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Zhang S, Yuan Y, Wang Z, Li J. The application of laser‑induced fluorescence in oil spill detection. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:23462-23481. [PMID: 38466385 DOI: 10.1007/s11356-024-32807-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 03/03/2024] [Indexed: 03/13/2024]
Abstract
Over the past two decades, oil spills have been one of the most serious ecological disasters, causing massive damage to the aquatic and terrestrial ecosystems as well as the socio-economy. In view of this situation, several methods have been developed and utilized to analyze oil samples. Among these methods, laser-induced fluorescence (LIF) technology has been widely used in oil spill detection due to its classification method, which is based on the fluorescence characteristics of chemical material in oil. This review systematically summarized the LIF technology from the perspective of excitation wavelength selection and the application of traditional and novel machine learning algorithms to fluorescence spectrum processing, both of which are critical for qualitative and quantitative analysis of oil spills. It can be seen that an appropriate excitation wavelength is indispensable for spectral discrimination due to different kinds of polycyclic aromatic hydrocarbons' (PAHs) compounds in petroleum products. By summarizing some articles related to LIF technology, we discuss the influence of the excitation wavelength on the accuracy of the oil spill detection model and proposed several suggestions on the selection of excitation wavelength. In addition, we introduced some traditional and novel machine learning (ML) algorithms and discussed the strengths and weaknesses of these algorithms and their applicable scenarios. With an appropriate excitation wavelength and data processing algorithm, it is believed that laser-induced fluorescence technology will become an efficient technique for real-time detection and analysis of oil spills.
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Affiliation(s)
- Shubo Zhang
- Department of Optical Science and Engineering, Fudan University, Shanghai, 200433, China
| | - Yafei Yuan
- Department of Sports Media and Information Technology, Shandong Sport University, Jinan, 250102, Shandong, China.
| | - Zhanhu Wang
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
| | - Jing Li
- Department of Optical Science and Engineering, Fudan University, Shanghai, 200433, China
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2
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Gong B, Mao S, Li X, Chen B. Mineral oil emulsion species and concentration prediction using multi-output neural network based on fluorescence spectra in the solar-blind UV band. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024. [PMID: 38470293 DOI: 10.1039/d3ay01820b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
The accurate monitoring of oil spills is crucial for effective oil spill recovery, volume determination, and cleanup. Oil slicks become emulsified under the effects of wind and waves, which increases the consistency of the oil spills. This phenomenon makes oil spills more challenging to handle and exacerbates environmental pollution. In this study, the variation of the solar-blind ultraviolet (UV) fluorescence spectra obtained from simulated oil spills with different oil types and oil-water ratios was investigated. By designing and constructing a multi-angle excitation and detection system, an apparent fluorescence peak of the oil emulsions was observed at around 290 nm under 220 nm excitation. By utilizing competitive adaptive reweighted sampling (CARS) and multi-output neural network algorithms, both the types and concentrations of the emulsified oils were obtained simultaneously. The classification accuracy for identifying the oil type exceeds 98%, and the mean absolute percentage error (MAPE) for concentration regression is around 2%. The results indicate that active solar-blind UV fluorescence could become a supplementary method for on-site oil spill detection to achieve comprehensive monitoring of oil spills. This study provides potential applications for UV-induced fluorescence spectrometry in oil spill on-site monitoring during the daytime.
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Affiliation(s)
- Bowen Gong
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, 130033, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shilei Mao
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, 130033, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xinkai Li
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, 130033, China.
| | - Bo Chen
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, 130033, China.
