Plant essential oils have become an important research hotspot in the field of natural products due to their excellent antioxidant activity. Maojian essential oil (MJEO) has shown significant research and development value due to its potential antioxidant mechanism. In response to the problems of high energy consumption and low efficiency in traditional extraction processes, this study used microwave-assisted hydrodistillation (MAHD) to extract MJEO. Based on single-factor experiments, response surface methodology (RSM) was applied to optimize the extraction parameters. The chemical composition of the obtained essential oil was subsequently analyzed using gas chromatography-mass spectrometry (GC-MS). The results showed that the extraction yield of MJEO was 0.1225% under the conditions of a microwave power of 406 W and a solid-to-liquid ratio of 1 : 4.1 g mL-1, and the CO2 emission of essential oil extracted by MAHD was reduced by 77.75% compared with traditional hydrodistillation (HD). GC-MS analysis showed that 32 components were isolated from MJEO, with linalool being the most abundant. The results of the antioxidant activity assay demonstrated that MJEO has an effective ability to scavenge free radicals. The above research findings provide an important theoretical basis for the high value utilization of medicinal plant resources. The application of MAHD technology not only significantly enhanced the extraction efficiency of MJEO but also offered scientific support for the comprehensive utilization of Maojian tea resources and the high-quality development of the tea industry, possessing both significant academic value and practical significance.

Heterologous expression of soluble proteins is a crucial method for studying gene function and enzyme structure. Escherichia coli is a widely used host for expressing foreign genes from plants, animals, and microorganisms. PaxASAT1, a member of the BAHD acyltransferase family, plays a pivotal role in the initial step of acylsucrose biosynthesis by catalyzing the formation of mono-acylsucrose from sucrose and fatty acyl-CoA. In this study, PaxASAT1 was expressed in BL21 (DE3) but with low solubility. Several strategies were evaluated to enhance soluble expression of PaxASAT1, including codon optimization, chaperone co-expression, soluble-tag fusion, and SEP-tag utilization. Results demonstrated that the inclusion of molecular chaperones like DnaK resulted in a slight improvement in the solubility of PaxASAT1. SUMO and MBP tag fusions significantly enhanced PaxASAT1 soluble expression. Notably, C9K-modified SEP tags substantially increased the solubility of PaxASAT1. This study provides a theoretical foundation for the soluble expression of acylsucrose acyltransferase in E. coli, paving the way for further research and applications in the field.

This study aimed to optimize the supercritical CO₂ (SC-CO2) extraction process for Eleutherococcus senticosus extracts (ESE) to maximize yield, and to evaluate the antioxidant activity of the obtained extracts. The extraction was performed using SC-CO2 under conditions below the boiling point to preserve heat-sensitive compounds. Key factors (temperature, pressure, and time) were investigated, and the optimal process conditions were determined using the response surface methodology. The antioxidant activity of ESE was assessed by measuring its scavenging effect on 2,2-diphenyl-1-picrylhydrazyl free radicals. The optimal extraction conditions were identified as a temperature of 60 °C, pressure of 30 MPa, and an extraction time of 2 h, yielding 1.88 ± 0.05

This paper presents a nonconventional semisupervised learning method for classifying near-infrared (NIR) data, designed for situations where not all data classes are known and labeled training data are sparse. Such requirements are commonly encountered in both industrial applications and scientific research contexts. The proposed method to tackle this challenge here is a designed process with LapDRegOSVM, which combines spectral segmentation with a Laplacian regularized one-class support vector machine and a dynamic decision rule. The learning process uses parallel LapDRegOSVM procedures, with each procedure identifying a single known class from mixed data. LapDRegOSVM improves upon traditional one-class SVMs (OSVM) and classical Laplacian regularized OSVM (LapOSVM) by leveraging information from unlabeled data through kernel reformation with manifold regularization and decision rule redefinition. A significant advancement of LapDRegOSVM lies in its refined decision rule, implemented via either a dynamic threshold or D-constrained K-means clustering. Results show that LapDRegOSVM outperforms standard OSVM and LapOSVM in utilizing unlabeled data and reregulated decision rule for achieving more accurate classification, particularly in handling "not available" (NA) data. The D-constrained K-means approach to the decision rule also proves superior to static thresholds. This semisupervised classification process achieves high accuracy and reliability in identifying expected classes within NIR spectra even with a substantial number of unknown classes, and all unknown classes remain under "NA" status postclassification, a capability rarely demonstrated by other learning methods.

In our study, chemical exploration of the Nicotiana tabacum L. symbiotic fungus Aspergillus sp. TE-65L led to the discovery of a new himeic acid derivative, japonimeic acid J (1), along with two previously reported compounds 2 and 3. The structure of the new compound was unequivocally elucidated using nuclear magnetic resonance spectroscopy and high-resolution electrospray ionization mass spectrometry. Furthermore, compounds 1 and 2 displayed strong anti-phytopathogenic potency against Alternaria alternata and Botrytis cinerea, with a minimum inhibitory concentration of 4 mu g/mL. This result suggests that they have considerable potential as new, naturally sourced agro-antibiotics for controlling fungal plant diseases.

