Algorithm 1. The normalized scoring algorithm.

Input: a vector of quality indexes of spinach X

Output: a matrix including the normalized weight Z and the normalized score P

n=cols(X); ex=mean(X); σ=Stdev(X); σ2=Var(X)

for i∈0…n−1   $z_i=\frac{x_i-ex}{\sigma /\sqrt{n}}$

c=mean(Z); s=Stdev(Z); s2=Var(Z)

$f(x)=\frac{1}{\sqrt{2\pi }s}{e}^{-\frac{{(x-c)}^2}{2s^2}}$ for i∈0…n−1   $p_i={\displaystyle {\int }_{-\infty }^{z_i}f(x)}dx$ return (Z; P)