SENSORS AND ACTUATORS B-CHEMICAL
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2025
·
IF 7.7
Jiao Chen,Xiaolong Xiong,Jingru Ye,Xinjia Shuai,Jun Zhou,Qingqing Liu,Chengzhi Huang,Hui Liu
Heavy metal pollution is pervasive, complex, and diverse, causing lasting and potentially toxic effects on human health. Therefore, it is necessary to develop high-throughput detection of heavy metals. In this work, we proposed a machine learning-based three-dimensional fluorescent sensor strategy for effective detection of heavy metal pollution. Specifically, a multi-emitting fluorescent "chemical nose/tongue" sensor was first constructed with two different colored CDs and 5,10,15,20-tetrakis(4-trimethylamino) phenyl porphyrin (TAPP) for discriminating nine heavy metals, and then the fluorescence fingerprints were obtained based on the changes in the three-dimensional fluorescence spectra influenced by various heavy metals. The results demonstrated that the sensor easily and rapidly discriminated the categories and concentrations of multiple single heavy metal ions and binary mixed heavy metal ions within the concentration range of 10 μM–0.5 mM. Subsequently, the data input way was optimized, and the results showed that the accuracy of image recognition in predicting categories and concentrations of heavy metal ions was 75.1 % and 64.4 %, and the accuracy of data recognition was 97.8 % and 100 %. After optimizing the learning model, the Random Forest (RF) model can predict the categories and concentrations of a binary heavy metal mixture with accuracies of 100 % and 91.8 %, respectively. In addition, machine learning-assisted fluorescent sensors showed promising applications in real tap water and river samples. In conclusion, compared to previous analysis methods, this approach achieved qualitative and quantitative analysis of heavy metals in complex environments through three-dimensional fluorescence spectra combined with the ML strategy. Notably, the sensor did not involve cumbersome synthesis of multiple sensing channels, making it easy to construct and cost-effective.
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
高影响因子
JOURNAL OF HAZARDOUS MATERIALS
·
2024
·
IF 12.2
Beibei Li,Ruijuan Qu,Ting Wang,Ruixue Guo,Jie Tian,Shuyi Li,Mostafa R. Abukhadra,Rehab Khaled Mahmoud,Zunyao Wang
In this investigation, we conducted a detailed analysis of the oxidation of 16 imidazole ionic liquid variants by Fe(VI) under uniform experimental setups, thereby securing a dataset of second-order reaction rate constants ( k obs ). This methodology ensures superior data consistency and comparability over traditional methods that amalgamate disparate data from varied studies. Utilizing 16 chemical structural parameters obtained via Density Functional Theory (DFT) as descriptors, we developed a Quantitative Structure Activity Relationship (QSAR) model. Through rigorous correlation analysis, Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Applicability Domain (AD) evaluation, we identified a pronounced negative correlation between the molecular orbital gap energy ( E gap) and k obs . MLR analysis further underscored E gap as a pivotal predictive variable, with its lower values indicating heightened oxidative reactivity towards Fe(VI) in the ionic liquids, leading the QSAR model to achieve a predictive accuracy of 0.95. Furthermore, we integrated an advanced machine learning approach - Random Forest Regression (RFR), which adeptly highlighted the critical factors influencing the oxidation efficiency of imidazole ionic liquids by Fe(VI) through elaborate decision trees , feature importance assessment, Recursive Feature Elimination (RFE), and cross-validation strategies. The RFR model demonstrated a remarkable predictive performance of 0.98. Both QSAR and RFR models pinpointed E gap as a key descriptor significantly affecting oxidation efficiency, with the RFR model presenting lower root mean square errors , establishing it as a more reliable predictive tool. The application of the RFR model in this study significantly improved the model's stability and the intuitive display of key influencing factors, introducing promising advanced analytical tools to the field of environmental chemistry .
研究领域:
环境化学
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计算化学
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产品关联
DOI
PubMed
高影响因子
CHEMISTRY OF MATERIALS
·
2025
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IF 7
Maxim Molokeev,Nicolay Golovnev,Andrey Zolotov,Shuai Zhang,Zhiguo Xia
Machine learning models were applied to predict the scintillation performances of organic–inorganic hybrid metal halides (OIMHs), focusing on their photoluminescent quantum yield (PLQY). Random Forest and Decision Tree algorithms identified the most critical structural parameter of organic molecules influencing the M···M distance between metal ions and correlated PLQY value, with an optimal distance of approximately 8 Å correlating with enhanced luminescence efficiency. This prediction was experimentally validated through the synthesis of several OIMH compounds, demonstrating strong agreement between predicted and measured PLQY values. The machine learning approach not only enabled the screening of efficient compounds but also deepened the understanding of how structural factors, such as the structure of organic molecules, govern scintillation properties. These findings underscore the potential of machine learning in accelerating the development of next-generation luminescent materials with improved performance, offering a powerful tool for future material design and optimization.
