Spectroscopic techniques coupled with chemometric approaches have been widely used for quality evaluation of agricultural and food (agri-food) products due to the nondestructive, simple, fast, and easy characters. However, these techniques face the issues or challenges of relatively weak robustness, generalizability, and applicability in modeling and prediction because they measure the aggregate amount of light interaction with tissues, resulting in the combined effect of absorption and scattering of photons. Optical property measurement could separate absorption from scattering, providing new insights into more reliable prediction performance in quality evaluation, which is attracting increasing attention. In this review, a brief overview of the currently popular measurement techniques, in terms of light transfer principles and data analysis algorithms, is first presented. Then, the emphases are put on the recent advances of these techniques for measuring optical properties of agri-food products since 2000. Corresponding applications on qualitative and quantitative analyses of quality evaluation, as well as light transfer simulations within tissues, were reviewed. Furthermore, the leading groups working on optical property measurement worldwide are highlighted, which is the first summary to the best of our knowledge. Finally, challenges for optical property measurement are discussed, and some viewpoints on future research directions are also given.
Keywords: Quality evaluation; absorption; fruits and vegetables; optical properties; scattering.
HighlightsEmerging techniques for measuring optical properties are briefly introducedQualitative analyses of maturity evaluation and defect detection are reviewedQuantitative analyses of attribute prediction and microstructure estimation are presentedLight transfer simulations based on optical properties are comprehensively discussedLeading groups are summarized for the first time, to the best of our knowledgeChallenges and prospects for optical property measurement are given.