Pectin has bad effects on the sensory quality of cigarettes. In order to reduce the pectin content in tobacco leaves, polygalacturonase (PG) gene was extracted from Aspergillus niger sw06, and recombinant plasmid pPICZαA was constructed and transformed into Pichia pastoris X33 to build an engineered strain X33/pPICZαA-PG. Transformant genomic fragment was 1,608 bp. The genomic fragment was amplified and recovered, and sequencing indicated that PG gene expression have been successfully inserted into P. pastoris expression vector. Positive clones were detected by SDS protein with a molecular weight of about 60 kDa. The enzyme production cycle of the recombinant strain was 36 h, and crude enzyme activity was 2872.91 U/mL. The fusion protein was purified by nickel Sepharose affinity chromatography. A clear band was detected and the concentration of recombinant protein was 8.1 μg/μL. It showed a good effect on degrading pectin after addition of the PG crude enzyme produced by recombinant yeast on the tobacco pulp. The optimized addition amount on process product line was 0.8%, which could reduce tobacco pulp pectin from 3.65 to 3.01% and achieve a degradation rate of 17.53%. Sensory evaluation showed that the effect was better when the addition amount of pulping was 0.4%.

Rehmannia radix is a kind of food and medicinal material, riched in monoterpenoids, phenylethanol glycosides, triterpenes, flavonoids, and other kinds of chemical compounds, among which monoterpenoids such as iridoids and ionones, and phenylethanol glycosides are the characteristic components of R. radix, with various biological activities. To excavate more characteristic active ingredients, the chemical compositions were identified from the 75% ethanol extract by v a variety of column methods and their hypoglycemic activities were evaluated in HepG2 cells. As a consequence, 13 compounds (1-13) were identified, including 4 iridoid compounds, 3 ionones, and 6 phenylethanol compounds, among them compound 3 was a new iridoid. The hypoglycemic activity showed that 7 compounds (1 similar to 2, 4 similar to 7 and 13) could promote glucose uptake of IR-HepG2 cells. And compound 6 could significantly upregulate protein levels of PI-3K, p-GSK3 beta, p-AKT and GLUT4, and significantly downregulated protein levels of PEPCK and G6Pase. These results revealed that compound 6 improved insulin resistance in HepG2 cells probably by activating PI-3K/AKT signaling pathway and inhibiting gluconeogenesis. These results succeeded in enriching the chemical composition of R. radix and provided an important scientific basis for the application of Rehmannia in the treatment of diabetes.

Two polysaccharide fractions were successfully isolated from the crude exopolysaccharide (EPS) of Scleroderma areolatum via gel filtration chromatography. Size exclusion chromatography/multi-angle laser light scattering (SEC/MALLS) analysis showed that the weight of average molecular weights (Mw) of these fractions was 3.162 ȕ 106 (Fr-I) and 2.613 ȕ 106 (Fr-II). Both EPS fractions exhibited a compact globular structure in aqueous solution. Monosaccharide composition analysis revealed that they were primarily composed of mannose and glucuronic acid. FT-IR spectral analysis identified prominent functional groups, such as hydroxyl and carboxylic, typical of heteropolysaccharides. When administered as supplements in high-fat diets to obese rats for 8 weeks, the EPS fractions reduced body weight, serum inflammatory factor levels, and significantly regulated serum lipid levels. Additionally, they increased the total concentration of short-chain fatty acids (SCFA) in colonic digesta. These results suggest a potential role for EPS in mitigating obesity and related metabolic disorders in high-fat diet-induced obese rats.

Synthesis and sweet performance of DGP for the first time. Intramolecular cyclization of 6- O -tosyl glucopyranoside catalyzed by TBAF. The crucial role of HFIP in intramolecular cyclization of 3,6-anhydro glucopyranoside.

This paper investigates the nonlinear relationship between tobacco harmful content tar reduction and laser perforation parameters. To find a model to demonstrate the relationship between the laser perforation parameters and the cigarette tar reduction level, an online platform based on Python Streamlit was built to collect and publish related data. After the initial analysis of the collected experimental data, the quadratic nonlinear regression model demonstrates a significant fit to the experimental data. However, although the nonlinear regression has much higher accuracy than the linear regression plane, the prediction normalized root mean squared error (NRMSE) is still high, over 10%, which indicates that the regression relationship is more complex than the simple quadratic function expression. On the other hand, the sample dataset used for modeling is very limited, which restricts its exploration and the development of a model comparable to those built with big data. To address this challenge for small sample size data in modeling this complex nonlinear relationship, a novel rational-quadratic Minkowski (RM)-based kernel was designed. This RM-kernel model acquires higher accuracy than other kernels in both SVM and Gaussian process regression. Furthermore, this new kernel also shows less sensitivity to hyperparameter change, the greater ability to capture complex relationships, and more flexibility than the RBF kernel and RQ kernel. Subsequently, the kernel-based RM regression model was successfully implemented for laser perforation parameter selection, yielding consistent results that align with human sensory test data.

