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Banaś J, Banaś M. Combined Application of Fluorescence Spectroscopy and Principal Component Analysis in Characterisation of Selected Herbhoneys. Molecules 2024; 29:749. [PMID: 38398501 PMCID: PMC10893536 DOI: 10.3390/molecules29040749] [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: 01/04/2024] [Revised: 01/23/2024] [Accepted: 02/02/2024] [Indexed: 02/25/2024] Open
Abstract
This study reports the use of front-face fluorescence spectroscopy with principal component analysis (PCA) as a tool for the characterisation of selected Polish herbhoneys (raspberry, lemon balm, rose, mint, black current, instant coffee, pine, hawthorn, and nettle). Fluorimetric spectra registered in the ranges ascribed to fluorescence of amino acids, polyphenols, vitamins, and products of Maillard's reaction enabled the comparison of herbhoney compositions. Obtained synchronous spectra combined with PCA were used to investigate potential differences between analysed samples and interactions between compounds present in them. The most substantial influence on the total variance had the intensities of polyphenols fluorescence. These intensities were the main factor differentiated by the analysed products.
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Affiliation(s)
- Joanna Banaś
- Department of Biotechnology and General Technology of Food, Faculty of Food Technology, University of Agriculture in Kraków, Balicka 122, 30-149 Kraków, Poland
| | - Marian Banaś
- Department of Power Systems and Environmental Protection Facilities, Faculty of Mechanical Engineering and Robotics, AGH University of Kraków, A. Mickiewicza 30, 30-059 Kraków, Poland;
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Xu J, Xu J, Tong Z, Yu S, Liu B, Mu X, Du B, Gao C, Wang J, Liu Z, Liu D. Impact of different classification schemes on discrimination of proteins with noise-contaminated spectra using laboratory-measured fluorescence data. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 296:122646. [PMID: 37003145 DOI: 10.1016/j.saa.2023.122646] [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: 12/02/2022] [Revised: 03/05/2023] [Accepted: 03/15/2023] [Indexed: 06/19/2023]
Abstract
Biological agents are important to detect and identify with respect to environmental contamination and public health. Noise contamination in fluorescent spectra is one of the contributors to the uncertainties of identification. In order to investigate the noise-tolerant capability provided by laboratory-measured excitation-emission matrix (EEM) fluorescence spectra that are used as a database, fluorescence properties of four proteinaceous biotoxin samples and ten harmless protein samples were characterized by EEM fluorescence spectra, and the predicting performance of models trained by laboratory-measured fluorescence data was tested and verified from validation data with noise-contaminated spectra. By means of peak signal of noise (PSNR) as an indicator of noise levels, the potential impact of noise contaminations on the characterization and discrimination of these samples was evaluated quantitatively. Different classification schemes utilizing multivariate analysis techniques of Principal Component Analysis (PCA), Random Forest (RF), and Multi-layer Perceptron (MPL) coupled with feature descriptors of differential transform (DT), Fourier transform (FT) and wavelet transform (WT) were conducted under different PSNR values. We systematically analyzed the performance of classification schemes by the case study at 20 PSNR and by statistical analysis from 1-100 PSNR. The results show that the spectral features with EEM-WT decreased the demanding number of input variables while retaining high performances in sample classification. The spectral features with EEM-FT presented the worst performance although having the largest number of features. The distributions of feature importance and contribution were found sensitive to noise contaminations. The classification scheme of PCA prior to MPL with EEM-WT as input presented an improvement in lower PSNR. These results indicate that robust features extracted by corresponding techniques are critical to enhancing the spectral differentiation capabilities among these samples and play an important role in eliminating the noise effect. The study of classification schemes for discriminating protein samples with noise-contaminated spectra presents tremendous potential for future developments in the rapid detection and identification of proteinaceous biotoxins based on three-dimensional fluorescence spectrometry.
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Affiliation(s)
- Jiwei Xu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Jianjie Xu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China.
| | - Zhaoyang Tong
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Siqi Yu
- Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Bing Liu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Xihui Mu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Bin Du
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Chuan Gao
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Jiang Wang
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Zhiwei Liu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Dong Liu
- Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei, 230031, Anhui, China
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Xu J, Xu J, Tong Z, Du B, Liu B, Mu X, Guo T, Yu S, Liu S, Gao C, Wang J, Liu Z, Zhang P. Performance of feature extraction method for classification and identification of proteins based on three-dimensional fluorescence spectrometry. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121841. [PMID: 36179565 DOI: 10.1016/j.saa.2022.121841] [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: 04/08/2022] [Revised: 09/01/2022] [Accepted: 09/02/2022] [Indexed: 06/16/2023]
Abstract
Three-dimensional excitation emission matrix (EEM) fluorescence spectroscopy was employed to discriminate protein samples comprising bovine serum albumin, neurotensin, ovalbumin, ricin, trypsin from bovine pancreas and trypsin from porcine pancreas. Two methods of feature extraction with and without parameterization were applied to the spectral data in order to evaluate their performance of discrimination between protein samples. The discrimination of protein samples was conducted by k-means clustering algorithm and eigenvalue extracting procedure based on principal component analysis (PCA). It was found that the method of feature extraction without parameterization performed best, correctly attributing 100% of the spectral data in the condition of two principal components (PCs) captured. Features extracted with spectral parameterization failed to separate ricin and trypsin from bovine pancreas in same condition. Without spectral parameterization, less dimensionality and unique principal components captured by PCA indicates the spectrally-resolved features of corresponding protein samples. By clustering using each spectrum at fixed excitation wavelength, excitation wavelengths matched with common intrinsic fluorophores were found to be more sensitive to the classification accuracy. Contributions of spectral features extracted from EEM to the principal components were discussed and demonstrated their feature differentiation capabilities among six protein samples. These results reveal that appropriate extraction approach of features in combination with PCA analysis could be used in discrimination of protein samples at species level as a spectroscopic diagnostic tool. Our study provides fundamental references about computational strategies when EEM are used to explore proteins in ambient environment.