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Ran X, Yu Y, Yang H, Tan X, Ran Z, Zhang R, Du G, Yang L. Handheld UV spectrophotometer device for detection of methamphetamine hydrochloride based on supramolecular sensing platform. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 305:123499. [PMID: 37832445 DOI: 10.1016/j.saa.2023.123499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/14/2023] [Accepted: 10/05/2023] [Indexed: 10/15/2023]
Abstract
The realization of drug detection in drug-using crime sites can provide law enforcement officials with direct evidence. This research has developed and demonstrated an easy-to-use handheld sensor that can quickly detect methamphetamine (MA) in the field. The core of the handheld UV spectrophotometer device (HUVSD) is the STM32F103 series of single-chip micro-controller, which has a 32-bit microcontroller and two embedded 12-bit high-precision analog-to-digital converter (ADC) modules. Through Bluetooth-wireless transmission protocol, the spectral information can be displayed in the cell phone's app, and it is possible to visually determine whether the test sample contains methamphetamine hydrochloride substances based on the characteristic peak at 410 nm. The readily available and inexpensive inducible compound 3A and the phosphate pillar[5]arene@silver nanoparticle (PP5@AgNPs) colloidal solution were used as the reactants. The PP5@AgNPs colloidal solution and 3A were mixed and reacted at room temperature, and the color changed to gray-black. The color change was caused by the aggregation of AgNPs induced by the molecular recognition between the induction compound 3A and PP5 on the AgNPs surface. After continuing to add the drug MA, the color of the colloidal solution turned yellow again. This is due to the occurrence of competitive molecular recognition, and the interaction between PP5 and 3A/MA was investigated by molecular docking simulations. The HUVSD has high sensitivity, simple equipment, time-saving, color change visualization and suitable for on-site deployment. It only needs a Pasteur pipette, which has great potential to realize rapid on-site detection.
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Affiliation(s)
- Xin Ran
- Yunnan Province Key Lab of Wood Adhesives and Glued Products, International Joint Research Center for Biomass Materials, Southwest Forestry University, Kunming 650224, China
| | - Yanbo Yu
- Yunnan Province Key Lab of Wood Adhesives and Glued Products, International Joint Research Center for Biomass Materials, Southwest Forestry University, Kunming 650224, China
| | - Hongxing Yang
- Yunnan Province Key Lab of Wood Adhesives and Glued Products, International Joint Research Center for Biomass Materials, Southwest Forestry University, Kunming 650224, China
| | - Xiaoping Tan
- Yunnan Province Key Lab of Wood Adhesives and Glued Products, International Joint Research Center for Biomass Materials, Southwest Forestry University, Kunming 650224, China; College of Chemistry and Chemical Engineering, Yangtze Normal University, Fuling, 408100, China.
| | - Zhiyong Ran
- Key Laboratory of Microsystems and Microstructures Manufacturing (Ministry of Education), School of Medicine and Health, Harbin Institute of Technology, Harbin 150001, China.
| | - Ruilin Zhang
- NHC Key Laboratory of Drug Addiction Medicine, School of Forensic Medicine, Kunming Medical University, Kunming 650500, China.
| | - Guanben Du
- Yunnan Province Key Lab of Wood Adhesives and Glued Products, International Joint Research Center for Biomass Materials, Southwest Forestry University, Kunming 650224, China
| | - Long Yang
- Yunnan Province Key Lab of Wood Adhesives and Glued Products, International Joint Research Center for Biomass Materials, Southwest Forestry University, Kunming 650224, China
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Ahmadinouri F, Parvin P, Rabbani AR. Assessment of asphaltene and resin fractions in crude oil using laser-induced fluorescence spectroscopy based on modified Beer-Lambert (LIFS-MBL). SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 304:123314. [PMID: 37672886 DOI: 10.1016/j.saa.2023.123314] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 07/11/2023] [Accepted: 08/28/2023] [Indexed: 09/08/2023]
Abstract
Crude oil is one of the most significant petrogenic sources of polycyclic aromatic compounds (PACs). These substances play an essential role in the pollution of the marine environment. Therefore, the rapid identification of this pollutant source and its fractions is vital. For this purpose, a fast and on-site method of laser-induced fluorescence spectroscopy based on modified Beer-Lambert (LIFS-MBL) is proposed here using solvent densitometry. Three optical parameters of the self-quenching (K), the extinction (α), and the peak concentration (Cp) are experimentally extracted from MBL graphs. Note that the parameters above are known to be unique characteristics of various crude oils. The corresponding compounds are generally classified into saturate, aromatic, resin, and asphaltene fractions, abbreviated as SARA. Differentiation among these fractions is achieved using the LIFS-MBL method by selecting the optimal excitation wavelength at 405 nm. This line effectively rules out the light aromatic rings and focuses on heavy fractions. The correlation of optical parameters with heavy oil fractions is verified according to analysis of variance. Statistical relations are proposed to calculate crude oil fractions values. The values of light fractions including saturate and aromatic components can also be determined by the heavy fractions. In this method, the test time is notably reduced from four days using the standard methods to less than half an hour according to the presented LIFS-MBL technique.
