Non-destructive hyperspectral imaging technology to assess the quality and safety of food: a review

Abstract

AbstractThe quality and safety of food can be evaluated using a variety of conventional and scientific methods. But all of those ways are time-consuming, laborious, and harmful. There are two primary types of processes used to gauge the quality and safety of foods: 1) Destructive methods (like gas chromatography, high performance liquid chromatography, enzyme linked immuno-sorbent assay, etc.); and 2) Non-destructive methods (such imaging methods, computer vision systems, fourier transform infrared spectroscopy, and near infrared spectroscopy). Techniques for imaging are frequently employed in the food industry to assess external quality. Imaging is the process of visualizing an object, while spectroscopy is the study of how energy is transferred from light to matter. Spectroscopy and imaging are used in the hyper spectral imaging approach. A method that may offer both spectral and spatial information about a component is called hyperspectral imaging (HSI). The HSI creates a hypercube out of spectral pictures at more than ten different wavelengths. A hypercube has three dimensions: two spatial (the x and y axes) and one spectral (λ). Fruits and vegetables, dairy goods, meat products, seafood, grains, and legumes are all evaluated for quality and safety using HSI. The HSI approach is excellent for identifying both internal and exterior food problems. Anthocyanin in grapes, Penicillium digitatum in mandarins, melamine in milk powder, and the amount of fat in cheese can all be detected using HSI. In addition to recognizing the muscles in lamb meat, HSI may also be used to assess the colour, pH, and tenderness of beef, the colour, pH, and drip loss of pork, and the presence of E. coli in pork. Additionally, HSI is utilized to identify Aspergillus niger in wheat and Aflatoxin B1 in maize. Chemometric instruments are essential to HSI. Large data storage and fast processors are needed. Improved models are required for quick and simple evaluation. The HSI has limits when it comes to microbiological contaminants’ metabolites detection and quantification, model optimization, and the development of more reliable models. Validation of developed models on several storage conditions. Combining HSI with Raman microscopic imaging (RMI) and fluorescence microscopic imaging (FMI) improves the ability to analyze microbes. Graphical Abstract