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The experimental calibration matrix for partial least squares calibration (PLS) method was designed with 20 samples.
The calibration matrix for partial least squares (PLS) regression was designed with 14 samples.
The experimental calibration matrix for partial least squares (PLS-1) calibration was designed with 31 samples.
The experimental calibration matrix for partial least squares (PLS) and orthogonal signal correction (OSC PLS) method was designed with 35 mixtures.
The analytical wavelengths of 400 700 nm were chosen and the experimental calibration matrix for partial least squares (PLS) was designed with 21 samples of 5.90 41.25, 0.30 29.35 and 0.64 41.30 ng ml−1 for cobalt, nickel and copper, respectively.
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The diagonal matrix (y-block) for partial least squares discriminatory analysis (PLS-DA) was generated in Excel and imported into PLS toolbox 4.0 (Eigenvector, Wenatchee, WA) operating in Matlab [26].
It operates by encoding the set of source trees as a matrix of partial binary characters, one character for each branch of each source tree, and then analyzing that matrix using a parsimony heuristic.
This data was used to generate a matrix for analysis by partial least square regression analysis [see Additional file 6].
Such an approach is based on computation of several independent Mantel tests using two different matrices and possibly a third matrix as a covariate for partial Mantel tests.
A non-Gaussian response matrix which includes probability for partial absorption events in the SCD is constructed using instrument parameters obtained from ground calibration.
In contrast, for full-matrix least squares we need the matrices of partial derivatives and.
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