## 3 Results
### Four Variables
#### 3.3.4 The Regression Results of the Other Literature  
The results of the RSM models of modern regression are listed in Table 7.  
[PERSON] et al. [46] reported the factors that influence the optimization of the microencapsulation of peanut sprouts. The influencing variables were x\({}_{1}\), water/oil ratio, x\({}_{2}\), first emulsifier, x\({}_{3}\), water/oil/water ratio, and x\({}_{4}\), second emulsifier. The response y was the yield of microencapsulation. The t-value and \(p\)-value of each variable were listed in the ANOVA table in the study [46]. The \(p\)-values of some variables were \(>\)0.05. However, the reported RSM equation was a complete model in the study. The results of modern regression revealed that only the variables x\({}_{1}\), x\({}_{2}\), x\({}_{4}\), and x\({}_{1}\)x\({}_{2}\) were influence factors.  
A study of optimizing germination conditions to improve the resveratrol content yield of peanut sprout was performed by [PERSON] et al. [47]. The affecting variables were x\({}_{1}\), soaking temperature, x\({}_{2}\), soaking time, x\({}_{3}\), germinal temperature, and x\({}_{4}\), germinal time. In the study, the RSM model was a complete equation, and it was used to produce the contour and response surface plots. The F-value and t-value of each variable have been listed in the ANOVA table in the study. Some variables did not significantly affect the response with \(p\)-values \(>\) 0.05. However, the authors proposed a complete model [47]. The adequate equation evaluated by the modern regression only involves the variables x\({}_{1}\), x\({}_{2}\), x\({}_{3}\), x\({}_{4}\), x\({}_{1}\)x\({}_{3}\), x\({}_{2}\)\({}^{2}\), and x\({}_{3}\)\({}^{2}\).  
[PERSON] and [PERSON] [48] studied the optimization of lead removal from aqueous solutions. The response, y\({}_{\text{LRC}}\), was the lead removal capacity. The influencing factors were x\({}_{1}\), pH, x\({}_{2}\), temperature, x\({}_{3}\), lead ion concentration, and x\({}_{4}\), adsorbent dose. The reported RSM equation was a complete equation. The curves of contour and response surface plots were presented. However, the ANOVA table in the study indicated that only six variables had a lower \(p\)-value (\(p<\) 0.05). The adequate regression evaluated with modern regression indicated that significant variables were x\({}_{1}\), x\({}_{2}\), x\({}_{3}\), x\({}_{4}\), x\({}_{1}\)x\({}_{4}\), x\({}_{2}\)\({}^{2}\), and x\({}_{3}\)\({}^{2}\). The normality test was failed (\(p=\) 0.023), and the constant variance test was passed.  
[PERSON] et al. [50] studied optimization for wild _Myrtus communus_ L. fruit by-products as a natural colorant source. The response was y\({}_{\text{TAC}}\) (total anthocyanin content). The influencing factors were x\({}_{1}\), pH, x\({}_{2}\), ultrasound power, x\({}_{3}\), time, and x\({}_{4}\), solid/liquid ratio. The authors reported a complete equation and used this model to produce contour and response surface plots. In our study, the adequately evaluated model with modern regression only involved four variables (x\({}_{1}\), x\({}_{2}\), x\({}_{3}\), and x\({}_{1}\)x\({}_{3}\)). No quadratic terms exist in this adequate equation. The normality test was failed (\(p=\) 0.011), and the constant variance test was passed.