The present study aimed to investigate the relationship between aroma types and chemical properties of flue-cured tobacco (FCT), and to explore the applicability of machine learning (ML) combined with feature derivation in the FCT industry. A total of 619 Sichuan FCT samples representing three aroma types (fresh-sweet, honey-sweet, and mellow-sweet) were utilized. Feature derivation was performed based on 51 raw chemical indices, followed by a three-tier key indicator selection process incorporating separability analysis, Random Forest (RF) importance ranking, and redundant feature elimination via correlation analysis. By comparing multiple machine learning models, the optimal model adapted to the Sichuan FCT dataset was screened out. Model parameter optimization was accomplished in combination with the genetic algorithm (GA), and finally, visual interpretation of the model's decision-making mechanism was realized by means of SHAP values. The results demonstrated that after three-tier screening, 9 key characteristic indices including rutin-malonic acid, rutin and chlorogenic acid et al were finally identified. The random forest (RF) algorithm was the optimal model for this dataset; after parameter optimization, the model achieved an F1-score of 88.3% and an accuracy of 93.5%, which greatly reduced the detection cost and improved the model's discrimination performance. Additionally, the SHAP value interpretation framework clearly reveals the intrinsic correlation between chemical characteristics and aroma types. This study not only enhances the efficiency of aroma type classification for Sichuan FCT but also clarifies the key chemical indicators associated with aroma traits. It further provides quantitative support for optimizing FCT quality through the targeted regulation of key component contents.

Biomass is greatly influenced by geographic location, soil composition, environment, and climate, making the efficient and accurate identification of growing areas highly significant. This study proposes a classification model for tobacco growing areas based on time series features from thermogravimetric analysis (TGA). This study combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) model to process the derivative thermogravimetric (DTG) data, aiming to uncover the inherent time series properties and the continuous and dynamic relationship between temperatures for classifying tobacco growing areas. By analyzing 375 tobacco samples from ten different provinces, CNN is employed to extract local features, while LSTM captures long-term dependencies in the DTG data. The dataset used in this study has a limited sample size, a wide variety of classes, and an imbalance in the number of samples across these classes. Despite these challenges, the model achieves 86.4% accuracy on the test set, significantly surpassing the performance of the traditional Support Vector Machine model, which only achieves 68.2% accuracy. Additionally, the model reveals key temperature ranges crucial for growing area classification associated with the pyrolysis temperature ranges of volatile components, hemicellulose, cellulose, lignin, and CaCO3 in the tobacco. This model lays the groundwork for the future use of geographical labels to accurately represent tobacco's style and quality, enabling more precise differentiation and improved quality control.

Light quality plays a crucial role in plant growth and development. Nevertheless, mechanistic insights regarding the effects of monochromatic red light and monochromatic blue light on the development of tobacco seedlings remain limited. This study aims to elucidate how monochromatic red and blue light specifically affect the growth of tobacco seedlings, with a focus on systemic nutrient allocation from roots. By analyzing morphological, physiological and transcriptomic changes in both shoots and roots, we aim to fill the gaps in our understanding of the interaction between light quality and nutrient absorption mechanisms. Physiological results indicated that red light promoted seedling growth and enhanced nitrogen (N)and potassium (K) uptake, whereas blue light exerted the opposite effect by inhibiting growth and reducing nutrient uptake. Transcriptome analysis revealed that genes related to N uptake (e.g., NRT1.1 was induced by red light and suppressed by blue light), N assimilation and K uptake were significantly regulated by monochromatic light in roots. Furthermore, various transcription factors (TFs) involved in phytohormone signal transduction were significantly regulated by blue and red light. These findings suggest that red and blue light may modulate phytohormone metabolism through TF–mediated pathways in tobacco roots. Overall, this study provides valuable insights into the effects of light quality on plant growth and underscores the importance of elucidating light-mediated mechanisms for improving crop yield and quality.

Endophytic fungi belonging to the Aspergillus genus have received substantial attention due to their notable secondary metabolic potential. In this study, chemical investigations using LC-MS/MS-based molecular networking on the endophytic fungus Aspergillus japonicus TE-739D led to the discovery of two new cyclohexadepsipeptides, namely japonamides C (1) and D (2), along with three known cyclodipeptides (3-5). Their structures, including the absolute configurations of the amino acid residues, were elucidated through spectroscopic data analysis and an optimized Marfey's method. The newly discovered compounds, japonamides C (1) and D (2), were screened for broad-spectrum cell proliferation inhibitory activity against 20 different human cell lines. The results indicated that both compounds displayed broad-spectrum antiproliferative activity against MKN-45, HCT116, TE-1, 5,637, CAL-62, and A-637 cells, with inhibition rates ranging from 55.0 to 72.3%. Moreover, the antibacterial activity of compounds 1-5 against two Gram-positive bacteria and two Gram-negative bacteria was also evaluated.

