Reducing Food Loss and Waste from Farm to Fork with IR Spectroscopy

Burke, Miranda and McAinsh, Martin (2025) Reducing Food Loss and Waste from Farm to Fork with IR Spectroscopy. PhD thesis, Lancaster University.

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Abstract

Food loss and waste is an issue that is often misunderstood and to many is entirely unknown. Food loss and waste have unprecedented impacts on our economy, and society and are fundamentally an environmental disaster. Important resources go to waste along with our food and the inappropriate disposal contributes to global greenhouse gas emissions by around 10%. With a changing climate making it harder to produce food for a growing population, it is more important than ever, that food loss and waste are significantly reduced. Fruit and vegetables are the most wasted food products due to their high perishability. This research explores one avenue for addressing food loss and waste by using infrared spectroscopy as a method for detecting molecular and biochemical processes involved in fruit and vegetable degradation and subsequent spoilage. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and near-infrared (NIR) spectroscopy are used to enhance our understanding of the biochemical processes occurring in post-harvest fruit stored in different temperature environments. Biochemical markers that may have important applications within the food supply chain have been described. Storage temperature conditions have shown notable effects on the biochemical signatures further supporting the applied use of post-harvest temperature control for shelf-life extension. Contributing factors to food waste include the fraudulent misrepresentation of fruit origins and farming practices, IR spectroscopy has been investigated as a method for accurate classification of these factors. Developmental processes in tomato plants have been analysed using nearinfrared spectroscopy to enhance our understanding of the biochemical processes involved. Spectroscopy was paired with powerful analytical chemometric analysis to provide data exploration, biomarker identification, and classification models. These approaches provide for high accuracy results in classifications and suggest suitability for use in an applied setting for detection and reducing food loss and waste in the supply chain.