A Review Towards Hyperspectral Imaging for Real-Time Quality Control of Food Products with an Illustrative Case Study of Milk Powder Production

Abstract

Hyperspectral imaging (HSI) is a relatively fast analytical method that is being used for quality testing of natural foods (e.g. fruits and vegetables). This method has been proposed to replace existing manual off-line sensory quality tests of processed foods (e.g. milk powder). This article reviews the current status of HSI for monitoring natural and processed food product quality and identifies key knowledge gaps in recent literature that may be responsible for the limited success of HSI, especially for processed foods (e.g. milk powder) quality testing at the lab and industrial scales. Furthermore, various strategies to cope with challenges associated with limited HSI success are discussed. From the current literature, HSI has been used primarily as an off-line machine vision technique for quality testing to replace existing manual sensory tests. However, HSI lacks scenarios of testing and controlling product quality in real time, especially for processed foods. Little has been reported to date on generalising suitable design choices for the ‘successful’ implementation of HSI. To address this deficiency, the potential of HSI to achieve real-time quality control was examined using the milk powder production process as a case study.