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Cui T, Ying Z, Zhang J, Guo S, Chen W, Zhou G, Li W. Strategies for the quality control of Chrysanthemi Flos: Rapid quantification and end-to-end fingerprint conversion based on FT-NIR spectroscopy. PHYTOCHEMICAL ANALYSIS : PCA 2024; 35:754-770. [PMID: 38282123 DOI: 10.1002/pca.3326] [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: 09/02/2023] [Revised: 12/15/2023] [Accepted: 12/26/2023] [Indexed: 01/30/2024]
Abstract
INTRODUCTION Chrysanthemi Flos (CF) is widely used as a natural medicine or tea. Due to its diverse cultivation regions, CF exhibits varying quality. Therefore, the quality and swiftness in evaluation holds paramount significance for CF. OBJECTIVE The aim of the study was to construct a comprehensive evaluation strategy for assessing CF quality using HPLC, near-infrared (NIR) spectroscopy, and chemometrics, which included the rapid quantification analyses of chemical components and the Fourier transform (FT)-NIR to HPLC conversion of fingerprints. MATERIALS AND METHODS A total of 145 CF samples were utilised for data collection via NIR spectroscopy and HPLC. The partial least squares regression (PLSR) models were optimised using various spectral preprocessing and variable selection methods to predict the chemical composition content in CF. Both direct standardisation (DS) and PLSR algorithms were employed to establish the fingerprint conversion model from the FT-NIR spectrum to HPLC, and the model's performance was assessed through similarity and cluster analysis. RESULTS The optimised PLSR quantitative models can effectively predict the content of eight chemical components in CF. Both DS and PLSR algorithms achieve the calibration conversion of CF fingerprints from FT-NIR to HPLC, and the predicted and measured HPLC fingerprints are highly similar. Notably, the best model relies on CF powder FT-NIR spectra and DS algorithm [root mean square error of prediction (RMSEP) = 2.7590, R2 = 0.8558]. A high average similarity (0.9184) prevails between predicted and measured fingerprints of test set samples, and the results of the clustering analysis exhibit a high level of consistency. CONCLUSION This comprehensive strategy provides a novel and dependable approach for the rapid quality evaluation of CF.
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Affiliation(s)
- Tongcan Cui
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Zehua Ying
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Jianyu Zhang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Shubo Guo
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Wei Chen
- Shanghai Zhen Ren Tang Pharmaceutical Co., Ltd, Shanghai, China
| | - Guifang Zhou
- Shanghai Zhen Ren Tang Pharmaceutical Co., Ltd, Shanghai, China
| | - Wenlong Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
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2
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Jiang D, Wang K, Li H, Zhang Y. Efficient Near-Infrared Spectrum Detection in Nondestructive Wood Testing via Transfer Network Redesign. SENSORS (BASEL, SWITZERLAND) 2024; 24:1245. [PMID: 38400402 PMCID: PMC10893441 DOI: 10.3390/s24041245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/11/2024] [Accepted: 02/11/2024] [Indexed: 02/25/2024]
Abstract
This study systematically developed a deep transfer network for near-infrared spectrum detection using convolutional neural network modules as key components. Through meticulous evaluation, specific modules and structures suitable for constructing the near-infrared spectrum detection model were identified, ensuring its effectiveness. This study extensively analyzed the basic network components and explored three unsupervised domain adaptation structures, highlighting their applications in the nondestructive testing of wood. Additionally, five transfer networks were strategically redesigned to substantially enhance their performance. The experimental results showed that the Conditional Domain Adversarial Network and Globalized Loss Optimization Transfer network outperformed the Direct Standardization, Piecewise Direct Standardization, and Spectral Space Transformation models. The coefficients of determination for the Conditional Domain Adversarial Network and Globalized Loss Optimization Transfer network are 82.11% and 83.59%, respectively, with root mean square error prediction values of 12.237 and 11.582, respectively. These achievements represent considerable advancements toward the practical implementation of an efficient and reliable near-infrared spectrum detection system using a deep transfer network.