Microorganisms play an important role in cigar tobacco leaves (CTLs) production, while the impact of varieties and origins on microorganisms and thereby on aroma profiles were rarely reported. In this study, the aroma profiles of wrapper and filler CTLs from three varieties, respectively, and filler CTLs from four origins were analyzed, and the bacterial and fungal communities of these CTLs and their correlation with aroma constituents were explored. The results showed that ketones and nitrogen heterocycles were dominant in wrapper and filler CTLs of different varieties and filler CTLs from different origins, their proportions in different CTLs were 22.34-58.28% and 19.45-53.62%, respectively. There were significant differences in the varieties and contents of aroma constituents in CTLs of different varieties and origins, which showed obvious variety and origin specificity. For example, the contents of aldehydes (92.24 μg/kg), ketones (1553.00 μg/kg), and alkenes (227.14 μg/kg) in the wrapper variety of AQ2 increased significantly, the contents of ketones (2027.21 μg/kg), alkenes (219.98 μg/kg), and total aroma constituents (4262.60 μg/kg) in the filler variety of QDQX2 increased, and the contents of ketones, nitrogen heterocycles, alkenes, and total aroma constituents in the origin of FXQX1 increased, which increased by 0.29-0.84, 0.44-0.96, 0.22-7.19, and 0.34-0.65 times. Besides, the microbial community diversity and structure were significantly affected by varieties and origins. There were obvious differences in the α- and β-diversity of microbial communities in different varieties and origins of CTLs. Varieties have little effect on the bacterial communities of wrapper and filler CTLs, and only the abundances of the dominant Corynebacterium (45.78-55.34% of wrapper and 29.97-60.90% of filler) and Staphylococcus (15.16-34.60% of wrapper and 22.12-46.70% of filler) were different, but there were significant differences in the composition and abundance of the fungal community. Different from the influence of varieties, the composition and abundance of bacterial communities were significantly changed by the origins, while only the abundances of the dominant Alternaria (6.45-46.73%), Aspergillus (5.72-32.24%), Wallemia (11.80-24.84%), and Cladosporium (1.12-10.89%) in the fungal community were different. Correlation analyses showed that different bacterial and fungal communities synergistically contributed to the formation of aroma profiles of CTLs, and the microbial communities contributing to the formation of aroma profiles were obviously different among wrapper and filler varieties and filler origins.

In recent years, image processing technology has been increasingly studied on intelligent unmanned platforms, and the differences in the shooting environment during tobacco baking pose challenges to image processing algorithms. To address this problem, an ensemble multi-dimensional randomization network (EMRNet) for intelligent recognition of tobacco baking stage is proposed. The first is to obtain the tobacco leaf area during the baking process. Then, a multi-dimensional randomization network (MRNet) is designed to recognize tobacco baking stage. The effectiveness of MRNet lies in multi-scale hidden layer feature extraction, which can effectively enhance the expression ability of features to overcome the impact of differences between different environments on the tobacco baking stage. Finally, MRNet is used as component learner for constructing an ensemble randomization network structure to distinguish the tobacco baking stage. On the constructed tobacco baking stage dataset, EMRNet achieves 89.14% accuracy with 642.96MFLOPs. Compared with SVM, MLP, BP, ELM, CRVFL and other algorithms, EMRNet shows excellent performance in accuracy and model complexity. The proposed method explores the application of image processing technology in crop baking and drying, providing theoretical support for intelligent baking technology.