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Affiliation(s)
- Fatemeh Ahmadinouri
- Department of Energy Engineering and Physics, Amirkabir University of Technology (Tehran Polytechnic), P.O. Box 15875-4413, Tehran, Iran
| | - Parviz Parvin
- Department of Energy Engineering and Physics, Amirkabir University of Technology (Tehran Polytechnic), P.O. Box 15875-4413, Tehran, Iran.
| | - Ahmad Reza Rabbani
- Petroleum Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), P.O. Box 15875-4413, Tehran, Iran
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Zhu L, Yang S, Xiao Z, Huang H, Yan K, Wang S. A portable Raspberry Pi-based spectrometer for on-site spectral testing. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023. [PMID: 37335311 DOI: 10.1039/d3ay00464c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2023]
Abstract
We designed a portable Raspberry Pi-based spectrometer, which mainly consists of a white LED acting as the wide-spectrum source, a reflection grating for light dispersion, and a CMOS imaging chip aiming at spectral recording. All the optical elements and Raspberry Pi were integrated using 3-D printing structures with a size of 118 mm × 92 mm × 84 mm, and home-built software was also designed for spectral recording, calibration, analysis, and display implemented with a touch LCD. Additionally, the portable Raspberry Pi-based spectrometer was equipped with an internal battery, thus supporting on-site applications. Tested by a series of verifications and applications, the portable Raspberry Pi-based spectrometer could reach a spectral resolution of 0.065 nm per pixel within the visible band and provide spectral detection with high accuracy. Therefore, it can be used for on-site spectral testing in various fields.
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Affiliation(s)
- Lin Zhu
- Jiangsu Province Engineering Research Center of Integrated Circuit Reliability Technology and Testing System, Wuxi University, Wuxi, Jiangsu, 214105 China
- OptiX+ Laboratory, School of Electronics and Information Engineering, Wuxi University, Wuxi, Jiangsu, 214105 China
- Computational Optics Laboratory, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Shuwei Yang
- School of Intelligent Science and Information Engineering, Xi'an Peihua University, Xi'an, Shaanxi, 710125, China
- Advanced Institute of Micro-Nano Intelligent Sensing (AIMNIS), School of Electronic Information Engineering, Xi'an Technological University, Xi'an, Shaanxi, 710032, China
| | - Zhibo Xiao
- Jiangsu Province Engineering Research Center of Integrated Circuit Reliability Technology and Testing System, Wuxi University, Wuxi, Jiangsu, 214105 China
- OptiX+ Laboratory, School of Electronics and Information Engineering, Wuxi University, Wuxi, Jiangsu, 214105 China
- Computational Optics Laboratory, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Huachuan Huang
- School of Manufacture Science and Engineering, Key Laboratory of Testing Technology for Manufacturing Process, Ministry of Education, Southwest University of Science and Technology, Mianyang, Sichuan, 621010, China
| | - Keding Yan
- Advanced Institute of Micro-Nano Intelligent Sensing (AIMNIS), School of Electronic Information Engineering, Xi'an Technological University, Xi'an, Shaanxi, 710032, China
| | - Shouyu Wang
- Jiangsu Province Engineering Research Center of Integrated Circuit Reliability Technology and Testing System, Wuxi University, Wuxi, Jiangsu, 214105 China
- OptiX+ Laboratory, School of Electronics and Information Engineering, Wuxi University, Wuxi, Jiangsu, 214105 China
- Single Molecule Nanobiology Laboratory (Sinmolab), Nanjing Agricultural University, Nanjing, Jiangsu, 210095, China.
