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Jiménez-Hernández G, Ortega-Gavilán F, Bagur-González MG, González-Casado A. Discrimination/Classification of Edible Vegetable Oils from Raman Spatially Solved Fingerprints Obtained on Portable Instrumentation. Foods 2024; 13:183. [PMID: 38254484 PMCID: PMC10814980 DOI: 10.3390/foods13020183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/29/2023] [Accepted: 12/29/2023] [Indexed: 01/24/2024] Open
Abstract
Currently, the combination of fingerprinting methodology and environmentally friendly and economical analytical instrumentation is becoming increasingly relevant in the food sector. In this study, a highly versatile portable analyser based on Spatially Offset Raman Spectroscopy (SORS) obtained fingerprints of edible vegetable oils (sunflower and olive oils), and the capability of such fingerprints (obtained quickly, reliably and without any sample treatment) to discriminate/classify the analysed samples was evaluated. After data treatment, not only unsupervised pattern recognition techniques (as HCA and PCA), but also supervised pattern recognition techniques (such as SVM, kNN and SIMCA), showed that the main effect on discrimination/classification was associated with those regions of the Raman fingerprint related to free fatty acid content, especially oleic and linoleic acid. These facts allowed the discernment of the original raw material used in the oil's production. In all the models established, reliable qualimetric parameters were obtained.
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Affiliation(s)
- Guillermo Jiménez-Hernández
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/Fuentenueva w/n, 18071 Granada, Spain; (G.J.-H.); (A.G.-C.)
| | - Fidel Ortega-Gavilán
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/Fuentenueva w/n, 18071 Granada, Spain; (G.J.-H.); (A.G.-C.)
- Animal Health Central Laboratory (LCSA), Department of Chemical Analysis of Residues, Ministry of Agriculture, Fisheries and Food, Camino del Jau w/n, 18320 Santa Fe, Spain
| | - M. Gracia Bagur-González
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/Fuentenueva w/n, 18071 Granada, Spain; (G.J.-H.); (A.G.-C.)
| | - Antonio González-Casado
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/Fuentenueva w/n, 18071 Granada, Spain; (G.J.-H.); (A.G.-C.)
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2
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Geng D, Chen X, Lu D, Chen B. Discrimination of different edible vegetable oils based on GC-IMS and SIMCA. CYTA - JOURNAL OF FOOD 2023. [DOI: 10.1080/19476337.2022.2160827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Dechun Geng
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Xinyu Chen
- Department of Physical Chemistry, University of Duisburg-Essen, Essen, Germany
| | - Daoli Lu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Bin Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
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3
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Arroyo-Cerezo A, Jimenez-Carvelo AM, Gonzalez-Casado A, Ruisanchez I, Cuadros-Rodriguez L. The potential of the spatially offset Raman spectroscopy (SORS) for implementing rapid and non-invasive in-situ authentication methods of plastic-packaged commodity foods – Application to sliced cheeses. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109522] [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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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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5
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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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6
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Sudhakar A, Chakraborty SK, Mahanti NK, Varghese C. Advanced techniques in edible oil authentication: A systematic review and critical analysis. Crit Rev Food Sci Nutr 2021; 63:873-901. [PMID: 34347552 DOI: 10.1080/10408398.2021.1956424] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Adulteration of edible substances is a potent contemporary food safety issue. Perhaps the overt concern derives from the fact that adulterants pose serious ill effects on human health. Edible oils are one of the most adulterated food products. Perpetrators are adopting ways and means that effectively masks the presence of the adulterants from human organoleptic limits and traditional oil adulteration detection techniques. This review embodies a detailed account of chemical, biosensors, chromatography, spectroscopy, differential scanning calorimetry, non-thermal plasma, dielectric spectroscopy research carried out in the area of falsification assessment of edible oils for the past three decades and a collection of patented oil adulteration detection techniques. The detection techniques reviewed have some advantages and certain limitations, chemical tests are simple; biosensors and nuclear magnetic resonance are rapid but have a low sensitivity; chromatography and spectroscopy are highly accurate with a deterring price tag; dielectric spectroscopy is rapid can be portable and has on-line compatibility; however, the results are susceptible to variation of electric current frequency and intrinsic factors (moisture, temperature, structural composition). This review paper can be useful for scientists or for knowledge seekers eager to be abreast with edible oil adulteration detection techniques.
