15062011-28062011

in this task 4, it contains 2 sub-tasks: it is conducted a screening experiment and response surface optimization experiment.

__Screening experiment__ in our group, we have got 4 factors for screening experiment that are colour, aroma, taste and flower. but John thought flower can't be one factor to influence our product, because in our group opinion, we do not wanna put flower in tea bag, we just put the flower between in the out of tea bag and inside of the package, then it depends to customers who wanna add flower or not. therefore, for our product, we only have three factors are color, aroma, and taste. we have to discuss with Peter again in order to know the level of each factor we want and need. the great suggestion from Dr. Peter is 3 factors with 2 level. so each assessor have 8 different samples, and we need 40 assessors, the total samples we need to prepare that should be 320 samples. we ordered the sensory test on Monday (27th of June), and get all results(one example is shown in below figure) and save all results in excel file. using R project to analyze the significant factors. we need to type in R script, that are:

tea <-rep(c(-1,-1,-1,-1,1,1,1,1),times=5) ratio<-rep(rep(c(-1,1),times=4),times=5) sieve<-rep(rep(c(-1,-1,1,1),times=2),times=5) 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=5) ratio <- rep(c(1.1,4.1), times= 4),times=5) sieve <- rep(rep(c(1,1,1.4,1.4),times=2),times=5) 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-2.6)/1.5,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=5) ratio <- rep(c(1.1,4.1), times= 4),times=5) sieve <- rep(rep(c(1,1,1.4,1.4),times=2),times=5) 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-2.6)/1.5,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 tea <- rep(c(1,1.5,1,1.5,1,1.5,1,1.5),times=5) ratio <- rep(c(1.1,4.1), times= 4),times=5) sieve <- rep(rep(c(1,1,1.4,1.4),times=2),times=5) 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-2.6)/1.5,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=5) ratio <- rep(c(1.1,4.1), times= 4),times=5) sieve <- rep(rep(c(1,1,1.4,1.4),times=2),times=5) 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-2.6)/1.5,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 then, the results of the 3D pictures of each factors are shown in below: here have 4 3D pictures, but only 2 picture show colorful, maybe it is a 3D graph. however, we need to discuss with Peter in order to know it is correct or incorrect. so we need to discuss with Dr. Peter, and find a significant factors for sensory analysis, after that, we can do prepare all samples for the sensory test on Thursday, and finally, some group-mates will starting with blogger and wiki-spaces in tomorrow.