Lipopeptide fermentation faces challenges of high costs and low yields, requiring advanced monitoring solutions. This study presents an integrated methodology combining ultraviolet-visible (UV-vis), attenuated total reflection mid infrared (ATR-MIR), and surface enhanced Raman scattering (SERS) spectroscopy with chemometrics. Fermentation samples were used to build partial least-squares regression models for predicting reducing sugars, cell concentration, and lipopeptide yield. The stability competitive adaptive reweighted sampling (sCARS) algorithm outperformed principal component analysis in feature extraction. Evaluation of four fusion strategies revealed that full feature fusion yielded the highest predictive accuracy. The combined UV-vis, ATR-MIR, and SERS approach achieved optimal performance for lipopeptide yield (determination coefficient of prediction, R2p = 0.986; root-mean-square error of prediction, RMSEP = 0.357) and cell concentration (R2p = 0.977, RMSEP = 0.010). This spectroscopic strategy enables efficient, non-destructive fermentation monitoring and shows considerable promise for industrial bioprocess optimization.

The flavor quality of fermented foods is largely shaped by microbial metabolism. Among these, those whose concentrations are typically at the parts-per-billion (ppb) or even lower concentrations but exert disproportionately important effects on sensory perception. Despite their potential significance, these low-abundance compounds remain insufficiently investigated. In this review, we define such compounds as dark metabolites, including both volatile and non-volatile components, and systematically summarize recent advances in their classification, flavor attributes, formation mechanisms, detection strategies, and functional potential. Dark metabolites in fermented foods mainly include sulfur-containing compounds, nitrogen-containing heterocycles, metal ion-small molecule complexes, selenium- and other trace-element-containing complexes, atypical esters, and oligomeric small molecules. Their formation is jointly regulated by precursor availability, microbial metabolism involving lactic acid bacteria, yeasts, and molds, and processing or storage conditions such as pH, oxygen availability, salinity, light exposure, and storage environment. These metabolites contribute to the complex flavor profiles of fermented foods through ultra-low odor thresholds and synergistic or masking interactions with major metabolites. In terms of analytical approaches, volatile dark metabolites are commonly characterized usinggas chromatography-olfactometry-mass spectrometry (GC-O-MS) and gas chromatography-ion mobility spectrometry (GC-IMS), which enable qualitative identification and odor activity evaluation. By contrast, nonvolatile dark metabolitesare more effectively analyzed using high-resolution mass spectrometry (HR-MS)and liquid chromatography-tandem mass spectrometry (LC-MS/MS). In addition, they may exhibit antioxidant, antibacterial, and anti-inflammatory activities, as well as potential roles in regulating gut health and mineral homeostasis. This findings provides a theoretical basis for their precise regulation and value-added application infermented foods.

Three novel antioxidant peptides (FPEF, LGFD, MGFGL, molecular weight <1000 Da) were isolated from Agaricus bisporus via sequential DA201-C macroporous resin, Sephadex G-100 gel filtration, and RP-HPLC. Molecular docking revealed strong Keap1 binding (energies: −8.2 to −8.7 kcal/mol) via hydrogen bonds and π-alkyl interactions. Cellular assays confirmed that all peptides alleviated H₂O₂-induced oxidative damage in HepG2 cells by reducing ROS/MDA levels, enhancing SOD/CAT/GSH-Px activities, and upregulating Nrf2-target genes (HO-1, NQO-1). Structural analysis indicated that FPEF's stability arose from balanced secondary structure and flexibility, rather than high ordered-structure content. FPEF exhibited superior stability under heat (100 °C), pH (3−11), and simulated gastrointestinal digestion (>90% retention), while MGFGL showed the highest ABTS radical scavenging activity (98.62% at 1.5 mg/mL). Collectively, FPEF shows promise as a natural antioxidant for functional food and nutricosmetic applications.

