Screening+Experiments

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One of our major roadblocks was the decision on the type of assessors we had to use for our sensory panel. The question was whether they should be trained or untrained! Tea was a tricky product and preferences could vary among assessors. To accept or taste a novel tea product, the assessor needed some level of experience with tea or to be precise green tea. The question was raised during the meeting with our guide and we decided to take it up with Dr Peter also before making a decision.

**13/6 and 14/6:** In our HoQ experiments in Unit 1 and 2, we were aware that the consumers accepted a greenish yellow colour to our product. We decided to study a little more about the effects of milling and selection of appropriate sieve sizes on the infusion time and absorbance of the product. We devised an experiment based on the milling experiment that we performed as part of our module in food processing. I suggested this experiment and it was helpful so that we could devise a proper infusion time of around 2 minutes that gave a greenish yellow colour, at sieve settings 1mm or 1.4mm and mill setting of 7 and 9 for rice and beans respectively. In the following days, we decided to work on modifications to the factory plan and some of us had to leave for the Heinz factory visit so a group meeting was not possible. But we allocated tasks to each member and this enabled us to work on the equipments and factory layout from home.

**21/6 to 24/6:** We had a discussion with Dr Peter about choosing the factors and levels for our screening experiments. We decided to use a factorial design approach from Design of Experiments techniques and our experiment contained 3 factors with 2 levels each. The team developed an experimental design with all possible combinations of factors (to include synergistic effects) and each of our assessors therefore had to assess 8 samples (23=8) i.e., One sample set for an assessor contained 8 samples. We decided to use 40 assessors and the total of number of samples then would be 320 (40*8=320). Dr Peter taught us how to design the questionnaire on Compusense and we were able to obtain a slot for the sensory experiments on Monday the 27th. The screening experiment is used to determine the correlation between the HOWs and the WHATs, where the HOWs are analyzed to determine if they alter the results and cause a significant change in the WHATs in the House of Quality. This was achieved by designing experiments to determine how each of these parameters affects the required response, which in our case we studied using acceptability tests with a 9-point scale. In Unit II, we had already worked out the product formulation and the processing conditions which are important to maintaining quality and consumer acceptability of the product. We were also able to determine which one of the HOWs, when altered results in a significant change in the WHATs. In order to identify the **best** **processing conditions** and the **optimal amount of key ingredients**, we identified the following factors which could significantly affect the quality or consumer acceptability of our tea.

Levels of HOWs in the experimental design

 Where: "**-**"shows **low** level of formulation  "**+**"shows **high** level of formulation  We used a factorial design approach from Design of Experiments techniques and our experiment contained 3 factors with 2 levels each. The team developed an experimental design with all possible combinations of factors (to include synergistic effects) and each of our assessors therefore had to assess 8 samples (23=8) i.e., One sample set for an assessor contained 8 samples. We used 40 assessors and the total of number of samples prepared was 320 (40*8=320). The tests were conducted in the sensory booths of the Food Processing Laboratory. The 8 samples used had the following processing conditions and key ingredient specifications:  **Sample 1**:  1g tea leaves; 4:1 ratio of cereals (0.8g) and pulses (0.2g); 1mm sieve size  **Sample 2**:  1g tea leaves; 1:1 ratio of cereals (0.5g) and pulses (0.5g); 1mm sieve size  **Sample 3**:  1g tea leaves; 4:1 ratio of cereals (0.8g) and pulses (0.2g); 1.4mm sieve size  **Sample 4**:  1g tea leaves; 1:1 ratio of cereals (0.5g) and pulses (0.5g); 1.4mm sieve size <span style="display: block; font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;"> **Sample 5**: <span style="display: block; font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;"> 1.5g tea leaves; 4:1 ratio of cereals (0.4g) and pulses (0.1g); 1mm sieve size <span style="display: block; font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;"> **Sample 6**: <span style="display: block; font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;"> 1.5g tea leaves; 1:1 ratio of cereals (0.25g) and pulses (0.25g); 1mm sieve size <span style="display: block; font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;"> **Sample 7**: <span style="display: block; font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;"> 1.5g tea leaves; 4:1 ratio of cereals (0.4g) and pulses (0.1g); 1.4mm sieve size <span style="display: block; font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;"> **Sample 8**: <span style="display: block; font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;"> 1.5g tea leaves; 1:1 ratio of cereals (0.25g) and pulses (0.25g); 1.4mm sieve size