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Affiliation(s)
- Dapeng Jiang
- College of Computer and Control Engineering, Northeast Forestry University, 26 Hexing Rd., Harbin 150040, China; (D.J.); (K.W.)
| | - Keqi Wang
- College of Computer and Control Engineering, Northeast Forestry University, 26 Hexing Rd., Harbin 150040, China; (D.J.); (K.W.)
| | - Hongbo Li
- College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China;
| | - Yizhuo Zhang
- College of Computer Science and Artificial Intelligence, Changzhou University, 1 Gehu Middle Rd., Changzhou 213164, China
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3
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Galvan D, de Aguiar LM, Bona E, Marini F, Killner MHM. Successful combination of benchtop nuclear magnetic resonance spectroscopy and chemometric tools: A review. Anal Chim Acta 2023; 1273:341495. [PMID: 37423658 DOI: 10.1016/j.aca.2023.341495] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/20/2023] [Accepted: 06/05/2023] [Indexed: 07/11/2023]
Abstract
Low-field nuclear magnetic resonance (NMR) has three general modalities: spectroscopy, imaging, and relaxometry. In the last twelve years, the modality of spectroscopy, also known as benchtop NMR, compact NMR, or just low-field NMR, has undergone instrumental development due to new permanent magnetic materials and design. As a result, benchtop NMR has emerged as a powerful analytical tool for use in process analytical control (PAC). Nevertheless, the successful application of NMR devices as an analytical tool in several areas is intrinsically linked to its coupling with different chemometric methods. This review focuses on the evolution of benchtop NMR and chemometrics in chemical analysis, including applications in fuels, foods, pharmaceuticals, biochemicals, drugs, metabolomics, and polymers. The review also presents different low-resolution NMR methods for spectrum acquisition and chemometric techniques for calibration, classification, discrimination, data fusion, calibration transfer, multi-block and multi-way.
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Affiliation(s)
- Diego Galvan
- Chemistry Institute, Universidade Federal de Mato Grosso do Sul (UFMS), 79070-900, Campo Grande, MS, Brazil; Chemistry Departament, Universidade Estadual de Londrina (UEL), 86.057-970, Londrina, PR, Brazil.
| | | | - Evandro Bona
- Post-Graduation Program of Food Technology (PPGTA), Universidade Tecnológica Federal do Paraná (UTFPR), Campus Campo Mourão, 87301-899, Campo Mourão, PR, Brazil; Post-Graduation Program of Chemistry (PPGQ), Universidade Tecnológica Federal do Paraná (UTFPR), Campus Curitiba, 80230-901, Curitiba, PR, Brazil
| | - Federico Marini
- Department of Chemistry, University of Rome "La Sapienza", Piazzale Aldo Moro 5, 00185, Rome, Italy
| | - Mário Henrique M Killner
- Chemistry Departament, Universidade Estadual de Londrina (UEL), 86.057-970, Londrina, PR, Brazil
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4
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de Souza Zangirolami M, Moya Moreira TF, Leimann FV, Valderrama P, Março PH. Texture profile and short-NIR spectral vibrations relationship evaluated through Comdim: The case study for animal and vegetable proteins. Food Control 2023. [DOI: 10.1016/j.foodcont.2022.109290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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5
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Galvan D, de Andrade JC, Effting L, Lelis CA, Melquiades FL, Bona E, Conte-Junior CA. Energy-dispersive X-ray fluorescence combined with chemometric tools applied to tomato and sweet pepper classification. Food Control 2023. [DOI: 10.1016/j.foodcont.2022.109326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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6
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Tan A, Wang Y, Zhao Y, Wang B, Li X, Wang AX. Near infrared spectroscopy quantification based on Bi-LSTM and transfer learning for new scenarios. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 283:121759. [PMID: 35985223 DOI: 10.1016/j.saa.2022.121759] [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: 05/01/2022] [Revised: 08/04/2022] [Accepted: 08/11/2022] [Indexed: 06/15/2023]
Abstract
This study proposed a deep transfer learning methodology based on an improved Bi-directional Long Short-Term Memory (Bi-LSTM) network for the first time to address the near infrared spectroscopy (NIR) model transfer issue between samples. We tested its effectiveness on two datasets of manure and polyglutamic acid (γ-PGA) solution, respectively. First, the optimal primary Bi-LSTM networks for cattle manure and the first batch of γ-PGA were developed by ablation experiments and both proved to outperform one-dimensional convolutional neural network (1D-CNN), Partial Least Square (PLS) and Extreme Learning Machine (ELM) models. Then, two types of transfer learning approaches were carried out to determine model transferability to non-homologous samples. For poultry manure and the second batch of γ-PGA, the obtained predicting results verified that the second approach of fine-tuning Bi-LSTM layers and re-training FC layers transcended the first approach of fixing Bi-LSTM layers and only re-training FC layers by reducing the RMSEPtarget of 23.4275% and 50.7343%, respectively. Finally, comparisons with fine-tuning 1D-CNN and other traditional model transfer methods further identified the superiority of the proposed methodology with exceeding accuracy and smaller variation, which decreased RMSEPtarget of poultry manure and the second batch of γ-PGA of 7.2832% and 48.1256%, 67.1117% and 80.6924% when compared to that acquired by fine-tuning 1D-CNN, Tradaboost-ELM and CCA-PLS which were the best of five traditional methods, respectively. The study demonstrates the potential of the Fine-tuning-Bi-LSTM enabled NIR technology to be used as a simple, cost effective and reliable detection tool for a wide range of applications under various new scenarios.
