## 1 Introduction  
Worldwide, increasingly significant public concerns about sub-quality foods being linked to increased morbidity, mortality, human suffering, and economic burden have been the drivers towards industry compliance for ensuring both high food quality and requisite safety standards [1]. Through the use of analytical procedures and the analysis of the resulting data, relevant information about food composition [2], its chemical composition and structure [3], physicochemical properties [4], and sensory characteristics [5] can be obtained. Due to the dynamism of a modern food industry and increasing consumer self-awareness, even established analytical techniques are constantly challenged. These are often aligned to expectations of low-cost analysis requiring low to nil sample preparation and environmentally-sustainable methods for assessment and quality monitoring. These attributes critically important and form essential attributes of modern production facilities [6] and an evolving and modern food industry also requires efficient and non-invasive technologies that are able to provide information about food quality [7; 8]. Different technologies can offer the food industry with these advantages, where molecular spectroscopy, for example near and mid infrared (NIR and MIR) are the most attractive due to their inherent characteristics (e.g., speed, low cost) [9; 10; 11]. Spectroscopy applications that are detailed in the literature by researchers in the field have been primarily rely on spot measurements using either the NIR or visible (Vis) regions of the electromagnetic spectrum. More recently, a diverse range of hyperspectral devices, including cameras and spectral imaging devices, are now readily available providing exciting new possibilities in food analysis and processing. This technology can acquire either single or multiple images at discrete wavelengths, presenting the potential for the detection of specific attributes relating to quality in an extensive range of raw materials and products using in the manufacture of foods [1; 2; 3; 4; 5; 6; 7; 8]. Overall, the availability of hyperspectral (HSI) and multispectral imaging (MSI) systems allowed for obtaining spatial, spectral, and multi-constituent information about the sample being analysed [1; 2; 3; 4; 5; 6; 7; 8]. For example, for decades, fruit assessment without off-line interruption and sample destruction was a challenge for producers, researchers and food safety agencies [1; 2; 3; 4; 5; 6; 7; 8]. Efforts have therefore been exerted towards the introduction and advancements of innovative imaging technologies for fast, non-invasive, and non-destructive monitoring of ripening and maturity stages of fresh produce in the farm and packing shed [1; 2; 3; 4; 5; 6; 7; 8]. In the last decade, vibrational spectroscopy, hyperspectral/multispectral imaging, and biomimetic sensors have started to feature with increasing prominence as rapid and efficient methods for the assessment of food quality, safety, and authentication [1; 2; 3; 4; 5; 6; 7; 8]. Moreover, these methods present useful alternatives to traditional expensive and time-consuming techniques, such as those on a microbiological platform. Spectral imaging can be classified as either hyperspectral (HSI) or multispectral imaging (MSI) [1; 2; 3; 4; 5; 6; 7; 8]. Multispectral imaging involves the acquisition of spectral images at few discrete and narrow wavebands (bandwidths of between 5 and 50 nm) and it is considered to be an improvement of hyperspectral imaging as this technology is cost effective [1; 2; 3; 4; 5; 6; 7; 8]. The result is the ability of MSI to simultaneously predict multiple components, providing a key advantage and rendering a promising future outlook with a single, automated image acquisition [1; 2; 3; 4; 5; 6; 7; 8]. Hyperspectral imaging relies on one of two sensing modes as in-line scanning (push broom) mode or as filter-based imaging mode [1; 2; 3; 4; 5; 6; 7; 8]. In-line scanning mode involves the imaging system scanning moving product items.