This paper reports a novel fluorescent aptasensor for the detection of acetamiprid pesticide with high sensitivity and selectivity based on the inner filter effect (IFE) of gold nanoparticles (AuNPs) toward fluorescent carbon dots (CDs). The aptasensor employs S-18 aptamer as the specific target recognition molecule and CDs as the signal transmission element. Free S-18 aptamer sequences can wrap the surfaces of AuNPs and enable their dispersion state even in the presence of high amounts of Tris-HCl salt, which can effectively quench the fluorescence of the CDs through the IFE. Upon adding acetamiprid pesticide, the free S-18 aptamer sequences are firstly exhausted to form some complexes; thus, the AuNPs tend to aggregate and their absorption spectrum no longer overlaps with the fluorescence emission spectrum of the CDs, which leads to obvious fluorescence recovery of the aptasensor. The properties of the CDs were extensively characterized, and the fluorescence quenching effects of the AuNPs toward the CDs were fully investigated. Additionally, the effects of some vital parameters, such as the type and amount of AuNPs, on the fluorescent aptasensor were also investigated. The proposed aptasensor has a detection limit as low as 1.08 μg L⁻¹ with a linear range of 5 to 100 μg L⁻¹ and shows high selectivity for acetamiprid over other control pesticides. Moreover, the aptasensor displays excellent accuracy and recovery in the detection of spiked real samples, suggesting that this fluorescent aptasensor can play important roles in the fields of food analysis and environmental detection.

Stacking fermentation is critical in sauce-flavor Baijiu production, but winter production often sees abnormal fermentations, like Waistline and Sub-Temp fermentation, affecting yield and quality. This study used three machine learning models (Logistic Regression, KNN, and Random Forest) combined with multi-omics (metagenomics and flavoromics) to develop a classification model for abnormal fermentation. SHAP analysis identified 13 Sub-Temp Fermentation and 9 Waistline microbial biomarkers, along with 9 Sub-Temp Fermentation and 12 Waistline flavor biomarkers. Komagataeibacter and Gluconacetobacter are key for normal fermentation, while Ligilactobacillus and Lactobacillus are critical in abnormal cases. Excessive acid and ester markers caused unbalanced aromas in abnormal fermentations. Additionally, ecological models reveal the bacterial community assembly in abnormal fermentations was influenced by stochastic factors, while the fungal community assembly was influenced by deterministic factors. RDA analysis shows that moisture significantly drove Sub-Temp fermentation. Differential gene analysis and KEGG pathway enrichment identify metabolic pathways for flavor markers. This study provides a theoretical basis for regulating stacking fermentation and ensuring Baijiu quality.

The objective of this study was to isolate and screen the mould microbiome of five Moutai‐flavor Daqu samples and to characterize and quantify the associated exogenous enzymes. In all, 11 and 18 mould genera were identified in the samples by, respectively, a culture‐dependent method and a culture‐independent high throughput sequencing method. Species of Aspergillus and Rhizopus were the dominant mould genera in the Moutai‐flavor Daqu samples. Analysis of enzyme production showed that Aspergillus versicolor in Daqu ZX01 exhibited the highest neutral proteinase activity (2671.0 ± 73.9 U/g) and Rhizomucor pusillus in Daqu DYT01 exhibited the highest saccharifying amylase production ability (1724.6 ± 11.0 U/g). Copyright © 2017 The Institute of Brewing & Distilling

To increase the safety and quality of baijiu and rice wine in China, controlling the use of traditional Xiaoqu by studying the beneficial yeasts present has recently been considered. The fungal diversity of six Chinese Xiaoqu including five traditional and one commercial samples was investigated to screen fermentative yeasts with low yields of higher alcohols. A high throughput sequencing approach detected fifteen fungal species with relative abundance higher than 1%, and displayed dissimilarities of fungal diversity among Xiaoqu samples. The 15 fungal species were composed of 11 filamentous fungi with Rhizopus arrhizus as the most common specie and four yeast species, containing Hyphopichia burtonii, Saccharomyces cerevisiae, Saccharomycopsis fibuligera, and Saccharomycopsis malanga. Classic culture-dependent approaches, including 5.8S-ITS-RFLP analysis and D1/D2 sequencing of the 26S rRNA gene, identified nine yeast species in the five traditional Chinese Xiaoqu. In addition to the four yeast species also detected by high throughput sequencing approach, the other five yeast species isolated were Clavispora lusitaniae, Cryptococcus neoformans, Komagataella pastoris, Trichosporon asahii, and Wickerhamomyces anomalus. Further micro-fermentations of rice wine were performed using 19 single yeast isolates, and after the fermentation of rice wine, higher alcohols and ethanol were analyzed by gas chromatography. Two yeast strains, Saccharomyces cerevisiae FBKL2.8022 and Wickerhamomyces anomalus FBKL2.8023, were found to have low yields of higher alcohols and could produce 11.70%vol and 7.10%vol ethanol separately. This study for the first time, to the best of our knowledge, explored the fungal resources in traditional Xiaoqu from different regions of Guizhou, China. The screened S. cerevisiae and W. anomalus strains could be used to establish specific starters to promote the standardization of the production of baijiu and rice wine.