Zinc-polysaccharide complexes are novel functional complexes formed through the chelation of Zn2+ with active groups (such as hydroxyl and carboxyl groups) on polysaccharide chains. This process not only alters the host structure and physicochemical properties of the polysaccharides but also synergistically enhances the bioactivity of both Zn2+ and the polysaccharides themselves, exhibiting multiple physiological functions including antioxidant, hypoglycemic, antitumor, and immunomodulatory effects. Due to their natural origin, high bioavailability, and excellent safety profile, zinc-polysaccharide complexes represent a promising new class of zinc supplements. Furthermore, they demonstrate significant potential in pharmaceutical applications and as agricultural feed additives. This paper systematically summarizes and compares the preparation methods of zinc-polysaccharide complexes, including plant transformation, fermentation, and chemically synthesized approaches, and analyzes their structural characteristics, biological activities, and application potential. At the same time, this paper highlights the challenges in current research, such as technical bottlenecks in large-scale production associated with different preparation methods, including the poor product uniformity and uncontrollable molecular weight associated with chemical synthesis, the susceptibility of fermentation methods to zinc toxicity, and the excessively long production cycle of plant-based conversion methods; insufficient structure-activity relationship (SAR) studies, most of which remain at the level of phenomenological descriptions; the lack of standardized clinical studies on in vivo pharmacokinetic profiles, long-term safety evaluations, and standardized quality control, and discusses future directions. This review aims to comprehensively summarize recent research progress on zinc-polysaccharide complexes, provide a theoretical reference for subsequent studies, and promote the continued development and practical application of these complexes.

This study employed mid-infrared attenuated total reflection (ATR-MIR) spectroscopy and chemometrics to monitor the submerged fermentation process of Tremella fuciformis (T. fuciformis). It investigated the effects of four different preprocessing methods on the performance of both the qualitative identification and the quantitative prediction models. The qualitative model, which employed unsupervised learning via Principal Component Analysis (PCA), analyzed ATR-MIR data, physicochemical parameters, and rheological parameters, clearly delineating distinct fermentation stages. The supervised Random Forest (RF) model optimized input variables through feature importance selection and PCA, achieving a classification accuracy of 97.5%. The quantitative model, Partial Least Squares Regression (PLSR), demonstrated strong predictive performance for reducing sugar, total sugar, tremella polysaccharide, and dry cell weight, with low root mean square error and high R2 values. This ATR-MIR spectroscopy-based chemometrics model offers valuable insights for food science and holds the potential for optimizing tremella polysaccharide production through precise fermentation control.

The lipopeptide fermentation process faces challenges of complexity, high cost and low yield. This study developed a real-time monitoring strategy using Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometrics to monitor Bacillus subtilis fermentation. Four spectral preprocessing methods (FD, SD, SG-FD, SG-SD) were evaluated for their effects on qualitative and quantitative models. Principal Component Analysis (PCA) successfully identified different fermentation stages from spectral and physicochemical data. For classification, Linear Discriminant Analysis (LDA) showed superior accuracy among classifiers (eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and LDA), with SG-SD preprocessing yielding the best generalization. In quantitative modeling, Partial Least Squares Regression (PLSR) models with preprocessing significantly outperformed raw data models. The SG-FD-PLSR model optimally predicted bacterial concentration (R2 = 0.9720, RMSEP = 0.0044), while SD-PLSR achieved the best performance for reducing sugar (R2 = 0.9762, RMSEP = 0.0897) and lipopeptide yield (R2 = 0.9406, RMSEP = 0.1095). The integration of ATR-FTIR with chemometrics provides an effective monitoring strategy, demonstrating strong potential for industrial-scale lipopeptide production.

The fermentation of Candida utilis has become a cost-effective and high-yield process, characterized by its high nutritional value, high productivity, and short fermentation time. To efficiently and comprehensively monitor the fermentation process of Candida utilis, this study employed an integrated data fusion system, based on attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy and colorimetry, combined with chemometrics to achieve efficient and comprehensive monitoring of the Candida utilis fermentation process. By analyzing the trends in key physicochemical indicators during fermentation, the fermentation process could be divided into four stages: 0-3 h, 3-9 h, 9-24 h, and 24-33 h. Principal component analysis (PCA) was employed to reduce the dimensionality of the highly collinear infrared spectral data to 1-10 principal components. Through the evaluation of four distinct machine learning algorithms, the optimal data fusion method and the best-performing machine learning model (random forest, with a classification accuracy of 95.30 %) were identified. PCA and linear discriminant analysis (LDA) revealed that samples from different fermentation times exhibited distinct clustering trends, although full differentiation was not achieved. To enhance classification accuracy, supervised learning models based on class labels were introduced. A comparison of classification accuracy between single signal and fusion signal approaches revealed that the optimal model achieved superior performance on the fusion dataset, with a 4-stage classification accuracy of 0.978, which was higher than that of the 11-stage classification. Quantitative prediction of key physicochemical parameters during the fermentation process was conducted using partial least squares regression (PLSR) and support vector regression (SVR) based on colorimeter dataset, ATR-MIR dataset, and fusion dataset. The data fusion strategy demonstrated excellent predictive performance for physicochemical indicators, particularly in predicting pH (R2p = 0.940, RMSEP = 0.210) and cell concentration (R2p = 0.946, RMSEP = 0.236). The color difference dataset exhibited the highest accuracy in predicting reducing sugar (R2p = 0.987, RMSEP = 0.988). The results demonstrated that, compared to PLSR, SVR demonstrated superior performance in these quantitative analysis tasks, enabling more accurate prediction of the physicochemical parameters in the Candida utilis fermentation process.

