SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
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2025
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IF 4.6
Yuxi Jiang,Ruifang Yang,Nanjing Zhao,Gaofang Yin,Hengxin Song,Gaoyong Shi,Peng Huang,Ming Gao
Groundwater, a vital freshwater resource, faces increasing contamination risks from chemical industrial parks discharging hazardous compounds such as phenol and toluene. Detecting these pollutants at low concentrations is essential to ensure water quality and protect against long-term hazards. A method combining fluorescence spectroscopy and Gaussian feature extraction is proposed for the identification and quantification of phenol and toluene in groundwater. Fluorescence excitation-emission matrix (EEM) spectra of phenol and toluene are first measured, followed by feature extraction using a Gaussian function. The extracted features are then employed for qualitative identification and quantitative determination via support vector machine (SVM) and partial least squares (PLS) regression, respectively. For qualitative identification, Gaussian feature extraction is compared with original feature and PCA-based feature extraction methods. For quantification, it is compared with peak picking and PCA-based feature extraction methods. The results show that after Gaussian feature extraction, the performance is significantly improved. The identification accuracy for single-component samples reached 95.24 %, while for mixture samples, the accuracy was 90 %. In quantitative analysis of mixture samples, the average relative error for phenol concentrations of 2 µg/L or higher and toluene concentrations of 600 µg/L was controlled around 10 %, while for phenol concentrations at 1 µg/L, the relative error was about 30 %. This approach enhances both identification and quantification performance, providing a reliable tool for the early detection and quantification of low-concentration contaminants in groundwater, with great potential for environmental protection.
研究领域:
环境科学
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分析化学
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产品关联
DOI
PubMed
高影响因子
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
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2021
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IF 4.098
Jiaqi Hu,Yanqiu Zou,Biao Sun,Xinyao Yu,Ziyang Shang,Jie Huang,Shangzhong Jin,Pei Liang
Pesticide detection is of tremendous importance in agriculture, and Raman spectroscopy/Surface-Enhanced Raman Scattering (SERS) has proven extremely effective as a stand-alone method to detect pesticide residues. Machine learning may be able to automate such detection, but conventional algorithms require a complete database of Raman spectra, which is not feasible. To bypass this problem, the present study describes a transfer learning method that improves the algorithm's accuracy and speed to extract features and classify Raman spectra. The transfer learning model described here was developed through the following steps: (1) the classification model was pre-trained using an open-source Raman spectroscopy database; (2) the feature extraction layer was saved after training; and (3) the training model for the Raman spectroscopy database was re-established while using self-tested pesticides and keeping the feature extraction layer unchanged. Three models were evaluated with or without transfer learning: CNN-1D, Resnet-1D, and Inception-1D, and they have improved the accuracy of spectrum classification by 6%, 2%, and 3%, with reduced training time and increased curve smoothness. These results suggest that transfer learning can improve the feature extraction capability and therefore accuracy of Raman spectroscopy models, expanding the range of Raman-based applications where transfer learning model can be used to identify the spectra of different substances.
研究领域:
分析化学
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机器学习
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农业科学
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DOI
PubMed
高影响因子
Scientific Programming
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2021
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IF 1.025
Tao Li,Yuanyuan Xu,Jiliang Luo,Jianan He,Shiming Lin
In order to improve the accuracy of amino acid identification, a model based on the convolutional neural network (CNN) and bidirectional gated recurrent network (BiGRU) is proposed for terahertz spectrum identification of amino acids. First, we use the CNN to extract the feature information of the terahertz spectrum; then, we use the BiGRU to process the feature vector of the amino acid time-domain spectrum, describe the time series dynamic change information, and finally achieve amino acid identification through the fully connected network. Experiments are carried out on the terahertz spectra of various amino acids. The experimental results show that the CNN-BiGRU model proposed in this study can effectively realize the terahertz spectrum identification of amino acids and will provide a new and effective analysis method for the identification of amino acids by terahertz spectroscopy technology.
研究领域:
生物信息学
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光谱学
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人工智能
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产品关联
DOI
高影响因子
Nanomaterials
·
2022
·Vol. 12
·期 12
·2114
·
IF 5.719
Bo Wang,Xiaoling Qin,Kun Meng,Liguo Zhu,Zeren Li
Terahertz (THz) spectroscopy is the de facto method to study the vibration modes and rotational energy levels of molecules and is a widely used molecular sensor for non-destructive inspection. Here, based on the THz spectra of 20 amino acids, a method that extracts high-dimensional features from a hybrid spectrum combined with absorption rate and refractive index is proposed. A convolutional neural network (CNN) calibrated by efficient channel attention (ECA) is designed to learn from the high-dimensional features and make classifications. The proposed method achieves an accuracy of99.9%and99.2%on two testing datasets, which are12.5%and23%higher than the method solely classifying the absorption spectrum. The proposed method also realizes a processing speed of 3782.46 frames per second (fps), which is the highest among all the methods in comparison. Due to the compact size, high accuracy, and high speed, the proposed method is viable for future applications in THz chemical sensors.
