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Wang Y, Li Z, Wang W, Liu P, Tan X, Bian X. Rapid quantification of single component oil in perilla oil blends by ultraviolet-visible spectroscopy combined with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 321:124710. [PMID: 38936207 DOI: 10.1016/j.saa.2024.124710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 06/23/2024] [Accepted: 06/24/2024] [Indexed: 06/29/2024]
Abstract
As a unconventional oil, perilla oil is much more expensive than conventional oils since it has the highest content of α-linolenic acid among vegetable oils. Thus the adulteration of perilla oil is serious, which needs to be solved. In this study, the single component oil in perilla oil blends were first quantitatively analyzed by ultraviolet-visible (UV-vis) spectroscopy combined with chemometric methods. Soybean oil and palm oil were added into perilla oil to form binary and ternary perilla oil blends. Partial least squares (PLS), back propagation-artificial neural network (BP-ANN), support vector regression (SVR) and extreme learning machine (ELM) were compared and the best model was selected for calibration. In order to improve the prediction performance of the calibration model, ten preprocessing methods and five variable selection methods were investigated. Results show that PLS was the best calibration method for binary and ternary perilla oil blends. For binary perilla oil blends, the correlation coefficients of prediction (Rp) obtained by PLS were both above 0.99, which does not need preprocessing and variable selection. For ternary perilla oil blends, after the best continuous wavelet transform (CWT) preprocessing and discretized whale optimization algorithm (WOA) variable selection, the Rp values obtained by the best model CWT-WOA-PLS were all above 0.97. This research provides a common framework for calibration of perilla oil blends, which maybe a promising method for quality control of perilla oil in industry.
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Affiliation(s)
- Yao Wang
- School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, PR China
| | - Zihan Li
- School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, PR China
| | - Wenqiang Wang
- School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, PR China
| | - Peng Liu
- School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, PR China
| | - Xiaoyao Tan
- School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, PR China
| | - Xihui Bian
- School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, PR China; NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Shandong University, Jinan, 250012, China.
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2
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Yang X, Pei J, He X, Wang Y, Wang L, Shen F, Li P, Fang Y. A novel method for determination of peroxide value and acid value of extra-virgin olive oil based on fluorescence internal filtering effect correction. Food Chem 2024; 441:138342. [PMID: 38176142 DOI: 10.1016/j.foodchem.2023.138342] [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: 08/03/2023] [Revised: 12/25/2023] [Accepted: 12/29/2023] [Indexed: 01/06/2024]
Abstract
Peroxide value (PV) and acid value (AV) are widely used indicators for evaluating oxidation degree of olive oils. Fluorescence spectroscopy has been extensively studied on the detection of oil oxidation, however, the detection accuracy is limited due to internal filtering effect (IFE). Due to the primary and secondary IFE, at least two wavelengths of absorption information are required. Least squares support vector regression (LSSVR) models for PV and AV were established based on two absorption coefficients (μa) at 375 nm and emission wavelength and one fluorescence intensity at corresponding wavelength. The regression results proved that the model based on 375 and 475 nm could reach the best performance, with the highest correlation coefficient for prediction (rp) of 0.889 and 0.960 for PV and AV respectively. Finally, the explicit formulations for PV and AV were determined by nonlinear least squares fitting, and the rp could reach above 0.94 for two indicators.
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Affiliation(s)
- Xiaoyun Yang
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing 210023, China
| | - Jingyu Pei
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing 210023, China
| | - Xueming He
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing 210023, China.