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Affiliation(s)
- Jiwei Xu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Jianjie Xu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China.
| | - Zhaoyang Tong
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Bin Du
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Bing Liu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Xihui Mu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Tengxiao Guo
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Siqi Yu
- School of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei, 230026, Anhui, China
| | - Shuai Liu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Chuan Gao
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Jiang Wang
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Zhiwei Liu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Pengjie Zhang
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
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4
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Rapid identification of rice geographical origin and adulteration by excitation-emission matrix fluorescence spectroscopy combined with chemometrics based on fluorescence probe. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
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Silveira AL, Barbeira PJS. A fast and low-cost approach for the discrimination of commercial aged cachaças using synchronous fluorescence spectroscopy and multivariate classification. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2022; 102:4918-4926. [PMID: 35266168 DOI: 10.1002/jsfa.11857] [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: 01/03/2022] [Revised: 03/07/2022] [Accepted: 03/10/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Cachaça is the distilled beverage typical of Brazil and can be subjected to the aging process in wooden barrels. In addition to oak barrels, cachaça is also aged in barrels of different Brazilian native woods, resulting in a wide variety of its sensory characteristics. In this work, 172 cachaças aged in bálsamo, jequitibá, oak, and umburana barrels were analyzed by synchronous fluorescence spectroscopy and by the classification methods of principal component analysis and partial least squares discriminant analysis. Spectra were preprocessed by the first derivative by Savitzky-Golay smoothing, using a filter width and polynomial order determined through face-centered central composite designs. Multivariate analysis was realized using the spectra recorded at different wavelength differences, and models were compared by the classification errors in the test sets. RESULTS The principal component analysis applied to the synchronous fluorescence spectra presented a tendency of separation by the wood used in the aging process, and the partial least squares discriminant analysis model constructed using the fluorescence spectra recorded at a wavelength difference of 30 nm provided better performance parameters (efficiency 91-97%, sensitivity 81-100%, and specificity 91-100%). CONCLUSION Synchronous fluorescence spectroscopy offers a promising approach for the classification of cachaças aged in bálsamo, oak, jequitibá, and umburana barrels, and the discriminant model can be used for routine analysis as a screening method. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Amanda Lemes Silveira
- ICEx, Departamento de Química, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
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Silveira AL, Barbeira PJS. Synchronous fluorescence spectroscopy and multivariate classification for the discrimination of cachaças and rums. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 270:120821. [PMID: 35007903 DOI: 10.1016/j.saa.2021.120821] [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: 11/03/2021] [Revised: 12/21/2021] [Accepted: 12/25/2021] [Indexed: 06/14/2023]
Abstract
Although cachaça and rum are distilled beverages obtained from the same raw material, they present differences in their chemical compositions. In this study, synchronous fluorescence spectroscopy was used combined with supervised classification models based on the partial least squares discriminant analysis to develop a rapid and low-cost model for discriminating between 50 cachaça and 40 rum samples. Partial least squares discriminant analysis models were constructed using synchronous fluorescence spectra recorded at wavelength differences of 10-100 nm. Initially, spectra were preprocessed by the first derivative with the Savitzky-Golay smoothing, and filter width and polynomial order were selected through face-centered central composite designs. For the construction and validation models, the spectra data were split into two datasets: the training and the test sets containing 60 (C, n = 33; R, n = 27) and 30 (C, n = 17; R, n = 13) samples, respectively. The best discrimination was achieved using fluorescence spectra recorded at wavelength difference 10 nm, allowing the discrimination of cachaça and rum with a classification efficiency of 98%. These results indicate that synchronous fluorescence spectroscopy offers a promising approach for the authentication of cachaças and rums.
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Affiliation(s)
- Amanda Lemes Silveira
- ICEx, Departamento de Química - Universidade Federal de Minas Gerais (UFMG), Av. Antônio Carlos, 6627, Pampulha, Belo Horizonte - MG CEP 31270-901, Brazil
| | - Paulo Jorge Sanches Barbeira
- ICEx, Departamento de Química - Universidade Federal de Minas Gerais (UFMG), Av. Antônio Carlos, 6627, Pampulha, Belo Horizonte - MG CEP 31270-901, Brazil.