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Affiliation(s)
- Ailing Tan
- School of Information and Science Engineering, Yanshan University, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao 066004, China
| | - Yunxin Wang
- School of Information and Science Engineering, Yanshan University, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao 066004, China.
| | - Yong Zhao
- School of Electrical Engineering, Yanshan University, The Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Qinhuangdao 066004, China
| | - Bolin Wang
- School of Information and Science Engineering, Yanshan University, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao 066004, China
| | - Xiaohang Li
- School of Information and Science Engineering, Yanshan University, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao 066004, China
| | - Alan X Wang
- Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76706, USA
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7
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8
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Parameter Visualization of Benchtop Nuclear Magnetic Resonance Spectra toward Food Process Monitoring. Processes (Basel) 2022. [DOI: 10.3390/pr10071264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
Abstract
Low-cost and user-friendly benchtop low-field nuclear magnetic resonance (NMR) spectrometers are typically used to monitor food processes in the food industry. Because of excessive spectral overlap, it is difficult to characterize food mixtures using low-field NMR spectroscopy. In addition, for standard compounds, low-field benchtop NMR data are typically unavailable compared to high-field NMR data, which have been accumulated and are reusable in public databases. This work focused on NMR parameter visualization of the chemical structure and mobility of mixtures and the use of high-field NMR data to analyze benchtop NMR data to characterize food process samples. We developed a tool to easily process benchtop NMR data and obtain chemical shifts and T2 relaxation times of peaks, as well as transform high-field NMR data into low-field NMR data. Line broadening and time–frequency analysis methods were adopted for data processing. This tool can visualize NMR parameters to characterize changes in the components and mobilities of food process samples using benchtop NMR data. In addition, assignment errors were smaller when the spectra of standard compounds were identified by transferring the high-field NMR data to low-field NMR data rather than directly using experimentally obtained low-field NMR spectra.
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9
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Lelis CA, Galvan D, Tessaro L, de Andrade JC, Mutz YS, Conte-Junior CA. Fluorescence spectroscopy in tandem with chemometric tools applied to milk quality control. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104515] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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10
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Lindner S, Burger R, Rutledge DN, Do XT, Rumpf J, Diehl BWK, Schulze M, Monakhova YB. Is the Calibration Transfer of Multivariate Calibration Models between High- and Low-Field NMR Instruments Possible? A Case Study of Lignin Molecular Weight. Anal Chem 2022; 94:3997-4004. [PMID: 35201769 DOI: 10.1021/acs.analchem.1c05125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Although several successful applications of benchtop nuclear magnetic resonance (NMR) spectroscopy in quantitative mixture analysis exist, the possibility of calibration transfer remains mostly unexplored, especially between high- and low-field NMR. This study investigates for the first time the calibration transfer of partial least squares regressions [weight average molecular weight (Mw) of lignin] between high-field (600 MHz) NMR and benchtop NMR devices (43 and 60 MHz). For the transfer, piecewise direct standardization, calibration transfer based on canonical correlation analysis, and transfer via the extreme learning machine auto-encoder method are employed. Despite the immense resolution difference between high-field and low-field NMR instruments, the results demonstrate that the calibration transfer from high- to low-field is feasible in the case of a physical property, namely, the molecular weight, achieving validation errors close to the original calibration (down to only 1.2 times higher root mean square errors). These results introduce new perspectives for applications of benchtop NMR, in which existing calibrations from expensive high-field instruments can be transferred to cheaper benchtop instruments to economize.