| | - Yue Wang
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing 210023, China
| | - Liu Wang
- Key Laboratory of Traceability for Agricultural Genetically Modified Organisms , Ministry of Agriculture and Rural Affairs, Hangzhou 310022, China
| | - Fei Shen
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing 210023, China
| | - Peng Li
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing 210023, China
| | - Yong Fang
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing 210023, China
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Bian X, Wang Y, Wang S, Johnson JB, Sun H, Guo Y, Tan X. A Review of Advanced Methods for the Quantitative Analysis of Single Component Oil in Edible Oil Blends. Foods 2022; 11:foods11162436. [PMID: 36010436 PMCID: PMC9407567 DOI: 10.3390/foods11162436] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/04/2022] [Accepted: 08/11/2022] [Indexed: 12/21/2022] Open
Abstract
Edible oil blends are composed of two or more edible oils in varying proportions, which can ensure nutritional balance compared to oils comprising a single component oil. In view of their economical and nutritional benefits, quantitative analysis of the component oils in edible oil blends is necessary to ensure the rights and interests of consumers and maintain fairness in the edible oil market. Chemometrics combined with modern analytical instruments has become a main analytical technology for the quantitative analysis of edible oil blends. This review summarizes the different oil blend design methods, instrumental techniques and chemometric methods for conducting single component oil quantification in edible oil blends. The aim is to classify and compare the existing analytical techniques to highlight suitable and promising determination methods in this field.
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Affiliation(s)
- Xihui Bian
- School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
- Shandong Provincial Key Laboratory of Olefin Catalysis and Polymerization, Shandong Chambroad Holding Group Co., Ltd., Binzhou 256500, China
- Correspondence: ; Tel./Fax: +86-22-83955663
| | - Yao Wang
- School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
| | - Shuaishuai Wang
- Shandong Provincial Key Laboratory of Olefin Catalysis and Polymerization, Shandong Chambroad Holding Group Co., Ltd., Binzhou 256500, China
| | - Joel B. Johnson
- School of Health, Medical & Applied Sciences, Central Queensland University, Bruce Hwy, North Rockhampton, QLD 4701, Australia
| | - Hao Sun
- School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
| | - Yugao Guo
- School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
| | - Xiaoyao Tan
- School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
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4
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Meng D, Ciyong G, Li L, Zhao Z, Zhang W, Du C. Application of ultraviolet-visible spectroscopy coupled with support vector regression for the quantitative detection of thiamethoxam in tea. APPLIED OPTICS 2022; 61:6186-6192. [PMID: 36256231 DOI: 10.1364/ao.463293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 06/24/2022] [Indexed: 06/16/2023]
Abstract
A model combining UV-visible (UV-Vis) spectroscopy and support vector regression (SVR) for the quantitative detection of thiamethoxam in tea is proposed. First, each original UV-Vis spectrum in the sample set is decomposed into some intrinsic mode functions (IMFs) and a residual via ensemble empirical mode decomposition. Next, the decomposed IMFs are reconstructed into high-frequency and low-frequency matrices, and the residuals are combined into a trend matrix. Then, the SVR is used to build regression sub-models between each matrix and the content of thiamethoxam in tea. Finally, the combination model is established by a weighted average of the sub-models. The prediction results are compared with SVR and SVR coupled with several preprocessing methods, and the results demonstrate the superiority of the proposed approach in the quantitative detection of thiamethoxam in tea.
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Lozano‐Castellón J, López‐Yerena A, Domínguez‐López I, Siscart‐Serra A, Fraga N, Sámano S, López‐Sabater C, Lamuela‐Raventós RM, Vallverdú‐Queralt A, Pérez M. Extra virgin olive oil: A comprehensive review of efforts to ensure its authenticity, traceability, and safety. Compr Rev Food Sci Food Saf 2022; 21:2639-2664. [DOI: 10.1111/1541-4337.12949] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 02/28/2022] [Accepted: 03/04/2022] [Indexed: 01/19/2023]
