A groundbreaking development in food quality control has emerged from the laboratories of the Universitat Rovira i Virgili (URV) in Spain, promising to revolutionize how the freshness and edibility of hazelnuts are assessed. The research team has successfully created an innovative non-destructive method employing near-infrared hyperspectral imaging (NIR-HSI) to detect oxidation in hazelnuts even while they are still inside their shells and packaging. This method bypasses the limitations of conventional approaches that often require sample destruction or lengthy preparatory procedures, opening new avenues for quality monitoring in the agriculture and food distribution sectors.
Hazelnuts, a vital component of Catalonia’s nut production—second only to almonds—play an important economic role in the region, chiefly in Tarragona. Given the strong export orientation of the Catalan nut cooperatives, ensuring the consistent quality and longevity of hazelnuts during packaging and distribution is paramount. Oxidation, a chemical degradation process fundamentally responsible for causing hazelnuts to turn rancid, compromises taste and shelf life. The research highlights how contact with oxygen and light accelerates oxidative reactions in the unsaturated fatty acids comprising the hazelnuts’ chemical structure, underscoring the importance of appropriate packaging to slow this degradation.
Traditional analytical methods for monitoring nut oxidation typically involve destructive chemical assays, use of reagents, and often necessitate physical sample preparation. The novel approach by the URV team introduces hyperspectral cameras capable of mapping the chemical composition across an entire surface area. Unlike conventional spectrometers that provide spectral data point-by-point, hyperspectral imaging captures spatial and spectral information simultaneously, creating detailed chemical profiles of the hazelnut sample without physical interference.
Central to the technique’s efficacy is the application of infrared radiation, which occupies a spectral range with longer wavelengths than visible light yet lower frequencies than green light, rendering it invisible to human vision. Infrared light interacts uniquely with organic molecules, which selectively absorb specific wavelengths corresponding to molecular vibrations—particularly those linked to oxidation byproducts. By harnessing these absorption patterns, the hyperspectral system discerns subtle chemical changes that indicate the degree of oxidation, enabling rapid and accurate quality evaluation.
One of the critical advantages of this technology is its ability to analyze all hazelnuts collectively within their packaging without removal or sample destruction, thus representing a paradigm shift toward more sustainable and efficient analytical practices in food sciences. This non-contact method does not require reagents or direct sample handling, aligning with growing trends in green analytical chemistry that seek to minimize environmental impact and occupational hazards.
Calibration and validation of the device involved subjecting hazelnut samples to various storage conditions over an extended period—78 days—to model how factors such as vacuum packaging, nitrogen atmospheres, atmospheric exposure, and light intensity influence oxidative degradation. These experimental conditions generated the spectral data necessary to build robust mathematical models correlating infrared absorbance with the physical and chemical states of the samples, ensuring precise predictive capability and real-world applicability.
The insights gained confirmed that exposure to atmospheric oxygen and light are the main catalysts accelerating oxidation, while storage duration proportionally increases oxidative products. Vacuum packaging emerged as the most effective preservation approach for maintaining nut quality over time, significantly retarding the rancidity onset. Light exposure, conversely, markedly destabilizes the product, reinforcing the need for protective packaging solutions that mitigate photodegradation during distribution and storage.
Going beyond laboratory quantification, the researchers correlated their spectral findings with sensory evaluations to ascertain if chemical changes detected by hyperspectral imaging translate into perceptible differences for consumers. Sensory panels reported increased rancidity in samples stored in light and air-exposed conditions, validating the hyperspectral technique’s relevance in predicting not only chemical composition but also product quality from an end-user perspective.
This research contributes to a larger scientific movement embracing non-destructive optical methods in quality control, offering industries competitive advantages through enhanced efficiency, reduced waste, and improved product standardization. While currently the instruments bear a high capital cost—often exceeding 50,000 euros—the technology is becoming increasingly accessible. Its adaptation is already progressing in adjacent applications such as distinguishing bitter versus sweet almond varieties and sorting plastics in recycling facilities, signaling a broader revolution in spectroscopic quality assessment.
At the heart of this innovation is Jokin Ezenarro, lead author and analytical chemist at URV, who remarked on the transformative potential of hyperspectral imaging systems. Reflecting on its future impact, he emphasized that the technology is “here to stay,” suggesting widespread adoption within the food industry is imminent as instruments become more cost-effective and user-friendly.
The significance of this work lies not only in preserving the integrity and flavor of a key agricultural product but also in ecological and economic dimensions. Reduced food loss through improved monitoring translates into lower resource waste and strengthens supply chains globally. Furthermore, the technology’s scalability across various food matrices holds promise for enhancing quality assurance protocols well beyond hazelnuts, impacting nuts, fruits, meats, and processed foods alike.
Ultimately, the integration of NIR-HSI with sophisticated chemometric models symbolizes a leap forward in analytical capabilities—uniting physics, chemistry, and data science to tackle age-old problems of food spoilage. As this method enters the commercial realm, producers, distributors, and consumers have the opportunity to benefit from unprecedented transparency, reliability, and sustainability in the nuts sector and beyond.
Subject of Research: Not applicable
Article Title: NIR-HSI for the non-destructive monitoring of in-bag hazelnut oxidation
News Publication Date: 16-Feb-2025
Web References: http://dx.doi.org/10.1016/j.saa.2025.125906
References: Jokin Ezenarro, Ines Saouabi, Ángel García-Pizarro, Daniel Schorn-García, Montserrat Mestres, Jose Manuel Amigo, Olga Busto, Ricard Boqué, NIR-HSI for the non-destructive monitoring of in-bag hazelnut oxidation, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Volume 333, 2025, 125906, ISSN 1386-1425.
Image Credits: Universitat Rovira i Virgili
Keywords
Chemical analysis, Oxidation, Infrared radiation, Chemical composition, Absorbance spectroscopy, Fatty acids
