IEEE SENSORS JOURNAL
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2024
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IF 4.3
Zhikang Rao,Boyu Guo,Jixiang Zu,Weiqiang Zheng,Ying Xu,Yuting Yang
In clinical diagnosis, the detection of dopamine (DA) is often interfered by epinephrine (EP), a neurotransmitter drug that shares a similar structure with DA, which affects the accurate prediction of DA concentration. In this study, an electrochemiluminescence (ECL) and differential pulse voltammetry (DPV) dual model biosensor combined with machine learning algorithm was proposed for improving the selective detection of DA under EP interference. First, reduced graphene and gold nanoparticles were applied to enhance the sensitivity and stability of the sensor in ECL and DPV measurement. Compared with single model sensing, the dual model sensor could combine the superiority of ECL and DPV while improving the accuracy and reliability of experimental data. Then, nine graphic features related to concentration prediction were extracted from the ECL and DPV response curves, and Pearson’s correlation coefficient (PCC), as well as random forest (RF) algorithm, was adopted for feature importance analysis. To achieve accurate signal decoupling, an intelligent biologically inspired algorithm, particle swarm optimization (PSO) algorithm, was introduced to optimize initial weights and thresholds of the traditional artificial neural network (ANN) for further model training based on multi-feature fusion, which greatly increased the prediction accuracy of DA in the presence of EP. The results demonstrated that DA could be accurately predicted in the concentration range of 9– 200 μM , and the R2 between the true and predicted values of DA concentration reached 0.99. The root-mean-square error (RMSE) and mean absolute error (MAE) of the test dataset were 5.77 and 4.32 μM , respectively. In addition, the PSO-ANN-assisted ECL-DPV dual model detection scheme has been successfully applied for the determination of DA in real samples, such as goat serum and artificial urine.
研究领域:
生物医学工程
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临床诊断
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电化学传感
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产品关联
DOI
高影响因子
PLoS One
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2019
·Vol. 14
·期 5
·e0216178
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IF 2.776
Anamya Ajjolli Nagaraja,Nicolas Fontaine,Mathieu Delsaut,Philippe Charton,Cedric Damour,Bernard Offmann,Brigitte Grondin-Perez,Frederic Cadet
The selection of optimal enzyme concentration in multienzyme cascade reactions for the highest product yield in practice is very expensive and time-consuming process. The modelling of biological pathways is a difficult process because of the complexity of the system. The mathematical modelling of the system using an analytical approach depends on the many parameters of enzymes which rely on tedious and expensive experiments. The artificial neural network (ANN) method has been successively applied in different fields of science to perform complex functions. In this study, ANN models were trained to predict the flux for the upper part of glycolysis as inferred by NADH consumption, using four enzyme concentrations i.e., phosphoglucoisomerase, phosphofructokinase, fructose-bisphosphate-aldolase, triose-phosphate-isomerase. Out of three ANN algorithms, the neuralnet package with two activation functions, "logistic" and "tanh" were implemented. The prediction of the flux was very efficient: RMSE and R2 were 0.847, 0.93 and 0.804, 0.94 respectively for logistic and tanh functions using a cross validation procedure. This study showed that a systemic approach such as ANN could be used for accurate prediction of the flux through the metabolic pathway. This could help to save a lot of time and costs, particularly from an industrial perspective. The R-code is available at: https://github.com/DSIMB/ANN-Glycolysis-Flux-Prediction.
