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Wang Q, Zou X, Chen Y, Zhu Z, Yan C, Shan P, Wang S, Fu Y. XGBoost algorithm assisted multi-component quantitative analysis with Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 323:124917. [PMID: 39094267 DOI: 10.1016/j.saa.2024.124917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 07/22/2024] [Accepted: 07/29/2024] [Indexed: 08/04/2024]
Abstract
To improve prediction performance and reduce artifacts in Raman spectra, we developed an eXtreme Gradient Boosting (XGBoost) preprocessing method to preprocess the Raman spectra of glucose, glycerol and ethanol mixtures. To ensure the robustness and reliability of the XGBoost preprocessing method, an X-LR model (which combined XGBoost preprocessing and a linear regression (LR) model) and a X-MLP model (which combined XGBoost preprocessing and a multilayer perceptron (MLP) model) were developed. These two models were used to quantitatively analyze the concentrations of glucose, glycerol and ethanol in the Raman spectra of mixed solutions. The proportion map of hyperparameters was firstly used to narrow down the search range of hyperparameters in the X-LR and the X-MLP models. Then the correlation coefficients (R2), root mean square of calibration (RMSEC), and root mean square error of prediction (RMSEP) were used to evaluate the models' performance. Experimental results indicated that the XGBoost preprocessing method achieved higher accuracy and generalization capability, with better performance than those of other preprocessing methods for both LR and MLP models.
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Affiliation(s)
- Qiaoyun Wang
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China; Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China.
| | - Xin Zou
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
| | - Yinji Chen
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
| | - Ziheng Zhu
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
| | - Chongyue Yan
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
| | - Peng Shan
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
| | - Shuyu Wang
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
| | - Yongqing Fu
- Faculty of Engineering & Environment, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
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Wang Q, Xu S, Zhu Z, Wang J, Zou X, Zhang C, Liu Q. High sensitivity and ultra-low concentration range photoacoustic spectroscopy based on trapezoid compound ellipsoid resonant photoacoustic cell and partial least square. PHOTOACOUSTICS 2024; 35:100583. [PMID: 38312807 PMCID: PMC10835439 DOI: 10.1016/j.pacs.2023.100583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/26/2023] [Accepted: 12/27/2023] [Indexed: 02/06/2024]
Abstract
A high sensitivity and ultra-low concentration range photoacoustic spectroscopy (PAS) gas detection system, which was based on a novel trapezoid compound ellipsoid resonant photoacoustic cell (TCER-PAC) and partial least square (PLS), was proposed to detect acetylene (C2H2) gas. In the concentration range of 0.5 ppm ∼ 10.0 ppm, the limit of detection (LOD) values of TCER-PAC-based PAS system without data processing was 66.4 ppb, which was lower than that of the traditional trapezoid compound cylindrical resonant photoacoustic cell (TCCR-PAC). The experimental results indicated that the TCER-PAC had higher sensitivity than of TCCR-PAC. Within the concentration range of 12.5 ppb ∼ 125.0 ppb, the LOD and limit of quantification (LOQ) of TCER-PAC-based PAS system combined with PLS regression algorithm were 1.1 ppb and 3.7 ppb, respectively. The results showed that higher detection sensitivity and lower LOD were obtained by PAS system with TCER-PAC and PLS than that of TCCR-PAC-based PAS system.
