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Johnson NAN, Adade SYSS, Haruna SA, Ekumah JN, Ma Y. Quantitative assessment of phytochemicals in chickpea beverages using NIR spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 307:123623. [PMID: 37989004 DOI: 10.1016/j.saa.2023.123623] [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: 06/12/2023] [Revised: 09/17/2023] [Accepted: 11/04/2023] [Indexed: 11/23/2023]
Abstract
The prospects of near-infrared (NIR) spectroscopy combined with effective variable selection algorithms for quantifying phytochemical compounds in chickpea beverages were investigated in this study. As reference measurement analysis, the phytochemicals were extracted and identified via high-performance liquid chromatography. Multivariate algorithms were then applied, analyzed, and evaluated using correlation coefficients of validation set (Rp), root mean square error of prediction (RMSEP), and residual predictive deviations (RPDs). Accordingly, the competitive adaptive reweighted sampling-partial least squares (CARS-PLS) model achieved superior performance for biochanin A (Rp = 0.933, RPD = 3.63), chlorogenic acid (Rp = 0.928, RPD = 3.52), p-coumaric acid (Rp = 0.900, RPD = 2.37), and stigmasterol (Rp = 0.932, RPD = 3.15), respectively. Hence, this study demonstrated that NIR spectroscopy paired with CARS-PLS could be used for nondestructive quantitative prediction of phytochemicals in chickpea beverages during manufacture and storage.
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Affiliation(s)
- Nana Adwoa Nkuma Johnson
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China; School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China
| | - Selorm Yao-Say Solomon Adade
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China; School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China; Department of Nutrition and Dietetics, Ho Teaching Hospital, Ho, Ghana.
| | - Suleiman A Haruna
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China; Department of Food Science and Technology, Kano University of Science andTechnology, Wudil, P.M.B 3244 Kano, Kano State, Nigeria
| | - John-Nelson Ekumah
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China; Department of Nutrition and Food Science, College of Basic and Applied Sciences, University of Ghana, P. O. Box LG 134, Legon, Ghana
| | - Yongkun Ma
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China.
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Ortiz-Romero C, Ríos-Reina R, García-González DL, Cardador MJ, Callejón RM, Arce L. Comparing the potential of IR-spectroscopic techniques to gas chromatography coupled to ion mobility spectrometry for classifying virgin olive oil categories. Food Chem X 2023; 19:100738. [PMID: 37389321 PMCID: PMC10300311 DOI: 10.1016/j.fochx.2023.100738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 05/24/2023] [Accepted: 06/02/2023] [Indexed: 07/01/2023] Open
Abstract
Virgin olive oil (OO) can be classified into three different categories: extra virgin, virgin and lampante. The official method for this classification, based on physicochemical analysis and sensory tasting, is considered useful and effective, although it is a costly and time-consuming process. The aim of this study was to assess the potential of some analytical techniques for classifying and predicting different OO categories to support official methods and to provide olive oil companies with a rapid tool to assess product quality. Thus, mid and near infrared spectroscopies (MIR and NIR) have been compared by using different instruments and with head-space gas chromatography coupled to an ion mobility spectrometer (HS-GC-IMS). High classification success rates in validation models were obtained using IR spectrometers (>70% and > 80% in average for ternary and binary classifications, respectively), although HS-GC-IMS showed greater classification potential (>85% and > 90%).