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Chung S, Loh A, Jennings CM, Sosnowski K, Ha SY, Yim UH, Yoon JY. Capillary flow velocity profile analysis on paper-based microfluidic chips for screening oil types using machine learning. JOURNAL OF HAZARDOUS MATERIALS 2023; 447:130806. [PMID: 36680906 PMCID: PMC9940998 DOI: 10.1016/j.jhazmat.2023.130806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 12/29/2022] [Accepted: 01/15/2023] [Indexed: 06/17/2023]
Abstract
We conceived a novel approach to screen oil types on a wax-printed paper-based microfluidic platform. Various oil samples spontaneously flowed through a micrometer-scale channel via capillary action while their components were filtered and partitioned. The resulting capillary flow velocity profile fluctuated during the flow, which was used to screen oil types. Raspberry Pi camera captured the video clips, and a custom Python code analyzed them to obtain the capillary flow velocity profiles. 106 velocity profiles (each with 125 frames for 5 s) were recorded from various oil samples to build a training database. Principal component analysis (PCA), support vector machine (SVM), and linear discriminant analysis (LDA) were used to classify the oil types into heavy-to-medium crude, light crude, marine fuel, lubricant, and diesel oils. The second-order polynomial SVM model with PCA as a pre-processing step showed the highest accuracy: 90% in classifying crude oils and 81% in classifying non-crude oils. The assay took less than 30 s from the sample to answer, with 5 s of the capillary action-driven flow. This simple and effective assay will allow rapid preliminary screening of oil types, enable early tracking, and reduce the number of suspect samples to be analyzed by laboratory fingerprinting analysis.
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Affiliation(s)
- Soo Chung
- Department of Biosystems Engineering, The University of Arizona, Tucson, AZ 85721, United States; Department of Biosystems Engineering, Integrated Major in Global Smart Farm, and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Andrew Loh
- Korea Institute of Ocean Science and Technology, Geoje-si, Gyeongsangnam-do 53201, Republic of Korea
| | - Christian M Jennings
- Department of Biomedical Engineering, The University of Arizona, Tucson, AZ 85721, United States
| | - Katelyn Sosnowski
- Department of Biomedical Engineering, The University of Arizona, Tucson, AZ 85721, United States
| | - Sung Yong Ha
- Korea Institute of Ocean Science and Technology, Geoje-si, Gyeongsangnam-do 53201, Republic of Korea
| | - Un Hyuk Yim
- Korea Institute of Ocean Science and Technology, Geoje-si, Gyeongsangnam-do 53201, Republic of Korea.
| | - Jeong-Yeol Yoon
- Department of Biosystems Engineering, The University of Arizona, Tucson, AZ 85721, United States; Department of Biomedical Engineering, The University of Arizona, Tucson, AZ 85721, United States.
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7
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Gong B, Zhang H, Wang X, Lian K, Li X, Chen B, Wang H, Niu X. Ultraviolet-induced fluorescence of oil spill recognition using a semi-supervised algorithm based on thickness and mixing proportion-emission matrices. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:1649-1660. [PMID: 36917485 DOI: 10.1039/d2ay01776h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
In recent years, marine oil spill accidents have been occurring frequently during extraction and transportation, and seriously damage the ecological balance. Accurate monitoring of oil spills plays a vital role in estimating oil spill volume, determination of liability, and clean-up. The oil that leaks into natural environments is not a single type of oil, but a mixture of various oil products, and the oil film thickness on the sea surface is uneven under the influence of wind and waves. Increasing the mixed oil film thickness dimension and the mix proportion dimension has been proposed to weaken the effect of the detection environment on the fluorescence measurement results. To preserve the relationships between the data of oil films with different thicknesses and the relationships between the data of oil films with different mixing proportions, the three-dimensional fluorescence spectral data of mixed oil films on a seawater surface were measured in the laboratory, producing a thickness-fluorescence matrix and a proportion-fluorescence matrix. The nonlinear variation of the fluorescence spectra was investigated according to the fluorescence lidar equation. This work pre-processes the data by sum normalization and two-dimensional principal component analysis (2DPCA) and uses the dimensionality reduction results as two feature-point views. Then, semi-supervised classification of collaborative training (co-training) with K-nearest neighbors (KNN) and a decision tree (DT) is used to identify the samples. The results show that the average overall accuracy of this coupling model can reach 100%, which is 20.49% higher than that of the thickness-only view. Using unlabeled data can reduce the cost of data acquisition, improve the classification accuracy and generalization ability, and provide theoretical significance and application prospects for discrimination of spectrally similar oil species in natural marine environments.
