Phenolic metabolites changes during baijiu fermentation through non-targeted metabonomic.

To investigate the changes of phenolic metabolite during different grains fermentation stages of Chinse Baijiu, the ultra-performance liquid chromatography-quadrupole time of-flight mass spectrometry (UHPLC-QTOF-MS) was applied to identify and analyze the different phenolic metabolites, combined with principal component analysis and partial least squares discriminant analysis. Results indicated that significant differences in phenolic metabolites during different fermentation stages were found. Among the 231 phenolic metabolites detected, 36, 31, 19, 23, 14, and 50 differential phenolic metabolites were screened between different groups using partial least squares discriminant analysis. Twelve metabolic pathways with high correlation of differential phenolic metabolites and 23 main participating differential metabolites were identified through KEGG metabolic pathway enrichment analysis. The present study preliminarily revealed the differences of phenolic metabolites at different fermentation stages, and providing a theoretical basis for the further improving of the taste and quality of Chinese Baijiu.

Changes in Polyphenols and Antioxidant Activity in Fermentation Substrate during Maotai-Flavored Liquor Processing.

To investigate the changes in phenols and antioxidant capacity in fermented grains during different stages of the fermentation process (Xiasha, Zaosha, and single-round stages) of Maotai-flavored liquor, the total phenolic contents of 61 samples, collected in different stages, were analyzed via the Folin-Ciocalteu method, and the phenolic compounds were then identified by high-performance liquid chromatography (HPLC). Subsequently, the antioxidant activities were determined using the DPPH free radical scavenging rate and ABTS and FRAP antioxidant capacities. The correlations among the total phenolic contents, individual phenolics, and three antioxidant activities of the samples were analyzed. The results show that the total phenolic contents of the fermented samples did not change significantly in the Xiasha and Zaosha stages but showed an upward trend in the single-round stage. A total of 12 phenol acids were identified in the fermented grains, including 5 phenolic acids (e.g., ferulic acid and caffeic acid), 4 flavonoids (e.g., luteolin and apigenin), and 3 proanthocyanidins (e.g., apigeninidin), for which the DPPH free radical scavenging rates and ABTS and FRAP antioxidant capacities of all of the fermented grain samples ranged from 78.91 ± 4.09 to 98.57 ± 1.52%, 3.23 ± 0.72 to 13.69 ± 1.40 mM Trolox, and 5.06 ± 0.36 to 14.10 ± 0.69 mM FeSO4, respectively. The total phenolic contents of the fermented grain samples were significantly and positively correlated with the ABTS and FRAP (p ≤ 0.05), while no significant correlations were found between total phenolic content and DPPH. In general, the total phenolic content, phenolic substances, and antioxidant capacity of the fermented grains exhibited changes during the fermentation process in liquor production, and the phenolic components contributed more to the antioxidant properties of the fermented grains. The present study provides a theoretical reference for analyzing the dynamic changes and antioxidant properties of functional phenolic components in fermented grains.

Enzyme-integrated metal-organic framework platform for cascade detection of α-amylase.

As one of the most important industrial enzymes, α-amylase is widely used in food processing, such as starch sugar and fermentation, bringing high added value to industry of more than a trillion dollars. We developed a multi-enzyme system (Glu&Gox@Cu-MOF-74) prepared by embedding α-glucosidase (Glu) and glucose oxidase (Gox) into the biomimetic metal-organic framework Cu-MOF-74 using in situ encapsulation within 15 min at room temperature for efficient and sensitive detection of α-amylase activity. Benefitting from the remarkable peroxidase-mimicking property and rigid skeleton of Cu-MOF-74, the biocatalytic platform exhibited excellent cascade activity and tolerance in various extremely harsh environments compared to natural enzymes. On this basis, a cascade biocatalytic platform was constructed for the detection of α-amylase activity with wide linear range (5-100 U/L) and low limit of detection (1.45 U/L). The colorimetric cascade scheme is important for the sensitive and selective determination of α-amylase in complex fermentation samples, and the detection time is short (∼0.5 h). This work provides new ideas for the detection of α-amylase based on the cascade amplification method.

Integration of multienzyme co-immobilization and biomimetic catalysis in magnetic metal-organic framework nanoflowers for α-amylase detection in fermentation samples.

