FOOD CHEMISTRY
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2019
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IF 5.399
Shao Hua Lu,Sha Sha Li,Bo Yin,Jia Ying Mi,Hong Lin Zhai
Diverse interferences often affect the determination of pesticide residues in various kinds of food, and complex pretreatments are necessary. An effective approach for the simultaneous quantitative analysis of multi-target components in different substrates was proposed, and applied to the determination of ternary pesticides in cherry tomatoes and red grape samples. By means of HPLC-DAD, the spectra were obtained under isocratic elution. Utilizing the Tchebichef image moments, unified quantitative models were established for the three target components in the samples with two different substrates, respectively. The correlation coefficients of leave-one-out cross-validation ( R loo - c v 2 ) of the obtained models were more than 0.9975. The predictive correlation coefficients ( R p 2 ) were more than 0.9831. Compared with the N-PLS and MCR-ALS methods, the proposed approach is not only more accurate and reliable, but also can extract essential information and expected to be a potential tool to rapidly quantify multi-component in different substrates without complex pretreatments.
研究领域:
分析化学
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食品科学
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化学计量学
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DOI
PubMed
高影响因子
JOURNAL OF HAZARDOUS MATERIALS
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2025
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IF 11.3
Xiaoqin Tang,Xinyue Li,Yuhan Tian,Qiang Zhang,Shuli Man,Weipan Peng,Long Ma
Foodborne illnesses and contamination are becoming increasingly severe global public health issues. In recent years, the resistance of pathogenic bacteria has increased significantly, which makes it particularly urgent to develop advanced detection technologies beyond traditional methods. Pf Ago ( Pyrocus furiosus Argonaute), a novel programmable nuclease with specific sequence targeting and cleavage capabilities, offers significant advantages over traditional CRISPR diagnostics (CRISPR-Dx) in molecular detection, particularly in flexible design and multiplexed detection. In this work, Pf Ago was innovatively integrated with the nucleic acid reverse-phase enhanced fluorescent lateral flow test strip (rLFTS) and smartphone for the first time, enabling ultrasensitive, portable and multiplexed molecular detection of pathogenic bacteria in a cross-validating manner. Pf Ago precisely recognized LAMP amplicons, triggering linker DNA cleavage that generated dual colorimetric/fluorescent signals on rLFTS. A 3D-printed visualizer equipped with a homemade App-enabled smartphone achieved single-cell sensitivity (1 CFU/mL) within 45 min, two orders of magnitude lower than traditional qPCR (∼100 CFU/mL). In summary, a conceptually novel cross-validating manner integrating Pf Ago with dual-mode rLFTS was constructed. It eliminated the reliance on large instruments, representing a significant step forward in developing an ultrasensitive, portable, accurate and multiplexed molecular detection technology for foodborne pathogens.
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DOI
PubMed
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
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
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2025
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IF 4.6
Xiulian Yin,Yingjin Li,Mingjie Wei,Yanni Ji,Qinghua Cao,Xiangfen Xue
Spectral retrieval method plays a crucial role in spectral analysis technology. Nevertheless, research in this area has not received the requisite advancement for fluorescence excitation-emission matrix (EEM) spectroscopy, which has been widely used in environmental monitoring and other fields. In this work, we developed a spectral retrieval method, the contour line-based image pattern recognition (CLIPR) technique, to interpret the EEM spectrum. A three-level retrieval mechanism, including the primary feature Lib1, qualitation Lib2 and quantitation Lib3 has been set up. Optimization efforts have been conducted on the accuracy and efficiency of noise removing, feature extraction and image retrieval methods. To demonstrate the technique, four model pollutants with very similar chemical structures and a metal-organic framework (MOF), MOF-74 (Zn), as a fluorescence probe providing more dimensional information were used. A total of 28 × 4 EEM map images were modeled and validated using the leave-one-out cross-validation method. Two batches with 5 × 4 samples each as unknown samples were selected to evaluate the performance of the CLIPR method. When taking the weight value set with 0.8, 0.1 and 0.1, for the validation sets with the concentration greater than 50 μM, the intra class similarity is above 79.61 % and the accuracy reached 98.81 % (83/84). The best quantitative result is PhNH 2 -S1, with the RMSE/True value 3.09 % and R 2 0.99. For the unknown sets, when Batch 1 greater than 35 μM and Batch 2 greater than 50 μM, the recognition accuracy reaches 100 %. The research results indicate that the model has a certain degree of stability and generalization and it is feasible to apply spectral retrieval method to interpret EEM information. We have taken a step forward in laying the foundation for large-scale reference data preparation in this field.
