Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 5 appraisals
Journal of agricultural and food chemistry
Serum-Free Media in Cellular Agriculture: Current Challenges, Design Strategies, and Future Prospects
Cellular agriculture (CA), an emerging and sustainable agricultural paradigm, offers a promising solution to global challenges in food security and environmental sustainability. However, the large-scale manufacturing of CA is hindered by its dependence on serum-based culture media, which are costly and compositionally variable and raise biosafety and animal welfare concerns. Addressing this bottleneck requires the development of affordable, reliable, and ethically compliant serum-free media (SFM). This perspective elucidates the functional roles of serum components, summarizes recent advances in serum substitutes, and highlights microorganism-derived substitutes as particularly promising because of their low cost, compositional stability, and scalability. Furthermore, we outline the evolution of SFM formulation from empirical and design-of-experiment-based optimization to multiomics-driven formulation and artificial intelligence (AI)-assisted design. Overall, this review provides a focused framework for understanding current challenges, design strategies, and future directions of SFM development for CA.
15 May 2026
Read appraisal →Food chemistry
A comprehensive review on wine authentication and traceability: Current issues, solutions and future perspectives
Wine, as a commercially significant and globally consumed beverage, faces persistent challenges regarding authenticity verification and origin traceability, which threaten both consumer interests and market fairness. This paper comprehensively summarizes prevalent types of wine fraud issues and reviews modern analytical techniques including spectroscopy, chromatography, mass spectrometry, and emerging intelligent sensing techniques alongside advanced machine learning approaches for enhancing authentication and traceability of wine. The study highlights recent applications of these methods in verifying wine authenticity and geographical origins, emphasizing their evolving integration. Despite considerable progress, future development should focus on rapid detection, integrating deep learning with traditional analytical techniques, building wine fingerprint database, and leveraging blockchain for supply chain transparency. These measures are crucial to ensure the authenticity and geographical origins of wine, thereby safeguarding the interests of all stakeholders, including consumers and producers.
17 Apr 2026
Read appraisal →Journal of agricultural and food chemistry
Dairy-Derived Aroma Release Modulated by Oral Mucin: In Vitro Study Insights from Multispectral, Computational Modeling, and Machine Learning
Salivary mucin plays a pivotal role in modulating aroma perception during dairy consumption. This study investigated the impact of mucin on the release of ten key milk fat-derived aroma compounds (including 3 aldehydes, 3 ketones, and 4 lactones) using headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). The results demonstrated that mucin significantly inhibited the release of most compounds, including 2-heptanone, 2-nonanone, 2-pentadecanone, heptanal, δ-hexalactone, δ-octalactone, and δ-decalactone, with a maximum binding percentage of 34.79 ± 9.63%. Sensory analysis confirmed perceptible differences. Thermodynamic and spectroscopic data revealed that the binding was spontaneous and primarily driven by hydrophobic interactions and hydrogen bonds involving phenylalanine and tyrosine residues, which induced significant conformational changes in the mucin secondary structure. Shapley additive explanations analysis based on the support vector regression model identified the number of H-bond acceptors as the most critical molecular feature. These findings provide insights for modulating aroma perception in dairy products.
17 Apr 2026
Read appraisal →Food chemistry
Machine learning-driven flavoromics: Decoding stage-specific volatile compound dynamics and sensory deterioration in stored infant formula
Infant formula (IF) is a vital nutritional source for infants. However, its sensory quality deteriorates during prolonged storage, impacting consumer acceptance. This study investigated the stage-specific sensory deterioration mechanisms and the predictive modeling of IFs during storage by integrating flavoromics, fatty acid dynamics, and machine learning. Quantitative descriptive analysis identified six key attributes driving quality loss, with one-stage IF showing the earliest and most pronounced decline. Volatile compound profiling revealed aldehydes (hexanal, octanal) and ketones as dominant off-flavor markers, correlating with lipid oxidation pathways. Fatty acid analysis showed the degradation of polyunsaturated fatty acids, particularly linoleic and α-linolenic acid, as key drivers of off-flavor generation. Using only six lipid-oxidation-derived volatiles, Random Forest and XGBoost models accurately predicted stage-specific sensory scores (with R2 > 0.85), directly linking fatty acid oxidation to flavor deterioration. This work establishes lipid oxidation as predominant degradation mechanism and provides practical, data-driven tool for shelf-life optimization.
17 Apr 2026
Read appraisal →Journal of agricultural and food chemistry
From Sensometabolomics to Rapid Analysis and Machine Learning: A Perspective on Modeling Wine Mouthfeel
Wine is an intriguing natural product involving biotransformation of grape constituents and continued chemical evolution during aging. Deep knowledge of the chemistry and sensory properties of wine has been developed, but gaps remain and new challenges emerge. Boundaries continue to be pushed with sensometabolomics used to identify important chemical contributors related to sensory acceptance and product quality. Rapid methods and timely access to results are also paramount for a digitally transformed industry, but masses of data are generated. Machine learning is quickly emerging as a versatile and powerful approach for handling multidimensional data sets and modeling intricate wine phenomena.
10 Apr 2026
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