INFORM May 2026

EXTRACTS & DISTILLATES INFORM 37

analyses. Finally, the challenges that still exist are discussed, such as the lack of methodological standardization and the need for quality control and analytical validation to ensure reliable and reproducible results. INSTRUMENTAL-ANALYTICAL INTEGRATION IN FOOD SENSORY EVALUATION: CURRENT TRENDS AND FUTURE HORIZONS Melikoglu, M., et al. , Microchemical Journal , 221, 116942, 2026. Sensory evaluation is a cornerstone of food science, bridging product characteristics with human perception. In a rapidly changing food landscape, its evolution is vital for assessing quality, developing new products, and gaining consumer insights. This paper reviews recent advancements (2020–2025) in sensory evaluation, highlighting three key trends. Firstly, there is a paradigm shift towards integrating state-of-the-art analytical chemistry methodologies with traditional sensory evaluation. Tools like high resolution separation techniques (e.g., GC–MS, SGC/GC 2 -O-MS), and intelligent sensor arrays (e-noses, e-tongues) are combined with chemometrics, machine learning, and artificial intelligence. This approach offers objective, precise, and high throughput predictive capabilities for deciphering complex food matrices. This significantly enhances quality assessment and reduces reliance on subjective human panels. Secondly, the literature explores the significant influence of production, processing, and storage conditions on sensory quality, providing practical analytical insights for optimizing the food supply chain and ensuring consistent product quality. Lastly, the review covers advancements in sensory methodologies and a deeper understanding of consumer perception and behavior, which are crucial for market success and addressing societal challenges like

food waste. Future research should focus on developing more robust and analytically generalizable predictive models, elucidating dynamic sensory perception, tailoring experiences for personalized nutrition, and applying analytical sensory science within sustainable food systems. This holistic and interdisciplinary approach is essential for addressing the complex challenges and opportunities in the future of food. NOVEL ANTIOXIDANT-ACTIVE FOOD PACKAGING BASED ON OLIVE OIL BYPRODUCTS EXTRACTS Schiavone, C., et al. , Food Chemistry , 504, 147951, 2026. Food waste reduction remains a major challenge in the European Union, where annual losses exceed 88 million tons. Active food packaging offers a sustainable strategy to extend shelf-life and maintain product quality. This study evaluated the antioxidant performance of natural extracts from olive by products stone, leaf and pomace obtained through a green ethanol based extraction. Polyphenolic profiles were determined by UHPLC DAD, and antioxidant capacity was measured using DPPH assays, with olive stone extract showing the best activity (EC50 = (63.6 ± 4.6) mg/L). Total biophenol contents ranged from 10 g/kg to 130 g/kg, with high levels of gallic acid and related polyphenols. Among all, olive stone extracts exhibited the strongest performance in terms of antioxidant power percentage ((84.4 ± 1.6) %AP). Packaging films incorporating these extracts significantly delayed lipid oxidation in minced beef meat with 50% fat, reducing it to 18.4% after

10 days versus 46.6% in the control, demonstrating strong potential for sustainable industrial application.

Bryan Yeh has over 30+ years of senior leader experience in agribusiness,

biofuels, energy, food, management consulting, renewable chemicals, synthetic biology, and water industries. He is based in Walnut Creek, California. The threat of edible oil adulteration has been around for a long time, especially when higher value oils such as olive oil, coconut oil or sesame oil are involved. While much effort has been placed on developing new methods that can detect traces of compounds consistent with adulteration, the advent of machine learning has given us another tool to use with existing, industry accepted methods that can result in improved reliability in adulteration detection. The first article evaluates the use of laser induced fluorescence coupled with machine learning to identify olive oil adulteration. The second article applied FTIR spectroscopy with machine learning for differentiating between pure and adulterated coconut and sesame oils. Researchers in the third article used machine learning with fatty acid gas chromatograph fingerprinting as a method to determine adulteration in peanut oil. In the fourth article, researchers demonstrated the use of a low cost electronic nose technology with machine learning as a rapid method to determine adulteration in avocado oil.