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Yanfang Wu

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Open access Aug 2026

Rare Biosphere Reveals a Decoupling Between Microbial Abundance and Intrinsic Physiological Potential in Shanxi Aged Vinegar Fermentation

The discrepancy between in situ microbial abundance and actual metabolic performance represents a critical challenge for interpreting microbial function from meta-omic data. Here, we integrated metagenomic and metatranscriptomic sequencing to investigate this decoupling between microbial abundance and cultivation-based physiological potential in Shanxi aged vinegar (SAV) solid-state fermentation. Lactobacillus acetotolerans dominated the community at both the genomic (40.89%) and transcriptomic (55.36%) levels, whereas Pediococcus acidilactici accounted for only 0.11%—a canonical rare-biosphere member. Source tracking via Sankey analysis showed that genes involved in acetate production were primarily attributed to Acetobacter pasteurianus, whereas genes involved in lactate production were predominantly associated with Lactobacillus spp. However, L. acetotolerans exhibited limited acid tolerance and lactic acid production, whereas the low-abundance P. acidilactici AAF1-5 displayed robust stress tolerance and superior lactic acid production under fermentation-relevant conditions—a striking contrast between microbial abundance and physiological performance. Metabolic interaction network analysis predicted that P. acidilactici may be co-inhibited by L. acetotolerans (Ixy = −2.737, resource competition) and A. pasteurianus (Ixy = −1.887, acid stress). To test whether ecological constraints, rather than intrinsic metabolic capacity, underlie this low abundance, we heterologously expressed the heat shock co-chaperone gene grpE from A. pasteurianus in P. acidilactici AAF1-5 as an experimental tool. The recombinant strain P. acidilactici-grpE exhibited significantly enhanced viability under acetic acid stress and, in simulated SAV fermentation, lactic acid content increased by 23.63% compared with the wild-type control. These results demonstrate that meta-omic abundance does not necessarily predict physiological performance and that low abundance may reflect ecological constraints rather than intrinsic functional deficiency. Our study provides an ecological framework for linking microbial abundance with physiological function beyond sequence-based abundance inference in complex fermentation microbiomes.

Yan-Fang Wu, Yan Li, Hanlin Chen et al. · Foods · 0 citations

#explainable ai Review Oct 2026

Machine learning-assisted spectroscopic approaches for pesticide residue detection in food matrices.

The widespread use of pesticides in modern agriculture has substantially improved food production while raising serious concerns regarding contamination and food safety. Considerable scientific effort has focused on improving methods for monitoring and detecting pesticide residues in food to enhance safety assurance. Although chromatography coupled with mass spectrometry remains the gold standard, its routine application is constrained by high costs, labor-intensive sample preparation, and prolonged analysis times. Recent advances in spectroscopic techniques, including surface-enhanced Raman spectroscopy (SERS), Raman spectroscopy, hyperspectral imaging (HSI), and near-infrared (NIR) spectroscopy, offer promising non-destructive, rapid, and sensitive alternatives for pesticide residue detection across diverse food matrices. When integrated with machine learning (ML), these approaches further improve predictive accuracy and analytical robustness. This review synthesizes recent advances in ML-assisted spectroscopic approaches for pesticide residue detection across diverse food matrices, with emphasis on analytical performance, preprocessing strategies, feature engineering, and model selection. Convolutional neural networks (CNNs), support vector machines (SVMs), random forests (RFs), and ensemble learning methods are increasingly used to improve classification and quantitative prediction. Across the reviewed studies, analytical performance was generally strong, with high classification accuracies, while the lowest reported detection limit was achieved using a SERS-CNN platform. Despite these advances, key limitations remain, including reliance on laboratory-spiked samples, small dataset sizes, matrix interference, inconsistent validation strategies, high computational demands associated with high-dimensional spectral data, and limited field validation. Future directions should focus on hybrid AI-driven sensors, IoT integration, advanced data augmentation, QuEChERS-assisted preprocessing, and explainable AI to improve real-world applicability and interpretability.

B. C. Ezenwanne, C. Okoye, Stanley Ebhohimhen Abhadiomhen et al. · Food Research International · 1 citation