研究领域:
生物信息学
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代谢工程
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计算生物学
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酶学
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DOI
PubMed
高影响因子
WATER SCIENCE AND TECHNOLOGY
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2024
·Vol. 89
·期 9
·2384-2395
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IF 2.5
Bing Wenrong,Li Xinyu,Liang Mingzhao,Zhou Xu,Zhang Jianfeng,Liang Jing
View large Download slide View large Download slide Close modal Cr(VI) and phenol commonly coexist in wastewater, posing a great threat to the environment and human health. However, it is still a challenge for microorganisms to degrade phenol under high Cr(VI) stress. In this study, the phenol-degrading strain Bacillus cereus ZWB3 was co-cultured with the Cr(VI)-reducing strain Bacillus licheniformis MZ-1 to enhance phenol biodegradation under Cr(Ⅵ) stress. Compared with phenol-degrading strain ZWB3, which has weak tolerance to Cr(Ⅵ), and Cr(Ⅵ)-reducing strain MZ-1, which has no phenol-degrading ability, the co-culture of two strains could significantly increase the degraded rate and capacity of phenol. In addition, the co-cultured strains exhibited phenol degradation ability over a wide pH range (7–10). The reduced content of intracellular proteins and polysaccharides produced by the co-cultured strains contributed to the enhancement of phenol degradation and Cr(Ⅵ) tolerance. The determination coefficients R 2 , RMSE, and MAPE showed that the BP-ANN model could predict the degradation of phenol under various conditions, which saved time and economic cost. The metabolic pathway of microbial degradation of phenol was deduced by metabolic analysis. This study provides a valuable strategy for wastewater treatment containing Cr(Ⅵ) and phenol. Co-culture can degrade phenol under high concentrations of Cr(VI). Co-culture improves the degraded rate and concentration of phenol under Cr(VI) stress. Co-culture synergistically enhances the ability to resist phenol and Cr(VI) toxicity. BP-ANN predicted the degradation of phenol under different conditions.
研究领域:
环境微生物学
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废水处理
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机器学习应用
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DOI
PubMed
高影响因子
CHEMOSPHERE
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2015
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IF 3.34
Feifei Sun,Qingni Yu,Jingke Zhu,Lecheng Lei,Zhongjian Li,Xingwang Zhang
Based on the solubility of 25 nitrogen-heterocyclic compounds (NHCs) measured by saturation shake-flask method, artificial neural network (ANN) was employed to the study of the quantitative relationship between the structure and pH-dependent solubility of NHCs. With genetic algorithm-multivariate linear regression (GA-MLR) approach, five out of the 1497 molecular descriptors computed by Dragon software were selected to describe the molecular structures of NHCs. Using the five selected molecular descriptors as well as pH and the partial charge on the nitrogen atom of NHCs ( Q N ) as inputs of ANN, a quantitative structure–property relationship (QSPR) model without using Henderson–Hasselbalch (HH) equation was successfully developed to predict the aqueous solubility of NHCs in different pH water solutions. The prediction model performed well on the 25 model NHCs with an absolute average relative deviation (AARD) of 5.9%, while HH approach gave an AARD of 36.9% for the same model NHCs. It was found that Q N played a very important role in the description of NHCs and, with Q N , ANN became a potential tool for the prediction of pH-dependent solubility of NHCs.
研究领域:
药物化学
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计算化学
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DOI
PubMed
高影响因子
CHEMOSPHERE
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2020
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IF 5.778
Guocheng Zhu,Nana Xiong,Chuang Wang,Zhongwu Li,Andrew S. Hursthouse
Removing dissolved organic matter (DOM) with polyaluminium chloride is one of the primary goals of drinking water treatment . In this study, a new HMW framework was proposed, which divided the factors affecting coagulation into three parts consisting of hydraulic condition (H), metal salt (M), and background water matrix (W). In this framework, H, M and W were assumed to be interacted with each other and combined to determine coagulation efficiency. We investigated the feasibility of the framework to determine the treatment efficiency through mathematical models. Results showed that non-linear artificial neural network (ANN) model was a better fit to the experimental data than the linear partial least squares (PLS) model: the ANN model could explain 76% of the total variations while the PLS could only explain 71%. The PLS did not follow the variations of observed values adequately. These experiments showed that the interaction between the HMW framework components were not simple linear relationships. The ANN model was able to optimize the composition of the HMW framework improving the efficiency of DOM removal through the components of HMW such as velocity gradient (G value), coagulant dosage, solution pH, and background water matrix. Overall, HMW framework is a new classification of factors affecting coagulation, leading to a better understanding of the coagulation process and sensitivity to influencing variables.