Fresh-cut apples are susceptible to enzymatic browning and spoilage. The objective of this study was to evaluate the effect of different coatings on fresh-cut apples and develop a predictive model for their shelf life. The apples were treated with antioxidant peptide from Candida utilis (CUH), carboxymethyl chitosan (CMCS), and composite coatings, and their physicochemical properties were subsequently evaluated. Key factors identified through correlation analysis of shelf life were used as input parameters for a partial least squares regression (PLSR) model to predict the shelf life of fresh-cut apples treated with different coatings. The results indicate that CUH, CMCS, and CUH-CMCS coatings effectively delay the deterioration of fresh-cut apples. Notably, the CUH-CMCS composite coating demonstrated superior performance, showing only a slight 3.50% increase in the browning index (BI) during storage. Minimal changes were observed in weight loss and firmness, while overall total acidity (TA) and pH exhibited slight decreases. Moreover, the levels of ascorbic acid (Vc), polyphenol oxidase (PPO), and peroxidase (POD) were significantly lower compared to the control group, and the total bacterial count increased by no more than 0.7 log CFU g(-1). The PLSR model accurately predicted the shelf life of fresh-cut apples treated with different coatings, with R-c(2) and R-p(2) values both exceeding 0.90. The research results indicate that the coating and model developed in this study offer a novel approach for preserving and managing fresh-cut apples.

BACKGROUND:Potato starch (PS) is widely used in food, but its application is limited because of its poor heat resistance and easy aging. Therefore, it is necessary to adopt some modification methods to improve its performance and expand its application range. RESULTS:To improve these shortcomings of PS, the effect of yeast β-glucan (YG) at different concentrations (0%, 1%, 2% and 3%, w/v) on the gelatinization, structure and in vitro digestive properties of PS were investigated. The interaction of YG with PS was different because of different molecular weights. The addition of YG reduced the peak viscosity and increased the final viscosity of PS. YG made the texture of PS gel softer, and the effect of low molecular weight YG was more obvious. YG enhanced the thermal stability of PS. Fourier transform infrared spectroscopy showed that YG and PS interacted through hydrogen bonds. In addition, YG reduced the digestibility of PS in vitro. CONCLUSION:Collectively, the addition of β-glucan to PS can serve as a new approach to enhance the technological properties of PS in food applications. These results will provide theoretical basis for PS to develop into functional food. © 2024 Society of Chemical Industry.

Lipopeptides have favorable biological activity and thus have great potential to serve as replacements. Whole genome sequencing was used in this work to pinpoint the gene clusters in Bacillus subtilis that code for secondary metabolites that have antibacterial qualities. Afterwards, the metabolic pathways responsible for the production of these compounds were clarified, and the lipopeptides were separated and identified using a combination of chromatography and spectroscopy methods, such as High-Performance Liquid Chromatography (HPLC), Fourier Transform Infrared Spectroscopy (FTIR), and Liquid Chromatography-Mass Spectrometry (LC-MS). Bacillus subtilis mainly synthesized surfactin homologs with carbon chain lengths ranging from C13 to C16 ([M +H] +: 994.6418; 1008.6586; 1022.6730; 1036.6896) and fengycin homologs with carbon chain lengths of C15 and C16 ([M +H] +: 1449.7937, 1463.8046). The isolated lipopeptides exhibited strong inhibitory effects against Escherichia coli and Staphylococcus aureus , with minimum inhibitory doses of 12.50 mg/L and 6.25 mg/L, respectively. Furthermore, lipopeptides demonstrated a dose-dependent inhibition of E. coli and S. aureus . The findings from SEM and SYTO 9/PI staining demonstrate that lipopeptides function by compromising the integrity of bacterial cell membranes. The results demonstrate the effectiveness of lipopeptides as antibacterial agents against foodborne pathogens, indicating their potential as substitutes for traditional antibiotics.

