Question about PLS regression

Hello useRs,

I am new user to R and also statistics. Why predicted results in this example 
are different? Is the order of variables in X matrix important?

library (pls)
set.seed (1)
Y1 <- c(1,2,3,4,5,6,7,8,9,10)
Y2 <- c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)
X1 <- rnorm(10,sd=0.2)
X2 <- rnorm(10,sd=1)
X3 <- rnorm(10,sd=0.1)
X4 <- rnorm(10,sd=0.1)
X5 <- rnorm(10,sd=0.1)

KAL <- data.frame(num=c(1:10))
KAL$Y <- as.matrix(cbind (Y1,Y2))
KAL$X <- as.matrix(cbind (X1,X2,X3,X4,X5))
KAL2 <- data.frame(num=c(1:10))
KAL2$Y <- as.matrix(cbind (Y1,Y2))
KAL2$X <- as.matrix(cbind (X5,X4,X3,X2,X1))

PLS <- plsr (Y~X,data=KAL, 4,validation = "CV")
PLS2 <- plsr (Y~X,data=KAL2,4, validation = "CV")

X1v <- rnorm(10,sd=0.1)
X2v <- rnorm(10,sd=1)
X3v <- rnorm(10,sd=0.1)
X4v <- rnorm(10,sd=0.1)
X5v <- rnorm(10,sd=0.1)
VAL <- data.frame(num=c(1:10))
VAL$X <- as.matrix(cbind(X1v,X2v,X3v,X4v,X5v))

predict (PLS,VAL,4)
predict (PLS2,VAL,4)	

Thank You,

Andris Jankevics