Abstract

An electronic nose and a colorimeter were used to sample post-harvest litchis stored in three different storage environments (room temperature, refrigerator and controlled atmosphere) in order to explore the feasibility of electronic nose for fruit surface color recognition. BP Neural Network (BPNN), Simple Correlation Analysis (SCA), Canonical Correlation Analysis (CCA) and Partial Least Squares Regression (PLSR) were used for data processing. The experimental results demonstrate that with the increasing of storage time, the rate of decrease of color values (L*, a*, b*) is the fastest for litchis stored at the room temperature, followed by litchis stored in a refrigerator environment and a controlled atmosphere environment. During storage, the change in sensors’ response is the fastest for litchis stored at room temperature, followed by litchis stored in a refrigerator environment and litchis stored in a controlled atmosphere environment. The BPNN can effectively classify the storage time of litchis stored in a refrigerator environment and in a controlled atmosphere environment. However, the BPNN classification effect for litchis stored at room temperature is poor. Both of the CCA and the SCA results show that a certain correlations exists between the surface color values of litchi and the electronic nose response of litchi. The PLSR result shows that the prediction effect of surface a* prediction in litchis stored in a refrigerator environment is good. This research demonstrates the feasibility of the electronic nose for fruit surface color recognition, thereby providing a reference for fruit quality monitoring.

Keywords:

Artificial olfactory, classification and recognition, electronic nose, litchi, storage, surface color,

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