Apricot ( Prunus armeniaca L.) is an important stone fruit in the world that originated in China. The flavor of apricot fruit is mainly determined by its sugar and acid components. Studies have shown that apricot landraces and traditional cultivars can be distinguished by determining the sugar and acid components of the fruit using high-performance liquid chromatography (HPLC) and machine learning approaches. Total soluble sugar, malic acid, and citric acid exhibited no significant differences between the two groups. However, correlation analysis indicated that sugar coordination was enhanced in traditional cultivars compared with landraces, while the organic acid composition was more diverse. Four machine learning models classified them with performance metrics above 0.85. UpSetR analysis identified oxalic acid, tartaric acid, and fructose as key indicators, further improving model performance vs. full features. This study provides a novel strategy based on machine learning to distinguish apricot domestication traits across periods. • This study compared the differences in sugar and acid components between landraces from the Pamir Plateau and traditional cultivars from southern Xinjiang. • This study employed the combination of High Performance Liquid Chromatography (HPLC) and machine learning to classify apricot groups from 2 distinct domestication periods. • This study identified oxalic acid, tartaric acid, and fructose as key features for the classification of the two apricot groups.
Li et al. (Sun,) studied this question.