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Affiliation(s)
- Bowen Gong
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, 130033, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China. @mails.ucas.ac.cn
| | - Hongji Zhang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, 130033, China.
| | - Xiaodong Wang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, 130033, China.
| | - Ke Lian
- Shanghai Institute of Spacecraft Equipment, Shanghai, 200240, China
| | - Xinkai Li
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, 130033, China.
| | - Bo Chen
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, 130033, China.
| | - Hanlin Wang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, 130033, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China. @mails.ucas.ac.cn
| | - Xiaoqian Niu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, 130033, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China. @mails.ucas.ac.cn
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Antela KU, Sáez-Hernández R, Cervera ML, Morales-Rubio Á, Luque MJ. Development of an automated colorimeter controlled by Raspberry Pi4. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:512-518. [PMID: 36625306 DOI: 10.1039/d2ay01532c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
A low-cost new instrument to carry out automated colorimetric analysis has been developed. The device consists of a carousel sampler, built by a 3D-printer, and a Raspberry Pi4-controlled signal measurement module based on the RGBC (red, green, blue and clear) responses of a TCS34725 color light-to-digital converter with IR filter. The device has been tested with calibration standards of different food dyes (Tartrazine, Red Allure AC and Brilliant Blue FCF) and three food samples containing one of each food dye. The new device provides R2 > 0.995 and a LOD of 1.1, 1.4 and 0.1 μmol L-1 for each food dye, respectively. The results are statistically comparable to those obtained with a conventional benchtop spectrophotometer. The proposed device achieves a reduction in sample and waste volume and in analysis time, minimizes the use of energy, and allows in situ measurements, being an automated method it is safer for operators in comparison to the reference method, yielding similar analytical results and following the principles of green analytical chemistry.
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Affiliation(s)
- Kevin U Antela
- Department of Analytical Chemistry, University of Valencia, Research Building, 46100 Burjassot, Valencia, Spain.
| | - Roberto Sáez-Hernández
- Department of Analytical Chemistry, University of Valencia, Research Building, 46100 Burjassot, Valencia, Spain.
| | - M Luisa Cervera
- Department of Analytical Chemistry, University of Valencia, Research Building, 46100 Burjassot, Valencia, Spain.
| | - Ángel Morales-Rubio
- Department of Analytical Chemistry, University of Valencia, Research Building, 46100 Burjassot, Valencia, Spain.
| | - M José Luque
- Optics Department, Physics Faculty, University of Valencia, 46100 Burjassot, Valencia, Spain
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Fabrication and application of three-dimensional nanocomposites modified electrodes for evaluating the aging process of Huangjiu (Chinese rice wine). Food Chem 2022; 372:131158. [PMID: 34601421 DOI: 10.1016/j.foodchem.2021.131158] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 08/08/2021] [Accepted: 09/14/2021] [Indexed: 02/08/2023]
Abstract
In this study, three modified glassy carbon electrodes based on three-dimensional conducting polymer nanocomposites (TDCPNs) were fabricated for evaluating the aging process of Huangjiu (Chinese rice wines). The electrochemical activity and experimental conditions of the TDCPNs modified electrodes were investigated by cyclic voltammetry, the aging information obtained by the modified electrodes were optimized by variance inflation factor (VIF). Principal components analysis (PCA), locally linear embedding (LLE), and locality preserving projection (LPP, which presented the best classification result) based on the optimized data were applied to classify the wine samples. Then, the dimensionality reduction data of PCA, LLE, and LPP were used as input variables of the logistic regression and extreme learning machine (ELM) for evaluating the aging process of Huangjiu, and the LLE-ELM method exhibited the best prediction results. These results demonstrated that the TDCPNs modified electrodes presented the potential for the quality analysis of food and beverages.
