JOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES
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2020
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IF 3.004
Youzhen Tan,Biying Chen,Cui Ren,Mingxin Guo,Juanxia Wang,Kexing Shi,Xia Wu,Yifan Feng
Sandalwood is one of the most valuable woods in the world. However, today's counterfeits are widespread, it is difficult to distinguish authenticity. In this paper, similar genus ( Dalbergia and Pterocarpus ) and confused species ( Gluta sp .) of sandalwood were quickly and efficiently identified. Rapid identification model based on 1 H NMR and decision tree (DT) algorithm was firstly developed for the identification of sandalwood, and the accuracy was improved by introducing the AdaBoost algorithm. The accuracy of the final model was above 95%. And the feature components between different species of sandalwood were further explored using UHPLC-QTOFMS and NMR spectrometry. The results showed that 183 compounds were identified, among which 99 were known components, 84 were unknown components. The 1 H NMR and 13 C NMR signals of 505 samples were assigned, among them, 14 compounds were attributed, characteristic chemical shift intervals with great differences in the model were analysed. Furthermore, the fragmentation pattern of different compounds from sandalwood, in both positive and negative ion ESI modes, was summarized. The results showed a potential and rapid tool based on DT, NMR spectroscopy and UHPLC-QTOFMS, which had performed great potential for rapid identification and feature analysis of sandalwood.
研究领域:
植物学
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分析化学
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药用植物研究
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产品关联
DOI
PubMed
高影响因子
FOOD CHEMISTRY
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2025
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IF 9.8
Mengyuan Li,Lei Jia,Xiaolei Zhao,Lina Zhang,Dan Zhao,Jun Xu,Tongqian Zhao
Developing analytical methods for simultaneous detection of multiple antibiotic residues is crucial for environmental protection and human health. In this study, a dual lanthanide fluorescence probe (GDP-Eu-Tb) based on nucleotides has been designed. The addition of quinolone antibiotics (QNs) quench the Eu 3+ fluorescence signal through the inner filter effect (IFE) and exhibit characteristic peaks, enabling ratio fluorescence detection of levofloxacin (LVLX), gatifloxacin (GTLX), and moxifloxacin (MXLX). A ratiometric fluorescence sensor array is constructed using a single sensor element (GDP-Eu-Tb), combined with principal component analysis (PCA) and decision tree (DT) algorithms to model the relationship between fluorescence intensity ratios (I 450 /I 616 , I 460 /I 616 , I 463/ I 616 , I 468 /I 616 ) and QNs. The performance of the DT model is evaluated using accuracy, precision, recall, and F1 score, with stability and generalizability confirmed by stratified ten-fold cross-validation. This approach demonstrates high sensitivity, selectivity and applicability and provides an effective solution for antibiotic residue detection.
研究领域:
环境科学
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分析化学
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生物医学
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产品关联
DOI
PubMed
高影响因子
Alexandria Engineering Journal
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2024
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IF 6.2
Tian-Ji Liu,Qing Yu,Yi-Tong Wang,Jun-Guo Li,Xiao-Man Wang,Le-Le Kang,Rui Ji,Fu-Ping Wang,Ya-Nan Zeng,Shuang Cai
This study investigates the biomass and carbon fixation rate of Chlorella sp. by the main valuable elements in converter steel slag in F/2 seawater medium. The results indicate that Ca, Mg, P, Si, Fe, and Mn can increase the biomass and carbon fixation rate of Chlorella sp. , while Cu, Zn, Cr, and Al can decrease the biomass and carbon fixation rate of Chlorella sp. . Three machine learning methods known as Back Propagation neural network (BPNN), decision tree (DT), and random forest (RF) were applied to construct the prediction model for the carbon fixation rate of Chlorella sp. based on real-life experimental data obtained from single factor experiments. The overall results exhibited that the BPNN model is better than the DT model and RF model to predict the carbon fixation rate of Chlorella sp. . Finally, the maximum carbon fixation rate for Chlorella sp. predicted by BPNN model is 50.86 mg/(L·d), which was 2.46 times that of the control group, under the optimum conditions of Ca 5.77 g/L, Mg 4.74 g/L, P 1.27 g/L, Si 6.31×10 −1 g/L, Fe 6.50×10 −4 g/L, Mn 5.00×10 −5 g/L, Cu 2.51×10 −6 g/L, Zn 4.98×10 −6 g/L, Cr 0 g/L, and Al 0 g/L in F/2 seawater medium.
