Principal Component Analysis Reconstructing Centred Data

Hi, I have the following dataset which I have performed PCA on:

DATASET =

   10.0000    6.0000
   11.0000    4.0000
    8.0000    5.0000
    3.0000    3.0000
    2.0000    2.8000
    1.0000    1.0000

As I understand, the "score" output multiplied by the "coeff" output reconstructs the centered data. I assume by centred data it means fixing the data to the origin as descrbed in this tutorial video "https://www.youtube.com/watch?v=FgakZw6K1QQ"? If so why does my data when centred manually not equal the outputs of score*coeff? The score*coeff results in:

>> score*coeff

ans =

    4.7231   -0.8092
    4.2295   -2.9901
    2.5422   -0.3156
   -2.5933    1.3053
   -3.4936    1.7842
   -5.4078    1.0253

But then, the mean of the first column minus every value in that column (for the centred values of the first variable) and the mean of the second column minus every value in that column (for the centred values of the second variable) equals different values, even though this is presumably how you centre the data around the origin?

>> CentredVariable1 = mean(DATASET(:,1))-DATASET(:,1)

CentredVariable1 =

   -4.1667
   -5.1667
   -2.1667
    2.8333
    3.8333
    4.8333
    
  >> CentredVariable2 = mean(DATASET(:,2))-DATASET(:,2)

CentredVariable2 =

   -2.3667
   -0.3667
   -1.3667
    0.6333
    0.8333
    2.6333