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Affiliation(s)
- Qiaoyun Wang
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
- Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China
| | - Shunyuan Xu
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
| | - Ziheng Zhu
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
| | - Jilong Wang
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
| | - Xin Zou
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
| | - Chu Zhang
- National Key Laboratory of Science and Technology on Tunable Laser, Harbin Institute of Technology, Harbin 150001, China
| | - Qiang Liu
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
- Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China
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3
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Xie M, Zhu Y, Li Z, Yan Y, Liu Y, Wu W, Zhang T, Li Z, Wang H. Key steps for improving bacterial SERS signals in complex samples: Separation, recognition, detection, and analysis. Talanta 2024; 268:125281. [PMID: 37832450 DOI: 10.1016/j.talanta.2023.125281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/09/2023] [Accepted: 10/05/2023] [Indexed: 10/15/2023]
Abstract
Rapid and reliable detection of pathogenic bacteria is absolutely essential for research in environmental science, food quality, and medical diagnostics. Surface-enhanced Raman spectroscopy (SERS), as an emerging spectroscopic technique, has the advantages of high sensitivity, good selectivity, rapid detection speed, and portable operation, which has been broadly used in the detection of pathogenic bacteria in different kinds of complex samples. However, the SERS detection method is also challenging in dealing with the detection difficulties of bacterial samples in complex matrices, such as interference from complex matrices, confusion of similar bacteria, and complexity of data processing. Therefore, researchers have developed some technologies to assist in SERS detection of bacteria, including both the front-end process of obtaining bacterial sample data and the back-end data processing process. The review summarizes the key steps for improving bacterial SERS signals in complex samples: separation, recognition, detection, and analysis, highlighting the principles of each step and the key roles for SERS pathogenic bacteria analysis, and the interconnectivity between each step. In addition, the current challenges in the practical application of SERS technology and the development trends are discussed. The purpose of this review is to deepen researchers' understanding of the various stages of using SERS technology to detect bacteria in complex sample matrices, and help them find new breakthroughs in different stages to facilitate the detection and control of bacteria in complex samples.
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Affiliation(s)
- Maomei Xie
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Yiting Zhu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Zhiyao Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Yueling Yan
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Yidan Liu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Wenbo Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Tong Zhang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin, 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of TCM, Tianjin, 301617, China.
| | - Haixia Wang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin, 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of TCM, Tianjin, 301617, China.
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Khodabakhshian R, Seyedalibeyk Lavasani H, Weller P. A methodological approach to preprocessing FTIR spectra of adulterated sesame oil. Food Chem 2023; 419:136055. [PMID: 37027973 DOI: 10.1016/j.foodchem.2023.136055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 03/03/2023] [Accepted: 03/26/2023] [Indexed: 04/08/2023]
Abstract
Fourier transform infrared (FTIR) spectroscopy is established as an effective and fast method for the confirmation of the authenticity of food and among other, edible oils. However, no standard procedure is available for applying preprocessing as a vital step in obtaining accurate results from spectra. This study proposes a methodological approach to preprocessing FTIR spectra of sesame oil adulterated with vegetable oils (canola oil, corn oil, and sunflower oil). The primary preprocessing methods investigated are orthogonal signal correction (OSC), standard normal variate transformation (SNV), and extended multiplicative scatter correction (EMSC). Other preprocessing methods are used both as standalone methods and in combination with the primary preprocessing methods. The preprocessing results are compared using partial least squares regression (PLSR). OSC alone or with detrending were the most accurate in predicting the adulteration level of sesame oil, with a maximum coefficient of prediction (R2p) range of 0.910 to 0.971 for different adulterants.
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Guo Z, Zhang J, Dong H, Sun J, Huang J, Li S, Ma C, Guo Y, Sun X. Spatio-temporal distribution patterns and quantitative detection of aflatoxin B 1 and total aflatoxin in peanut kernels explored by short-wave infrared hyperspectral imaging. Food Chem 2023; 424:136441. [PMID: 37244182 DOI: 10.1016/j.foodchem.2023.136441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 05/17/2023] [Accepted: 05/19/2023] [Indexed: 05/29/2023]
Abstract
Aflatoxin contamination in peanut kernels seriously harms the health of humans and causes significant economic losses. Rapid and accurate detection of aflatoxin is necessary to minimize its contamination. However, current detection methods are time-consuming, expensive and destructive to samples. Therefore, short-wave infrared (SWIR) hyperspectral imaging coupled with multivariate statistical analysis was used to investigate the spatio-temporal distribution patterns of aflatoxin, and quantitatively detect the aflatoxin B1 (AFB1) and total aflatoxin in peanut kernels. In addition, Aspergillus flavus contamination was identified to prevent the production of aflatoxin. The result of validation set demonstrated that SWIR hyperspectral imaging could predict the contents of the AFB1 and total aflatoxin accurately, with residual prediction deviation values of 2.7959 and 2.7274, and limits of detection of 29.3722 and 45.7429 μg/kg, respectively. This study presents a novel method for the quantitative detection of aflatoxin and offers an early warning system for its potential application.