A segmented Transformer feature extraction network is proposed to address the challenges posed by near infrared spectroscopy,including high dimensionality,susceptibility to noise interference,and high spectral similarity among samples from different provinces.This network is applied to tobacco leaf origin identification to enhance classification accuracy.First,based on the one-dimensional structure of near infrared spectroscopy,an embedding layer is designed to compress features using one-dimensional convolution.Second,the data is divided into three parts along the spectral dimension using sliding windows.The Transformer architecture is improved to extract spectral features,addressing the issue of computational inefficiency caused by a large number of spectral bands.Last,to adapt to the characteristics of spectral data,a regression head with multiple layers of one-dimensional convolution is designed to predict the origin of the samples.To validate the effectiveness of the proposed algorithm,several comparative experiments are conducted,comparing classification accuracy,precision,recall,and other metrics with other algorithms.The superiority of each structure in the model is verified.The experimental results demonstrate that the proposed model effectively utilizes the spectral structure for feature extraction and noise suppression,successfully accomplishing the task of tobacco leaf origin identification.

The interaction between support and noble metal plays a crucial role in heterogeneous catalysis design.However,how to tune metal support interactions to optimize the activity still needs further exploration.CeO2 was introduced to promote CO oxidation over Ir/TiO2 by adjusting the interaction strength between iridium(Ir)and CeO2.The strong interaction between Ir and CeO2 blocks CO adsorption and causes low CO oxidation activity.However,introducing CeO2 on Ir/TiO2 produces localized interaction between Ir and CeO2,which can tune the surface electronic state of Ir,so a"volcano curve"relationship between CO oxidation activity and electronic state is built.Limited amount of CeO2 on Ir/TiO2(Ir/Ce0.2Ti)leads to CO complete oxidization at 22 ℃,and a new pathway for CO oxidation was explored.The study demon-strates that the utilization of tuning interaction strength between active metal and support is a potential method to increase the catalytic activity.

针对传统烟叶配方设计客观性不强、设计效率低的问题,提出了一种基于近红外光谱表征品质的烟叶配方设计方法:利用偏最小二乘法建立烟叶化学成分、部位、香型的近红外光谱模型,并预测烟叶品质指标;以品质指标为多维向量构建综合质量表征指数,依据"相似相替"规则,参照目标配方中的原烟等级,筛选相似的替代烟叶并组成候选烟叶集合,采用线性规划求解方法计算替代配方的构成比例.利用指标评价与感官验证相结合的方式,验证 2020年度四川烟叶配方设计效果.结果表明:替代配方的烟叶部位、烟叶总糖、还原糖、总植物碱、总氮含量等与目标配方的相对误差小于 6%;除刺激性指标外,替代配方烟叶香气量、清晰度、透发性、成团性、杂气、余味及劲头等感官质量指标与目标配方的差异不显著.

目的 探讨邢台地区儿童期肥胖与中枢性性早熟之间的关联,为该地区儿童性早熟的一级预防提供科学依据.方法 选取2020年5月至2021年4月在邢台市第三医院就诊并诊断为中枢性性早熟的234例年龄在6～9岁的患者作为病例组,并以1:1配比选取234例同期于该院儿科就诊的未患中枢性性早熟的儿童作为对照组.采用多因素条件Logistic回归模型分析儿童超重肥胖与中枢性性早熟的关系.结果 女童中,与对照组比较,病例组的出生体重较高、6个月纯母乳喂养率较低、母亲月经初潮年龄较小(χ2/u值分别为7.39、6.04、6.63,P<0.05);在男童中,病例组6个月纯母乳喂养率低于对照组(χ2=7.60,P<0.05),其他方面差异均无统计学意义(P>0.05).多因素回归分析显示,调整出生体重、6个月纯母乳喂养、家庭人均年收入、母亲超重、父亲超重、母亲月经来潮年龄等混杂因素后,与正常儿童相比,女童超重、肥胖增加了中枢性性早熟的发生风险(超重:OR=2.0,95％CI:1.1～3.7;肥胖:OR=1.8,95％CI:1.2～2.8);男童中,调整混杂因素前后,超重和肥胖均表现为与中枢性性早熟无关联(P>0.05).结论 河北邢台地区的女童超重和肥胖可能是中枢性性早熟的危险因素.

目的 探讨饮食联合运动干预对儿童期肥胖的影响.方法 选取2020年10月至2021年5月收治的肥胖儿童 108例,随机数字表法分为常规组和研究组,每组 54例.常规组采用常规干预,研究组在常规组基础上采用饮食联合运动干预,对比 2组干预前、干预 6 个月的体重、体重指数(BMI)、腰围、体脂百分比、内脏脂肪面积以及血脂水平.结果 干预 6 个月,研究组的体重、BMI均低于常规组,腰围小于常规组(P＜0.05);研究组的体脂百分比、内脏脂肪面积均低于常规组(P＜0.05);研究组的总胆固醇、三酰甘油、低密度蛋白、高密度蛋白水平低于常规组(P＜0.05).结论 在儿童期肥胖患儿中采用饮食联合运动干预,能有效减轻其肥胖症状,降低血脂水平.