<span style="display: block; font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;"> **Test Design** <span style="display: block; font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;"> The samples were prepared as per requirement. They were prepared carefully to minimize assessment errors. Since our product required strict temperature maintenance, hot water was only poured into the tea & its ingredients when a panel of 6 assessors was present at the sensory booths. The tea was given an infusion time of two minutes 30 seconds and then sieved and served to the assessor in pre-warmed ceramic cups. The optimum serving temperature of the beverage is 60-75oC. Randomly assigned 3-digit codes were used to identify each test sample and a balanced block design was used for sample presentation order. The test was completed within a four hour period in the food tech laboratory. <span style="display: block; font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;"> **Sensory analysis procedures** <span style="display: block; font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;"> Acceptability testing was achieved with the help of a 9-point hedonic scaling, in which the 40 assessors tasted each test sample (labeled with random 3-digit code) and indicated their degree of acceptance. Compusense 5 was used to conduct the sensory analysis. <span style="display: block; font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;"> **Sensory Panel** <span style="display: block; font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;"> The panel consisted of 40 untrained assessors who judged a coded test sample based on its colour, aroma and taste and indicated their degree of acceptability for each sample on the 9-point hedonic scale. They were instructed prior to the sensory test by the analyst concerned and were asked to drink a cup of water in between the samples to cleanse their palate. Consent forms were duly signed by all the assessors before tasting the samples. <span style="display: block; font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;"> **Test conditions** <span style="display: block; font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;"> The 8 samples and their replicates were prepared accordingly prior to the test. They were prepared and stored such that one could just pour hot water into them and allow an infusion time of two minutes before serving it to the sensory panel. The analysts informed the assessors about the test and instructed them to rate the acceptability for each test sample (labeled with a random 3-digit code). All samples were presented in the same manner to each of the assessors and they evaluated the samples in individual test booths, ensuring no discussion between the assessors.

<span style="font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;">**Statistical Analysis: Results and Discussion** <span style="font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;">Our experiments examined the factors: Amount of tea leaves, the cereal: pulse ratio and the sieve size to determine if these factors have any effect on the colour, aroma, taste and overall accetibility of cereal tea. Two levels for amount of tea in grams (1, 1.5), two different ratios (grams) (1:4, 4:1) and two sieve sizes (mm) (1, 1.4), each of these were repeated two times and in total there were 320 experimental runs (40 replicates per combination of factors). <span style="font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;">Following ANOVA and examination of the residual plots, response surface optimisation was done using RSM (Response Surface Methodology) analysis i.e., we used our sensory panel data to fit a response surface for each of the WHATs. <span style="font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: justify;">The results obtained are shown in the figures below:

<span style="display: block; font-family: Arial,Helvetica,sans-serif; font-size: 110%; text-align: left;">Careful analysis of the results show that the HOWs chosen (the higher and lower levels) failed to generate any significantchange in the responses to the WHATs. Moreover, it is observed that on an average the assessors have rated all the samples quite low and this proves to be a roadblock to the optimisation procedure. The team has to look into the possible causes and repeat this task again at the beginning of the next unit.

