2019
DOI: 10.1007/s13197-019-03891-7
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Microwave-assisted deep eutectic solvent extraction of phenolic antioxidants from onion (Allium cepa L.) peel: a Box–Behnken design approach for optimization

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Cited by 61 publications
(39 citation statements)
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“…For the optimization study, a BBD experimental design was used to obtain the variable combinations for the experimental runs ( Table 2 ). This enabled the experimental runs of a smaller number of samples with reliable results, because the number of levels for the factors is minimized (from five to three in our case) [ 45 , 46 ], and it generates less extreme experimental combinations compared to a central composite design (CCD) [ 47 ]. The prediction precision around the supposed optimum is similar to the CCD, because the center point level is repeated (three times in the current study), but with fewer runs [ 48 ].…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…For the optimization study, a BBD experimental design was used to obtain the variable combinations for the experimental runs ( Table 2 ). This enabled the experimental runs of a smaller number of samples with reliable results, because the number of levels for the factors is minimized (from five to three in our case) [ 45 , 46 ], and it generates less extreme experimental combinations compared to a central composite design (CCD) [ 47 ]. The prediction precision around the supposed optimum is similar to the CCD, because the center point level is repeated (three times in the current study), but with fewer runs [ 48 ].…”
Section: Resultsmentioning
confidence: 99%
“…The prediction precision around the supposed optimum is similar to the CCD, because the center point level is repeated (three times in the current study), but with fewer runs [ 48 ]. However, the efficiency of BBD was shown to be higher than CCD and three-level full factorial [ 28 , 45 ], providing a model with a better fit. Thus, by using the BBD, the number of experiments was significantly reduced compared to the fully factorial design with five levels as in the preliminary study.…”
Section: Resultsmentioning
confidence: 99%
“…Chi-square value (χ 2 ) measures the accuracy of models and determines the acceptability of data with the expected distribution data. It can be calculated using the formula (Bhushan and Girirajsinh 2019) in Eq. (31):where C experimental and C predicted refer to the experimental and predicted values of the concentration responses, respectively, and n refers to the sample points taken.…”
Section: Statistical Modeling and Evaluationmentioning
confidence: 99%
“…For the optimization study a BBD experimental design was used to obtain the variable combinations for the experimental runs (Table 2). This enabled the experimental runs of a smaller number of samples with reliable results because the number of levels for the factors is minimized (from 5 to 3 in our case) (Pal and Jadeja 2019; Rodsamran and Sothornvit 2019) and it generates less extreme experimental combinations compared to Central Composite Design (CCD) (Otto 2016). The prediction precision around the supposed optimum is similar to the CCD because the center point level is repeated (3 times in the current study), but with fewer runs (Addinsoft 2020).…”
Section: Optimization Studymentioning
confidence: 99%