Mokarram Marzieh, Amin Hosein, Khosravi Mohammad R
Department of Range and Watershed Management, College of Agriculture and Natural Resources of Darab Shiraz University Shiraz Iran.
Department of Plant Production, College of Agriculture and Natural Resources of Darab Shiraz University Shiraz Iran.
Food Sci Nutr. 2019 Aug 30;7(10):3176-3184. doi: 10.1002/fsn3.1149. eCollection 2019 Oct.
In this research, some characteristic qualities of orange fruits such as vitamin C and acid content; weight; fruit and skin diameter; and red (R), green (G), and blue (B) values of the RGB color model for 70 samples were used to predict the taste of orange grown in Darab, southeast of Fars Province, Iran, by multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS). To use MLR, firstly the most important input data were selected, and then, the best model to predict the taste of orange was applied. In this research, methodology of ANFIS consisted of selection of dependent orange taste, fuzzification, fuzzy inference rule, membership function, and defuzzification process. The predictive capability of these models was evaluated by various descriptive statistical indicators such as mean square error () and determination coefficient ( ). The results showed that the prediction performance of the MLR model has a strong significant relationship between orange taste and vitamin C (0.897), red color (0.901), and blue color (0.713). Also, the results of ANFIS model showed that with low error for train and check data increased the most accuracy for prediction of orange taste. Moreover, the results indicated that the success rate of taste determination for orange is higher by using ANFIS compared to the MLR. This research was to provide valuable information for orange taste.
在本研究中，利用70个样本的橙子果实的一些特征品质，如维生素C和酸含量、重量、果实和果皮直径以及RGB颜色模型的红色（R）、绿色（G）和蓝色（B）值，通过多元线性回归（MLR）和自适应神经模糊推理系统（ANFIS）来预测伊朗法尔斯省东南部达拉布种植的橙子的味道。为了使用MLR，首先选择最重要的输入数据，然后应用预测橙子味道的最佳模型。在本研究中，ANFIS的方法包括选择相关的橙子味道、模糊化、模糊推理规则、隶属函数和去模糊化过程。通过各种描述性统计指标，如均方误差（ ）和决定系数（ ）来评估这些模型的预测能力。结果表明，MLR模型的预测性能表明橙子味道与维生素C（0.897）、红色（0.901）和蓝色（0.713）之间存在很强的显著关系。此外，ANFIS模型的结果表明，训练和检验数据的误差较低，提高了橙子味道预测的准确性。此外，结果表明，与MLR相比，使用ANFIS确定橙子味道预测的成功率更高。本研究旨在为橙子味道提供有价值的信息。