This study focused on the modification of lentinan (LNT) and its degradation product, dLNT, through the nitric acid-sodium selenite method, resulting in the synthesis of 4 distinct seleno-polysaccharide (Se-LNTs). The selenium moiety was found to be connected to the C6 position of LNT in the form of selenate. The selenization process led to a notable reduction in molecular weight and an augmentation in chain rigidity. As the molecular weight decreased and chain rigidity increased, the cellular uptake of Se-LNT diminished. Additionally, the uptake pathway transitioned from macropinocytosis to caveolin-mediated endocytosis (CVME). In vitro cell experiments showed that all four Se-LNTs showed obvious anti-tumor activity, and Se-LNT-2 had higher selenium content and cellular uptake rate, showing better inhibitory effect. Furthermore, Se-LNTs were observed to induce a substantial production of reactive oxygen species (ROS) in HCT116 cells. The excessive ROS levels triggered mitochondrial peroxidation damage, escalating mitochondrial inner membrane permeability. This cascade event eventually led to the activation of caspase-3, ultimately leading to apoptosis in HCT116 cells and substantiating the anti-tumor effects of Se-LNTs. The comprehensive investigation into the structural modifications, cellular uptake mechanisms and biological activities of Se-LNTs underscores their potential as promising agents for anti-tumor applications.

Renowned for their distinctive aromas, terpenoid flavor compounds and their precursors are widely used in medicine, food, and the flavor and fragrance industries. Rapid advances in synthetic biology, including the modification of microbial chassis cells, the design of synthetic pathways for novel target products, and the integration of large-scale microbial fermentation, have enabled the development of microbial cell factories for the green and efficient production of terpenoid flavor compounds and their precursors, offering broader market potential. This review examines common biosynthetic mechanisms, recent progress in the field, and strategies for enhancing the biosynthetic efficiency of terpenoid flavor compounds and their precursors. This study aims to support the advancements of sustainable production technologies and promote industrial application within the flavor and fragrance sector.

This paper describes a design of an improved self-made Bruker NIR cup and analyzes the effect of the equipment modification to fit the Cambridge filter pad, which enhances experimental efficiency and reduces operational complexity. A self-made NIR cup based on the classical NIR cup is designed to speed up the operation process and reduce the experiment's time cost. To estimate the effect of this equipment modification, the NIR spectra from the classical sample cup and the new self-made cup are compared and analyzed. Furthermore, the quality evaluation results from NIR data of the two cups are also compared according to a distance metric chemometrics method, which shows quality analytical values between these two cups are approaching each other while the experiment efficiency is improved. • This paper introduces a newdesign of a self-made container cup improved from the Bruker's traditional sample container cup to better fit the filter pad and improve the experiment efficiency and convenience. • This paper also analyzes the effect of this container cup change by comparing the NIR spectra before and after modification.

The expansion of power systems has necessitated a comprehensive examination of power communication technology, a critical component ensuring system safety. This paper employs bibliometric and information visualization techniques to comprehensively analyze the past three decades of research in power communication. Specifically, CiteSpace software was utilized to visualize the literature in databases like Web of Science, constructing a knowledge graph that includes author collaboration networks, institution cooperation networks, keyword clusters, and timeline diagrams. This approach highlights key research areas, international differences in focus, and contributes to knowledge sharing and technological advancement.

Five kinds of exopolysaccharides (EPS) were obtained by fermentation of Scleroderma areolatum Ehrenb. with sucrose, glucose, maltose, lactose, and fructose as carbon sources. Antioxidant abilities of the obtained EPSs were evaluated by inhibiting AAPH, HO·, and glutathione (GS·) induced oxidation of DNA and quenching 2,2'-azinobis (3-ethylbenzothiazoline-6-sulfonate) cationic radical (ABTS· and galvinoxyl radicals. The effects of carbon sources on the antioxidant properties of EPSs could be examined. The results showed that five EPSs can effectively inhibit radicals induced oxidation of DNA, and the thiobarbituric acid reactive substances (TBARS) percentages were 44.7%-80.8%, 52.3%-77.5%, and 44.7%-73.3% in inhibiting AAPH, HO·, and GS· induced oxidation of DNA, respectively. All five EPSs could scavenge ABTS· and galvinoxyh, and exhibit superior activity in scavenging free radicals. Antioxidant abilities of EPS with fructose as carbon source were highest among five EPS.