研究领域:
材料科学
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光学
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人工智能
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DOI
高影响因子
APPLIED OPTICS
·
2023
·Vol. 62
·期 4
·1027-1034
·
IF 1.9
Shangjun Lin,Jie Chen,Wentao Liu,Zhenyun Peng,Zhencheng Chen,Zhencheng Chen,Fangrong Hu,Fangrong Hu
To achieve classification and concentration detection of cancer biomarkers, we propose a method that combines terahertz (THz) spectroscopy, metasurface sensors, and machine learning. A metasurface sensor suitable for biomarker detection was designed and fabricated with five resonance frequencies in the range of 0.3–0.9 THz. We collected biomarkers of five types and nine concentrations at 100 sets of time-domain spectra per concentration. The spectrum is processed by noise reduction and fast Fourier transform to obtain the frequency-domain spectrum. Five machine learning algorithms are used to analyze time- and frequency-domain spectra and ascertain which algorithm is more suitable for the classification of the biomarker THz spectrum. Experimental results show that random forest can better distinguish five biomarkers with an accuracy of 0.984 for the time-domain spectrum. For the frequency-domain spectrum, the support vector machine performs better, with an accuracy of 0.989. For biomarkers at different concentrations, we used linear regression to fit the relationship between biomarker concentration and frequency shift. Experimental results show that machine learning can distinguish different biomarker species and their concentrations by the THz spectrum. This work provides an idea and data processing method for the application of THz technology in biomedical detection.
研究领域:
生物医学工程
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机器学习
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光谱学
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产品关联
DOI
PubMed
高影响因子
FOOD RESEARCH INTERNATIONAL
·
2025
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IF 8
Yingbin Zhang,Xuwei Chen,Dingding Chen,Li Zhu,Guoqing Wang,Zhongxiu Chen
The taste of Chinese green tea is highly diverse. In this study, a combination of unsupervised and supervised learning methods was utilized to develop a model for classifying and predicting typical Chinese green tea taste. Three clustering methods were assessed based on quantitative descriptive analysis (QDA) results, with the Hierarchical-K means method chosen to classify 88 tea infusions into seven distinct taste types. Electronic tongue sensors, near-infrared spectroscopy, and metabolomics, along with the analysis of key chemical constituents, were applied to construct various datasets as model data. The performance of four multivariate statistical methods and six artificial intelligence algorithms was compared across the three datasets. Dataset 3, comprising chemical components, taste activity value (Tav), and their ratios, achieved the highest accuracy. The random forest (RF) model achieved the highest accuracy (0.98) and Kappa value (0.97) in predictions. The results indicate that key chemical components, Tav, and their relationships are more critical for classifying green tea taste. This study can provide a more accurate representation and prediction of typical Chinese tea taste profiles from a consumer standpoint. Significant variations in sensory attributes and chemical composition were observed among the identified taste categories, with the MU type displaying the lowest TavTC (total Tav of catechins)/TavTAA (total Tav of amino acids) ratio, indicating the strongest umami and sweetness characteristics. The findings of this study offer the potential for the development of personalized tea products, thereby contributing to an enhanced consumer experience.
研究领域:
食品科学
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机器学习
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代谢组学
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产品关联
DOI
PubMed
高影响因子
ACS Applied Materials & Interfaces
·
2025
·
IF 8.2
Jia Qi,Caiyun Fang,Chunhui Deng,Fangying Shi,Qunyan Yao
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive and lethal cancer, typically diagnosed at advanced stages due to its asymptomatic onset and challenges in early detection. To address the critical need for the early diagnosis of PDAC, we developed a laser desorption/ionization mass spectrometry (LDI-MS) platform based on mesoporous silica-modified magnetic graphene (MG@mSiO2). MG@mSiO2 exhibited exceptional ultraviolet (UV) absorption, efficient ionization, and minimal background interference, enabling high-resolution profiling of serum metabolic fingerprints (SMFs). Based on the extracted SMFs, we constructed a Random Forest (RF) model to classify PDAC patients, high-risk (HR) individuals, and healthy controls (HC), achieving an accuracy of 97.5% in the independent test set. Additionally, a six-metabolite biomarker panel was identified, showing strong diagnostic potential with sensitivity and accuracy exceeding 89.1% for distinguishing HC from PDAC. When coupled with the serological marker carbohydrate antigen 19–9 (CA19–9), the integrated strategy delivered significantly improved diagnostic performance, achieving high accuracy ranging from 95.3% to 100% in distinguishing HR and PDAC patients from HC. Furthermore, metabolic pathway analysis revealed key pathways associated with PDAC progression, providing mechanistic insights into the disease. This work provides a powerful diagnostic tool for PDAC screening, establishing a foundation for early detection and precision medicine in clinical practice.