研究领域:
环境科学
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化学工程
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DOI
PubMed
高影响因子
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
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2025
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IF 8.5
Guanfeng Deng,Pingjin Li,Heng Liang,Tao Chen,Lijun Zhou,Hongyu Yang,Xiaoyu Jiang,Chunbang Ding,Shiling Feng
Camellia oleifera leaves were byproduct of the C. oleifera industry which was rich in polysaccharides. Deep eutectic solvent-dual enzyme system (DES-dEAE) was established to achieve the simultaneous hydrolysis reaction of dual enzymes and DES extraction. Currently, there was a lack of research methods that simultaneously deal with the relationship between DES and enzymes and the effect of DES on the polysaccharide extraction rate. In the study, artificial neural network (ANN) and conductor-like screening model for real solvents (COSMO-RS) were used to screen the most suitable DES by predicting the pH and activity coefficient of DESs. Choline chloride and sorbitol constituted the best DES and were used to optimize the process conditions of DES-dEAE by response surface method (RSM). The polysaccharide yield was verified to be 21.46 mg/g with the optimal process conditions: liquid-solid ratio of 33:1, enzyme ratio of 3:1, extraction time of 15 min and water content of 40 %. The C. oleifera polysaccharides obtained by water, DES, and DES-dEAE extractions were observed by scanning electron microscopy and cell wall damage effect of DES-dEAE was confirmed microscopically. The study optimized the complex polysaccharide extraction system using bioinformatics methods, contributing new ideas to the development of simulated extraction.
研究领域:
植物学
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生物化学
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食品科学
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计算生物学
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产品关联
DOI
PubMed
高影响因子
CHEMICAL ENGINEERING JOURNAL
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2025
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IF 13.2
Ju-Xiang Chen,Jia-Wei Song,Nai-Yun Gao,Yu-Qiong Gao,Yi-Xuan Hu
Sodium p-perfluorous nonenoxybenzenesulfonate (OBS), as a novel perfluorinated compound (PFC), has garnered significant attention due to its widespread application. However, its molecular structure is highly stable, making it difficult to degrade. This experiment employs an ANN model and SHAP interpretation method to optimize DBD plasma for highly effective removal of OBS. Under multi-objective optimization of the ANN model (treatment time: 20 min; pH: 4; conductivity: 36 μS/cm; initial concentration: 37.5 mg/L; input power: 40.5 W), the OBS removal rate and energy yield were 91.8 % and 7.659 g/kW•h, respectively. In SHAP analysis, treatment time has the greatest impact on both removal rate and energy yield. Even in the presence of interfering inorganic anions (NO 3 − , HCO 3 − , and Cl − ), DBD plasma could achieve an effective removal rate of over 60 % for OBS. Free radical quenching experiments, density functional theory (DFT), and LC-MS indicate that OBS undergoes under the influence of •OH, •O 2 − , 1 O 2 , and e aq − , involving desulfonation processes, ether cleavage and perfluorinated chain shortening processes. According to ECOASR and T.E.S.T. toxicity analyses, although some toxic byproducts may be generated during the OBS removal process, the environmental risk was low. In complex real-world aquatic environments, the OBS still achieves an effective removal rate exceeding 70 %, demonstrating the broad application prospects of DBD plasma.