Multiple enzymes induce biological cascade catalysis is essential in nature and industrial production. However, the shortcomings of enzymes, including unsatisfactory stability, reusability, and sensitivity in harsh microenvironment, have restricted their broader use. Here, we report a facile method for fabricating a cascade system by combining the benefits of immobilized enzymes and biomimetic catalysis based on magnetic metal-organic framework nanoflowers (mMOFNFs). mMOFNFs prepared through the layered double hydroxide-derived strategy exhibited remarkable peroxidase-like activity and accessible amino interface, enabling it to serve not only as a reliable carrier for α-glucosidase and glucose oxidase fixation, but also as a nanozyme participating in cascade. On this basis, a colorimetric biosensor of excellent sensitivity and selectivity for α-amylase detection was constructed with a wide range (2-225 U L-1), low detection limit (2.48 U L-1), and rapid operation (30 min). This work provides a versatile strategy for establishing multi-enzyme cascade systems and rapid analysis of α-amylase.

High-sensitivity profiling of dipeptides in sauce-flavor Baijiu Daqu by chemical derivatization and ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry.

Dipeptides in sauce-flavor Baijiu Daqu are protein degradation products during the fermentation of Daqu, which are believed to play a crucial role in the flavor and quality of Baijiu. Herein, we integrated dansyl chloride derivatization with ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS) for comprehensively profiling dipeptides in Daqu. The derivatization efficiency was higher than 99.1 % for all 17 dipeptide standards under the optimized derivatization conditions. In total, 118 dipeptides were detected in Daqu. The method was validated and the analytical characteristics including the linearity (spanned across 2-4 orders of magnitude), precision (1.2-19.9 %), limit of detection (varied from 1.1 to 53.4 pmol/mL) and the stability (3.6-15.8 %) are satisfactory. The usefulness of the method was examined by studying the distribution characteristics of dipeptides in Daqu under different production conditions. The present method provides an effective and robust strategy for comprehensively analyzing dipeptide compounds in complex biological samples.

Fusion data from FT-IR and MALDI-TOF MS result in more accurate classification of specific microbiota.

Microbes are usually present as a specific microbiota, and their classification remains a challenge. MALDI-TOF MS is particularly successful in library-based microbial identification at the species level as it analyzes the molecular weight of peptides and ribosomal proteins. FT-IR allows more accurate classification of bacteria at the subspecies level due to the high sensitivity, specificity and repeatability of FT-IR signals from bacteria, which is not achievable with MALDI-TOF MS. Previous studies have shown that more accurate identification results can be obtained by the fusion of FT-IR and MALDI-TOF MS spectral data. Here, we constructed 20 groups of model microbiota samples and used FT-IR, MALDI-TOF MS, and their fusion data to classify them. Hierarchical clustering analysis (HCA) showed that the classification accuracy of FT-IR, MALDI-TOF MS, and the fusion data was 85%, 90%, and 100%, respectively. These results indicate that both FT-IR and MALDI-TOF MS can effectively classify specific microbiota, and the fusion of their spectral data could improve the classification accuracy. The FT-IR and MALDI-TOF MS data fusion strategy may be a promising technology for specific microbiota classification.

MALDI-TOF MS Is an Effective Technique To Classify Specific Microbiota.

MALDI-TOF MS is well-recognized for single microbial identification and widely used in research and clinical fields due to its specificity, speed of analysis, and low cost of consumables. Multiple commercial platforms have been developed and approved by the U.S. Food and Drug Administration. Matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) has been used for microbial identification. However, microbes can present as a specific microbiota, and detection and classification remain a challenge. Here, we constructed several specific microbiotas and tried to classify them using MALDI-TOF MS. Different concentrations of nine bacterial strains (belonging to eight genera) constituted 20 specific microbiotas. Using MALDI-TOF MS, the overlap spectrum of each microbiota (MS spectra of nine bacterial strains with component percentages) could be classified by hierarchical clustering analysis (HCA). However, the real MS spectrum of a specific microbiota was different than that of the overlap spectrum of component bacteria. The MS spectra of specific microbiota showed excellent repeatability and were easier to classify by HCA, with an accuracy close to 90%. These results indicate that the widely used MALDI-TOF MS identification method for individual bacteria can be expanded to classification of microbiota. IMPORTANCE MALDI-TOF MS can be used to classify specific model microbiota. The actual MS spectrum of the model microbiota was not a simple superposition of every single bacterium in a certain proportion but had a specific spectral fingerprint. The specificity of this fingerprint can enhance the accuracy of microbiota classification.