Variable (wavelength) selection is critical in the multivariate calibration of spectra that improves prediction performance and provides clearer interpretation. Most variable selection methods use Y information (chemical reference of interest) to evaluating the importance of variables. In this work, a novel variable selection is proposed that requires no reference information. Replicate spectra of a sample under various acquisition conditions were used with an index to evaluate the importance of variables. The results show that the proposed method achieves better results than the original partial least squares model with a few available calibration samples. When a large number of calibration samples are available, this method provided comparable results to the state-of-the-art variable selection methods while reduced the risk of model over-fitting.

为探索利用近红外光谱技术快捷、全面评价片烟质量的可行性,以2015—2017年初烤烟叶和复烤片烟为研究材料,使用偏最小二乘法等数据处理方法建立了片烟常规化学成分、香型、部位、感官质量的近红外光谱预测模型,验证模型效果后对2018—2019年度云南片烟质量进行了预测和评价.结果表明,2019年度较2018年度,化学成分呈现总糖、还原糖极显著下降、总氮极显著上升的趋势;清香型指数略有下降;两年上、中、下部位指标差异不显著;近红外感官质量模型评价的70个片烟中有57个片烟与感官评价结果相符,预测准确率达到81.4％,且年度间差异极显著;近红外预测结果与感官评吸结果具有较高的一致性.利用近红外技术评价片烟综合质量可以为卷烟配方设计提供参考.

为提高在有限样本条件下近红外预测复烤片烟产地的准确率,提出了一种基于级联分类的复烤片烟产地预测方法,该方法首先通过近红外光谱判断样本的香型属性并构建香型模型作为中间层,再使用LDA或P LS方法在单一香型框架下构建产地模型进行产地预测.以全国主要烟叶产区的复烤片烟为对象进行验证,结果表明:通过引入香型模型作为中间层,基于LDA的分类模型产地预测准确率由83.33％提升至94.44％;基于P LS的分类模型的准确率则由72.22％提升至86.11％.在有限样本数据和不引入新的模型参数的条件下,该方法有效降低了复烤片烟的产地误判比例.

为考察打叶复烤过程中关键工艺参数变化对云产烟叶模块质量的影响,选取"清甜香"风格特征适中的文山模块烟叶为研究对象,根据Box-Behnken Design(BBD)设计原理,以润叶含水率、润叶温度和复烤温度为主要影响因素,复烤叶片含水率和CCUI值为响应值,采用响应曲面法优化打叶复烤工艺条件,并建立相应的预测数学模型.结果表明:复烤叶片含水率、CCUI值与各关键工艺参数之间的变化均符合二次方程模型(R2分别为0.9451和0.8557),复烤温度对叶片含水率和CCUI值影响均较大,优化后最佳工艺参数为润叶含水率17.5％、润叶温度58.0℃、复烤温度406.0℃;优化条件下复烤叶片含水率9.627％、CCUI值0.851,其CCUI值较正常生产提高3.780％,内在品质提升显著(P<0.01),大片率和叶含梗率降低明显,干草香、正甜香和青香等香韵风格特征和感官品质更加凸显,说明适当提高润叶温度和复烤温度更有利于降低复烤叶片大片率、提升中小片率,彰显云产"清甜香"特征适中的烟叶模块风格和感官品质.

Pectin is a major component in many agricultural feedstocks. Despite the wide use in industrial production of cellulases and hemicellulases, the fungus Trichoderma reesei lacks a complete enzyme set for pectin degradation. In this study, three representative pectinolytic enzymes were expressed and screened for their abilities to improve the efficiency of T. reesei enzymes on the conversion of different agricultural residues. By replacing 5 % of the T. reesei proteins, endopolygalacturonase and pectin lyase remarkably increased the release of sugars from inferior tobacco leaves. In contrast, pectin methylesterase showed the strongest improving effect (by 31.1 %) on the hydrolysis of beetroot residue. The pectin in beetroot residue was only mildly degraded with the supple-mentation of pectin methylesterase, which allowed the extraction of pectin keeping the original emulsifying activity with a 51.1 % higher yield. The results provide a basis for precise optimization of lignocellulolytic enzyme systems for targeted valorization of pectin-rich agricultural residues.