__**R script used**__
tea <-rep(c(-1,-1,-1,-1,1,1,1,1),times=40) ratio<-rep(rep(c(-1,1),times=4),times=40) sieve<-rep(rep(c(-1,-1,1,1),times=2),times=40) tea.design<-data.frame(tea=factor(tea),ratio=factor(ratio),sieve=factor(sieve)) tea.design

tea <- rep(c(1,1.5,1,1.5,1,1.5,1,1.5),times=40) ratio <- rep(c(50,), 80),times=40) sieve <- rep(rep(c(1,1,1.4,1.4),times=2),times=40)  R1<-c(8,9,9,9,9,9,9,9)  R2<-c(3,7,7,4,6,7,7,6)  R3<-c(2,3,6,2,4,5,4,4)  R4<-c(6,7,4,3,2,7,6,2)  R5<-c(2,1,4,4,6,2,4,3)  R6<-c(3,4,6,4,7,3,4,4)  R7<-c(7,6,6,7,7,6,6,6)  R8<-c(2,4,3,4,5,5,6,5)  R9<-c(2,5,5,5,5,5,5,5)  R10<-c(5,6,7,3,3,4,6,4)  R11<-c(4,4,4,4,4,4,4,4)  R12<-c(7,4,7,6,4,5,6,6)  R13<-c(3,6,6,7,4,7,4,7)  R14<-c(5,6,8,6,6,8,6,5)  R15<-c(5,5,6,6,5,5,4,5)  R16<-c(4,7,7,6,7,6,7,6)  R17<-c(7,8,8,8,7,7,8,8)  R18<-c(5,5,3,5,5,6,5,6)  R19<-c(5,4,5,5,5,6,6,3)  R20<-c(6,7,7,7,7,7,5,6)  R21<-c(6,6,7,6,8,7,7,5)  R22<-c(3,6,5,5,5,4,5,6)  R23<-c(4,6,5,7,6,7,6,6)  R24<-c(4,5,7,5,4,4,4,3)  R25<-c(6,6,6,6,6,6,6,7)  R26<-c(8,7,7,6,7,7,7,7)  R27<-c(8,7,6,6,7,6,6,7)  R28<-c(6,5,3,7,4,5,6,4)  R29<-c(6,6,7,6,6,7,5,6)  R30<-c(3,6,6,6,6,6,6,6)  R31<-c(2,5,4,3,3,3,3,3)  R32<-c(7,7,7,8,7,9,8,7)  R33<-c(7,6,7,7,7,7,6,7) R34<-c(7,6,5,5,5,5,6,5) R35<-c(6,8,7,7,8,8,8,6) R36<-c(5,5,4,5,5,5,5,5) R37<-c(7,5,5,5,6,3,5,5) R38<-c(2,4,6,5,5,3,5,5) R39<-c(6,4,7,5,4,6,6,7) R40<-c(6,6,4,5,7,6,6,4) response<-c(R1,R2,R3,R4,R5,R6,R7,R8,R9,R10,R11,R12,R13,R14,R15,R16,R17,R18,R19,R20,R21,R22,R23,R24,R25,R26,R27,R28,R29,R30,R31,R32,R33,R34,R35,R36,R37,R38,R39,R40) design<-data.frame(tea=factor(tea),ratio=factor(ratio),sieve=factor(sieve),response) design

design.aov<-aov(response~tea*ratio*sieve,data=design) summary(design.aov)

png("residuals_design.png") oldpar<-par(oma=c(0,0,3,0),mfrow=c(2,2)) plot(design.aov) par(oldpar) dev.off

library(rsm) design.rsm<-data.frame(tea,ratio,sieve,response) design.CR<-coded.data(design.rsm,x1~(tea-1.25)/0.25,x2~(ratio-65)/15,x3~(sieve-1.2)/0.2) design.CR

design.rs1 <- rsm(response ~ FO(x1,x2,x3)+TWI(x1,x2,x3), data=design.CR) summary (design.rs1)

png("colour response.png",width = 1000, height = 480) par(mfrow = c(1,2)) persp(design.rs1, ~x1+x2,col = rainbow(50),contours = "colors", xlab=c("tea (x1)", "ratio (x2)"),  at=list(x3="1"),zlab = "colour response", cex.lab=1.2) contour(design.rs1, ~x1+x2,col = rainbow(10), xlab=c("tea (x1)", "ratio (x2)"),labcex=1.5,at=list(x3="1")) dev.off