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Loh A, Ha SY, Kim D, Lee J, Baek K, Yim UH. Development of a portable oil type classifier using laser-induced fluorescence spectrometer coupled with chemometrics. JOURNAL OF HAZARDOUS MATERIALS 2021; 416:125723. [PMID: 33812272 DOI: 10.1016/j.jhazmat.2021.125723] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 03/16/2021] [Accepted: 03/18/2021] [Indexed: 06/12/2023]
Abstract
Due to the recurrent small spills, oil pollution along coastal regions is still a major environmental issue. Standardized oil fingerprinting techniques are useful for oil spill identifications, but time- and resource-consuming. There have been ongoing needs for simple yet rapid approach for field screening of oil spill. Laser induced fluorescence (LIF) technology can be incorporated into a spectrometer, and with the integration of chemometrics can be consolidated as a potentially useful portable oil type classification device. Using a LIF spectrometer, 775 oil spectra were calibrated into supervised classification models and validated with 162 oil spectra. Reliability of the device to accurately remove background emission from fluorescence spectra was verified. Prediction performance and model robustness were further validated by comparison between commonly used classification models such as partial least square discriminant analysis (PLS-DA) and support vector machine-discriminant analysis (SVM-DA). Robustness in both models were comparable with PLS-DA having a lower number of misclassification (PLS-DA: 5.50%, SVM-DA: 13.8%) while SVM-DA having a lower number of unassigned samples (PLS-DA: 10.9%; SVM-DA: 16 1.39%). This study explicitly demonstrated the development of a new convenient and handy device which can be used as part of the screening process for oil spill fingerprinting.
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Affiliation(s)
- Andrew Loh
- Oil and POPs Research Group, Korea Institute of Ocean Science and Technology, Geoje 53201, Republic of Korea
| | - Sung Yong Ha
- Oil and POPs Research Group, Korea Institute of Ocean Science and Technology, Geoje 53201, Republic of Korea
| | - Donghwi Kim
- Oil and POPs Research Group, Korea Institute of Ocean Science and Technology, Geoje 53201, Republic of Korea
| | - Joonseok Lee
- MachTech Co., Ltd., Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
| | - Kyonghoon Baek
- MachTech Co., Ltd., Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
| | - Un Hyuk Yim
- Oil and POPs Research Group, Korea Institute of Ocean Science and Technology, Geoje 53201, Republic of Korea; Department of Ocean Science, Korea University of Science and Technology, Daejeon 34113, Republic of Korea.
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11
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Tonelli A, Mangia V, Candiani A, Pasquali F, Mangiaracina TJ, Grazioli A, Sozzi M, Gorni D, Bussolati S, Cucinotta A, Basini G, Selleri S. Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications. SENSORS 2021; 21:s21103552. [PMID: 34065190 PMCID: PMC8160707 DOI: 10.3390/s21103552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/10/2021] [Accepted: 05/16/2021] [Indexed: 12/12/2022]
Abstract
Single-board computers (SBCs) and microcontroller boards (MCBs) are extensively used nowadays as prototyping platforms to accomplish innovative tasks. Very recently, implementations of these devices for diagnostics applications are rapidly gaining ground for research and educational purposes. Among the available solutions, Raspberry Pi represents one of the most used SBCs. In the present work, two setups based on Raspberry Pi and its CMOS-based camera (a 3D-printed device and an adaptation of a commercial product named We-Lab) were investigated as diagnostic instruments. Different camera elaboration processes were investigated, showing how direct access to the 10-bit raw data acquired from the sensor before downstream imaging processes could be beneficial for photometric applications. The developed solution was successfully applied to the evaluation of the oxidative stress using two commercial kits (d-ROM Fast; PAT). We suggest the analysis of raw data applied to SBC and MCB platforms in order to improve results.