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Affiliation(s)
- Clemente Ortiz-Romero
- Department of Analytical Chemistry, Campus of International Excellence in Agrifood (ceiA3), Marie Curie Annex Building, University of Córdoba, Campus de Rabanales, E-14071 Córdoba, Spain
| | - Rocío Ríos-Reina
- Dpto. de Nutrición y Bromatología, Toxicología y Medicina Legal, Facultad de Farmacia, Universidad de Sevilla, C/P. García González n°2, E-41012 Sevilla, Spain
| | | | - María José Cardador
- Department of Analytical Chemistry, Campus of International Excellence in Agrifood (ceiA3), Marie Curie Annex Building, University of Córdoba, Campus de Rabanales, E-14071 Córdoba, Spain
| | - Raquel M Callejón
- Dpto. de Nutrición y Bromatología, Toxicología y Medicina Legal, Facultad de Farmacia, Universidad de Sevilla, C/P. García González n°2, E-41012 Sevilla, Spain
| | - Lourdes Arce
- Department of Analytical Chemistry, Campus of International Excellence in Agrifood (ceiA3), Marie Curie Annex Building, University of Córdoba, Campus de Rabanales, E-14071 Córdoba, Spain
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3
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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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Moe Htet TT, Cruz J, Khongkaew P, Suwanvecho C, Suntornsuk L, Nuchtavorn N, Limwikrant W, Phechkrajang C. PLS-regression-model-assisted raman spectroscopy for vegetable oil classification and non-destructive analysis of alpha-tocopherol contents of vegetable oils. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.104119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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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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Jurado-Campos N, Rodríguez-Gómez R, Arroyo-Manzanares N, Arce L. Instrumental Techniques to Classify Olive Oils according to Their Quality. Crit Rev Anal Chem 2021; 53:139-160. [PMID: 34260314 DOI: 10.1080/10408347.2021.1940829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
This review includes an update of the publications on quality classification of olive oils into extra, virgin or lampante olive oil categories. Nowadays, the official method to carry out this classification is time-consuming and, sometimes, it is not systematic and/or objective. It is based on conventional physicochemical analysis and on a sensorial tasting of olive oils carried out by a panel of experts. The aim of this review was to explore and give value to the alternative techniques reported in the bibliography to complement the current official methods established for that classification of olive oils. Specifically considered were non-separation and separation analytical techniques which could contribute to correctly classify olive oils according to their physicochemical and/or sensorial characteristics. An in-depth description has been written on the methods used to differentiate these three types of olive oils and the main advantages and disadvantages of the proposed procedures. The techniques here reviewed could be a real and fast option to complement or even substitute some of the analysis included in the official method. Finally, general trends and detected difficulties found to address this issue have been discussed throughout the article.
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Affiliation(s)
- Natividad Jurado-Campos
- Department of Analytical Chemistry, Institute of Fine Chemistry and Nanochemistry, International Agrifood Campus of Excellence (ceiA3), University of Córdoba, Córdoba, Spain
| | - Rocío Rodríguez-Gómez
- Department of Analytical Chemistry, Institute of Fine Chemistry and Nanochemistry, International Agrifood Campus of Excellence (ceiA3), University of Córdoba, Córdoba, Spain
| | - Natalia Arroyo-Manzanares
- Department of Analytical Chemistry, Faculty of Chemistry, Regional Campus of International Excellence "Campus Mare-Nostrum", University of Murcia, Murcia, Spain
| | - Lourdes Arce
- Department of Analytical Chemistry, Institute of Fine Chemistry and Nanochemistry, International Agrifood Campus of Excellence (ceiA3), University of Córdoba, Córdoba, Spain
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8
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Authentication of the Botanical and Geographical Origin and Detection of Adulteration of Olive Oil Using Gas Chromatography, Infrared and Raman Spectroscopy Techniques: A Review. Foods 2021; 10:foods10071565. [PMID: 34359435 PMCID: PMC8306465 DOI: 10.3390/foods10071565] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/01/2021] [Accepted: 07/02/2021] [Indexed: 01/18/2023] Open
Abstract
Olive oil is among the most popular supplements of the Mediterranean diet due to its high nutritional value. However, at the same time, because of economical purposes, it is also one of the products most subjected to adulteration. As a result, authenticity is an important issue of concern among authorities. Many analytical techniques, able to detect adulteration of olive oil, to identify its geographical and botanical origin and consequently guarantee its quality and authenticity, have been developed. This review paper discusses the use of infrared and Raman spectroscopy as candidate tools to examine the authenticity of olive oils. It also considers the volatile fraction as a marker to distinguish between different varieties and adulterated olive oils, using SPME combined with gas chromatography technique.
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9
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Abamba Omwange K, Al Riza DF, Saito Y, Suzuki T, Ogawa Y, Shiraga K, Giametta F, Kondo N. Potential of front face fluorescence spectroscopy and fluorescence imaging in discriminating adulterated extra-virgin olive oil with virgin olive oil. Food Control 2021. [DOI: 10.1016/j.foodcont.2021.107906] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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10
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Azizian H, Dugan MER, Kramer JKG. Clarifications of the Carbonyl and Water Absorptions in Fourier Transform Near Infrared Spectra from Extra Virgin Olive Oil. EUR J LIPID SCI TECH 2021. [DOI: 10.1002/ejlt.202000288] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | - Michael E. R. Dugan
- Lacombe Research and Development Centre Agriculture and Agri‐Food Canada Lacombe Alberta T4L 1W1 Canada
| | - John K. G. Kramer
- Research and Development Center Agriculture and Agri‐Food Canada Guelph Ontario N1G 5C9 Canada
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11
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State-of-the-Art of Analytical Techniques to Determine Food Fraud in Olive Oils. Foods 2021; 10:foods10030484. [PMID: 33668346 PMCID: PMC7996354 DOI: 10.3390/foods10030484] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/13/2021] [Accepted: 02/18/2021] [Indexed: 12/26/2022] Open
Abstract
The benefits of the food industry compared to other sectors are much lower, which is why producers are tempted to commit fraud. Although it is a bad practice committed with a wide variety of foods, it is worth noting the case of olive oil because it is a product of great value and with a high percentage of fraud. It is for all these reasons that the authenticity of olive oil has become a major problem for producers, consumers, and legislators. To avoid such fraud, it is necessary to develop analytical techniques to detect them. In this review, we performed a complete analysis about the available instrumentation used in olive fraud which comprised spectroscopic and spectrometric methodology and analyte separation techniques such as liquid chromatography and gas chromatography. Additionally, other methodology including protein-based biomolecular techniques and analytical approaches like metabolomic, hhyperspectral imaging and chemometrics are discussed.