研究领域:
环境科学
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等离子体化学
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化学工程
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水处理技术
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机器学习应用
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毒理学
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产品关联
DOI
高影响因子
Journal of Environmental Chemical Engineering
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2022
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IF 7.968
Abdul Latif,Ahsan Maqbool,Runzhan Zhou,Muhammad Arsalan,Kai Sun,Youbin Si
This study represents one of the first attempts towards bisphenol A (BPA) degradation by Trametes versicolor laccase immobilized on Ba-alginate beads. The effect of input variables including temperature (20–40 °C), BPA concentration (2–10 mg/L) and time (10–90 min) were studied using Box-Behnken design (BBD) and artificial neural network (ANN). The maximum BPA degradation of 84.34% was obtained when the temperature was 40 °C, BPA concentration 2 mg/L and time 50 min, which was accurately predicted by BBD (83.48%) and ANN (84.33%), proving the accuracy of prediction for both models. The values of R 2 and MSE for BBD were found to be 0.98, 9.88, while for ANN were 0.97, 38.25, respectively. Based on higher R 2 value and lower MSE, BBD was slightly better than ANN. The immobilized laccase showed higher storage stability than free laccase by retaining 68.64% and 44.62% of their activity at the same experimental conditions. Furthermore, BPA transformation was confirmed by Fourier-transform infrared spectroscopy (FTIR) analysis. GC-MS had detected the oxidative degradation products from BPA. Results showed that Ba-alginate immobilized laccase could be a promising biocatalyst in treating organic pollutants .
研究领域:
环境科学
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生物技术
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产品关联
DOI
高影响因子
ACS Omega
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2023
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IF 4.1
Angxin Tong,Xiaojun Tang,Haibin Liu,Honghu Gao,Xiaofei Kou,Qiang Zhang
The aim of this study is to enhance the classification performance of the back-propagation-artificial neural network (BP-ANN) algorithm for NaCl, NaOH, β-phenylethylamine (PEA), and their mixture, as well as to avoid the defects of the artificial bee colony (ABC) algorithm such as prematurity and local optimization. In this paper, a method that combined an improved adaptive artificial bee colony (IAABC) algorithm and BP-ANN algorithm was proposed. This method improved the ABC algorithm by adding an adaptive local search factor and mutation factor; meanwhile, it can enhance the abilities of the global optimization and local search of the ABC algorithm and avoid prematurity. The extracted score vectors of the principal component of the ultraviolet (UV) spectrum were used as the input variable of the BP-ANN algorithm. The IAABC algorithm was used to optimize the weight and threshold of the BP-ANN algorithm, and the iterative algorithm was repeated until the output accuracy was reached. The output variable was the classification results of NaCl, NaOH, PEA, and the mixture. Meanwhile, the proposed IAABC-BP-ANN algorithm was compared with discriminant analysis (DA), sigmaid-support vector machine (SVM), radial basis function-SVM (RBF-SVM), BP-ANN, and ABC-BP-ANN. Then, the above algorithms were used to classify NaCl, NaOH, PEA, and the mixture, respectively. In the experiment, four indicators, accuracy, recall, precision, and F-score, were used as the evaluation criteria. In addition, the regression equation parameters of the mixture for the testing set were obtained by BP-ANN, ABC-BP-ANN, and IAABC-BP-ANN models. All of the results showed that IAABC-BP-ANN exhibits better performance than other algorithms. Therefore, IAABC-BP-ANN combined with UV spectroscopy is a potential identification tool for the detection of NaCl, NaOH, PEA, and the mixture.