Affiliation(s)
- Julián Lozano‐Castellón
- Department of Nutrition, Food Science and Gastronomy, XIA, Faculty of Pharmacy and Food Sciences Institute of Nutrition and Food Safety (INSA‐UB) University of Barcelona Barcelona Spain
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y la Nutrición (CIBERObn) Instituto de Salud Carlos III (ISCIII) Madrid Spain
| | - Anallely López‐Yerena
- Department of Nutrition, Food Science and Gastronomy, XIA, Faculty of Pharmacy and Food Sciences Institute of Nutrition and Food Safety (INSA‐UB) University of Barcelona Barcelona Spain
| | - Inés Domínguez‐López
- Department of Nutrition, Food Science and Gastronomy, XIA, Faculty of Pharmacy and Food Sciences Institute of Nutrition and Food Safety (INSA‐UB) University of Barcelona Barcelona Spain
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y la Nutrición (CIBERObn) Instituto de Salud Carlos III (ISCIII) Madrid Spain
| | - Aina Siscart‐Serra
- Department of Nutrition, Food Science and Gastronomy, XIA, Faculty of Pharmacy and Food Sciences Institute of Nutrition and Food Safety (INSA‐UB) University of Barcelona Barcelona Spain
| | - Nathalia Fraga
- Department of Nutrition, Food Science and Gastronomy, XIA, Faculty of Pharmacy and Food Sciences Institute of Nutrition and Food Safety (INSA‐UB) University of Barcelona Barcelona Spain
| | - Samantha Sámano
- Department of Nutrition, Food Science and Gastronomy, XIA, Faculty of Pharmacy and Food Sciences Institute of Nutrition and Food Safety (INSA‐UB) University of Barcelona Barcelona Spain
| | - Carmen López‐Sabater
- Department of Nutrition, Food Science and Gastronomy, XIA, Faculty of Pharmacy and Food Sciences Institute of Nutrition and Food Safety (INSA‐UB) University of Barcelona Barcelona Spain
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y la Nutrición (CIBERObn) Instituto de Salud Carlos III (ISCIII) Madrid Spain
| | - Rosa M Lamuela‐Raventós
- Department of Nutrition, Food Science and Gastronomy, XIA, Faculty of Pharmacy and Food Sciences Institute of Nutrition and Food Safety (INSA‐UB) University of Barcelona Barcelona Spain
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y la Nutrición (CIBERObn) Instituto de Salud Carlos III (ISCIII) Madrid Spain
| | - Anna Vallverdú‐Queralt
- Department of Nutrition, Food Science and Gastronomy, XIA, Faculty of Pharmacy and Food Sciences Institute of Nutrition and Food Safety (INSA‐UB) University of Barcelona Barcelona Spain
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y la Nutrición (CIBERObn) Instituto de Salud Carlos III (ISCIII) Madrid Spain
| | - Maria Pérez
- Department of Nutrition, Food Science and Gastronomy, XIA, Faculty of Pharmacy and Food Sciences Institute of Nutrition and Food Safety (INSA‐UB) University of Barcelona Barcelona Spain
- Laboratory of Organic Chemistry, Faculty of Pharmacy and Food Sciences University of Barcelona Barcelona Spain
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6
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Rifna EJ, Pandiselvam R, Kothakota A, Subba Rao KV, Dwivedi M, Kumar M, Thirumdas R, Ramesh SV. Advanced process analytical tools for identification of adulterants in edible oils - A review. Food Chem 2022; 369:130898. [PMID: 34455326 DOI: 10.1016/j.foodchem.2021.130898] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 07/16/2021] [Accepted: 08/16/2021] [Indexed: 12/16/2022]
Abstract
This review summarizes the use of spectroscopic processes-based analytical tools coupled with chemometric techniques for the identification of adulterants in edible oil. Investigational approaches of process analytical tools such asspectroscopy techniques, nuclear magnetic resonance (NMR), hyperspectral imaging (HSI), e-tongue and e-nose combined with chemometrics were used to monitor quality of edible oils. Owing to the variety and intricacy of edible oil properties along with the alterations in attributes of the PAT tools, the reliability of the tool used and the operating factors are the crucial components which require attention to enhance the efficiency in identification of adulterants. The combination of process analytical tools with chemometrics offers a robust technique with immense chemotaxonomic potential. These involves identification of adulterants, quality control, geographical origin evaluation, process evaluation, and product categorization.