研究领域:
环境科学
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生物技术
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机器学习
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产品关联
DOI
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
Zhenli Sun,Yiyan Zhang,Zijin Hong,Jingjing Du
Surface-enhanced Raman spectroscopy (SERS) has emerged as a powerful analytical tool for dye monitoring in environmental waters; however, challenges remain in resolving complex mixtures due to spectral overlap and limited signal reproducibility. To address this issue, a dual-channel SERS system integrated with machine learning (ML) was developed. Positively and negatively charged polyelectrolyte-modified Fe 3 O 4 @SiO 2 -Au substrates were employed for the selective detection of anionic and cationic dyes, respectively. Benefiting from the magnetic properties of the substrates, the system enables simultaneous and efficient capture of charge-contrasting dye mixtures. Several classification ML models, including linear discriminant analysis (LDA), k-nearest neighbor (KNN), and decision tree, were constructed to distinguish SERS data from various mixed dyes. The best-performing models, LDA and KNN, achieved classification accuracies exceeding 92 %, and a multimodel ensemble algorithm combining LDA and KNN was designed to harness the strengths of both models, resulting in a classification accuracy of 98 % for mixed dyes. This integrated approach enhances the efficiency and precision of Raman spectroscopic analysis while reducing the need for manual intervention, offering strong potential for high-precision pollutant detection and real-world application in analytical chemistry.
研究领域:
分析化学
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光谱学
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环境监测
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机器学习
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纳米技术
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产品关联
DOI
PubMed
高影响因子
ANALYTICAL AND BIOANALYTICAL CHEMISTRY
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2023
·Vol. 415
·期 9
·1699-1707
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IF 4.3
Peng Shirun,Lu Dechan,Zhang Bohan,You Ruiyun,Chen Jingbo,Xu Huiyao,Lu Yudong
Liquid biopsies have significance for early colon cancer screening and improving patient survival. Recently, several researchers have applied surface-enhanced Raman spectroscopy (SERS) for the label-free and non-invasive detection of serum. Most of these studies performed the assay using a mixture of noble metal nanoparticles (NMNPs) with serum. However, SERS analysis of serum remains a challenge in terms of reproducibility and stability, as NMNPs tend to aggregate when mixed with serum, resulting in a non-uniform distribution of hot spots. Here, we report on the non-invasive identification of colon cancer (CC) using an internal standard (IS)–calibrated label-free serum SERS assay in combination with machine learning. Serum SERS spectra of 50 CC patients and 50 health volunteers have been obtained using silver nanoparticle (Ag NP) colloid and mercaptopropionic acid–modified Ag NPs (Ag NPs–MPA) as the SERS substrates. Decision tree (DT), random forest (RF), and principal component and linear discriminant analysis (PCA–LDA) algorithms were utilized to establish the diagnosis model for SERS spectra data classifying. The results show that the RF model provides a high diagnostic accuracy compared to PCA–LDA. Following calibration with IS molecules, high diagnostic accuracy of over 90% and 100% specificity can be achieved with DT, RF, and PCA–LDA algorithms to differentiate between cancer and normal groups. The results from this exploratory work demonstrate that serum SERS detection combined with multivariate statistical methods and IS calibration has great potential for the non-invasive and label-free detection of CC.