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Affiliation(s)
- Anjali Sudhakar
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Bhopal, India
| | - Subir Kumar Chakraborty
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Bhopal, India
| | - Naveen Kumar Mahanti
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Bhopal, India
| | - Cinu Varghese
- Rural Development Centre, Indian Institute of Technology, Kharagpur, India
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7
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Soltani Firouz M, Rashvand M, Omid M. Rapid identification and quantification of sesame oils adulteration using low frequency dielectric spectroscopy combined with chemometrics. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2020.110736] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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8
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Oliveri P, Malegori C, Mustorgi E, Casale M. Qualitative pattern recognition in chemistry: Theoretical background and practical guidelines. Microchem J 2021. [DOI: 10.1016/j.microc.2020.105725] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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9
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A Sensor-Based Methodology to Differentiate Pure and Mixed White Tequilas Based on Fused Infrared Spectra and Multivariate Data Treatment. CHEMOSENSORS 2021. [DOI: 10.3390/chemosensors9030047] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Mexican Tequila is one of the most demanded import spirits in Europe. Its fast-raising worldwide request makes counterfeiting a profitable activity affecting both consumers and legal distillers. In this paper, a sensor-based methodology based on a combination of infrared measurements (IR) and multivariate data analysis (MVA) is presented. The case study is about differentiating two categories of white Tequila: pure Tequila (or ‘100% agave’) and mixed Tequila (or simply, Tequila). The IR spectra were treated and fused with a low-level approach. Exploratory data analysis was performed using PCA and partial least squares (PLS), whilst the authentication analyses were carried out with PLS-discriminant analysis (DA) and soft independent modeling for class analogy (SIMCA) models. Results demonstrated that data fusion of IR spectra enhanced the outcomes of the authentication models capable of differentiating pure from mixed Tequilas. In fact, PLS-DA presented the best results which correctly classified all fifteen commercial validation samples. The methodology thus presented is fast, cheap, and of simple application in the Tequila industry.
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10
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Characterization and authentication of olive, camellia and other vegetable oils by combination of chromatographic and chemometric techniques: role of fatty acids, tocopherols, sterols and squalene. Eur Food Res Technol 2020. [DOI: 10.1007/s00217-020-03635-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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11
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Rocha WFDC, do Prado CB, Blonder N. Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods. Molecules 2020; 25:E3025. [PMID: 32630676 PMCID: PMC7411792 DOI: 10.3390/molecules25133025] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 06/25/2020] [Accepted: 06/29/2020] [Indexed: 11/16/2022] Open
Abstract
Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.
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Affiliation(s)
- Werickson Fortunato de Carvalho Rocha
- National Institute of Metrology, Quality and Technology (INMETRO), Av. N. S. das Graças, 50, Xerém, Duque de Caxias 25250-020, RJ, Brazil; (W.F.C.R.); (C.B.d.P.)
- National Institute of Standards and Technology (NIST), 100 Bureau Drive, Stop 8390 Gaithersburg, MD 20899, USA
| | - Charles Bezerra do Prado
- National Institute of Metrology, Quality and Technology (INMETRO), Av. N. S. das Graças, 50, Xerém, Duque de Caxias 25250-020, RJ, Brazil; (W.F.C.R.); (C.B.d.P.)