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Affiliation(s)
- Simon Lindner
- Department of Chemistry and Biotechnology, FH Aachen University of Applied Sciences, Jülich 52428, Germany.,Department of Natural Sciences, Bonn-Rhein-Sieg University of Applied Sciences, Rheinbach 53359, Germany
| | - René Burger
- Department of Natural Sciences, Bonn-Rhein-Sieg University of Applied Sciences, Rheinbach 53359, Germany
| | - Douglas N Rutledge
- Université Paris-Saclay, INRAE, AgroParisTech, UMR SayFood, Paris 75005, France.,National Wine and Grape Industry Centre, Charles Sturt University, Wagga Wagga 2650, Australia
| | - Xuan Tung Do
- Department of Natural Sciences, Bonn-Rhein-Sieg University of Applied Sciences, Rheinbach 53359, Germany
| | - Jessica Rumpf
- Department of Natural Sciences, Bonn-Rhein-Sieg University of Applied Sciences, Rheinbach 53359, Germany
| | - Bernd W K Diehl
- Spectral Service AG, Emil-Hoffmann-Straße 33, Köln 50996, Germany
| | - Margit Schulze
- Department of Natural Sciences, Bonn-Rhein-Sieg University of Applied Sciences, Rheinbach 53359, Germany
| | - Yulia B Monakhova
- Department of Chemistry and Biotechnology, FH Aachen University of Applied Sciences, Jülich 52428, Germany.,Spectral Service AG, Emil-Hoffmann-Straße 33, Köln 50996, Germany.,Institute of Chemistry, Saratov State University, Saratov 410012, Russia
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11
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Gunning Y, Taous F, El Ghali T, Gibbon JD, Wilson E, Brignall RM, Kemsley EK. Mitigating instrument effects in 60 MHz 1H NMR spectroscopy for authenticity screening of edible oils. Food Chem 2022; 370:131333. [PMID: 34788960 DOI: 10.1016/j.foodchem.2021.131333] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 10/01/2021] [Accepted: 10/03/2021] [Indexed: 11/04/2022]
Abstract
Low field (60 MHz) 1H NMR spectroscopy was used to analyse a large (n = 410) collection of edible oils, including olive and argan, in an authenticity screening scenario. Experimental work was carried out on multiple spectrometers at two different laboratories, aiming to explore multivariate model stability and transfer between instruments. Three modelling methods were employed: Partial Least Squares Discriminant Analysis, Random Forests, and a One Class Classification approach. Clear inter-instrument differences were observed between replicated data collections, sufficient to compromise effective transfer of models based on raw data between instruments. As mitigations to this issue, various data pre-treatments were investigated: Piecewise Direct Standardisation, Standard Normal Variates, and Rank Transformation. Datasets comprised both phase corrected and magnitude spectra, and it was found that that the latter spectral form may offer some advantages in the context of pattern recognition and classification modelling, particularly when used in combination with the Rank Transformation pre-treatment.
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Affiliation(s)
- Yvonne Gunning
- Quadram Institute Bioscience, Norwich Research Park, Colney, Norwich NR4 7UQ, UK
| | - Fouad Taous
- Centre National de l'Energie des Sciences et des Techniques Nucléaires (CNESTEN) Rabat, Morocco
| | - Tibari El Ghali
- Centre National de l'Energie des Sciences et des Techniques Nucléaires (CNESTEN) Rabat, Morocco
| | | | - E Wilson
- Quadram Institute Bioscience, Norwich Research Park, Colney, Norwich NR4 7UQ, UK
| | | | - E Kate Kemsley
- Quadram Institute Bioscience, Norwich Research Park, Colney, Norwich NR4 7UQ, UK.
