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. Author manuscript; available in PMC: 2023 Jun 28.
Published in final edited form as: Food Chem. 2021 Oct 26;373(Pt B):131471. doi: 10.1016/j.foodchem.2021.131471

Table 2B.

Machine learning for regression of olive oil adulterated by soybean oil.

Methods PCA+ LNR LNR with L1 Penalty LNR with L2 Penalty LNR with Elastic net Penalty PLS Regression PCA+ RF RF PCA+ Boosting Boosting
Training time (s) 0.002 3.394 0.022 1.972 2.943 0.476 1.298 0.060 2.987
R 2 0.997 0.997 0.999 0.995 1.000 0.997 0.996 1.000 1.000
MSE 2.357 2.448 0.883 4.474 0.001 3.147 3.393 0.056 0.001
Predicted R2 0.984 0.975 0.984 0.974 0.954 0.963 0.959 0.966 0.954
MSPE 15.089 22.722 14.851 24.237 42.986 34.535 38.021 31.535 42.666

Note: LNR (linear regression), PLS (partial least square), RF (random forest), MSE (mean squared error), R2 (coefficient of determination).