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Affiliation(s)
- E J Rifna
- Department of Food Process Engineering, National Institute of Technology, Rourkela 769008, Odisha, India
| | - R Pandiselvam
- Physiology, Biochemistry and Post-Harvest Technology Division, ICAR - Central Plantation Crops Research Institute, Kasaragod 671 124, Kerala, India.
| | - Anjineyulu Kothakota
- Agro-Processing & Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Trivandrum 695 019, Kerala, India.
| | - K V Subba Rao
- Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Madhuresh Dwivedi
- Department of Food Process Engineering, National Institute of Technology, Rourkela 769008, Odisha, India
| | - Manoj Kumar
- Chemical and Biochemical Processing Division, ICAR-Central Institute for Research on Cotton Technology, Matunga, Mumbai 400019, India
| | - Rohit Thirumdas
- Department of Food Process Technology, College of Food Science and Technology, PJTSAU, Telangana, India
| | - S V Ramesh
- Physiology, Biochemistry and Post-Harvest Technology Division, ICAR - Central Plantation Crops Research Institute, Kasaragod 671 124, Kerala, India
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7
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Lebanov L, Paull B. Smartphone-based handheld Raman spectrometer and machine learning for essential oil quality evaluation. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2021; 13:4055-4062. [PMID: 34554153 DOI: 10.1039/d1ay00886b] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
We present a method, utilising a smartphone-based miniaturized Raman spectrometer and machine learning for the fast identification and discrimination of adulterated essential oils (EOs). Firstly, the approach was evaluated for discrimination of pure EOs from those adulterated with solvent, namely benzyl alcohol. In the case of ylang-ylang EO, three different types of adulteration were examined, adulteration with solvent, cheaper vegetable oil and a lower price EO. Random Forest and partial least square discrimination analysis (PLS-DA) showed excellent performance in discriminating pure from adulterated EOs, whilst the same time identifying the type of adulteration. Also, utilising partial least squares regression analysis (PLS) all adulterants, namely benzyl alcohol, vegetable oil and lower price EO, were quantified based on spectra recorded using the smartphone Raman spectrometer, with relative error of prediction (REP) being between 2.41-7.59%.
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Affiliation(s)
- Leo Lebanov
- Australian Centre for Research on Separation Science (ACROSS), School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia.
- ARC Industrial Transformation Research Hub for Processing Advanced Lignocellulosics Products (PALs), School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia
| | - Brett Paull
- Australian Centre for Research on Separation Science (ACROSS), School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia.
- ARC Industrial Transformation Research Hub for Processing Advanced Lignocellulosics Products (PALs), School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia
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8
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Wu X, Bian X, Lin E, Wang H, Guo Y, Tan X. Weighted multiscale support vector regression for fast quantification of vegetable oils in edible blend oil by ultraviolet-visible spectroscopy. Food Chem 2020; 342:128245. [PMID: 33069537 DOI: 10.1016/j.foodchem.2020.128245] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 08/30/2020] [Accepted: 09/26/2020] [Indexed: 12/20/2022]
Abstract
Weighted multiscale support vector regression combined with ultraviolet-visible (UV-Vis) spectra for quantitative analysis of edible blend oil is proposed. In the approach, UV-Vis spectra of the training set are decomposed into a certain number of intrinsic mode functions (IMFs) and a residue by empirical mode decomposition (EMD) at first. Then support vector regression (SVR) sub-models are built on each IMF and residue. For prediction set, the spectra are decomposed as done on the training set and the final predictions are obtained by integrating SVR sub-model predictions by weighted average. The weight of the sub-model is the reciprocal of the fourth power of the root mean square error of cross-validation (RMSECV). For predicting peanut oil in binary blend oil and sesame oil in ternary blend oil, the proposed method has superiority in root mean square error of prediction (RMSEP) and correlation coefficient (R) compared with SVR and partial least squares (PLS).
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Affiliation(s)
- Xinyan Wu
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, People's Republic of China; School of Environmental Science and Engineering, Tiangong University, Tianjin 300387, People's Republic of China
| | - Xihui Bian
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, People's Republic of China; School of Chemistry and Chemical Engineering, Tiangong University, Tianjin 300387, People's Republic of China.
| | - En Lin
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, People's Republic of China; School of Chemistry and Chemical Engineering, Tiangong University, Tianjin 300387, People's Republic of China
| | - Haitao Wang
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, People's Republic of China; School of Environmental Science and Engineering, Tiangong University, Tianjin 300387, People's Republic of China
| | - Yugao Guo
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, People's Republic of China; School of Chemistry and Chemical Engineering, Tiangong University, Tianjin 300387, People's Republic of China
| | - Xiaoyao Tan
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, People's Republic of China; School of Chemistry and Chemical Engineering, Tiangong University, Tianjin 300387, People's Republic of China.
