Seeing the invisible: exploiting non-targeted techniques to detect cold and mechanical damage in kiwifruit

Mechanical damage and exposure to cold are among the main causes of quality deterioration in fresh produce during harvest, storage and distribution (Pruski, 2011). Such damage, often invisible to naked eye, compromises sensory and nutritional quality, leading to economic losses (Hou et al., 2023). Furthermore, conventional quality control methods – typically destructive and time-intensive – don’t meet the needs of Food Quality 4.0 (Hassoun et al., 2023). In response to this challenge, the study aims to develop and validate advanced non-targeted techniques for early detection of mechanical and cold-induced damage in kiwifruit produce. A representative sample of kiwifruit (Actinidia deliciosa, cv. Hayward) was stored under optimal refrigeration conditions (1°C, ≥ 90% relative moisture). To simulate real supply chain impacts, mechanical damage was induced by applying three force levels (approximately 18, 27, and 36 N) (Ahmadi, 2012). The progression of damage was then monitored using a multi-modal approach, combining Near-Infrared Spectroscopy (NIR), NIR Hyperspectral Imaging (NIR-HSI), thermography, and electronic nose. Additionally, conventional analyses – including texture, pH, °Brix, and enzymatic activity (polyphenol oxidase and peroxidase) – provided a comprehensive understanding of the deterioration processes. NIR and NIR-HSI successfully detected internal structure changes and moisture distribution, which were linked to texture softening and chemical alterations. These spectroscopic techniques made it possible to identify first signs of damage, before external signs became visible. Additionally, thermography revealed temperature gradients indicative of tissue breakdown, whereas electronic nose detected changes in the aromatic profile associated with enzymatic activities. Chemometric models, based on non-destructive data, showed that mechanical impact combined with cold stress influenced the deterioration. This approach accurately classified samples by damage level, showing that non-targeted techniques could detect hidden damages not visible externally. This study validates the use of advanced non-targeted techniques as reliable tools for real-time quality monitoring of fresh produce. By enabling early damage detection and enhancing cold chain management, this approach has significant potential to reduce waste, extend shelf life, and optimise supply chain efficiency.