With the development of near-infrared (NIR) spectroscopy, various calibration transfer algorithms have been proposed, but such algorithms are often based on the same distribution of samples. In machine learning, calibration transfer between types of samples can be achieved using transfer learning and does not need many samples. This paper proposed an instance transfer learning algorithm based on boosted weighted extreme learning machine (weighted ELM) to construct NIR quantitative analysis models based on different instruments for tobacco in practical production. The support vector machine (SVM), weighted ELM, and weighted ELM-AdaBoost models were compared after the spectral data were preprocessed by standard normal variate (SNV) and principal component analysis (PCA), and then the weighted ELM-TrAdaBoost model was built using data from the other domain to realize the transfer from different source domains to the target domain. The coefficient of determination of prediction (R 2) of the weighted ELM-TrAdaBoost model of four target components (nicotine, Cl, K, and total nitrogen) reached 0.9426, 0.8147, 0.7548, and 0.6980. The results demonstrated the superiority of ensemble learning and the source domain samples for model construction, improving the models' generalization ability and prediction performance. This is not a bad approach when modeling with small sample sizes and has the advantage of fast learning.

A sensor combining a polydimethylsiloxane-coated Mach-Zehnder interferometer with a tilted fiber Bragg grating is proposed for dual-parameter sensing of liquid temperature and refractive index. Mach Zehnder interference is used for high sensitivity temperature sensing while the core mode of tilted fiber Bragg grating is used to distinguish the temperature range and avoid spectral overlap. Meanwhile, the cut-off mode of tilted fiber Bragg grating is used for refractive index sensing, which can effectively eliminate crosstalk between temperature and refractive index. The average temperature sensitivity of the sensor can reach 4.88, 5.15, 4.53 and 4.38 nm/ degrees C over the range of 20, 30, 40 and 50 degrees C, respectively. The refractive index is in the range of 1.3330-1.3614, and the sensing sensitivity can reach 521.92 nm/RIU. Compared with other optical fiber sensors, the sensor proposed in this paper has the advantages of simple fabrication, compact structure, stable mechanical structure and high sensitivity, which is expected to be applied in the sensing field where refractive index and temperature need to be detected simultaneously.

Autothermal process which involves the introduction of oxygen to the pyrolysis process has been regarded as a better option for commercialization of biomass pyrolysis technologies. The presence of oxygen in the pyrolysis system will inevitably affect the thermal decomposition process, especially the release of products. However, few attentions have been paid to the influence of oxygen on in-situ evolution of chemical structure and emission of different product as pyrolysis process proceeded which are helpful for us to understand the role played by oxygen. Herein, we systematically demonstrated the effect of oxygen on the thermal decomposition of tobacco, a special lignocellulosic biomass material. The influential mechanisms of oxygen on the thermal decomposition process were explored via a series of in-situ characterization techniques including in-situ DRIFTS, in-situ XPS, Raman, and TG-MS. We found that the presence of oxygen was beneficial to oxygen and hydrogen removal process (e.g., dehydration, decarboxylation and de-carbonylation) during biomass pyrolysis, thus promoting the generation of aromatic char structure. In addition, the char formed under oxygen exhibited a more ordered structure with the larger aromatic crystallite size. Oxygen influenced the relative content of volatile and semi-volatile products, despite not affecting their types.

目的 观察分析克洛己新干混悬剂治疗小儿支气管肺炎临床疗效.方法 选取2019年1月至2020年1月在我院接受治疗的78例小儿支气管肺炎患儿作为本次研究对象,采取随机对照的方式分组,将其分为对照组39例与研究组39例,对照组患儿采取头孢曲松钠注射液治疗,研究组患儿采取克洛己新干混悬剂治疗,分析比较两组患儿的临床症状改善时间以及治疗前后实验室指标变化情况.结果 治疗前两组患儿WBC、hs-CRP、PCT等指标比较,差异无统计学意义(P>0.05),治疗后,研究组患儿WBC、hs-CRP、PCT等指标明显低于对照组,差异有统计学意义(P<0.05);研究组患儿治疗后咳嗽、发热、肺啰音、气喘等症状消失时间明显短于对照组,差异有统计学意义(P<0.05).结论 对小儿支气管肺炎患儿采取克洛己新干混悬剂治疗,能有效改善相关实验室指标水平,促进临床症状改善,值得临床推广.