Natto is a functional food, but it produces an unpleasant smell during the fermentation process. To eliminate the unacceptable smell, Lactobacillus bulgaricus and Bacillus subtilis were used to co-ferment soybeans in this study. The optimal conditions of co-fermentation anaerobic fermentation were: fermentation time 20 h, temperature 35 °C, strain ratio 1:1, and inoculum amount 4

Currently, the synthesis of polysaccharide metal ion complex is predominantly categorized into three approaches: chemical synthesis, physical synthesis and biosynthesis method. According to research, polysaccharide metal ion complexes usually exhibit superior biological activities than metal ions or polysaccharides alone, including antioxidant, hypoglycemic, hypolipidemic, immune regulation, anti-tumor, anti-inflammatory and antibacterial activities. In this paper, the preparation process, biological activity and structural characteristics of polysaccharide metal ion complexes were reviewed, aiming to provide references for the development of new therapeutic drugs and adjuvants. And the complexation of Tremella fuciformis polysaccharides and metal ions was prospected.

Seven novel antioxidant peptides (AWF, LWQ, WIY, YLW, LAYW, LPWG, and LYFY) exhibiting a superior activity compared to trolox were identified through in silico screening. Among these, the four peptides (WIY, YLW, LAYW, and LYFY) displayed notably enhanced performance, with ABTS activity 2.58-3.26 times and ORAC activity 5.19-8.63 times higher than trolox. Quantum chemical calculations revealed that the phenolic hydroxyl group in tyrosine and the nitrogen-hydrogen bond in the indole ring of tryptophan serve as the critical sites for antioxidant activity. These findings likely account for the potent chemical antioxidant activity. The corn peptides also exerted a protective effect against AAPH-induced cytomorphologic changes in human erythrocytes by modulating the antioxidant system. Notably, LAYW exhibited the most pronounced cytoprotective effects, potentially due to its high content of hydrophobic amino acids.

This study proposes an efficient method for monitoring the submerged fermentation process of Tremella fuciformis (T. fuciformis) by integrating electronic nose (e-nose), electronic tongue (e-tongue), and colorimeter sensors using a data fusion strategy. Chemometrics was employed to establish qualitative identification and quantitative prediction models. The Pearson correlation analysis was applied to extract features from the e-nose and tongue sensor arrays. The optimal sensor arrays for monitoring the submerged fermentation process of T. fuciformis were obtained, and four different data fusion methods were developed by incorporating the colorimeter data features. To achieve qualitative identification, the physicochemical data and principal component analysis (PCA) results were utilized to determine three stages of the fermentation process. The fusion signal based on full features proved to be the optimal data fusion method, exhibiting the highest accuracy across different models. Notably, random forest (RF) was shown to be the most accurate pattern recognition method in this paper. For quantitative prediction, partial least squares regression (PLSR) and support vector regression (SVR) were employed to predict the sugar content and dry cell weight during fermentation. The best respective predictive R2 values for reducing sugar, tremella polysaccharide and dry cell weight were found to be 0.965, 0.988, and 0.970. Furthermore, due to its ability to capture nonlinear data relationships, SVR had superior performance in prediction modeling than PLSR. The results demonstrated that the combination of electronic sensor fusion signals and chemometrics provided a promising method for effectively monitoring T. fuciformis fermentation.

Summary This research was designed to examine the effects of activated carbon dosage and pH on various attributes of corn protein hydrolysate, including decolourization efficiency, nitrogen component, amino acid (AA) composition, protein secondary structure, and antioxidant activity. Optimal pigment elimination of occurred within the pH range of 2.0–4.0, utilising the hydrolysate as the control. Remarkably, the removal of trichloroacetic acid (TCA) insoluble peptide was evident at pH 4.0. The decolourization process elicited considerable reduction of the aromatic AA, exhibiting pronounced decreases of acidic AA (Glu and Asp) within the pH range of 2.0–4.0, and basic AA (His, Lys, and Arg) within the range of 6.0–8.0. Meanwhile, infrared spectroscopy analyses indicated that a dosage of 0.5% activated carbon at pH 4.0, effectively preserved the secondary structure and ratios of the control, maintaining its antioxidant activity. These results provide insights into optimisation strategies for decolourization in the food industry.

