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Chen Y, Wu HL, Wang T, Wu JN, Liu BB, Ding YJ, Yu RQ. Rapid detection and quantification of adulteration in saffron by excitation-emission matrix fluorescence combined with multi-way chemometrics. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:1391-1398. [PMID: 37801402 DOI: 10.1002/jsfa.13028] [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: 03/29/2023] [Revised: 09/11/2023] [Accepted: 10/06/2023] [Indexed: 10/08/2023]
Abstract
BACKGROUND Saffron has gained people's attention and love for its unique flavor and valuable edible value, but the problem of saffron adulteration in the market is serious. It is urgent for us to find a simple and rapid identification and quantitative estimation of adulteration in saffron. Therefore, excitation-emission matrix (EEM) fluorescence combined with multi-way chemometrics was proposed for the detection and quantification of adulteration in saffron. RESULTS The fluorescence composition analysis of saffron and saffron adulterants (safflower, marigold and madder) were accomplished by alternating trilinear decomposition (ATLD) algorithm. ATLD and two-dimensional principal component analysis combined with k-nearest neighbor (ATLD-kNN and 2DPCA-kNN) and ATLD combined with data-driven soft independent modeling of class analogies (ATLD-DD-SIMCA) were applied to rapid detection of adulteration in saffron. 2DPCA-kNN and ATLD-DD-SIMCA methods were adopted for the classification of chemical EEM data, first with 100% correct classification rate. The content of adulteration of adulterated saffron was predicted by the N-way partial least squares regression (N-PLS) algorithm. In addition, new samples were correctly classified and the adulteration level in adulterated saffron was estimated semi-quantitatively, which verifies the reliability of these models. CONCLUSION ATLD-DD-SIMCA and 2DPCA-kNN are recommended methods for the classification of pure saffron and adulterated saffron. The N-PLS algorithm shows potential in prediction of adulteration levels. These methods are expected to solve more complex problems in food authenticity. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Yue Chen
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, China
| | - Hai-Long Wu
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, China
| | - Tong Wang
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, China
| | - Juan-Ni Wu
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, China
| | - Bing-Bing Liu
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, China
| | - Yu-Jie Ding
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, China
| | - Ru-Qin Yu
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, China
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Long W, Lei G, Guan Y, Chen H, Hu Z, She Y, Fu H. Classification of Chinese traditional cereal vinegars and antioxidant property predication by fluorescence spectroscopy. Food Chem 2023; 424:136406. [PMID: 37216781 DOI: 10.1016/j.foodchem.2023.136406] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 04/15/2023] [Accepted: 05/10/2023] [Indexed: 05/24/2023]
Abstract
In this work, a rapid and accurate strategy for classification of Chinese traditional cereal vinegars (CTCV) and antioxidant property predication was proposed by using the combination fluorescence spectroscopy and machine learning. Three characteristic fluorescent components were extracted by parallel factor analysis (PARAFAC), which have correlations greater than 0.8 with antioxidant activity of CTCV obtained by Pearson correlation analysis. Machine learning methods, including linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA) and N-way partial least squares discriminant analysis (N-PLS-DA), were used for the classification of different types of CTCV, and the correct classification rates was higher than 97%. The antioxidant property of CTCV were further quantified by using optimized variable-weighted least-squares support vector machine based on particle swarm optimization (PSO-VWLS-SVM). The proposed strategy provides a basis for further research on antioxidant active ingredients and antioxidant mechanisms of CTCV, and enable the continued exploration and application of CTCV from different types.
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Affiliation(s)
- Wanjun Long
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China
| | - Guanghua Lei
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China
| | - Yuting Guan
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China
| | - Hengye Chen
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China
| | - Zikang Hu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China
| | - Yuanbin She
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, PR China.
| | - Haiyan Fu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China.
