## 1 Introduction  
Maize (_Zea mays_ L) is one main cereal crop that has been broadly cultivated worldwide for human consumption and animal feed. However, maize is susceptible to be infected with toxicigenic fungi, such as _Fusarium_ spp. (_F. verticillioides, Gibberella moniliformis, F. graminearum_) and _Aspergillus_ spp. (_A. niger_, _A. flavus_, _A. parasitic_s), in the field and/or post-harvest conditions. Different toxicigenic fungi can produce relevant toxic metabolites, for example, aflat by _A. flavus_ and _A. parasitic_s,obratoroxins by _A. niger_, fumonisins by _Fusarium_ spp. [1; 2]. Among the various toxins, aflatoxin is highly toxic and carcinogenic to animals and humans [3; 4]. Therefore, there has been a mass of studies focusing on detection of aflat and fungi on maize to prevent health threats to people resulting from consumption of infected kernels. Traditional methods to detect fungal contamination on cereals are microbiological methods and diagnostic media for toxigenic fungi identification, and immunological methods for toxin detection. However, these methods are usually time-consuming, professional, expensive, labour-intensive and contaminative by introducing harmful chemical reagents [2; 5; 6; 7; 8]. In recent years, hyperspectral imaging, which was originally developed for remote sensing applications, emerges and becomes one of the most potential techniques for fast, non-destructive detection of food quality and safety [9; 10; 11]. Hyperspectral imaging integrates conventional imaging and spectroscopy to obtain both spatial and spectral information of the object [12; 13]. It allows interactive analysis between spectral and surface properties of samples based on each pixel point of images acquired with a wide wavelength range of spectral such as UV-visible, near infrared (NIR) or infrared (IR) [2]. Data of hyperspectral images are three-dimensional arrays of the form, X (\(m\times k\times\lambda\)), where \(m\) and \(k\) are the two axes of spatial information of the image, and \(\lambda\) is the number of wavelengths of the spectra. Hyperspectral imaging also has become a promising non-destructive technique for detection of toxin and fungi contamination on cereals. [PERSON] [14] studied hyperspectral Bright Green-Yellow Fluorescence (BGYF) imaging to detect single corn kernels contaminated with aflatoxin. [PERSON] et al. [15] used multispectral imaging (720-940 nm) to predict fumonisin content of milled maize. [PERSON] et al. [16; 17; 18] studied the use of hyperspectral imaging, with the range of 400-1000 nm and 1000-2500 nm, to detect aflatoxin B1 on maize kernels, which were artificially titrated on the kernel surface in the laboratory and inoculated with _Aspergillus flavus_ conidia in the field, respectively. [PERSON] et al. [19] verified the feasibility of short wave infrared (SWIR) hyperspectroscopy to detect aflatoxin on the surface of three different corn varieties (yellow, white, and purple). The results showed that the hyperspectral imaging technique can be used to detect the aflatoxin on or in maize kernels. However, research also found that it is hard to directly detect mycotoxins non-destructively. Therefore, some studies turn around to detect the contamination caused by fungi or mycotoxins, which affects other chemical and optical properties of whole kernels, and these changes can be detected with NIR hyperspectral imaging or spectroscopy [17; 20; 21; 22]. Moreover, compared to toxin detection, detection of toxicigenic fungi on cereals is important for the objective of early detection, which is significant in that it can prevent contamination in the form of mycotoxins in the food chain. [PERSON] et al. and [PERSON] et al. [23; 24] studied the application of near-infrared reflectance hyperspectral imaging for detection of wheat kernels infected with different fungi. [PERSON] et al.