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Affiliation(s)
- Alessandro Tonelli
- DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, Italy; (A.T.); (V.M.); (A.C.); (F.P.); (T.J.M.); (A.G.); (M.S.)
| | - Veronica Mangia
- DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, Italy; (A.T.); (V.M.); (A.C.); (F.P.); (T.J.M.); (A.G.); (M.S.)
| | - Alessandro Candiani
- DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, Italy; (A.T.); (V.M.); (A.C.); (F.P.); (T.J.M.); (A.G.); (M.S.)
| | - Francesco Pasquali
- DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, Italy; (A.T.); (V.M.); (A.C.); (F.P.); (T.J.M.); (A.G.); (M.S.)
| | - Tiziana Jessica Mangiaracina
- DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, Italy; (A.T.); (V.M.); (A.C.); (F.P.); (T.J.M.); (A.G.); (M.S.)
| | - Alessandro Grazioli
- DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, Italy; (A.T.); (V.M.); (A.C.); (F.P.); (T.J.M.); (A.G.); (M.S.)
| | - Michele Sozzi
- DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, Italy; (A.T.); (V.M.); (A.C.); (F.P.); (T.J.M.); (A.G.); (M.S.)
| | - Davide Gorni
- H&D S.R.L., Strada Langhirano 264/1a, 43124 Parma, Italy;
| | - Simona Bussolati
- Dipartimento di Scienze Medico-Veterinarie, Via del Taglio 10, 43126 Parma, Italy; (S.B.); (G.B.)
| | - Annamaria Cucinotta
- Dipartimento di Ingegneria e Architettura, University of Parma, Parco Area delle Scienze, 181/A, 43124 Parma, Italy;
| | - Giuseppina Basini
- Dipartimento di Scienze Medico-Veterinarie, Via del Taglio 10, 43126 Parma, Italy; (S.B.); (G.B.)
| | - Stefano Selleri
- Dipartimento di Ingegneria e Architettura, University of Parma, Parco Area delle Scienze, 181/A, 43124 Parma, Italy;
- Correspondence: ; Tel.: +39-052-190-5763
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Loh A, Soon ZY, Ha SY, Yim UH. High-throughput screening of oil fingerprint using FT-IR coupled with chemometrics. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 760:143354. [PMID: 33158524 DOI: 10.1016/j.scitotenv.2020.143354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 10/20/2020] [Accepted: 10/22/2020] [Indexed: 06/11/2023]
Abstract
An important element of the oil spill emergency response is the ability to rapidly identify the properties of oil spilled. Chemometrics provides large numbers of multivariate analysis tools that allow for more extensive use of data. Fourier transformed infrared spectroscopy coupled with classification and prediction models such as partial least square (PLS) and PLS-DA (discriminant analysis) allows the rapid identification of oil type and characteristics. By searching for the maximum covariance with the variables of interest, PLS allows the visualization of relations between samples and variables. The framework of this study is based on two main steps: The first is classification of oil and the second is prediction of physicochemical properties. Separated into four main categories: crude, light fuel, heavy fuel, and lubricant, spectrums of 92 oils were calibrated to predict the oil type and physicochemical properties of 26 oils. The predictability and robustness of the model was further validated using weathered oil. The classification and prediction models have accuracy of >95%. Most of the PLS models have root mean square error of calibration and prediction ranging from 0.10-3.07 and 0.3-2.8, respectively. External cross validations using weathered oils showed high prediction accuracy (relative standard deviations <5%). By increasing the number of oil type and samples, this approach is a promising method and can be included as part of the oil spill fingerprinting protocols.
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Affiliation(s)
- Andrew Loh
- Oil and POPs Research Group, Korea Institute of Ocean Science and Technology, Geoje 53201, Republic of Korea
| | - Zhi Yang Soon
- Oil and POPs Research Group, Korea Institute of Ocean Science and Technology, Geoje 53201, Republic of Korea; Department of Ocean Science, Korea University of Science and Technology, Daejeon 34113, Republic of Korea
| | - Sung Yong Ha
- Oil and POPs Research Group, Korea Institute of Ocean Science and Technology, Geoje 53201, Republic of Korea
| | - Un Hyuk Yim
- Oil and POPs Research Group, Korea Institute of Ocean Science and Technology, Geoje 53201, Republic of Korea; Department of Ocean Science, Korea University of Science and Technology, Daejeon 34113, Republic of Korea.
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