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12
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Quantitative Analysis of Methanol in Methanol Gasoline by Calibration Transfer Strategy Based on Kernel Domain Adaptive Partial Least Squares(kda-PLS). Chem Res Chin Univ 2022. [DOI: 10.1007/s40242-022-1327-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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13
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Sefid-Sefidehkhan Y, Salehniya H, Khoshkam M, Amiri M. Transfer of multivariate calibration model for simultaneous electrochemical determination of ascorbic acid and uric acid. J CHEM SCI 2021. [DOI: 10.1007/s12039-021-01982-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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14
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Hou X, Wang X, Hu Y, Chen Y, Huang G, Nie S. A One-Dimensional U-Net-Based Calibration-Transfer Method for Low-Field Nuclear Magnetic Resonance Signals. Anal Chem 2021; 93:10469-10476. [PMID: 34270205 DOI: 10.1021/acs.analchem.1c00765] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The reconstruction of the statistical analysis model of an instrument is a time-consuming and expensive process. Herein, the feasibility of spectral model calibration-transfer application to the same type of low-field nuclear magnetic resonance (LF-NMR) instrument was investigated using a one-dimensional U-net (1D U-net). Unlike conventional calibration-transfer algorithms such as direct standardization (DS), the 1D U-net network can reduce the error between the master and slave instruments through iterative cycles. The calibration-transfer ability was verified; three experiments that entailed the use of edible oil and copper sulfate (CuSO4) samples were implemented. The analysis of the spectral responses and feature analysis of the edible oil samples revealed that the signal of the slave instrument calibrated using the 1D U-net most resembled the signal of the master instrument, and its relative residual value was reduced to 0.0045. Further analysis of the CuSO4 concentration prediction showed that on the support vector regression (SVR) model constructed using the master instrument, the signal of the slave instrument calibrated by the 1D U-net was more similar to the response of the master instrument, and its root mean square error (RMSE) was only 0.01606 mmol/L. Thus, 1D U-net is a viable candidate for calibration-transfer applications to LF-NMR instruments.
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Affiliation(s)
- Xuewen Hou
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Xin Wang
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Ying Hu
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yang Chen
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Gang Huang
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
| | - Shengdong Nie
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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15
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Galvan D, Tanamati AAC, Casanova F, Danieli E, Bona E, Killner MHM. Compact low-field NMR spectroscopy and chemometrics applied to the analysis of edible oils. Food Chem 2021; 365:130476. [PMID: 34237562 DOI: 10.1016/j.foodchem.2021.130476] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 04/08/2021] [Accepted: 06/24/2021] [Indexed: 10/21/2022]
Abstract
Compact nuclear magnetic resonance (NMR) spectroscopy combined with chemometric tools opens new perspectives for NMR use. This work compares the potential of 43, 60 and 400 MHz NMR spectroscopy for quality control of edible oils. Partial least squares regression (PLSR) and support vector regression (SVR) models built on the three NMR devices had equivalent performances for fatty acids and iodine value, and the models built with the low field spectra were equivalent to the high field. Moreover, performances for calibration indicated that most of the models built with medium/or high-resolution fields presented reproducibility values lower than the minimum accepted by the American Oil Chemists' Society (AOCS). Compared to classical methods, this new approach allows the application of medium resolution devices as a sample screening tool in analytical laboratories since it allows the spectrum obtention in a few seconds, without the need for sample preparation or the use of deuterated solvents.
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Affiliation(s)
- Diego Galvan
- Departamento de Química, Universidade Estadual de Londrina, 86.057-970 Londrina, Brazil.
| | - Ailey Aparecida Coelho Tanamati
- Programa de Pós-Graduação em Tecnologia de Alimentos, Universidade Tecnológica Federal do Paraná, Câmpus - Campo Mourão, 87.301 899 Campo Mourão, Brazil
| | | | | | - Evandro Bona
- Programa de Pós-Graduação em Tecnologia de Alimentos, Universidade Tecnológica Federal do Paraná, Câmpus - Campo Mourão, 87.301 899 Campo Mourão, Brazil
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16
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Galvan D, Aquino A, Effting L, Mantovani ACG, Bona E, Conte-Junior CA. E-sensing and nanoscale-sensing devices associated with data processing algorithms applied to food quality control: a systematic review. Crit Rev Food Sci Nutr 2021; 62:6605-6645. [PMID: 33779434 DOI: 10.1080/10408398.2021.1903384] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed.
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Affiliation(s)
- Diego Galvan
- Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
| | - Adriano Aquino
- Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
| | - Luciane Effting
- Chemistry Department, State University of Londrina (UEL), Londrina, PR, Brazil
| | | | - Evandro Bona
- Post-Graduation Program of Food Technology (PPGTA), Federal University of Technology Paraná (UTFPR), Campo Mourão, PR, Brazil
| | - Carlos Adam Conte-Junior
- Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
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