tea <- rep(c(1,1.5,1,1.5,1,1.5,1,1.5),times=40) ratio <- rep(c(50,80), times= 4),times=40) sieve <- rep(rep(c(1,1,1.4,1.4),times=2),times=40)  R1<-c(2,2,2,4,4,2,5,1)  R2<-c(4,6,3,6,7,3,4,6)  R3<-c(3,2,3,3,4,6,6,3)  R4<-c(3,8,4,6,6,8,8,6)  R5<-c(2,1,6,6,6,2,1,2)  R6<-c(3,6,6,6,2,4,2,7)  R7<-c(3,7,2,1,1,2,3,5)  R8<-c(2,2,2,5,4,6,5,6)  R9<-c(2,3,5,6,6,7,6,5)  R10<-c(6,7,6,5,6,5,7,7)  R11<-c(3,4,3,3,4,3,3,3)  R12<-c(4,2,5,2,2,5,5,5)  R13<-c(3,2,6,7,4,6,7,7)  R14<-c(2,1,6,1,2,1,4,3)  R15<-c(4,4,3,3,5,5,5,6)  R16<-c(4,5,2,7,3,8,7,6)  R17<-c(7,8,8,7,8,8,8,8)  R18<-c(4,3,4,5,5,4,5,5)  R19<-c(4,3,6,4,5,4,6,6)  R20<-c(6,7,7,6,8,7,6,5)  R21<-c(6,3,4,6,8,6,6,5)  R22<-c(2,5,6,6,5,6,6,7)  R23<-c(6,7,8,4,6,6,6,7)  R24<-c(2,4,5,5,4,3,2,2)  R25<-c(6,6,6,6,6,6,4,7)  R26<-c(8,6,6,3,3,7,2,7)  R27<-c(8,5,7,6,5,7,4,6)  R28<-c(6,4,1,1,2,4,1,6)  R29<-c(4,6,3,5,5,4,2,5)  R30<-c(4,3,7,6,6,2,2,1)  R31<-c(5,3,3,3,3,3,3,3)  R32<-c(8,9,8,8,8,8,8,8)  R33<-c(7,6,7,7,7,7,6,7) R34<-c(7,3,5,3,5,3,5,4) R35<-c(8,7,8,8,8,3,3,8) R36<-c(6,4,3,6,3,5,3,4) R37<-c(8,4,4,2,3,1,2,3) R38<-c(2,4,6,4,6,3,3,7) R39<-c(6,7,6,5,5,7,7,7) R40<-c(6,6,6,7,4,6,4,5) response<-c(R1,R2,R3,R4,R5,R6,R7,R8,R9,R10,R11,R12,R13,R14,R15,R16,R17,R18,R19,R20,R21,R22,R23,R24,R25,R26,R27,R28,R29,R30,R31,R32,R33,R34,R35,R36,R37,R38,R39,R40) design<-data.frame(tea=factor(tea),ratio=factor(ratio),sieve=factor(sieve),response) design

design.aov<-aov(response~tea*ratio*sieve,data=design) summary(design.aov)

png("taste.png") oldpar<-par(oma=c(0,0,3,0),mfrow=c(2,2)) plot(design.aov) par(oldpar) dev.off

library(rsm) design.rsm<-data.frame(tea,ratio,sieve,response) design.CR<-coded.data(design.rsm,x1~(tea-1.25)/0.25,x2~(ratio-65)/15,x3~(sieve-1.2)/0.2) design.CR

design.rs1 <- rsm(response ~ FO(x1,x2,x3)+TWI(x1,x2,x3), data=design.CR) summary (design.rs1)

png("taste response.png",width = 1000, height = 480) par(mfrow = c(1,2)) persp(design.rs1, ~x1+x2,col = rainbow(50),contours = "colors", xlab=c("tea (x1)", "ratio (x2)"),  at=list(x3="1"),zlab = "taste response3", cex.lab=2) contour(design.rs1, ~x1+x2,col = rainbow(10), xlab=c("tea (x1)", "ratio (x2)"),labcex=2,at=list(x3="1")) dev.off

