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Table 1 Regression coefficients, R2, and probability value of the response surface models

From: Optimizing the acceleration of Cheddar cheese ripening using response surface methodology by microbial protease without altering its quality features

Regression coefficient pH (Y1) ADV (Y2) Moisture (Y3) aw (Y4) SN [(%), Y5] Fat [(%), Y6] Overall acceptability (Y7)
b0 3.22 22.156 31.27 0.87167 60.4 41.98 32.3
b1 0.96 − 7.752 3.47 0.04122 − 16.8 − 1.75 − 3.0
b2 102.14 − 438.700 − 277.16 − 1.89539 − 1812.0 − 363.49 − 853.9
b3 − 0.71 7.457 − 0.83 − 0.01209 14.3 − 2.40 − 10.0
b12 − 0.10 0.652 − 0.43 − 0.00551 2.0 − 0.02 0.1
b22 − 1629.07 − 764.094 2827.35 − 2.94037 30,030.3 1381.18 14,895.8
b32 − 0.12 − 0.363 0.19 − 0.00385 0.2 − 0.78 0.5
b12 − 11.58 115.769 32.60 0.34731 60.7 53.63 − 9.3
b13 0.00 − 0.474 − 0.08 0.00204 − 2.5 0.71 1.3
b23 22.96 − 52.445 − 67.11 0.23852 26.1 42.82 78.5
R2 0.99 0.95 0.96 0.94 0.94 0.94 0.92
Regression (P-value) 0.00a 0.00a 0.00a 0.00a 0.00a 0.00a 0.00a
  1. b0, b1, b2 and b3: the estimated regression coefficient for the main linear effects. b12, b22 and b32: the estimated regression coefficient for quadratic effects. b12, b13 and b23: the estimated regression coefficient for the interaction effects. 1: purification factor of protease (PF), 2: protease concentration (%); 3: ripening time (month)
  2. aSignificant (P ≤ 0.05)