## 1 Introduction  
Aroma is an apple quality attribute consisting of not only one critical component but a complex mixture of volatiles which relies on volatile compounds, their concentrations and odor thresholds. Currently, more than 300 volatiles have been identified in apples [1]. Most of the volatiles are classified into esters (78-92% of total volatiles), alcohols (6-16% of total volatiles), aldehydes and ketones [2]. However, the compositions and concentrations of volatiles are affected by many factors, including stage of maturity [3; 4; 5], storage conditions [3], climates [6] and terrori effect [7]. For instance, aldehydes are dominant in immature apples. However, the concentrations of aldehydes decrease during the apple ripening period, while esters and alcohols become the main volatiles with their concentrations increasing [8]. Additionally, apple variety is another key factor that has a great influence on volatiles. Studies on various varieties reveal many differences both in volatiles' composition and concentration [4; 9; 10; 11]. There are many typical methods of volatile extraction, including purge and trap, dynamic headspace, liquid-liquid extraction and solvent assisted flavor evaporation. The headspace solid phase micro-extraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS) technique is frequently applied to fruit [7; 9; 10; 12; 13; 14] volatile detection, due to its advantages of being easy, convenient and environmentally friendly. Multivariate analysis has been employed to discriminate and classify fruit based on volatile profiles in many studies. Principal component analysis (PCA) is a useful tool to distinguish the differences among samples and study the relationship between samples and volatiles. It is frequently applied to fruit, including apple [9; 10; 12], peach [13] and kiwifruit [15]. However, PCA cannot be applied to sample classification. Although PCA may indicate clear separation by a bi-dimensional plot, a complementary technique is also demanded in parallel [16]. Partial least squares discriminant analysis (PLS-DA) is one of the widely used methods to classify different groups of samples. This pattern recognition method has been previously studied in apple [12; 17] and other fruits [15; 18]. Although volatile concentrations can reflect the contribution to volatile compounds, not all volatiles contribute to the aroma. Odor threshold values should also be taken into account. For example, although the amounts of some specific volatile compounds were low, they may have a greater contribution to the aroma due to their low odor threshold. [PERSON] et al. [19] reported methyl 2-methylbutyrate, ethyl 2-methylbutyrate, isobutyl acetate, ethyl butanoate, isopentyl formate, butyl acetate, hexyl acetate, hexal and hexanol were the odor-active compounds in Fuji apple. Similar results were found by [PERSON] et al. [5]; hexyl acetate, hexyl propanoate, hexyl 2-methylbutyrate, ethyl 2-methylbutyrate, 2-methylbutyl acetate and butyl 2-methylbutanoate were considered the odor-active compounds in Golden Reinders apples. There were several seasonal factors that affect volatile emissions such as temperature and precipitation. High temperature may inhibit enzyme activity, and water deficit limits the volatile potential [20]. However, excessive rain may also reduce the content of volatiles [21]. There was no consistent conclusion on whether the season has an influence on volatiles that is stronger than cultivar differences. [PERSON] et al. [22], [PERSON] et al. [23], and [PERSON] et al. [21], demonstrated that seasonal effects were stronger than cultivar effects on melon and strawberry aromas. However, [PERSON] et al. [17], demonstrated there was no seasonal effect on ancient apple cultivars, which could be explained by ancient apple cultivars being more resistant to adverse climatic changes.