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Rapid authentication of green tea grade by excitation-emission matrix fluorescence spectroscopy coupled with multi-way chemometric methods. Eur Food Res Technol 2022. [DOI: 10.1007/s00217-022-04174-w] [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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4
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Authentication of craft and industrial beers by excitation-emission matrix fluorescence spectroscopy and chemometrics. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107650] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Long WJ, Wu HL, Wang T, Dong MY, Chen LZ, Yu RQ. Fast identification of the geographical origin of Gastrodia elata using excitation-emission matrix fluorescence and chemometric methods. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 258:119798. [PMID: 33892304 DOI: 10.1016/j.saa.2021.119798] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 03/30/2021] [Accepted: 04/04/2021] [Indexed: 05/28/2023]
Abstract
Geographical origin is an important factor affecting the quality of traditional Chinese medicine. In this paper, the identification of geographical origin of Gastrodia elata was performed by using excitation-emission matrix fluorescence and chemometric methods. Firstly, excitation-emission matrix (EEM) fluorescence spectra of Gastrodia elata samples from different geographical origins were obtained. And then three chemometric methods, including multilinear partial least squares discriminant analysis (N-PLS-DA), unfold partial least squares discriminant analysis (U-PLS-DA), and k-nearest neighbor (kNN) method, were applied to build discriminant models. Finally, 45 Gastrodia elata samples could be differentiated from each other by these classification models according to their geographical origins. The results showed that all models obtained good classification results. Compared with the N-PLS-DA and U-PLS-DA, kNN got more accurate and reliable classification results and could identify Gastrodia elata samples from different geographical origins with 100% accuracy on the training and test set. Therefore, the proposed method was available for easily and quickly distinguishing the geographical origin of Gastrodia elata, which can be considered as a promising alternative method for determining the geographic origin of other traditional Chinese medicines.
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Affiliation(s)
- Wan-Jun Long
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China
| | - Hai-Long Wu
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China.
| | - Tong Wang
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China
| | - Ming-Yue Dong
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China
| | - Lu-Zhu Chen
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China
| | - Ru-Qin Yu
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China
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6
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Fang H, Wu HL, Wang T, Long WJ, Chen AQ, Ding YJ, Yu RQ. Excitation-emission matrix fluorescence spectroscopy coupled with multi-way chemometric techniques for characterization and classification of Chinese lager beers. Food Chem 2020; 342:128235. [PMID: 33051102 DOI: 10.1016/j.foodchem.2020.128235] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 09/25/2020] [Accepted: 09/25/2020] [Indexed: 01/04/2023]
Abstract
This paper proposed excitation-emission matrix fluorescence spectroscopy coupled with multi-way chemometric techniques for characterization and classification of Chinese pale lager beers produced by different manufacturers. The undiluted and diluted beer samples presented different fluorescence fingerprints. Three-way and four-way parallel factor analysis (PARAFAC) were used to decompose the skillfully constructed three-way and four-way data arrays, respectively, to further achieve beer characterization and feature extraction. Based on the features extracted in different ways, four strategies for beer classification were proposed. In each strategy, three supervised classification methods including linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA) and k-nearest neighbor (kNN) were used to build discriminant models. By comparison, PARAFAC-data fusion-kNN method in strategy 3 and four-way PARAFAC-kNN method in strategy 4 obtained the best classification results. The classification strategy based on four-way sample-excitation-emission-dilution level data array was proposed to solve the problem of beer classification for the first time.
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Affiliation(s)
- Huan Fang
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, People's Republic of China
| | - Hai-Long Wu
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, People's Republic of China.
| | - Tong Wang
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, People's Republic of China.
| | - Wan-Jun Long
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, People's Republic of China
| | - An-Qi Chen
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, People's Republic of China
| | - Yu-Jie Ding
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, People's Republic of China
| | - Ru-Qin Yu
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, People's Republic of China
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7
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Zhang YY, Zhang Q, Zhang YM, Wang WW, Zhang L, Yu YJ, Bai CC, Guo JZ, Fu HY, She Y. A comprehensive automatic data analysis strategy for gas chromatography-mass spectrometry based untargeted metabolomics. J Chromatogr A 2020; 1616:460787. [DOI: 10.1016/j.chroma.2019.460787] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 12/09/2019] [Accepted: 12/11/2019] [Indexed: 02/01/2023]
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8
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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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9
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Hamedpour V, Sasaki Y, Zhang Z, Kubota R, Minami T. Simple Colorimetric Chemosensor Array for Oxyanions: Quantitative Assay for Herbicide Glyphosate. Anal Chem 2019; 91:13627-13632. [PMID: 31556601 DOI: 10.1021/acs.analchem.9b02822] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Although the determination of oxyanions due to correlation with metabolic processes and diseases is in high demand, most of the developed methods are suffering from a shortage of a capability of on-site analysis, sensitivity, and user-friendliness. This paper introduces the first colorimetric chemosensor array targeting various anions including glyphosate. The proposed sensor benefits from some notable features such as utilizing only commercially available reagents, recognizing similarly structured compounds by biomaterial-free sensors, and providing a fingerprint-like response originating from pattern recognition. The detection mechanism is based on an anion sensing strategy named coordination binding-based sensor array (CBSA). In CBSA, competitive coordinative bonding of a metal ion (Zn2+) between a catechol dye (i.e., indicator) and target anions occurs, and changes in the optical properties of the dye represent the target's concentration. For data processing, two chemometrical techniques including linear discrimination analysis (LDA) and an artificial neural network (ANN) for pattern classification and regression/prediction purposes were successfully employed, respectively. Finally, the proposed chemosensor was subjected to glyphosate samples (commercial herbicide and tap water samples) and produced satisfactory results.