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Affiliation(s)
- Zhen Guo
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong 255049, China
| | - Jing Zhang
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong 255049, China
| | - Haowei Dong
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong 255049, China
| | - Jiashuai Sun
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong 255049, China
| | - Jingcheng Huang
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong 255049, China
| | - Shiling Li
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong 255049, China
| | - Chengye Ma
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong 255049, China
| | - Yemin Guo
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong 255049, China
| | - Xia Sun
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong 255049, China
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6
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Wang Q, Song S, Li L, Wen D, Shan P, Li Z, Fu Y. An extreme learning machine optimized by differential evolution and artificial bee colony for predicting the concentration of whole blood with Fourier Transform Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 292:122423. [PMID: 36750009 DOI: 10.1016/j.saa.2023.122423] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 01/23/2023] [Accepted: 01/27/2023] [Indexed: 06/18/2023]
Abstract
Raman spectroscopy, with its advantages of non-contact nature, rapid detection, and minimum water interference, is promising for non-invasive blood detection or diagnosis in clinic applications. However, there is a critical issue that how to accurately analyze blood composition by Raman spectroscopy. In this study, we apply extreme learning machine (ELM) algorithm and a multivariate calibration regression model to analyze the results from Raman spectroscopy and determine the component's concentrations in blood samples, including glucose, cholesterol, and triglyceride. Self-adaption differential evolution artificial bee colony (SADEABC) algorithm was further applied to increase the data's accuracy and robustness. The obtained data for coefficient of determination, root mean square error of calibration, root mean square error of prediction, and relative percent deviation, were 0.9822, 0.3993, 0.3827, and 6.6679 for glucose, 0.9786, 0.2104, 0.2088 and 5.9533 for cholesterol, and 0.9921, 0.2744, 0.3433 and 10.5075 for triglyceride, respectively. Results demonstrated that the model based on SADEABC-ELM show much better prediction data than those models based on the ELM and ABC-ELM.
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Affiliation(s)
- Qiaoyun Wang
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China; Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China.
| | - Shuai Song
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
| | - Lei Li
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
| | - Da Wen
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
| | - Peng Shan
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
| | - Zhigang Li
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
| | - YongQing Fu
- Faculty of Engineering & Environment, Northumbria University, Newcastle Upon Tyne NE1 8ST, UK
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7
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Hefnawy MA, Fadlallah SA, El-Sherif RM, Medany SS. Competition between enzymatic and non-enzymatic electrochemical determination of cholesterol. J Electroanal Chem (Lausanne) 2023. [DOI: 10.1016/j.jelechem.2023.117169] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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8
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Application of visible-near-infrared hyperspectral imaging technology coupled with wavelength selection algorithm for rapid determination of moisture content of soybean seeds. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.105048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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9
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Todaro B, Begarani F, Sartori F, Luin S. Is Raman the best strategy towards the development of non-invasive continuous glucose monitoring devices for diabetes management? Front Chem 2022; 10:994272. [PMID: 36226124 PMCID: PMC9548653 DOI: 10.3389/fchem.2022.994272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 08/24/2022] [Indexed: 11/27/2022] Open
Abstract
Diabetes has no well-established cure; thus, its management is critical for avoiding severe health complications involving multiple organs. This requires frequent glycaemia monitoring, and the gold standards for this are fingerstick tests. During the last decades, several blood-withdrawal-free platforms have been being studied to replace this test and to improve significantly the quality of life of people with diabetes (PWD). Devices estimating glycaemia level targeting blood or biofluids such as tears, saliva, breath and sweat, are gaining attention; however, most are not reliable, user-friendly and/or cheap. Given the complexity of the topic and the rise of diabetes, a careful analysis is essential to track scientific and industrial progresses in developing diabetes management systems. Here, we summarize the emerging blood glucose level (BGL) measurement methods and report some examples of devices which have been under development in the last decades, discussing the reasons for them not reaching the market or not being really non-invasive and continuous. After discussing more in depth the history of Raman spectroscopy-based researches and devices for BGL measurements, we will examine if this technique could have the potential for the development of a user-friendly, miniaturized, non-invasive and continuous blood glucose-monitoring device, which can operate reliably, without inter-patient variability, over sustained periods.