The current trend is the promotion of antioxidants that are beneficial for both health and the environment. Candida utilis have garnered considerable attention due to their commendable attributes such as non-toxicity and the ability to thrive in waste. Therefore, Candida utilis was used as raw material to isolate and identify new antioxidant peptides by employing methods such as ultrafiltration, DEAE Sepharose Fast Flow, and liquid chromatography-tandem mass spectrometry. The antioxidant mechanism of peptides was investigated by molecular docking. The properties of antioxidant peptides were evaluated using a variety of computational tools. This study resulted in the identification of two novel antioxidant peptides. According to the molecular docking results, the antioxidant mechanism of Candida utilis peptides operates by obstructing the entry to the myeloperoxidase activity cavity. The (-) CDOCKER energy of antioxidant peptides was 6.2 and 6.1 kcal/mol, respectively. Additionally, computer predictions indicated that antioxidant peptides exhibited non-toxicity and poor solubility.

The second-generation of iron supplements is known for its instability, thus leading to the emergence of polysaccharide iron complexes as a novel third-generation iron supplement, which has garnered significant scholarly attention. Yet, there is a limited body of academic literature on the stability and biological activity of polysaccharide iron complexes. The objective of this study is to investigate the stability, release of Fe3+, and biological activity of fermented Tremella polysaccharide after complexation with Fe3+ in order to address the research gaps in this field. The combination of Fe3+ and polysaccharide was confirmed successful by scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, X-ray diffraction, and circular dichroism spectroscopy. The release rate of the fermented Tremella polysaccharide-Fe3+ complex in simulated gastric fluid for 0.5 h was found to be 76% +/- 2%, indicating a favorable release profile. Additionally, the complex exhibited excellent viscosity stability, while the introduction of Fe3+ into Tremella polysaccharide resulted in improved thermal and solution stability. Moreover, the complex exhibits excellent antioxidant and hypoglycemic activity in vitro. The results demonstrate that the complex possesses exceptional stability and dual efficacy in terms of iron replenishment and nutrition, thus positioning it as a potential key player among novel iron supplements.

Nattokinase (NK) is a thrombolytic enzyme extracted from natto, which can be used to prevent and treat blood clots. However, it is sensitive to the environment, especially the acidic environment of human stomach acid, and its effect of oral ingestion is minimal. This study aims to increase NK's oral and storage stability by embedding NK in microcapsules prepared with chitosan (CS) and gamma- polyglutamic acid ( gamma- PGA). The paper prepared a doublelayer NK oral delivery system by layer self-assembly and characterized its stability and in vitro simulated digestion. According to the research results, the bilayer putamen structure has a protective effect on NK, which not only maintains high activity in various environments (such as acid-base, high temperature) and long-term storage (60 days), but also effectively protects the loaded NK from being destroyed in gastric fluid and achieves its slow release. This work has proved the feasibility of the design of bilayer putamen structure in oral administration and has good fibrolytic activity. Therefore, the novel CS/ gamma- PGA microcapsules are expected to be used in nutraceutical delivery systems.

γ-Polyglutamic acid is a kind of biomaterial and environmentally friendly polymer material with the characteristics of water solubility and good biocompatibility. It has a wide range of applications in medicine, food, cosmetics and other fields. This article reviews the preparation, characterization and medical applications of γ-polyglutamic acid nanoparticles. Nanoparticles prepared by using γ- polyglutamic acid not only had the traditional advantages of enhancing drug stability and slow-release effect, but also were simple to prepare without any biological toxicity. The current methods of nanoparticle preparation mainly include the ion gel method and solvent exchange method, which use the total electrostatic force, van der Waals force, hydrophobic interaction force and hydrogen bond force between molecules to embed materials with different characteristics. At present, there are more and more studies on the use of γ-polyglutamic acid to encapsulate drugs, and the research on the mechanism of its encapsulation and sustained release has gradually matured. The development and application of polyglutamic acid nanoparticles have broad prospects.