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Affiliation(s)
- Vahid Hamedpour
- Institute of Industrial Science , The University of Tokyo , 4-6-1 Komaba , Meguro-ku, Tokyo 153-8505 , Japan
| | - Yui Sasaki
- Institute of Industrial Science , The University of Tokyo , 4-6-1 Komaba , Meguro-ku, Tokyo 153-8505 , Japan
| | - Zhoujie Zhang
- Institute of Industrial Science , The University of Tokyo , 4-6-1 Komaba , Meguro-ku, Tokyo 153-8505 , Japan
| | - Riku Kubota
- Institute of Industrial Science , The University of Tokyo , 4-6-1 Komaba , Meguro-ku, Tokyo 153-8505 , Japan
| | - Tsuyoshi Minami
- Institute of Industrial Science , The University of Tokyo , 4-6-1 Komaba , Meguro-ku, Tokyo 153-8505 , Japan
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10
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Wang T, Wu HL, Long WJ, Hu Y, Cheng L, Chen AQ, Yu RQ. Rapid identification and quantification of cheaper vegetable oil adulteration in camellia oil by using excitation-emission matrix fluorescence spectroscopy combined with chemometrics. Food Chem 2019; 293:348-357. [DOI: 10.1016/j.foodchem.2019.04.109] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 04/09/2019] [Accepted: 04/28/2019] [Indexed: 10/26/2022]
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11
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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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12
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Zhu J, Zhu F, Li L, Cheng L, Zhang L, Sun Y, Wan X, Zhang Z. Highly discriminant rate of Dianhong black tea grades based on fluorescent probes combined with chemometric methods. Food Chem 2019; 298:125046. [PMID: 31260981 DOI: 10.1016/j.foodchem.2019.125046] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 02/18/2019] [Accepted: 02/18/2019] [Indexed: 01/08/2023]
Abstract
We established a novel Dianhong black tea grades discriminant analytic technique based on a fluorescence image along with carbon quantum dots (CDs) as fluorescent probes. Different grades of Dianhong black tea contain different various amounts of tea polyphenols. Tea polyphenols can quench the fluorescent intensity of CDs, resulting in different fluorescent peaks; Dianhong black tea grades can then be discriminated through the use of principal component analysis and Bayesian analysis. Compared with the additional data processing required in other methods, the advantage of our method is that the fluorescence curve can be used directly, and it achieves satisfactory results. We firstly used CDs combined with chemometrics to identify eight grades of Dianhong black tea, and we also provide a new method that improves the identification rate using nanotechnology to avoid performing complex data processing.
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Affiliation(s)
- Jing Zhu
- State Key Laboratory of Tea Plant Biology and Utilization, and Department of Applied Chemistry, Anhui Agricultural University, Hefei 230036, Anhui, China
| | - Fengyuan Zhu
- State Key Laboratory of Tea Plant Biology and Utilization, and Department of Applied Chemistry, Anhui Agricultural University, Hefei 230036, Anhui, China
| | - Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, and Department of Applied Chemistry, Anhui Agricultural University, Hefei 230036, Anhui, China
| | - Linlin Cheng
- State Key Laboratory of Tea Plant Biology and Utilization, and Department of Applied Chemistry, Anhui Agricultural University, Hefei 230036, Anhui, China
| | - Liang Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, and Department of Applied Chemistry, Anhui Agricultural University, Hefei 230036, Anhui, China
| | - Yue Sun
- State Key Laboratory of Tea Plant Biology and Utilization, and Department of Applied Chemistry, Anhui Agricultural University, Hefei 230036, Anhui, China
| | - Xiaochun Wan
- State Key Laboratory of Tea Plant Biology and Utilization, and Department of Applied Chemistry, Anhui Agricultural University, Hefei 230036, Anhui, China.
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, and Department of Applied Chemistry, Anhui Agricultural University, Hefei 230036, Anhui, China.
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