# Construction of Color Prediction Model for Damaged Korla Pears during Storage Period
## 1 Introduction
According to the abovementioned works in the literature, the color changes in fruits during the storage period have been successfully evaluated, yielding relatively good results. However, the color changes in the damaged Korla fragrant pears during the storage period have been scarcely reported in the literature. At present, the damaged fragrant pears during the storage period are only eliminated by conducting simple and rude regular examinations. Nonetheless, this approach not only increases the operation links and labor costs but also cannot accurately recognize the storage quality. Hence, a high-efficiency and low-cost method to predict the colors of fragrant pears is urgently needed. To this end, the adaptive neuro-fuzzy inference system (ANFIS) model has arisen as a promising solution. In 1965, Professor [PERSON] of the University of California broke through the classical geometric theory and first proposed the concept of membership function to express the fuzzy nature of things, which laid the foundation for fuzzy theory [15]. More specifically, [PERSON] and [PERSON] established the fuzzy inference model Takagi-Sugeno (TS) in 1985 and 1988, respectively [16]. TS is a fuzzy system with strong adaptability, which can automatically update the parameter values of membership functions [17]. ANFIS is a fuzzy inference system based on the TS model. It can realize the three basic processes of fuzzy control, fuzzy inference, and anti-fuzzification based on neuro networks. The learning mechanism of neural networks is utilized to automatically extract rules from samples, and then the fuzzy inference control rules are adjusted. It has the comparative advantages of self-adaptation, self-organization, and self-learning [18]. On top of that, it has been already applied to predict the quality of fruits and vegetables, achieving promising results. [PERSON] et al. [19] studied the influencing laws of ripeness and storage time on the hardness of Korla fragrant pears and predicted fruit colors based on the ANFIS model. The authors found that the ANFIS model with a membership function of gbellmf (generalized bell) achieved the optimal prediction effect. In another interesting work, [PERSON] et al. [20] predicted the reserves of ascorbic acid (AA) of fresh-cut pineapples during the storage process based on the ANFIS model and proved that the ANFIS model with a triangular membership function (trimf) achieved the best prediction effect. Although good effects on the quality prediction of fruits and vegetables by using the ANFIS model have been achieved so far, the damage to the Korda pear color during storage based on the application of this method has been scarcely reported in the literature. Under this perspective, in this work, the variation law of the color of the damaged fragment pears during the storage period was systematically investigated, and color prediction models were constructed with eight different membership functions in ANFIS. The optimal model was selected and verified. Finally, the colors of the fragrant pears were scientifically and accurately predicted. Moreover, the ANFIS model can provide valuable theoretical references to precisely predict the quality of Korda fragrant pears during storage.