研究领域:
生物医学工程
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分析化学
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机器学习
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癌症诊断
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纳米技术
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DOI
高影响因子
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
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2023
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IF 1.5
Mingju Chen,Anle Cui,Zhengxu Duan,Xingzhong Xiong
Currently, evaluating the quality of strong-flavor Baijiu (SFB) heavily relies on subjective sensory analysis, resulting in large deviations in evaluation. However, as there are no existing evaluation criteria for SFB quality, this study aimed to extract trace components and design an evaluation model using gas chromatography–mass spectrometry (GC–MS). First, the key component data was analyzed using principal component analysis (PCA) and sparse principal component analysis (SPCA) to identify the most important principal components that represent the SFB samples. Second, KNN, DT, SVM, and BP analyses were then employed on the principal component data to determine the grade of the SFB samples. Finally, a price prediction model based on SPCA+BP was established to objectively evaluate the quality and price of SFB. The experimental results show that the proposed method can effectively realize the distinction and price prediction of SFB.
研究领域:
食品科学
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分析化学
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机器学习
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化学计量学
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DOI
高影响因子
Food Science of Animal Resources
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2021
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IF 2.471
Xiao-Guang Dong,Li-Bing Gao,Hai-Jun Zhang,Jing Wang,Kai Qiu,Guang-Hai Qi,Shu-Geng Wu
This study discriminated fatty acid profile and flavor characteristics of BeijingYou Chicken (BYC) as a precious local breed and Dwarf Beijing You Chicken (DBYC)eggs. Fatty acid profile and flavor characteristics were analyzed to identifydifferences between BYC and DBYC eggs. Four classification algorithms were usedto build classification models. Arachidic acid, oleic acid (OA), eicosatrienoicacid, docosapentaenoic acid (DPA), hexadecenoic acid, monounsaturated fattyacids (MUFA), polyunsaturated fatty acids (PUFA), unsaturated fatty acids (UFA)and 35 volatile compounds had significant differences in fatty acids andvolatile compounds by gas chromatography-mass spectrometry (GC-MS)(p<0.05). For fatty acid data, k-nearest neighbor (KNN) and supportvector machine (SVM) got 91.7% classification accuracy. SPME-GC-MS datafailed in classification models. For electronic nose data, classificationaccuracy of KNN, linear discriminant analysis (LDA), SVM and decision tree wasall 100%. The overall results indicated that BYC and DBYC eggs could bediscriminated based on electronic nose with suitable classification algorithms.This research compared the differentiation of the fatty acid profile andvolatile compounds of various egg yolks. The results could be applied toevaluate egg nutrition and distinguish avian eggs.
研究领域:
食品科学
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化学计量学
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动物科学
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分析化学
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产品关联
DOI
PubMed
高影响因子
APPLIED CATALYSIS B-ENVIRONMENTAL
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2024
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IF 20.2
Longde Jiang,Jingde Luan,Haowei Zhang,Yu Bai,Yu Zhang,Wengang Liu,Zheng Yan,Haiting Zhao
Photocatalytic H 2 production from seawater splitting is a promising method for sustainable energy conversion technology. The vanadium-oxygen binary doped carbon nitride (VO X -CN) is successfully prepared for H 2 production. Benefiting from the electronic structure alteration, the maximum H 2 yield on VO X -CN in artificial seawater reaches 4475.74 μmol h −1 g −1 , 10 times higher than that of CN in deionized water. The impact of ions on the photocatalytic activity of VO 2 -CN is followed by Na + > Mg 2+ > K + > Ca 2+ . The adsorption of electrolyte ions promotes the formation of delocalized electron clouds on the surface of VO 2 -CN, facilitating more electrons to participate in H + reduction. Screening based on six machine models, the decision tree regression algorithm is used to reveal the interaction between cation type, their concentration and H 2 yield. Pearson heatmap indicates that Na + , Mg 2+ , and K + ions are positively correlated with H 2 yield, whereas Ca 2+ shows a negative correlation. The interactions among Na + , Mg 2+ and K + atoms facilitate H 2 production in multi-ion system, whereas Ca 2+ ions do the opposite, especially for high concentration. This work proposes new insights into the interference on H 2 yield between the structure alteration of CN framework and the change of ion type and concentration.
