adjusted. It can be hard to only use a Shewhart chart, because sometimes the measurements are inside the limits but a little too high or low. This will be visualized in the CUSUM charts because a systematic difference from the target value will result in a CUSUM falling outside the limits. This means that the fat content should be adjusted upwards at 2019-04-16 (see figure 12) for semi-skimmed milk and downwards at 2019-04-12 (see figure 13) for whole milk.

4.5 Conclusion One of the aims with the project was to apply SPC to measure the variation of fat content in milk, which was done. Both a Shewhart chart and a CUSUM chart was used. The recommendation is that to analyze the fat content in semi-skimmed milk and whole milk, the sample should be taken directly after the pasteurizer after about 20 minutes from production start of that certain product. After sampling, the sample should be analyzed in Foss MilkoScan FT2 to get the fat content. From the instrument, a measurement is received, which is inserted into a software to be able to visualize both a Shewhart chart and CUSUM chart. If the measurements fall outside the second limit, three times the standard deviation, in the Shewhart chart or outside the limits (H) in the

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CUSUM chart, the standardization before the pasteurizer in question should be considered. It is not known whether the standardization can improve the fat content to reach the goal of being within 0.03 % units from the target value. This is because that the recommendation has not been applied in the process yet, but it will be in the near future. Continuing studies in this project aim at verifying how many measurements that is acceptable to fall outside the limits before an adjustment must be done. Another study that can be done is to detect how big the adjustment should be. The Alfast is not linear and therefor hard to adjust. This project could also be applied for statistical process control of other products. In the future, to simplify the analysis, an online measurement could be used. Data would then be plotted in a chart frequently and a lot of work would be reduced, and the process could be analyzed in real-time.

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