## 1 Introduction  
_Aspergillus flavus_ and the closely related subspecies _Aspergillus parasiticus_ have long been recognized as major contaminants of organic and nonorganic items [1]. They can contaminate a wide range of agricultural products, lead to sensorial, nutritional changes like pigmentation, discoloration, and rotting [2]. Moreover, they can produce aflatoxin under appropriate conditions [3]. If they infectsfood and feeds (such as maize, rice, peanuts, etc. ), the aflatoxin may cause mycotoxicoses in animals and humans [4]. Although the presence of _Aspergillus_ spp. does not necessarily indicate the occurrence of aflatoxin, it presents the possibility of the appearance of aflatoxin [5]. Accordingly, it is important and necessary to early detect and identify toxic fungi. Traditional culturing methods are the gold standard for microorganism detection. Identification of the fungal growth is mainly based on the macroscopic and microscopic biological characteristics of the fungus. In specific condition, total viable count, biomass, and metabolite production concentrations were utilized to describe fungal growth [6; 7; 8; 9], while fungal DNA extraction, polymerase chain reaction (PCR), and agarose gel electrophoresis were investigated for accurate fungal detection [10]. However, traditional culturing and biological analysis were time consuming and laborious; in particular, they required additional chemicals and even produced toxic by-products. Thus, non-destructive optical methods for the detection of microorganisms are becoming increasingly important [11]. Hyperspectral imaging (HSI) is an optical imaging technique that combines conventional imaging and vibrational spectroscopy and provides both spatial and spectral information simultaneously [12; 13]. The spatial and spectral information are contained in a 3D hyperspectral image dataset, which contains two spatial dimensions and one spectral dimension [14]. Thus in the hyperspectral image, the spectrum of each pixel and image of each wavelength band can be analysed synergistically. In recent years, hyperspectral imaging was widely used to detect fungal contamination on cereals. [PERSON] et al. used visible-near infrared (Vis/NIR) hyperspectral imaging (400-1000 nm) to early detect maize kernels inoculated with different toxicine fungi. Results indicated that the method could identify _A. flavus_ and _A. niger_ contaminants on the maize, starting at 48 h from inoculation [15]. [PERSON] et al. used near-infrared hyperspectral imaging (800-2600 nm)-determined pistachio kernels infected by toxicine and atoxigenic _A. flavus_ (KK11 and R5) and classified the kernel infection by those two fungi at the last fungal growing stage [16]. [PERSON] et al. detected the contamination level of maize kernels inoculated with _A. parasitic_ by using hyperspectral images with a spectral range of 1000-2500 nm. The studies showed that hyperspectral imaging has become a promising non-destructive technique for detecting the fungal contamination levels and fungal species on cereals [17]. However, fungal development on food product is complex; thus, to detect fungi in its early stage, it is necessary to investigate microorganisms in a controlled manner and environment first. For example, there have been some publications that have reported the use of hyperspectral imaging to detect fungi plated on the culture medium. [PERSON] et al. identified five types of toxicine fungi on day 5 of growth using a hyperspectral image, and all five fungi can be easily separated by only three wavelength bands (743, 458 and 541 nm) [18]. [PERSON] et al. classified toxicine and atoxigenic _A. flavus_ using a hyperspectral image under UV light and halogen light source [19]. This research focused on discriminating the species of the fungi only at one fixed mature stage of growth. In recent years, there have also been some studies that have analyzed the development of fungi. [PERSON] et al. constructed growth curves of three _Fusarium_ spp. and visualized the radial growth zones within the colonies [20; 21].