Comparison of Sensory Evaluation Models for Strawberry
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Abstract:
In order to make the result of sensory evaluation of strawberry more objective and independent of subjective factors such as evaluator's evaluation experience, age and health status, in this study, ten physicochemical flavor indicators of strawberry, such as catalase and polyphenol oxidase, were used as the input data, and the sensory evaluation score was used as the output data. The grey wolf optimizer-based support vector machine was used to establish a sensory evaluation prediction model of strawberry. To verify the superiority of the proposed model, this established model was compared with the particle swarm optimization support vector machine model, convolutional neural network model and long-/short-term memory network model. In order to ensure fully the effectiveness of the proposed model, the experiment was repeated 20 times and the mean value of each index was calculated. The root mean square error and the mean absolute error of the proposed model were 0.28 and 0.24, respectively (lower than the error indices for particle swarm optimization support vector machine (0.46 and 0.38), for long short time memory network (1.24 and 0.99), and for convolutional neural network (0.88 and 0.75). The experimental results showed that the grey wolf optimizer-based support vector machine model had the highest prediction accuracy, the highest stability, and the smallest error. The results can provide a reference for the evaluation of strawberry’s sensory evaluation score.