研究领域:
医学
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生物化学
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诊断学
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产品关联
DOI
PubMed
高影响因子
Foods
·
2024
·Vol. 13
·期 6
·897
·
IF 4.7
Chengzhi Wang,Xiaping Fu,Ying Zhou,Feng Fu
Deoxynivalenol (DON) is a harmful fungal toxin, and its contamination in wheat flour poses a food safety concern globally. This study proposes the combination of fluorescence hyperspectral imaging (FHSI) and qualitative discrimination methods for the detection of excessive DON content in wheat flour. Wheat flour samples were prepared with varying DON concentrations through the addition of trace amounts of DON using the wet mixing method for fluorescence hyperspectral image collection. SG smoothing and normalization algorithms were applied for original spectra preprocessing. Feature band selection was carried out by applying the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the random frog algorithm on the fluorescence spectrum. Random forest (RF) and support vector machine (SVM) classification models were utilized to identify wheat flour samples with DON concentrations higher than 1 mg/kg. The results indicate that the SG–CARS–RF and SG–CARS–SVM models showed better performance than other models, achieving the highest recall rate of 98.95% and the highest accuracy of 97.78%, respectively. Additionally, the ROC curves demonstrated higher robustness on the RF algorithm. Deep learning algorithms were also applied to identify the samples that exceeded safety standards, and the convolutional neural network (CNN) model achieved a recognition accuracy rate of 97.78% for the test set. In conclusion, this study demonstrates the feasibility and potential of the FHSI technique in detecting DON infection in wheat flour.
研究领域:
食品安全
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光谱学
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机器学习
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DOI
PubMed
高影响因子
JOURNAL OF FOOD COMPOSITION AND ANALYSIS
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2024
·
IF 4
Weizhe Wang,Ruirui Liu,Yufang Su,Suozai Ren,Yanmei Xi,Yun Huang,Juan Wang,Lixiang Lan,Xuelu Chi,Baoguo Sun,Nasi Ai
Odorous raw milk poses a growing threat to dairy product quality, negatively impacting both producers and consumers. However, methods for effectively identifying odorous raw milk have not been systematically established. In this study, 30 raw milk samples (RMSs) collected from different pastures were classified into 18 fresh RMSs and 12 odorous RMSs through sensory evaluation. Quantitative datasets of volatile compounds in RMSs were obtained using HS-SPME–GC–MS. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models identified nine key differential volatile compounds distinguishing fresh from odorous RMSs. Among these, hexanal and octanal were identified as potent odorants contributing to the off-odor in raw milk. Based on nine key difference volatile compounds, support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF) algorithmic models were successfully constructed to classify RMSs to identify odorous RMSs. All models achieved classification accuracy exceeding 0.9, with the RF model performing the best, achieving an accuracy of 1.0. This work provides a reference and available workflow for identifying and labeling odorous RMSs.
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
高影响因子
LWT-FOOD SCIENCE AND TECHNOLOGY
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2023
·
IF 6
Zhichao Xiong,Wanzhen Feng,Dongzhou Xia,Jixin Zhang,Yuming Wei,Tiehan Li,Junlan Huang,Yujie Wang,Jingming Ning
The authenticity of the geographical origin of agricultural products has received widespread attention. Tea tree varieties, processing processes and origins influence the quality and price of raw pu-erh tea (RPT). This study distinguished RPT from 10 different production areas through headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) combined with orthogonal partial least squares–discriminant analysis (OPLS-DA) model and machine learning algorithms. Among the thirty-five types of common volatiles identified, pentanal, heptanal, naphthalene, cedrol, and 2,6-di- tert -butylbenzoquinone were considered the key differential compounds distinguishing the 10 different production areas of the RPT samples through the screening of variable importance in projection values of the OPLS-DA and coefficient weights of the linear discriminant analysis function. Among them, heptanal and 2,6-di- tert -butylbenzoquinone had the highest content in West of Bingdao and the lowest content in Nannuoshan. The random forest algorithm achieved a discrimination accuracy of 98.4% based on the discrimination of five key compounds in 63 RPT samples. The random forest model was demonstrated to be reliable and valid by using receiver operating characteristic curves (area under the curve = 0.7603). The study results serve as a reference for the differentiation of 10 production areas of RPT.