tea <- rep(c(1,1.5,1,1.5,1,1.5,1,1.5),times=40) ratio <- rep(c(50,80), times= 4),times=40) sieve <- rep(rep(c(1,1,1.4,1.4),times=2),times=40)  R1<-c(3,3,3,4,3,4,6,2)  R2<-c(5,6,6,5,6,4,5,6)  R3<-c(2,2,4,3,6,6,6,3)  R4<-c(3,7,6,4,6,8,7,6)  R5<-c(2,1,6,7,7,5,1,6)  R6<-c(5,6,6,6,4,6,4,7)  R7<-c(3,7,2,1,2,3,4,6)  R8<-c(2,2,2,5,4,6,5,6)  R9<-c(2,3,5,6,6,7,5,5)  R10<-c(5,7,6,5,6,6,7,7)  R11<-c(3,5,3,3,4,4,4,4)  R12<-c(5,2,5,4,4,5,6,6)  R13<-c(3,2,6,7,4,6,7,7)  R14<-c(3,3,7,3,4,2,4,4)  R15<-c(4,5,4,4,5,5,5,6)  R16<-c(4,7,2,7,4,8,7,6)  R17<-c(7,7,8,7,8,8,8,8)  R18<-c(5,3,3,5,5,5,5,5)  R19<-c(5,4,6,4,5,5,6,5)  R20<-c(6,7,7,7,7,7,6,5)  R21<-c(5,4,4,6,7,7,6,5)  R22<-c(3,5,5,4,5,6,5,5)  R23<-c(6,6,8,5,6,7,6,7)  R24<-c(2,4,4,4,4,4,3,3)  R25<-c(6,6,6,6,6,6,6,6)  R26<-c(9,6,6,3,5,7,3,7)  R27<-c(8,6,6,6,4,6,5,6)  R28<-c(6,3,1,1,1,4,2,7)  R29<-c(5,6,5,5,6,5,4,6)  R30<-c(4,3,5,6,6,4,2,1)  R31<-c(3,3,3,3,3,3,3,3)  R32<-c(8,8,8,8,7,9,8,7)  R33<-c(7,6,7,7,7,7,6,7) R34<-c(7,4,5,3,5,4,4,4) R35<-c(8,7,9,8,7,7,7,8) R36<-c(6,5,3,6,4,5,3,4) R37<-c(8,4,3,2,3,1,2,3) R38<-c(3,4,6,5,6,4,4,6) R39<-c(6,5,6,5,5,6,7,7) R40<-c(5,6,6,7,6,6,5,6) response<-c(R1,R2,R3,R4,R5,R6,R7,R8,R9,R10,R11,R12,R13,R14,R15,R16,R17,R18,R19,R20,R21,R22,R23,R24,R25,R26,R27,R28,R29,R30,R31,R32,R33,R34,R35,R36,R37,R38,R39,R40) design<-data.frame(tea=factor(tea),ratio=factor(ratio),sieve=factor(sieve),response) design

design.aov<-aov(response~tea*ratio*sieve,data=design) summary(design.aov)

png("overall.png") oldpar<-par(oma=c(0,0,3,0),mfrow=c(2,2)) plot(design.aov) par(oldpar) dev.off

library(rsm) design.rsm<-data.frame(tea,ratio,sieve,response) design.CR<-coded.data(design.rsm,x1~(tea-1.25)/0.25,x2~(ratio-65)/15,x3~(sieve-1.2)/0.2) design.CR

design.rs1 <- rsm(response ~ FO(x1,x2,x3)+TWI(x1,x2,x3), data=design.CR) summary (design.rs1)

png("overall response.png",width = 1000, height = 480) par(mfrow = c(1,2)) persp(design.rs1, ~x1+x2,col = rainbow(50),contours = "colors", xlab=c("tea (x1)", "ratio (x2)"),  at=list(x3="1"),zlab = "overall response3", cex.lab=2) contour(design.rs1, ~x1+x2,col = rainbow(10), xlab=c("tea (x1)", "ratio (x2)"),labcex=2,at=list(x3="1")) dev.off