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DOI
PubMed
高影响因子
LWT-FOOD SCIENCE AND TECHNOLOGY
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2025
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IF 6.6
Weilong Peng,Lijun Wang,Xiaoguang Wang,Yaya Wang,Weimei Wang,Junjie Huang,Ruigang Zhou,Chaoyang Chen,Ruonan Bo,Mingjiang Liu,Jingui Li
A novel method for extracting polyphenols from Moringa oleifera Lam. ( M. oleifera ) using natural deep eutectic solvents (NADES) has been developed, with extraction conditions optimized via an artificial neural network-genetic algorithm (ANN-GA) to maximize efficiency. Traditional extraction methods for polyphenols from M. oleifera leaves, such as ethanol extraction, are often associated with low efficiency, environmental concerns, and high operational costs. Furthermore, the empirical selection of solvents and extraction conditions is time-consuming and lacks precision. In this study, 28 NADES were employed to predict the solubility of polyphenol molecules through a conductor-like screening model for realistic solvents (COSMO-RS) model. Based on the results, eight highly effective NADES combinations were selected and experimentally verified. The betaine-maleic acid (Bet-MA) combination with the ultrasonic-assisted extraction showed the highest extraction rate of M. oleifera leaves polyphenol (MOLP), which was 1.56 times that of ultrasonic-assisted ethanol solvent. Simplex centroid mixture design (SCMD) and two-level factor experiments were conducted to optimize the ratio of betaine, maleic acid, and water and identify the most influential extraction factors on MOLP yield. The ANN-GA model was further applied to refine the extraction conditions and analyze their interactions on MOLP content. Additionally, antioxidant assays demonstrated that MOLP extracted with the Bet-MA solvent displayed significant antioxidant activity. In conclusion, this study demonstrates that COSMO-RS and ANN-GA may serve as powerful tools for solvent selection and extraction process optimization, offering a green and efficient approach for polyphenol extraction from M. oleifera leaves. The findings highlight the potential of NADES-based extraction methods for broader applications in the extraction of various bioactive substances, contributing to the development of sustainable and environmentally friendly extraction technologies.
研究领域:
植物化学
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食品科学
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环境科学
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产品关联
DOI
高影响因子
JOURNAL OF CHROMATOGRAPHY A
·
2026
·
IF 4
Qiuyue Hu,Ruochen Qi,Xindi Feng,Dian Jin,Li Sun,Zuoxiang Zeng,Feng Liang,Zhongli Xiong,Zhen Liu
Deep eutectic solvents (DESs) have emerged as promising green alternatives to conventional organic solvents for the sustainable extraction of bioactive compounds from medicinal plants. In this study, a DES-based extraction system enhanced by ultrasound was employed to extract flavonoids from Scutellariae Radix (SR). A hybrid optimization strategy integrating Box-Behnken design (BBD), artificial neural network (ANN) modeling, and genetic algorithm (GA) was utilized to maximize the extraction efficiency. Under the optimized conditions, the predicted yield of flavonoids reached 194.58 mg/g, which was in close agreement the experimental value (194.03 mg/g). The purified DES extracts exhibited strong antioxidant activity. After five consecutive extraction cycles, the DES maintained its structure stability and retained a high extraction yield with only a 11.39% decrease. The characterization of the solid extraction residue indicated the microstructure of SR was disrupted by the DES, facilitating the release of flavonoids. In addition, mechanistic studies based on quantum chemical calculations revealed strong hydrogen bonding and orbital interactions between the DES and flavonoids as key contributors to the extraction process. These results demonstrate the potential of DESs as tunable and reusable media for the efficient extraction of natural products, supported by both experimental data and molecular-level insights.
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产品关联
DOI
PubMed
高影响因子
FOOD CHEMISTRY
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2024
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IF 8.5
Baoqing Bai,Siyuan Meng,Yanli Guo,Ziqing Zhang,Rui Chen,Tao Bo,Jinhua Zhang,Sanhong Fan,Yukun Yang
In this study, vortex-assisted liquid-liquid microextraction (VA-LLME) based on hydrophobic deep eutectic solvents (HDES) was used to efficiently and sustainably extract five phenolic acids and tetramethylpyrazine (TMP) from Shanxi aged vinegar (SAV). The VA-LLME technique was employed to investigate the extraction mechanism of HDES with the best extraction performance for the target compounds using a conductor-like screening model for real solvents (COSMO-RS). An artificial neural network combined with a genetic algorithm (ANN-GA) was developed to optimize the extraction conditions based on single-factor and response surface methodology, while also analyzing the interactive effects on the phenolic acids and TMP in the extracted solution during the extraction phase. The optimized conditions were determined, and the greenness of the procedure was evaluated using an analytical greenness metric, indicating that this technique can serve as a green alternative for the determination of phenolic acids and TMP in SAV.
研究领域:
食品科学
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分析化学
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产品关联
DOI
PubMed
高影响因子
危险品化学品经营许可证（带存储）