| | - Niksa Blonder
- National Institute of Standards and Technology (NIST), 100 Bureau Drive, Stop 8390 Gaithersburg, MD 20899, USA
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12
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Identification and quantification of counterfeit sesame oil by 3D fluorescence spectroscopy and convolutional neural network. Food Chem 2020; 311:125882. [DOI: 10.1016/j.foodchem.2019.125882] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 10/23/2019] [Accepted: 11/08/2019] [Indexed: 12/20/2022]
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13
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Bikrani S, Jiménez-Carvelo AM, Nechar M, Bagur-González MG, Souhail B, Cuadros-Rodríguez L. Authentication of the Geographical Origin of Margarines and Fat-Spread Products from Liquid Chromatographic UV-Absorption Fingerprints and Chemometrics. Foods 2019; 8:E588. [PMID: 31752349 PMCID: PMC6915439 DOI: 10.3390/foods8110588] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 11/13/2019] [Accepted: 11/16/2019] [Indexed: 11/25/2022] Open
Abstract
Fat-spread products are a stabilized emulsion of water and vegetable oils. The whole fat content can vary from 10 to 90% (w/w). There are different kinds, which are differently named, and their composition depends on the country in which they are produced or marketed. Thus, having analytical solutions to determine geographical origin is required. In this study, some multivariate classification methods are developed and optimised to differentiate fat-spread-related products from different geographical origins (Spain and Morocco), using as an analytical informative signal the instrumental fingerprints, acquired by liquid chromatography coupled with a diode array detector (HPLC-DAD) in both normal and reverse phase modes. No sample treatment was applied, and, prior to chromatographic analysis, only the samples were dissolved in n‑hexane. Soft independent modelling of class analogy (SIMCA) and partial least squares-discriminant analysis (PLS-DA) were used as classification methods. In addition, several classification strategies were applied, and performance of the classifications was evaluated applying proper classification metrics. Finally, 100% of samples were correctly classified applying PLS-DA with data collected in reverse phase.
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Affiliation(s)
- Sanae Bikrani
- Department of Chemistry, Faculty of Sciences, University of Abdelmalek Essaâdi, Av. Sebta, Mhannech II, 93002 Tetouan, Morocco; (S.B.); (M.N.); (B.S.)
| | - Ana M. Jiménez-Carvelo
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, C/ Fuentenueva, s/n, E-18071 Granada, Spain; (M.G.B.-G.); (L.C.-R.)
| | - Mounir Nechar
- Department of Chemistry, Faculty of Sciences, University of Abdelmalek Essaâdi, Av. Sebta, Mhannech II, 93002 Tetouan, Morocco; (S.B.); (M.N.); (B.S.)
| | - M. Gracia Bagur-González
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, C/ Fuentenueva, s/n, E-18071 Granada, Spain; (M.G.B.-G.); (L.C.-R.)
| | - Badredine Souhail
- Department of Chemistry, Faculty of Sciences, University of Abdelmalek Essaâdi, Av. Sebta, Mhannech II, 93002 Tetouan, Morocco; (S.B.); (M.N.); (B.S.)
| | - Luis Cuadros-Rodríguez
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, C/ Fuentenueva, s/n, E-18071 Granada, Spain; (M.G.B.-G.); (L.C.-R.)
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14
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Authentication of the geographical origin of extra-virgin olive oil of the Arbequina cultivar by chromatographic fingerprinting and chemometrics. Talanta 2019; 203:194-202. [DOI: 10.1016/j.talanta.2019.05.064] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 05/08/2019] [Accepted: 05/13/2019] [Indexed: 12/12/2022]
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15
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Musa M, Ayoko GA, Ward A, Rösch C, Brown RJ, Rainey TJ. Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity. Cells 2019; 8:E851. [PMID: 31394865 PMCID: PMC6721732 DOI: 10.3390/cells8080851] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 07/26/2019] [Accepted: 08/02/2019] [Indexed: 12/04/2022] Open
Abstract
Microalgae are swift replicating photosynthetic microorganisms with several applications for food, chemicals, medicine and fuel. Microalgae have been identified to be suitable for biofuels production, due to their high lipid contents. Microalgae-based biofuels have the potential to meet the increasing energy demands and reduce greenhouse gas (GHG) emissions. However, the present state of technology does not economically support sustainable large-scale production. The biofuel production process comprises the upstream and downstream processing phases, with several uncertainties involved. This review examines the various production and processing stages, and considers the use of chemometric methods in identifying and understanding relationships from measured study parameters via statistical methods, across microalgae production stages. This approach enables collection of relevant information for system performance assessment. The principal benefit of such analysis is the identification of the key contributing factors, useful for decision makers to improve system design, operation and process economics. Chemometrics proffers options for time saving in data analysis, as well as efficient process optimization, which could be relevant for the continuous growth of the microalgae industry.