tea <- rep(c(1,1.5,1,1.5,1,1.5,1,1.5),times=40) ratio <- rep(c(50,80), times= 4),times=40) sieve <- rep(rep(c(1,1,1.4,1.4),times=2),times=40)  R1<-c(7,7,8,8,8,9,7,9)  R2<-c(5,3,7,4,6,7,6,3)  R3<-c(5,4,5,5,6,4,6,4)  R4<-c(2,6,2,2,6,8,6,8)  R5<-c(6,4,6,8,7,8,3,8)  R6<-c(6,6,6,5,5,6,6,6)  R7<-c(2,3,3,3,2,5,4,4)  R8<-c(4,3,3,2,5,5,6,6)  R9<-c(5,5,5,5,5,6,6,5)  R10<-c(5,7,6,4,5,4,5,4)  R11<-c(4,6,4,5,4,4,5,5)  R12<-c(6,6,5,4,5,5,6,5)  R13<-c(3,6,7,7,6,7,6,7)  R14<-c(7,7,8,6,1,7,8,7)  R15<-c(5,5,5,5,5,5,5,5)  R16<-c(4,8,3,7,5,8,6,6)  R17<-c(7,7,8,8,8,8,8,8)  R18<-c(5,4,4,4,5,5,5,5)  R19<-c(6,5,5,3,4,5,5,4)  R20<-c(6,6,7,7,7,8,6,6)  R21<-c(4,6,4,7,6,6,6,5)  R22<-c(5,5,5,5,7,6,6,6)  R23<-c(7,5,7,6,7,7,6,8)  R24<-c(2,6,5,4,3,4,4,3)  R25<-c(6,6,6,6,6,6,5,6)  R26<-c(9,7,5,8,7,6,7,6)  R27<-c(7,8,6,6,6,5,7,6)  R28<-c(6,4,1,4,4,5,4,4)  R29<-c(4,6,5,6,7,6,6,6)  R30<-c(5,4,5,4,4,4,3,3)  R31<-c(5,3,3,3,3,3,3,3)  R32<-c(6,6,7,8,7,9,8,6)  R33<-c(7,6,7,7,7,7,7,7) R34<-c(8,6,5,7,6,7,6,3) R35<-c(8,7,8,8,9,4,9,8) R36<-c(7,6,4,5,5,5,5,6) R37<-c(5,3,4,1,1,3,5,5) R38<-c(3,5,6,4,5,4,4,5) R39<-c(6,6,6,6,5,6,5,5) R40<-c(5,7,6,6,5,5,6,6) response<-c(R1,R2,R3,R4,R5,R6,R7,R8,R9,R10,R11,R12,R13,R14,R15,R16,R17,R18,R19,R20,R21,R22,R23,R24,R25,R26,R27,R28,R29,R30,R31,R32,R33,R34,R35,R36,R37,R38,R39,R40) design<-data.frame(tea=factor(tea),ratio=factor(ratio),sieve=factor(sieve),response) design

design.aov<-aov(response~tea*ratio*sieve,data=design) summary(design.aov)

png("aroma.png") oldpar<-par(oma=c(0,0,3,0),mfrow=c(2,2)) plot(design.aov) par(oldpar) dev.off

library(rsm) design.rsm<-data.frame(tea,ratio,sieve,response) design.CR<-coded.data(design.rsm,x1~(tea-1.25)/0.25,x2~(ratio-65)/15,x3~(sieve-1.2)/0.2) design.CR

design.rs1 <- rsm(R ~ FO(x1,x2,x3)+TWI(x1,x2,x3), data=design.CR) summary (design.rs1)

png("aroma response3.png",width = 1000, height = 480) par(mfrow = c(1,2)) persp(design.rs1, ~x1+x2,col = rainbow(50),contours = "colors", xlab=c("tea (x1)", "ratio (x2)"),  at=list(x3="1"),zlab = "aroma response3", cex.lab=2) contour(design.rs1, ~x1+x2,col = rainbow(10), xlab=c("tea (x1)", "ratio (x2)"),labcex=2,at=list(x3="1")) dev.off

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