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Basalekou M, Kyraleou M, Kallithraka S. Authentication of wine and other alcohol-based beverages—Future global scenario. FUTURE FOODS 2022. [DOI: 10.1016/b978-0-323-91001-9.00028-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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Ranaweera RKR, Gilmore AM, Capone DL, Bastian SEP, Jeffery DW. Spectrofluorometric analysis combined with machine learning for geographical and varietal authentication, and prediction of phenolic compound concentrations in red wine. Food Chem 2021; 361:130149. [PMID: 34082385 DOI: 10.1016/j.foodchem.2021.130149] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/21/2021] [Accepted: 05/15/2021] [Indexed: 12/13/2022]
Abstract
Fluorescence spectroscopy is rapid, straightforward, selective, and sensitive, and can provide the molecular fingerprint of a sample based on the presence of various fluorophores. In conjunction with chemometrics, fluorescence techniques have been applied to the analysis and classification of an array of products of agricultural origin. Recognising that fluorescence spectroscopy offered a promising method for wine authentication, this study investigated the unique use of an absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) technique for classification of red wines with respect to variety and geographical origin. Multi-block data analysis of A-TEEM data with extreme gradient boosting discriminant analysis yielded an unrivalled 100% and 99.7% correct class assignment for variety and region of origin, respectively. Prediction of phenolic compound concentrations with A-TEEM based on multivariate calibration models using HPLC reference data was also highly effective, and overall, the A-TEEM technique was shown to be a powerful tool for wine classification and analysis.
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Affiliation(s)
- Ranaweera K R Ranaweera
- Department of Wine Science and Waite Research Institute, The University of Adelaide (UA), PMB 1, Glen Osmond, South Australia 5064, Australia
| | - Adam M Gilmore
- HORIBA Instruments Inc., 20 Knightsbridge Rd., Piscataway, NJ 08854, United States
| | - Dimitra L Capone
- Department of Wine Science and Waite Research Institute, The University of Adelaide (UA), PMB 1, Glen Osmond, South Australia 5064, Australia; Australian Research Council Training Centre for Innovative Wine Production, UA, PMB 1, Glen Osmond, South Australia 5064, Australia
| | - Susan E P Bastian
- Department of Wine Science and Waite Research Institute, The University of Adelaide (UA), PMB 1, Glen Osmond, South Australia 5064, Australia; Australian Research Council Training Centre for Innovative Wine Production, UA, PMB 1, Glen Osmond, South Australia 5064, Australia
| | - David W Jeffery
- Department of Wine Science and Waite Research Institute, The University of Adelaide (UA), PMB 1, Glen Osmond, South Australia 5064, Australia; Australian Research Council Training Centre for Innovative Wine Production, UA, PMB 1, Glen Osmond, South Australia 5064, Australia.
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Kong D, Cui Y, Kong L, Wang S. Classification of oil pollutants based on excitation-emission matrix fluorescence spectroscopy and two-dimensional discriminant analysis. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 228:117799. [PMID: 31806481 DOI: 10.1016/j.saa.2019.117799] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 11/10/2019] [Accepted: 11/14/2019] [Indexed: 06/10/2023]
Abstract
Excitation-Emission Matrix (EEM) Fluorescence spectroscopy and two-dimensional (2D) discriminant analysis were used to classify oil pollutants. Firstly, the EEM fluorescence spectroscopy of the oil samples was collected using the FS920 steady-state fluorescence spectrometer, and EEM was preprocessed by removing scattering and normalization. Secondly, EEM was analyzed and characterized by parallel factor analysis (PARAFAC). Finally, all the collected samples were divided into training and test sets by the Kennard-Stone algorithm. The classification models of the training set samples were established by using 2D-PCA, 2D-LDA, PARAFAC-2DPCA, PARAFAC-2DLDA, PARAFAC-LDA and NPLS-DA algorithms, respectively. These models were then used to classify test set samples. The classification performance of the used models was assessed by accuracy, sensitivity and specificity. The best classification results in the used models were obtained by using 2D-PCA and 2D-LDA with 95% and 95% accuracy, respectively. These results provide an important reference for classification of oil pollutants.
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Affiliation(s)
- Deming Kong
- School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China; Department of Telecommunications and Information Processing, Ghent University, B-9000 Ghent, Belgium
| | - Yaoyao Cui
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China.
| | - Lingfu Kong
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Shutao Wang
- School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
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Sikorska E, Włodarska K, Khmelinskii I. Application of multidimensional and conventional fluorescence techniques for classification of beverages originating from various berry fruit. Methods Appl Fluoresc 2020; 8:015006. [DOI: 10.1088/2050-6120/ab6367] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Developing an Excitation-Emission Matrix Fluorescence Spectroscopy Method Coupled with Multi-way Classification Algorithms for the Identification of the Adulteration of Shanxi Aged Vinegars. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-019-01586-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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