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9
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Meenu M, Cai Q, Xu B. A critical review on analytical techniques to detect adulteration of extra virgin olive oil. Trends Food Sci Technol 2019. [DOI: 10.1016/j.tifs.2019.07.045] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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10
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Linear and non-linear quantification of extra virgin olive oil, soybean oil, and sweet almond oil in blends to assess their commercial labels. J Food Compost Anal 2019. [DOI: 10.1016/j.jfca.2018.09.010] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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11
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Lastra-Mejías M, Torreblanca-Zanca A, Aroca-Santos R, Cancilla JC, Izquierdo J, Torrecilla JS. Characterization of an array of honeys of different types and botanical origins through fluorescence emission based on LEDs. Talanta 2018; 185:196-202. [DOI: 10.1016/j.talanta.2018.03.060] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 03/17/2018] [Accepted: 03/21/2018] [Indexed: 11/29/2022]
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12
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Garrido-Delgado R, Eugenia Muñoz-Pérez M, Arce L. Detection of adulteration in extra virgin olive oils by using UV-IMS and chemometric analysis. Food Control 2018. [DOI: 10.1016/j.foodcont.2017.10.012] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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13
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Durán Merás I, Domínguez Manzano J, Airado Rodríguez D, Muñoz de la Peña A. Detection and quantification of extra virgin olive oil adulteration by means of autofluorescence excitation-emission profiles combined with multi-way classification. Talanta 2018; 178:751-762. [DOI: 10.1016/j.talanta.2017.09.095] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Revised: 09/27/2017] [Accepted: 09/30/2017] [Indexed: 11/27/2022]
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14
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Mbogning Feudjio W, Ghalila H, Nsangou M, Majdi Y, Mbesse Kongbonga Y, Jaïdane N. Fluorescence Spectroscopy Combined with Chemometrics for the Investigation of the Adulteration of Essential Oils. FOOD ANAL METHOD 2017. [DOI: 10.1007/s12161-017-0823-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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15
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Aroca-Santos R, Cancilla JC, Pariente ES, Torrecilla JS. Neural networks applied to characterize blends containing refined and extra virgin olive oils. Talanta 2016; 161:304-308. [PMID: 27769410 DOI: 10.1016/j.talanta.2016.08.033] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2016] [Revised: 08/07/2016] [Accepted: 08/10/2016] [Indexed: 12/18/2022]
Abstract
The identification and quantification of binary blends of refined olive oil with four different extra virgin olive oil (EVOO) varietals (Picual, Cornicabra, Hojiblanca and Arbequina) was carried out with a simple method based on combining visible spectroscopy and non-linear artificial neural networks (ANNs). The data obtained from the spectroscopic analysis was treated and prepared to be used as independent variables for a multilayer perceptron (MLP) model. The model was able to perfectly classify the EVOO varietal (100% identification rate), whereas the error for the quantification of EVOO in the mixtures containing between 0% and 20% of refined olive oil, in terms of the mean prediction error (MPE), was 2.14%. These results turn visible spectroscopy and MLP models into a trustworthy, user-friendly, low-cost technique which can be implemented on-line to characterize olive oil mixtures containing refined olive oil and EVOOs.
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Affiliation(s)
- Regina Aroca-Santos
- Departamento de Ingeniería Química, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, Madrid 28040, Spain
| | - John C Cancilla
- Departamento de Ingeniería Química, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, Madrid 28040, Spain
| | - Enrique S Pariente
- Departamento de Ingeniería Química, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, Madrid 28040, Spain
| | - José S Torrecilla
- Departamento de Ingeniería Química, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, Madrid 28040, Spain.
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