The ongoing evolution of power communication technology and the deepening implementation of smart grid construction have significantly elevated the complexity and significance of power communication systems. However, conventional simulation software often necessitates installation on specific operating systems and hardware, accompanied by intricate interfaces and operational procedures, which hinder users from engaging in swift and adaptable activities. To address these challenges, this paper presents a simulation platform developed in JavaScript, designed to enhance the performance and reliability of PLC systems within a smart grid environment. Currently, this web-based simulation platform primarily focuses on power line communication signal attenuation simulation, which is crucial for estimating the transmission distance of PLC signals.

Near infrared spectra are typically high dimensional data and widely used in various industries and scientific areas. Various machine learning algorithms have been employed to classify the NIR spectral data. This paper compares the performance of three popular models: the Kolmogorov-Arnold Network (KAN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), in classifying Mah map datasets which are derived from preprocessed NIR spectra. This study investigates the performance of three prominent machine learning models Multilayer Perceptron (MLP), Kolmogorov-Arnold Network Network (KAN), and Support Vector Machine (SVM), in classifying high-dimensional Near Infrared (NIR) data within the tobacco industry. The impact of hidden layer architecture on classification accuracy was examined for both MLP and KAN networks. While KAN demonstrated superior accuracy and stability with L-BFGS optimization, particularly when employing two hidden layers, it exhibited inferior performance with Adam optimization compared to MLP. With one hidden layer and same hidden neuron number, KAN can provide slightly better classification accuracy in high dimension than both MLP and SVM. However, SVM proved generally stable across various input dimensions, achieving high classification accuracy.

为了解不同品牌中支卷烟产品之间的烟气特征差异,选取了广西中烟真龙品牌中支系列的H和Y品种以及河南中烟黄金叶品牌的S,L,N品种共5款市售卷烟进行研究,用GC/MS,GC以及吸烟机对两种品牌中支卷烟主流烟气中的香气成分和烟气常规成分(烟碱、CO和焦油含量)进行检测,同时利用主成分分析和相关性分析方法寻求不同品牌中支卷烟风格差异,明晰不同品牌中支卷烟烟气特征差异.实验结果表明:①通过主成分分析可知,两种品牌卷烟在烟气香味成分上具有明显差异.真龙品牌的2款卷烟香气以赋予烟气果香的针叶烯以及赋予烟气花香、木香特征的金合欢醇为主要香气特征;而品种S以赋予烟气烟草本香及甜香的巨豆三烯酮、2,3-二氢-3,5-二羟基-6-甲基-4H-吡喃-4-酮为主要香气特征;品种L,N以赋予烟气强烈花香的3-羟基β-二氢大马酮、丁香酚、5-羟甲基糠醛为主要香气特征.②两种品牌卷烟均符合低焦油卷烟标准,但黄金叶3款卷烟烟气常规成分含量基本高于真龙的2款卷烟.③相关性分析表明每一款卷烟的烟气常规成分组间检测相似度均在99％以上,说明5款卷烟烟气常规成分具有极强的稳定性;品种H与其他卷烟烟气常规成分的相似度极低,最低至27.5％,但品种Y与其他卷烟的烟气常规成分相似度极高,尤其是与L的相似度达到99.0％以上,说明烟气常规成分不能单独作为区分不同品牌卷烟的指标.

In order for normal operation, performance optimization, failure detection, and efficient management of the Power Line Communication (PLC) system, accurate prediction of PLCSI (Power Line Communication State Information) is essential. This paper aims to build a website based on streamlit framework that can visualize and compare the performance of Long Short-Term Memory (LSTM) networks and Support Vectors Machine (SVM) in predicting PLCSI.

Quality control is important for tobacco industry and tobacco leaf is the source material for cigarettes product. For a certain brand’s products, without known standard samples as center, it is difficult to detect outliers of unknown groups with classical PCA. Although classical PCA has been widely used in NIRS for tobacco, the accuracy of classical PCA can not satisfy the industrial requirements to correctly classify the products and identify the outliers. Therefore the robust sparse PCA (RSPCA) here is used for tobacco leaf NIR process, which has advantages over both robust PCA (RPCA) and classical PCA (CPCA) that the RSPCA can suppress the effect of outliers through sparse loadings and has robust dimension projection. Thus RSPCA brings in higher accuracy for tobacco leaf source classification and outlier detection compared to classical PCA. With Eigenvalue Decomposition Discriminant Analysis (EDDA), a Gaussian component based supervised classification method, the tobacco leaf sources from different quality levels are well classified according to the robust score distance(SD) and orthogonal distance(OD) of RSPCA. Furthermore, the principal components (PCs) based classification and SD-OD based classification are also compared between the three types of PCA, which shows the RSPCA SD-OD based classification has the best performance.