研究领域:
生物化学
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人工智能
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分子传感
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DOI
高影响因子
SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY
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2024
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IF 3.2
Yujiang Gou,Xinglan Fu,Shilin Zhao,Panyu He,Chunjiang Zhao,Guanglin Li
Identification of soil contamination types is of great scientific significance for soil remediation and environmental pollution control. However, traditional identification methods for soil determination are time-consuming, laborious, and complicated. Here, we proposed an accurate method for identifying soil contamination types based on laser-induced breakdown spectroscopy (LIBS) and an improved convolutional neural network (CNN) model. The spectral feature extraction-based multiple attention residual network (SFEMARNet) model was constructed to extract detailed features by spectral feature extraction (SFE) modules, and highlight useful features by multiple attention residual (MAR) modules in LIBS spectral data. In addition, deep learning models and machine learning models were used to identify the data. The results showed that the SFEMARNet model achieved an accuracy of 98.75% on the test set. The recall, precision, and F1-score of the models reached 98.78%, 98.75%, and 98.76%, respectively, which were significantly better than the three deep learning models and of four machine learning models. It seems that the SFEMARNet model combined with LIBS technology may be a potential method for the accurate identification of soil contamination types.
研究领域:
环境科学
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人工智能
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土壤学
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DOI
高影响因子
Journal of Food Measurement and Characterization
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2023
·Vol. 17
·期 5
·4627-4637
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IF 3.4
Hu Jun,Zhan Chaohui,Xu Zhen,Shi Hongyang,Wang Hao,He Yong,Ouyang Aiguo
The abuse of clenbuterol hydrochloride (CL) is a serious threat to people’s life and health. The traditional THz detection method can only detect the sample at the milligram level, which is not suitable for trace analysis of the sample. In this paper, a THz metamaterial enhanced micro-nano structure was designed based on the electromagnetic theory to realize the highly sensitive detection of the trace CL by THz spectroscopy. Firstly, four pretreatment methods were used to correct the spectrum, including AirPLS, AsLS, Backcor and BEADS. Secondly, four algorithms, namely, PCA, CARS, UVE, and SPA were used to extract features from the spectra. Finally, the PLS and LS-SVM models are established. The AirPLS-CARS-LS-SVM model has the best result, with the R p of 0.9743, the RMSEP of 2.90 × 10 –5 , and the LOD of 9.67 × 10 –5 mg/mL. This study will facilitate the rapid, nondestructive and highly sensitive detection of the trace CL in food. Besides, this study has significant implications for promoting the detection of other types of clenbuterol and trace substances. Graphical abstract
研究领域:
分析化学
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生物医学工程
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食品安全检测
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光谱学
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纳米技术
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DOI
高影响因子
MINERALS ENGINEERING
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2025
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IF 5
Hao Wang,Qinshan Liu,Mengdi Xu,Lei Xie,Yaowen Xing,Xiahui Gui
Flotation is a core technology for the upgrading and purification of sulfide ores, and it has been widely applied in industrial production. In industrial flotation processes, operators heavily rely on visual inspection of froth conditions to adjust operating parameters, which leads to strong subjectivity, poor stability, and delayed feedback. In recent years, machine vision technology has been widely adopted for flotation froth image classification. However, most existing methods rely on traditional clustering or other classification models, which struggle to handle practical challenges such as label scarcity and large variations in operating conditions. Moreover, the mapping between image features and flotation conditions remains unclear. To address these challenges, this study proposed a semi-supervised clustering-based method for fine classification of flotation froth images, aiming to establish a quantitative relationship between image features and flotation conditions. First, based on a self-constructed flotation experiment platform under multiple operating conditions, froth images were collected under varying impeller speeds and collector dosages. Subsequently, multidimensional features including color, texture, and size were extracted. A mutual information-based method was then employed for dimensionality reduction and feature selection, resulting in compact feature vectors. On this basis, an “Eff-Collector” kernel function was constructed by incorporating operating conditions. Unsupervised clustering pseudo-labels were used to guide Gaussian kernel-based clustering, thereby building a semi-supervised clustering model constrained by operating conditions. This method effectively improves froth image classification accuracy under limited labeling conditions and enhances the ability of clustering results to represent actual operating conditions. The average accuracy of operating condition identification from froth images exceeds 94.89 %, with the standard deviation of major clustering categories across multiple trials remaining below 3 %. These results provide a solid theoretical foundation and a practical technical pathway for reagent regulation and intelligent monitoring in flotation processes.