研究领域:
材料科学
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催化
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能源转换
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化学工程
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机器学习
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产品关联
DOI
高影响因子
Gels
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2025
·Vol. 11
·期 9
·744
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IF 5.3
Wenhe Liu,Xuhui Wu,Mengmeng Yang,Yuhan Huang,Zhanyang Xu,Mingze Yao,Yikui Bai,Feng Zhang
In the design of gel phase change composite wall materials for solar greenhouses, the alteration of material composition could directly affect the thermal performance of gel phase change composite wall materials. In order to obtain better suitable gel phasechange composite wall material for solar greenhouses, Na2HPO4·12H2O-based gel phasechange materials with different content of ingredient (Na2SiO3·9H2O, C35H49O29, KCl, and nano-α-Fe2O3) were obtained via the Taguchi method and machine learning algorithms, such as Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting Trees (GBDT). The result shows that the GBDT is more suitable for the thermal performance optimization prediction of gel phase change composite wall materials, including time cooling (TC), latent heat of phase change (ΔHm), supercooling degree (ΔT), and phase change temperature (Tm). The determination coefficient (R2) of time cooling (TC), latent heat of phase change (ΔHm), supercooling degree (ΔT), and phase change temperature (Tm) by GBDT are 0.9987, 0.99965, 1, and 0.9995, respectively. The mean absolute error (MAE) coefficient percentage of supercooling degree (ΔT), phase change temperature (Tm), latent heat of phase change (ΔHm), and time of cooling (TC) by GBDT are 0.32%, 0.25%, 0.17%, and 0.26%, respectively. The root mean square error (RMSE) of supercooling degree (ΔT), phase change temperature (Tm), latent heat of phase change (ΔHm), and time of cooling (TC) by GBDT are 0.41%, 0.32%, 0.19%, and 0.35%, respectively. The optimal result predicted by GBDT is Na2HPO4·12H2O + 5% Na2SiO3·9H2O + 12% KCl + 0.2% Nano-α-Fe2O3+ 3% C35H49O29, which was verified by experiments.
研究领域:
可再生能源
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建筑节能
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相变材料
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机器学习应用
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热能存储
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温室技术
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产品关联
DOI
PubMed
高影响因子
SENSORS AND ACTUATORS B-CHEMICAL
·
2023
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IF 8.4
Rui Chen,Jinjie You,Yuge Chen,Zeming Zhang,Aili Sun,Hua Liu,Xizhi Shi
A highly selective and sensitive fluorescence sensor, using the decision tree (DT) machine learning, was successfully fabricated for the quantitative detection of Cu 2+ , which is based on near-infrared carbonized polymer dots (r-CPDs). Using o-phenylenediamine and 1,3,5-benzene tricarboxylic acid as raw materials first to synthesize the r-CPDs (quantum yield of 32.9%), which pyrrole NH ring can specifically form metal chelates with Cu 2+ hiding the electron leap result in the fluorescence burst, by a one-step hydrothermal synthesis method. Furthermore, the fluorescence sensor based on the r-CPDs was fabricated for ultra-sensitively detect Cu 2+ in the range of 0.5–80.0 nM (R 2 =0.9986) in aqueous environments and aquatic products with relative standard deviation (RSD) below 4.4% and a limit of detection (LOD) of 0.24 nM. Combined with the machine learning algorithm model, the r-CPDs fluorescence color changes accompanied by different Cu 2+ concentrations were classified. A self-developed smartphone application equipped with 3D printing technology to prepare portable cartridges successfully applied to rapid real-time detection of trace Cu 2+ in various practical samples. The experimental results show that the method has not only convenient for calculating but also accurate. Generally, this study displayed a perfect fusion of r-CPDs fluorescence sensors with machine learning which applied to the determination of Cu 2+ in environmental and aquatic products.
研究领域:
环境科学
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分析化学
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机器学习
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材料科学
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传感器技术
✓ 已验证
产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）