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Affiliation(s)
- Biagio Todaro
- NEST Laboratory, Scuola Normale SuperiorePisa, Italy
- Correspondence: Biagio Todaro, ; Stefano Luin,
| | - Filippo Begarani
- P.B.L. SRL, Solignano, PR, Italy
- Omnidermal Biomedics SRL, Solignano, PR, Italy
| | - Federica Sartori
- P.B.L. SRL, Solignano, PR, Italy
- Omnidermal Biomedics SRL, Solignano, PR, Italy
| | - Stefano Luin
- NEST Laboratory, Scuola Normale SuperiorePisa, Italy
- NEST, Istituto Nanoscienze, CNR, Pisa, Italy
- Correspondence: Biagio Todaro, ; Stefano Luin,
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Alle M, Bandi R, Sharma G, Dadigala R, Lee SH, Kim JC. Gold nanoparticles spontaneously grown on cellulose nanofibrils as a reusable nanozyme for colorimetric detection of cholesterol in human serum. Int J Biol Macromol 2022; 201:686-697. [PMID: 35104471 DOI: 10.1016/j.ijbiomac.2022.01.158] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 01/17/2022] [Accepted: 01/26/2022] [Indexed: 12/14/2022]
Abstract
Recently, gold nanoparticles (AuNPs) are extensively used as peroxidase mimics. However, low catalytic activity, high synthesis cost, substrate-induced aggregation in reaction medium and difficulty in recovery and reuse still remain as major challenges. Here, a novel, simple, spontaneous, and reagent-less in-situ method for the production of AuNPs using dialdehyde cellulose nanofibrils (DACNF) is proposed. AuNPs synthesis time and size were greatly influenced by aldehyde content and the optimal aldehyde content for ultra-small AuNPs (≈10 nm) was 2.1 mM/g. AuNPs@DACNFs exhibited broad-spectrum peroxidase activity and steady-state kinetics revealed their better kinetic parameters (low Km and high Vmax) over horseradish peroxidase (HRP). AuNPs@DACNFs was further converted into paper strip, which served as a biosensor for H2O2 and cholesterol detection. The proposed method exhibited wide linear response in the range of 10-90 μM and 0.05-0.45 mM, and detection limit of 0.39 μM and 1.9 μM for H2O2 and cholesterol, respectively. Great shelf life and reusability were evident by FE-SEM and ICP-OES analysis. The smartphone application "Color Grab" was used to enable the portable onsite detection. The results of cholesterol detection in human serum samples were in agreement with clinically observed values, suggesting the great potential of the probe in disease diagnosis.
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Affiliation(s)
- Madhusudhan Alle
- Institute of Forest Science, Kangwon National University, Chuncheon 24341, Republic of Korea
| | - Rajkumar Bandi
- Institute of Forest Science, Kangwon National University, Chuncheon 24341, Republic of Korea
| | - Garima Sharma
- Department of Biomedical Science & Institute of Bioscience and Biotechnology, Kangwon National University, Chuncheon 24341, Republic of Korea
| | - Ramakrishna Dadigala
- Institute of Forest Science, Kangwon National University, Chuncheon 24341, Republic of Korea
| | - Seung-Hwan Lee
- Institute of Forest Science, Kangwon National University, Chuncheon 24341, Republic of Korea; Department of Forest Biomaterials Engineering, College of Forest and Environmental Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea.
| | - Jin-Chul Kim
- Department of Biomedical Science & Institute of Bioscience and Biotechnology, Kangwon National University, Chuncheon 24341, Republic of Korea.