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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产品关联
DOI
高影响因子
LWT-FOOD SCIENCE AND TECHNOLOGY
·
2025
·
IF 6.6
Yiyuan Chen,He Huang,Ruiyang Yin,Shuo Wang,Jiaxin Hong,Yashuai Wu,Liyun Guo,Yumei Song,Dongrui Zhao,Jinyuan Sun,Xiaotao Sun,Mingquan Huang,Jinchen Li,Baoguo Sun
Beer is one of the most widely consumed alcoholic beverages worldwide. Nowadays, consumers have increasingly prioritized personalized and high-quality products. Beer distilled spirits represents a notable example of this trend. However, the key quality factors between beer and its distilled spirits remain unclear, which significantly impedes the development of beer derivatives. In this study, a total of 260 trace components were identified by gas chromatography-mass spectrometry. Among them, 82 dual-effect flavor factors were identified through flavor expression evaluation (Osme, AEDA, OAV, and TAV). The random forest model, correlation analysis, and adding validation experiments were employed to screen eight key flavor quality factors (3-methyl-butanol, ethyl hexadecanoate, phenethyl acetate, γ -nonalactone, ethyl octanoate, 4-ethylguaiacol, maltol and α -terpineol) that significantly influenced the sensory attributes of malt aroma, caramel aroma, fermentation aroma, sweet taste, bitter taste, and astringency. The aforementioned key flavor quality factors can be employed as a primary indicator for the monitoring and optimization of quality processes in both beer and beer distilled spirits.
研究领域:
食品科学
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代谢组学
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产品关联
DOI
高影响因子
EUROPEAN JOURNAL OF LIPID SCIENCE AND TECHNOLOGY
·
2016
·Vol. 119
·期 3
·1500463
·
IF 1.953
Lingyi Liu,Chuanrong Hu,Lianliang Liu,Sihong Zhang,Ke Chen,Dongping He
A simple and rapid classification model for olive and Camellia oil was proposed based on ion mobility spectrometry (IMS) fingerprints and chemometric model (peak detection and random forest algorithm). Results indicated that IMS fingerprint spectra by second-derivative algorithm could completely separate 64 olive oil and 79 Camellia oil samples used in this study by simply calculating the peak area. Random forest algorithm was employed to establish discriminant model for olive oil adulterated by Camellia oil. Simulated adulteration detection showed that the accuracy rate of discriminant model is 96.4% as two of 55 samples were identified as blending olive oil. All these results suggested that IMS could be an effective method to detect the adulterated olive oils by Camellia oil. Practical applications: Camellia oil is much similar to olive oil no matter in the physicochemical properties and fatty acid profiles. Thereby, olive oil has been one of the most frequent targets for the adulteration by Camellia oil. This study aimed to provide a rapid method to detect and separate olive oil and Camellia oil by a portable IMS device, by using fingerprints spectra, peak detection (first- and second-derivative algorithm), and random forest algorithm. Results indicated that the classification and discriminant model established in this work was doable for the adulteration detection in the industry. Based on fingerprints spectra, peak detection (first- and second-derivative algorithm) and random forest algorithm, IMS detection is doable for the rapid detection and separation of olive and Camellia oil.
研究领域:
食品科学
·
分析化学
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产品关联
DOI
高影响因子
ADVANCED FUNCTIONAL MATERIALS
·
2025
·
IF 19
Delong Han,Yuting Cai,Xinze Wang,Weining Zhang,Xusheng Li,Zhaoru Hou,Jiahui Liu,Dengke Song,Wenlong Xu
Traditional hydrogels tend to freeze and lose performance at low temperatures, limiting their applications. Additionally, hydrogels need to exhibit low hysteresis, excellent cycling stability, and self-adhesion to ensure high-quality signal acquisition in complex environments. To address this challenge, this study designed a dual-network gel in a glycerol (Gly)/H 2 O solvent system. Due to the combination of chemical and physical crosslinking (hydrogen bonding and electrostatic interactions), the resulting gel exhibits skin-adaptive modulus, high cycling stability, anti-freezing ability, body temperature-induced adhesion, and excellent electrical performance, making it suitable for wearable sensors at low temperatures. Based on this gel, a single-electrode triboelectric nanogenerator (gel-TENG) is developed, achieving efficient conversion of mechanical energy into electrical energy. Further applied to a smart insole, it successfully enabled real-time visualization of plantar pressure distribution and skiing motion recognition. Using a random forest machine learning algorithm, the system accurately classified 11 basic skiing motions, achieving a classification accuracy of 97.1%. This study advances flexible sensors and self-powered systems, supporting intelligent materials research in extreme environments.
研究领域:
材料科学
·
生物医学工程
·
机器学习
·
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）