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Affiliation(s)
- Mutah Musa
- Biofuel Engine Research Facility, School of Chemistry, Physics and Mechanical Engineering, Science and Engineering Faculty, Queensland University of Technology (QUT), Queensland 4000, Australia.
| | - Godwin A Ayoko
- Environmental Technologies Discipline, School of Chemistry, Physics and Mechanical Engineering, Science and Engineering Faculty, Queensland University of Technology, Queensland 4000, Australia
| | - Andrew Ward
- Queensland Urban Utilities (QUU), Innovation Centre, Main Beach Road Myrtletown QLD 4008, Australia
- Advanced Water Management Centre (AWMC), University of Queensland (UQ), St Lucia, Brisbane, Queensland 4072, Australia
| | - Christine Rösch
- Institute for Technology Assessment and Systems Analysis (ITAS), Karlsruhe Institute of Technology, 76021 Karlsruhe, Germany
| | - Richard J Brown
- Biofuel Engine Research Facility, School of Chemistry, Physics and Mechanical Engineering, Science and Engineering Faculty, Queensland University of Technology (QUT), Queensland 4000, Australia
| | - Thomas J Rainey
- Biofuel Engine Research Facility, School of Chemistry, Physics and Mechanical Engineering, Science and Engineering Faculty, Queensland University of Technology (QUT), Queensland 4000, Australia.
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16
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Esteki M, Shahsavari Z, Simal-Gandara J. Food identification by high performance liquid chromatography fingerprinting and mathematical processing. Food Res Int 2019; 122:303-317. [DOI: 10.1016/j.foodres.2019.04.025] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 04/09/2019] [Accepted: 04/10/2019] [Indexed: 01/31/2023]
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17
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Jiménez-Carvelo AM, González-Casado A, Bagur-González MG, Cuadros-Rodríguez L. Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review. Food Res Int 2019; 122:25-39. [PMID: 31229078 DOI: 10.1016/j.foodres.2019.03.063] [Citation(s) in RCA: 123] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 03/25/2019] [Accepted: 03/26/2019] [Indexed: 12/31/2022]
Abstract
In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.
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Affiliation(s)
- Ana M Jiménez-Carvelo
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain.
| | - Antonio González-Casado
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain
| | - M Gracia Bagur-González
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain
| | - Luis Cuadros-Rodríguez
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain
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18
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Jiménez-Carvelo AM, Cruz CM, Olivieri AC, González-Casado A, Cuadros-Rodríguez L. Classification of olive oils according to their cultivars based on second-order data using LC-DAD. Talanta 2018; 195:69-76. [PMID: 30625602 DOI: 10.1016/j.talanta.2018.11.033] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 11/06/2018] [Accepted: 11/09/2018] [Indexed: 11/26/2022]
Abstract
Second-order data acquired using liquid chromatography coupled to a diode array detector were used to classify extra virgin olive oils samples according to their cultivars. The chromatographic fingerprints from the epoxidised fraction were obtained using normal-phase liquid chromatography. To reduce the data matrices two strategies were employed: (1) multivariate curve resolution-alternating least squares (MCR-ALS) and (2) a new strategy proposed in this work based on the fusion of the mean data profiles in both spectral and time domains. Several conventional chemometric tools were then applied to both raw and reduced data: principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA), soft independent modelling of class analogies (SIMCA) and n-way partial least-squares-discriminant analysis (NPLS-DA). Furthermore, an emergent multivariate classification method known as random forest (RF) has been first applied to second-order data. It was shown that RF is more efficient than conventional tools. Indeed, the obtained sensibility, specificity and accuracy are 1.00, 0.92 and 0.95 respectively; these performance metrics are significantly better than the values found for the other methods.