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Pian F, Wang Q, Wang M, Shan P, Li Z, Ma Z. A shallow convolutional neural network with elastic nets for blood glucose quantitative analysis using Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 264:120229. [PMID: 34371316 DOI: 10.1016/j.saa.2021.120229] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/17/2021] [Accepted: 07/23/2021] [Indexed: 06/13/2023]
Abstract
In this paper, a one-dimensional shallow convolutional neural network structure combined with elastic nets (1D-SCNN-EN) was firstly proposed to predict the glucose concentration of blood by Raman spectroscopy. A total of 106 different blood glucose spectra were obtained by Fourier transform (FT) Raman spectroscopy. The one-dimensional shallow convolutional neural network, with elastic nets added to the full connected layer, was presented to capture multiple deep features and reduce the complexity of the model. The 1D-SCNN-EN model has a better performance than conventional approaches (partial least squares and support vector machine). The root mean squared error of calibration (RMSEC), the root mean squared error of prediction (RMSEP), the determination coefficient of prediction (RP2), and the residual predictive deviation of prediction (RPD) were 0.10262, 0.11210, 0.99403, and 12.94601, respectively. The experiment results showed that the 1D-SCNN-EN model has a higher prediction accuracy and stronger robustness than the other regression models. The overall studies indicated that the 1D-SCNN-EN model looked promising for predict the glucose concentration of blood by Raman spectroscopy when the sample size is small.
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Affiliation(s)
- Feifei Pian
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China; Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China
| | - Qiaoyun Wang
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China; Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China.
| | - Mingxuan Wang
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China; Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China
| | - Peng Shan
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
| | - Zhigang Li
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
| | - Zhenhe Ma
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China
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12
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Bel’skaya LV, Sarf EA, Solomatin DV. Application of FTIR Spectroscopy for Quantitative Analysis of Blood Serum: A Preliminary Study. Diagnostics (Basel) 2021; 11:diagnostics11122391. [PMID: 34943626 PMCID: PMC8700755 DOI: 10.3390/diagnostics11122391] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 12/14/2021] [Accepted: 12/17/2021] [Indexed: 11/16/2022] Open
Abstract
The aim of this study was to analyze the possibility of simultaneous determination of the concentration of components from the characteristics of FTIR spectra using the example of a model blood serum. To prepare model solutions, a set of freeze-dried control sera based on bovine blood serum was used, certified for approximately 38 parameters. Based on the values of the absorbance and areas of absorption bands in the FTIR spectra of model solutions, a regression equation was constructed by solving a nonlinear problem using the generalized reduced gradient method. By using the absorbance of the absorption bands at 1717 and 3903 cm−1 and the areas of the absorption bands at 616, 3750, and 3903 cm−1, it is possible to simultaneously determine the concentrations of 38 components with an error of less than 0.1%. The results obtained confirm the potential clinical use of FTIR spectroscopy as a reagent-free express method for the analysis of blood serum. However, its practical implementation requires additional research, in particular, analysis of real blood serum samples and validation of the method.
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Affiliation(s)
- Lyudmila V. Bel’skaya
- Biochemistry Research Laboratory, Omsk State Pedagogical University, 644099 Omsk, Russia;
- Correspondence:
| | - Elena A. Sarf
- Biochemistry Research Laboratory, Omsk State Pedagogical University, 644099 Omsk, Russia;
| | - Denis V. Solomatin
- Department of Mathematics and Mathematics Teaching Methods, Omsk State Pedagogical University, 644043 Omsk, Russia;
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