## 1 Introduction  
Over the past 20 years, hyperspectral imaging (HSI) has become an invaluable tool for food safety and quality applications [1; 2]. Spoilage and contamination of food and agricultural products are ongoing concerns for the food industry. Recent applications of hyperspectral imaging for food safety include detection of mold in peanuts [3; 4], lead pollution in lettuce leaves [5], and Fusarium head blight in wheat kernels and wheat flour [6]. Food fraud, the intentional misrepresentation of food or food ingredients for economic gain, is another major food safety issue that has been addressed with hyperspectral imaging. For example, this technology has been applied for identifying fillets of less expensive species of fish that have been marketed and sold as more expensive red snapper (_Lutjanus campechanus_) fillets [7; 8]. Hyperspectral imaging has been a staple of agriculture monitoring, with initial applications dating back to the 1970s. Early applications include large-scale remote monitoring of land and agriculture from the Landsat-I satellite [9], monitoring of crop yield [10], and detection of plant disease and invasive species [11]. While agriculture applications have remained constant since these early examples, the methods have changed with new technologies enabling more localized analysis. Unmanned aerial vehicles (UAVs) have become attractive survey platforms for local, detailed aerial monitoring efforts [12] and advancements in computing technology and miniaturization of HSI devices have enabled the construction of new systems for in-field crop analysis [13]. Hyperspectral imaging devices are complex systems that can be characterized by the method with which the full spatial-spectral data cube is obtained. Data cubes can be acquired by spatial scanning, spectral scanning, or by a combination of these methods [14]. With spatial scanning imagers, light is collected at a point or along a line and dispersed into its spectral components by a dispersive optic such as prism or diffraction grating. This point or line is then scanned over the target area through the physical motion of the sensor, reflection from a scanning mirror, or physical motion of the target object. With spectral scanning imagers, the full spatial content is collected by the image sensor for individual wavelengths in sequence. Collection of the wavelengths is typically accomplished by switching wavelengths through filter wheels, electronically controlled liquid crystal tunable filters (LCTF), or acousto-optic tunable filters (AOTF) [15]. Despite successes in the food safety and agriculture industries, hyperspectral imaging does have disadvantages, mostly due to the data cube being constructed from individual components collected in a time-sequential manner. This can be an error-prone process, especially for high-speed imaging applications. Another category of the hyperspectral imager, the snapshot imager, overcomes these issues by combining an array of optics to collect both the spatial and spectral information simultaneously. Usually, this means some compromise in either the spectral or spatial domain. All of these solutions tend to be both complex and costly [16]. In research and discovery, it is unknown which wavelengths will be significant and which are redundant. In many cases, once the spectral characteristics for a particular targeted application are understood, there can be a significant reduction in the complexity of the spectral imaging system. Issues common to all hyperspectral imager types are the significant computing power required and the large file sizes of the data cubes, especially in applications involving larger fields of view. Attempts to address these issues have included the application of compressive sensing [17; 18; 19], deep neural networks [20], and methods centered around principal component analysis (PCA) [21]. Each of these solutions has its own limitations in terms of heavy computational requirements and large file sizes for data cube analysis.