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Affiliation(s)
- Ana M Jiménez-Carvelo
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, C/ Fuentenueva s/n, E-18071, Granada, Spain.
| | - Carlos M Cruz
- Department of Organic Chemistry, Faculty of Sciences, University of Granada, C/ Fuentenueva s/n, E-18071, Granada, Spain
| | - Alejandro C Olivieri
- Instituto de Química Rosario (IQUIR-CONICET), Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Suipacha 531, S2002LRK, Rosario, Argentina
| | - Antonio González-Casado
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, C/ Fuentenueva s/n, E-18071, Granada, Spain
| | - Luis Cuadros-Rodríguez
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, C/ Fuentenueva s/n, E-18071, Granada, Spain
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19
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A review on the application of chromatographic methods, coupled to chemometrics, for food authentication. Food Control 2018. [DOI: 10.1016/j.foodcont.2018.06.015] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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20
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Discrimination and classification of extra virgin olive oil using a chemometric approach based on TMS-4,4'-desmetylsterols GC(FID) fingerprints of edible vegetable oils. Food Chem 2018; 274:518-525. [PMID: 30372973 DOI: 10.1016/j.foodchem.2018.08.128] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 08/23/2018] [Accepted: 08/28/2018] [Indexed: 11/23/2022]
Abstract
A single out-line HPLC-GC (FID) analytical method is applied to acquire the chromatographic fingerprint characteristic of the TMS-4,4'-desmetylsterol derivative fraction of several marketed edible vegetable oils in order to identify and discriminate the most valuable extra-virgin olive oils from the other vegetal oils (canola, corn, grape seed, linseed, olive pomace, peanut, rapeseed, soybean, sesame, seeds (non-specified composition but usually a blend of corn and sunflower) and sunflower). The natural structure of the preprocessed data undergoes a preliminary exploration using principal component analysis and heat map-based cluster analysis. A partial least squares-discriminant model is first trained from 53 oil samples (only 3 latent variables) and externally validated from 18 test oil samples. No classification errors are found and all the test samples are correctly classified. Additional classification models are also built in order to discriminate among vegetables-oil families and excellent results have been also achieved.
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Quantification of extra virgin olive oil in dressing and edible oil blends using the representative TMS-4,4′-desmethylsterols gas-chromatographic-normalized fingerprint. Food Chem 2018; 239:1192-1199. [DOI: 10.1016/j.foodchem.2017.07.069] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Revised: 07/07/2017] [Accepted: 07/13/2017] [Indexed: 11/17/2022]
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Chemometric classification and quantification of olive oil in blends with any edible vegetable oils using FTIR-ATR and Raman spectroscopy. Lebensm Wiss Technol 2017. [DOI: 10.1016/j.lwt.2017.07.050] [Citation(s) in RCA: 79] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Milanez KDTM, Nóbrega TCA, Nascimento DS, Insausti M, Band BSF, Pontes MJC. Multivariate modeling for detecting adulteration of extra virgin olive oil with soybean oil using fluorescence and UV–Vis spectroscopies: A preliminary approach. Lebensm Wiss Technol 2017. [DOI: 10.1016/j.lwt.2017.06.060] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Obisesan KA, Jiménez-Carvelo AM, Cuadros-Rodriguez L, Ruisánchez I, Callao MP. HPLC-UV and HPLC-CAD chromatographic data fusion for the authentication of the geographical origin of palm oil. Talanta 2017; 170:413-418. [DOI: 10.1016/j.talanta.2017.04.035] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 04/06/2017] [Accepted: 04/11/2017] [Indexed: 11/25/2022]
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