Zhu G, Raghavan GSV, Li Z. Estimating the Moisture Ratio Model of Cantaloupe Slices by Maximum Likelihood Principle-Based Algorithms.
PLANTS (BASEL, SWITZERLAND) 2023;
12:941. [PMID:
36840289 PMCID:
PMC9959644 DOI:
10.3390/plants12040941]
[Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 02/10/2023] [Accepted: 02/16/2023] [Indexed: 06/18/2023]
Abstract
As an agricultural plant, the cantaloupe contains rich nutrition and high moisture content. In this paper, the estimation problem of the moisture ratio model during a cantaloupe microwave drying process was considered. First of all, an image processing-based cantaloupe drying system was designed and the expression of the moisture ratio with regard to the shrinkage was built. Secondly, a maximum likelihood principle-based iterative evolution (MLP-IE) algorithm was put forward to estimate the moisture ratio model. After that, aiming at enhancing the model fitting ability of the MLP-IE algorithm, a maximum likelihood principle-based improved iterative evolution (MLP-I-IE) algorithm was proposed by designing the improved mutation strategy, the improved scaling factor, and the improved crossover rate. Finally, the MLP-IE algorithm and MLP-I-IE algorithm were applied for estimating the moisture ratio model of cantaloupe slices. The results showed that both the MLP-IE algorithm and MLP-I-IE algorithm were effective and that the MLP-I-IE algorithm performed better than the MLP-IE algorithm in model estimation and validation.
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