Abstract Beer spoilage by microorganisms can lead to significant economic losses and quality defects in the brewing industry. Current molecular detection methods often lack sufficient specificity or require complex instrumentation, limiting their practicality for on-site use. This study developed a rapid and specific polymerase spiral reaction (PSR) assay for detecting Pediococcus acidilactici , a significant spoilage bacterium in beer production. From 13 candidate target genes, target PaciT2 was successfully cloned into a plasmid vector and used for subsequent assay development. Following multiple sequence alignment of its conserved region, three primer sets were designed and screened. The first primer set was ultimately used to establish and optimize the PSR assay protocol. The optimized PSR assay demonstrated exceptional specificity, producing positive results exclusively for P. acidilactici while showing no cross-reactivity with over 20 non-target strains, including six closely related lactic acid bacteria (LAB) species and various other foodborne pathogens. The assay achieved a high sensitivity, with a detection limit of 277.8 fg/μL. This combination of specificity and sensitivity renders the PSR method particularly suitable for monitoring P. acidilactici within the complex microbial communities of brewery environments. Overall, the established assay provides a rapid, equipment-independent, and highly reliable alternative to conventional PCR, offering significant advantages for on-site quality control in beer production.
Extracts from untreated and cold atmospheric plasma (CAP)-pretreated Scutellaria baicalensis were complexed with hydroxypropyl-beta-cyclodextrin (HP-beta-CD) and incorporated into poly(vinyl alcohol)/chitosan (PVA/CS) electrospun nanofiber membranes for grape preservation. 1H NMR supported host-guest inclusion, whereas FTIR and XRD indicated physical embedding and intermolecular interactions within the nanofiber matrix. Inclusion-complex incorporation altered spinning-solution properties, generally shifted fiber-diameter distributions toward smaller values, improved UV-visible light shielding, and caused no apparent deterioration in thermal behavior. DPPH radical-scavenging activity increased from 6.53 % for PVA/CS to 79.38 % for the membrane containing 3 % CAP/ICs. The composite membranes also inhibited Staphylococcus aureus and Escherichia coli. After 8 days at 25 degrees C, wrapped grapes showed lower weight loss, better firmness retention, smaller changes in total soluble solids and peel color, and less shriveling than the unwrapped control. These membranes show potential as active inner layers or preservation inserts for fresh-grape packaging.
Dietary fibers (DF) can help preserve the resistance of resistant starch (RS) after thermal processing, but the effects of fibers with contrasting hydration properties and solubility on its physicochemical properties have not been systematically compared under identical conditions. This study therefore investigated the effects of wheat bran fiber (WB), an insoluble DF with low hydration properties, and psyllium fiber (PS), a soluble DF with excellent hydration properties, on the physicochemical properties of RS. PS significantly enhanced pasting viscosity, apparent viscosity, viscoelastic moduli (G ' and G '') and hydration properties of RS, and formed a veil-like layer on starch granules, whereas WB showed limited effects. PS also disrupted crystalline and short-range molecular order, but WB did not. Both fibers reduced thermal stability, with PS showing a greater effect. These findings imply PS may suit RS-based formulations requiring viscosity and water retention, while WB may suit applications desiring minimal structural interference.
Optimal cold storages of post-harvest fruits are necessary to enhance preservation, improve energy efficiency, reduce manual monitoring and improve food security along with quality especially in agricultural economies like Pakistan. An experiment using object detection and adaptive temperature control is uniquely performed in a humid subtropical climate. A YOLO (You Only Look Once) object detection algorithm detects categories of fruit to adaptively control the set-point temperature of a cold storage refrigeration system. The algorithm differentiates fresh/stale bananas and apples to control compressor power of a 0.1-ton refrigerator. The object detecting system is 92 % accurate in sensing correct fruit. Adaptive temperature control through the compressor saves 30 % energy for bananas and 12 % for apples compared to conventional systems whilst passively regulating humidity and extending shelf-life of the fruit by 50 %. The methodology of this pilot-study can be extended to different foods beneficial for agriculture and transport sectors.
This study aimed to evaluate the capability of near-infrared (NIR) spectroscopy combined with multivariate regression algorithms to predict physicochemical properties and characterize ripening-stage progression of Fuji apple. Spectral preprocessing included SNV and smoothing, and three regression algorithms (PLSR, PCR, and SVM) were applied. Results demonstrated that predictive performance strongly depended on the property type. For physical properties, particularly firmness and L*, SVM achieved the highest prediction accuracy (R-2 = 0.890 and 0.891, RMSE = 3.951 and 3.449, RPD = 3.00 and 2.92, respectively), indicating excellent model robustness and industrial applicability. In contrast, a* and b* showed poor predictability (R-2 < 0.40, RPD approximate to 1.0-1.2). For chemical properties, starch-related properties showed the best performance (Starch%: R-2 = 0.854, RMSE = 8.518, RPD = 2.33 using SVM; TA: R-2 = 0.858, RMSE = 0.087, RPD = 2.66 using PLS), whereas TSS and pH showed consistently weak predictive performance across all algorithms (R-2 < 0.32, RPD < 1.3). Overall, NIR spectroscopy combined with appropriate regression modeling proved highly effective for predicting structural and starch-related maturity indicators, whereas prediction of TSS and pH requires further methodological refinement.
