本文探讨了利用荧光高光谱成像技术对 aflatoxin 污染的单个玉米粒进行分类的方法。Aflatoxin 是一种由 Aspergillus flavus 和 Aspergillus parasiticus 等真菌产生的有毒次生代谢物，对人类和动物健康构成威胁。目前主要依赖于 TLC 和 HPLC 等分析方法检测，但这些方法成本高且耗时。研究中使用长波紫外激发下的可见近红外（VNIR）高光谱相机对受污染和未受污染的玉米粒进行成像，并通过最大似然和二进制编码算法进行像素分类，最高分类准确率达到88%。这种方法为粮食行业，特别是玉米行业提供了一种快速、非破坏性的 aflatoxin 检测手段。
摘要：
Aflatoxins are toxic secondary metabolites of the fungi Aspergillus flavus and Aspergillus parasiticus, among others. Aflatoxin contaminated corn is toxic to domestic animals when ingested in feed and is a known carcinogen associated with liver and lung cancer in humans. Consequently, aflatoxin levels in food and feed are regulated by the Food and Drug Administration (FDA) in the US, allowing 20 ppb (parts per billion) limits in food and 100 ppb in feed for interstate commerce. Currently, aflatoxin detection and quantification methods are based on analytical tests including thin-layer chromatography (TCL) and high performance liquid chromatography (HPLC). These analytical tests require the destruction of samples, and are costly and time consuming. Thus, the ability to detect aflatoxin in a rapid, nondestructive way is crucial to the grain industry, particularly to corn industry. Hyperspectral imaging technology offers a non-invasive approach toward screening for food safety inspection and quality control based on its spectral signature. The focus of this paper is to classify aflatoxin contaminated single corn kernels using fluorescence hyperspectral imagery. Field inoculated corn kernels were used in the study. Contaminated and control kernels under long wavelength ultraviolet excitation were imaged using a visible near-infrared (VNIR) hyperspectral camera. The imaged kernels were chemically analyzed to provide reference information for image analysis. This paper describes a procedure to process corn kernels located in different images for statistical training and classification. Two classification algorithms, Maximum Likelihood and Binary Encoding, were used to classify each corn kernel into "control" or "contaminated" through pixel classification. The Binary Encoding approach had a slightly better performance with accuracy equals to 87% or 88% when 20 ppb or 100 ppb was used as classification threshold, respectively.
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