研究领域:
光谱分析
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环境监测
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模式识别
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荧光光谱技术
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化学传感
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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
Yolandani,Dandan Liu,Fredy Agil Raynaldo,Mokhtar Dabbour,Xueli Zhang,Zhongyuan Chen,Qingzhi Ding,Lin Luo,Haile Ma
The bitterness of soy protein isolate hydrolysates prepared using five proteases at varying degree of hydrolysis (DH) and its relation to physicochemical properties, i.e., surface hydrophobicity (H 0 ), relative hydrophobicity (RH), and molecular weight (MW), were studied and developed for predictive modelling using machine learning. Bitter scores were collected from sensory analysis and assigned as the target, while physicochemical properties were assigned as features. The modelling involved data pre-processing with local outlier factor; model development with support vector machine, linear regression, adaptive boosting and K-nearest neighbors algorithms; and performance evaluation by 10-fold stratified cross-validation. The results indicated that alcalase hydrolysates were the most bitter, followed by protamex, flavorzyme, papain, and bromelain. Distinctive correlation results were found among the physicochemical properties, influenced by the disparity of each protease. Among the features, the combination of RH-MW fitted various classification models and resulted in the best prediction performance.
研究领域:
食品科学
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化学工程
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机器学习
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感官评价
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DOI
PubMed
高影响因子
MICROCHEMICAL JOURNAL
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2023
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IF 4.8
Shiyi Chen,Feiyang Tian,Juan Peng,Yan Meng,Yu Wang,Xiaogang He,Qian Wang,Ganghui Chu
Chemical warfare agents, especially organophosphorus warfare agents are a variety of various chemical substances employed in warfare that can massively cause damage to the organism even death. The partial catalytic function of Universitetet i Oslo-66 (UiO-66, a zirconium-based metal–organic framework) produced via de novo synthesis is similar to calf intestinal alkaline phosphatase (CIAP, EC 3.1.3.1) and has a lower usage cost, is expected to be a mimetic enzyme for hydrolyzing organophosphorus warfare agents due to the fact that it has lattice defects. 0.1–5.6 mL of acetic acid was added as a regulator to form different lattice defects. Typically, mimetic enzyme activity tests often require expensive reagents as substrates, and the experiment processes are time-consuming and inconvenient. As a result, a novel approach based on near-infrared spectroscopy (NIRs) was developed in this research for predicting UiO-66 mimetic enzyme activity. p -Nitrophenyl phosphate disodium salt ( p NPP) was selected as a lower toxic organophosphorus warfare agent to conduct the subsequent simulant research. The absorbance of the mimetic enzyme catalytic system was evaluated using Ultraviolet–Visible (UV–Vis) spectroscopy to investigate the amount of its activity and compare it with CIAP activity. The synthesized samples’ enzyme activities ranged from 1.91 to 6.67 U/g and their catalytic costs ranged from 0.65 to 2.26 CNY. The powdered samples' near-infrared spectra were obtained. The sample’s enzyme activity data were linked with each sample’s original NIR spectra to establish the NIR model. A partial least squares (PLS) model of mimetic enzyme activity was developed by coupling spectral preprocessing with a variable screening technique. The root-mean-square error of cross-validation (RMSECV), the correlation coefficient of validation set (R) value, and the ratio of prediction to deviation (RPD) value were employed as assessment model indicators. The near-infrared spectrum model was developed by merging the best 2nd derivative spectral preprocessing with the Competitive Adaptive Reweighted Sampling (CARS) variable screening method. This model's RMSECV was 0.41 U/g, the correlation coefficient of calibration set (R_cv) value was 0.916, the root-mean-square error of prediction (RMSEP) set was 0.52 U/g, R was 0.948, and RPD was 2.51. As a result, the established model could be implemented to quantify the activity of UiO-66 quickly by including more variations of de novo -synthesized samples. The prediction method is simple, rapid, low cost and adaptable to be the theoretical and practical basis for further studying other interdisciplinary research work in biomimetic enzymology and spectroscopy.
研究领域:
化学
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材料科学
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酶学
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光谱学
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毒理学
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DOI
高影响因子
危险品化学品经营许可证（带存储）