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产品关联
DOI
高影响因子
Energy & Environmental Materials
·
2025
·
IF 14.1
Xinmiao Li,Ying Li,Huifang Jiang,Yancheng Chen,Zhuangzhuang Ma,Zhifeng Shi,Di Chen,Guozhen Shen
All-optically controlled artificial synapses for neuromorphic vision offer unique advantages in simplifying circuit design and minimizing power consumption, meeting the application demands of the current artificial intelligence era. However, developing all-optically controlled devices that combine high performance and high reproducibility remains a significant challenge. In this work, we demonstrate an all-optically controlled artificial synapse based on ZnO and Cs 2 CoCl 4 single crystal connected structure, which integrates light information sensing and processing capabilities. Relying on the simple series-connected structure, as well as the positive photoconductance of ZnO and the negative photoconductance of Cs 2 CoCl 4 , the optically controlled bidirectional synaptic plasticity is realized under ultraviolet and visible light stimulation without additional voltage modulation in the all-optically controlled synapse. In addition, leveraging its ultraviolet-enhanced feature extraction and visible-suppression capabilities, the all-optically controlled synapse can act as denoising units in bioinformation preprocessing and weight-updating units in feature recognition. The proposed all-optically controlled synapses exhibit excellent information perception, low-level noise reduction, and high-level cognition functions for bioinformation recognition under complex light conditions. We believe that this work can provide structural-level insights and inspirations in the design and fabrication of all-optically controlled synapses to promote the application for efficient neuromorphic vision in the future.
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DOI
高影响因子
Journal of Physical Chemistry Letters
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2025
·
IF 4.6
Yanyi Yang,Xia Long,Jiaqing Dai,Xiaochi Liu,Duokai Zheng,Juexian Cao,Yong Hu
Interpretability is fundamental in the precise identification of single-molecule charge transport, and its absence in deep learning models is currently the major barrier to the usage of such powerful algorithms in the field. Here, we have pioneered a novel identification method employing fusion attention-based deep learning technologies. Central to our approach is the innovative neural network architecture, SingleFACNN, which integrates convolutional neural networks with a fusion of multihead self-attention and spatial attention mechanisms. Our findings demonstrate that SingleFACNN accurately classifies the three-type and four-type STM-BJ data sets, leveraging the convolutional layers’ robust feature extraction and the attention layers’ capacity to capture long-range interactions. Through comprehensive gradient-weighted class activation mapping and ablation studies, we identified and analyzed the critical features impacting classification outcomes with remarkable accuracy, thus enhancing the interpretability of our deep learning model. Furthermore, SingleFACNN’s application was extended to mixed samples with varying proportions, achieving commendable prediction performance at low computational cost. Our study underscores the potential of SingleFACNN in advancing the interpretability and credibility of deep learning applications in single-molecule charge transport, opening new avenues for single-molecule detection in complex systems.
研究领域:
机器学习
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分子电子学
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计算物理学
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纳米技术
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人工智能
✓ 已验证
产品关联
DOI
PubMed
高影响因子
ANALYTICAL CHEMISTRY
·
2024
·
IF 6.7
Yonghong Zha,Yansong Li,Jianhua Zhou,Xiaolan Liu,Ki Soo Park,Yu Zhou
Given the harmful effect of pesticide residues, it is essential to develop portable and accurate biosensors for the analysis of pesticides in agricultural products. In this paper, we demonstrated a dual-mode fluorescent/intelligent (DM-f/DM-i) lateral flow immunoassay (LFIA) for chloroacetamide herbicides, which utilized horseradish peroxidase-IgG conjugated time-resolved fluorescent nanoparticle probes as both a signal label and amplification tool. With the newly developed LFIA in the DM-f mode, the limits of detection (LODs) were 0.08 ng/mL of acetochlor, 0.29 ng/mL of metolachlor, 0.51 ng/mL of Propisochlor, and 0.13 ng/mL of their mixture. In the DM-i mode, machine learning (ML) algorithms were used for image segmentation, feature extraction, and correlation analysis to obtain multivariate fitted equations, which had high reliability in the regression model with R2 of 0.95 in the range of 2 × 102–2 × 105 pg/mL. Importantly, the practical applicability was successfully validated by determining chloroacetamide herbicides in the corn sample with good recovery rates (85.4 to 109.3%) that correlate well with the regression model. The newly developed dual-mode LFIA with reduced detection time (12 min) holds great potential for pesticide monitoring in equipment-limited environments using a portable test strip reader and laboratory conditions using ML algorithms.
研究领域:
分析化学
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环境科学
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机器学习
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农业科学
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生物技术
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产品关联
DOI
PubMed
高影响因子
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