Abstract

Vegetable freshness evaluation is of great significance to ensure the quality of vegetables and realize fine production. Existing vegetable freshness evaluation methods have difficulty realizing rapid online evaluation and industrial applications due to such disadvantages as being susceptible to subjective factors, complicated operation, large computation, and high hardware cost. To solve the above problems, a rapid online vegetable freshness evaluation method was developed based on the single turnover chlorophyll fluorescence parameters $F_o^\prime $, $F_v^\prime /F_m^\prime$ and ${\sigma ^\prime _{{\rm{PSII}}}}$. A freshness evaluation model for spinach and swamp cabbage was established based on a classification and regression tree algorithm, using $F_o^\prime $, $F_v^\prime /F_m^\prime$ and ${\sigma ^\prime _{{\rm{PSII}}}}$ as sample features. The model divided the freshness of spinach and swamp cabbage into three grades: good, medium, and poor, and the leave-one-out cross validation results showed that the freshness evaluation accuracies of spinach and swamp cabbage reached 98.1% and 94.3%, respectively.

© 2022 Optica Publishing Group

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