Near-infrared hyperspectral and multispectral imaging principies and applications in the quality of fruits and vegetables

Abstract

In recent years, imaging technologies (hyperspectral and multispectral) are being widely investigated and applied as a non-destructive, reliable, and accurate technique to monitor the quality and composition of agricultural products. Over two decades, hyperspectral imaging (HSI) has developed as a promising technology for qualitative and quantitative analysis of fruits and vegetables. The ability to integrate spatial and spectral information in the form of a hypercube is one of the significant advantages of HSI over spectroscopy and other imaging technologies. It enables mapping of the spatial variability or distribution of those Parameters within the sample. Like HSI, multispectral imaging (MSI) is also gaining interest in real-time application in the grading and/or packaging of fruits and vegetables. Hundreds of images over a contiguous wavelength in HSI brings algorithmic processing complexities, whereas fewer wavelengths in multispectral imaging reduces algorithmic complexities and enables faster processing with reliable results. In summary, these imaging technologies are powerful and reliable techniques for the analysis of agricultural products. This chapter will focus on the theory and principles of near infrared HSI and MSI technologies, their components, the mode of image acquisition, and image processing techniques. Finally, the recent application of these imaging techniques for predicting physicochemical properties, antioxidants, chemical components, texture, defects, maturity classification, shape, and size of fruits and agricultural products are presented.