## 1 Introduction  
Food quality plays a vital role in the elucidation and development of preferences of consumers and processors, among other end-users. Food quality is basically perceived by the consumer according to its appearance, texture, and taste. Recently, food quality monitoring also required rapid analytical procedures [1]. Food quality ensures these products meet standards related to nutrition, freshness, and taste, while consistency appeals to consumers. Also, food safety is defined as avoiding the contamination of biological, chemical, and physical hazards to prevent adverse public health consequences, including foodborne illness. Moreover, large lots of low-quality food can be produced before processing difficulties are observed [2; 3]. These again result in food losses. As a result of this fact, some intrinsic and extrinsic critical parameter checking methodologies have been developed; these are much faster, more reliable, and non-destructive [4; 5; 6]. Hyperspectral imaging is a non-destructive approach that finds an important application in food safety for authentication [7; 8; 9] as well as microbial [10; 11], chemical, and physical contaminant identifications within the food industry [12; 13; 14; 15]. The hyperspectral imaging system of the qualification of foods captures the spectral reflectance of food items across a wide range of wavelengths, which may be used to identify chemical and physical properties related to moisture, freshness, and contamination. It captures comprehensive spectral information at each pixel, creating spatially resolved maps that differentiate between distinct food components or parameters of quality and permit non-destructive analysis in real time [6; 11]. The main challenges of hyperspectral imaging rely on extracting interesting information from high-dimensional data often containing redundant information [15]. Other relevant challenges are sensor noise, changing illumination conditions, and environmental conditions, sample heterogeneity, and anisotropy [16; 17]. The development of an efficient algorithm and chemometric techniques is essential to reduce the dimensionality in the hyperspectral data for improving the practical applications in real-time food monitoring using HSI. These algorithms can reduce computational time, improve model performance, and increase robustness by removing irrelevant variables and redundancies [18]. Machine learning can help in food safety, contaminant detection, spoilage prediction, and quality assessment through the analysis of large datasets received from sensors. This is enabled by training algorithms with historical data on the identification of patterns, risk classification, or making real-time predictions based on new input data [19; 20; 21; 22; 23; 24]. With ML came the offspring of AI, which encompasses a wide range of algorithms drawing meaningful information from data and using the knowledge to teach themselves for better classification [25; 26; 27] and prediction [28; 29; 30] within the food safety and quality sections. Also, deep learning relies on an artificial neural network composed of several layers, which automatically learn hierarchical features from raw data, with gradual changes to abstract and nuanced representations. DL in some cases is stronger than traditional ML and can process large volumes of big data in unstructured information represented through images and videos without manual feature extraction, hence improving accuracy and scalability [16; 29; 31; 32; 33; 34]. The literature indicates that AI catalyzes new revolution hyperspectral image and data analysis. Moreover, the effective preprocessing and deliberate choice of wavelengths in hyperspectral imaging are recommended to reduce extraneous factors that can affect the quality of measured spectra, such as the morphological variations and light-scattering characteristics of different food items [35; 36; 33].