## 1 Introduction  
Floral scent plays an important role in the reproductive processes of many plants, as well as in guaranteeing the yield and quality of many economically valuable plants. It also enhances the aesthetic properties of ornamental plants and cut flowers. Traditional scent analysis in plants has typically relied on sensory evaluation methods that perceive types and intensity of odors. Odor intensity is very complex, due to the effect of the odor detection threshold (ODT). ODT is the lowest concentration of a certain odor compound that is perceivable by the human olfactory system. The threshold of a chemical compound may change due to its shape, polarity, partial charges, or the addition of other compounds [1]. Therefore, sensory evaluation has various limitations, including strong subjectivity and poor repeatability. Chromatographic techniques, such as gas chromatography-mass spectrometry (GC-MS), solid-phase micro extraction (SPME), and headspace analysis, have increasingly been used to identify and quantify the aromatic components of fragrant plants, such as _Silene latifolia_[2], rose [3], _Luculia pinceana_[4], and _Osmanthus fragrans_[5]. However, these methods often fail to provide a global fingerprint of the scent sample, as the compounds detected are typically dependent on the selected sample pretreatment method. Furthermore, they are hampered by their complex technology, high running costs, and prolonged analysis time. In response to chromatographic technologies, an electronic nose (E-nose) has emerged as an olfactory simulation test tool that allows for the high-throughput analysis of volatile organic compounds in a complex matrix [6]. Using specific nano sensor arrays which reflect the changes in conductivity produced by the adsorption of compounds together with pattern recognition software, a global fingerprint of the volatile components in a sample can be obtained. In contrast to chromatographic techniques, which focus on the separation and detection of individual chemical components, the E-nose relies on response and recognition technology, which can rapidly identify and separate complex odors [6]. In addition, the E-nose is able to accurately distinguish the odor of complex samples at a low cost without the need to quantitatively analyze each individual component in the test sample, as is required for GC-MS. Thus far, the E-nose has primarily been used in food processing [7], evaluating the shelf-life of fruits, vegetables [8; 9], meat, and aquatic products [10; 11], and determining the authenticity of tobacco, alcohol, and other beverages [12; 13]. Scholars have recently attempted to train the E-nose to predict the response of the human sensory system to particular odors. For instance, the E-nose was trained to predict odor pleasantness [14] and was found to correlate well with the human data (above the 0.60 level) for single-component odorants [15]. However, none of the models in the pattern recognition software are able to accurately predict the human values or pleasantness for more than a few descriptors. Hence, the prediction of human sensory ratings from instrumental measurements is still arguably the greatest challenge of sensor-based machine olfaction. With regard to floral scent detection, the E-nose has been applied to germplasm differentiation [16; 17], flowering stage distinction [18], and flower organ differentiation [19]. If the E-nose can be applied to the fast screening of the interested taxon, such as a strong scent taxon or a special floral scent type, and accurately mapped to sensory evaluation, it would have very important applications in the evaluation of floral scent. E-nose technology has not been previously applied to the evaluation of scent intensity in crabapple. In this study, using headspace solid-phase micro extraction coupled with gas chromatography-mass spectrometry (HS-SPME)-GC-MS in conjunction with the E-nose, we evaluated the scent characteristics of crabapple (_Malus_, Rosaceae) taxa of different scent intensities.