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12
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Salah WA, Nofal M. Review of some adulteration detection techniques of edible oils. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:811-819. [PMID: 32833235 DOI: 10.1002/jsfa.10750] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 08/19/2020] [Accepted: 08/24/2020] [Indexed: 05/27/2023]
Abstract
Edible oils have economical and nutritional benefits. These oils offer nutrients that are essential to human health because they are the primary source of mono- and poly-unsaturated fats. Moreover, edible oils are used in home cooking and industrial food manufacturing. Therefore, edible oils have a considerable demand worldwide. However, some edible oils, such as olive oil, are more expensive than any other vegetable oils. Thus, oils such as olive oil are mixed with cheap edible oils as a result of the high price difference. Accordingly, adulteration in edible oils to obtain additional profit for the producer becomes a major issue of high concern for consumers. Moreover, adulteration in edible oils can cause several problems that affect consumer health. Therefore, the need for a sensitive, accurate and suitable method to detect the adulteration is highly considered. We provide a brief review of the different methods and techniques used to detect adulteration in edible oils, especially olive oil, with the aim of promoting consumer awareness of the authenticity of edible oils. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Wael A Salah
- Department of Electrical Engineering, College of Engineering and Technology, Palestine Technical University - Kadoorie (PTUK), Tulkarm, Palestine
| | - Mays Nofal
- Faculty of Graduate Studies, Palestine Technical University - Kadoorie (PTUK), Tulkarm, Palestine
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Pattern recognition based on machine learning identifies oil adulteration and edible oil mixtures. Nat Commun 2020; 11:5353. [PMID: 33097723 PMCID: PMC7584611 DOI: 10.1038/s41467-020-19137-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 09/23/2020] [Indexed: 11/12/2022] Open
Abstract
Previous studies have shown that each edible oil type has its own characteristic fatty acid profile; however, no method has yet been described allowing the identification of oil types simply based on this characteristic. Moreover, the fatty acid profile of a specific oil type can be mimicked by a mixture of 2 or more oil types. This has led to fraudulent oil adulteration and intentional mislabeling of edible oils threatening food safety and endangering public health. Here, we present a machine learning method to uncover fatty acid patterns discriminative for ten different plant oil types and their intra-variability. We also describe a supervised end-to-end learning method that can be generalized to oil composition of any given mixtures. Trained on a large number of simulated oil mixtures, independent test dataset validation demonstrates that the model has a 50th percentile absolute error between 1.4–1.8% and a 90th percentile error of 4–5.4% for any 3-way mixtures of the ten oil types. The deep learning model can also be further refined with on-line training. Because oil-producing plants have diverse geographical origins and hence slightly varying fatty acid profiles, an online-training method provides also a way to capture useful knowledge presently unavailable. Our method allows the ability to control product quality, determining the fair price of purchased oils and in-turn allowing health-conscious consumers the future of accurate labeling. Fraudulent adulteration of edible oils is based on the fact that their characteristic fatty acid profile can be mimicked with mixtures of other oil types. Here, the authors use a deep learning method to uncover fatty acid patterns discriminative for ten different plant oil types and to discern composition of mixtures.
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Li X, Zhang L, Zhang Y, Wang D, Wang X, Yu L, Zhang W, Li P. Review of NIR spectroscopy methods for nondestructive quality analysis of oilseeds and edible oils. Trends Food Sci Technol 2020. [DOI: 10.1016/j.tifs.2020.05.002] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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An Artificial Intelligence Approach for Italian EVOO Origin Traceability through an Open Source IoT Spectrometer. Foods 2020; 9:foods9060834. [PMID: 32630427 PMCID: PMC7353555 DOI: 10.3390/foods9060834] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 06/17/2020] [Indexed: 12/18/2022] Open
Abstract
Extra virgin olive oil (EVOO) represents a crucial ingredient of the Mediterranean diet. Being a first-choice product, consumers should be guaranteed its quality and geographical origin, justifying the high purchasing cost. For this reason, it is important to have new reliable tools able to classify products according to their geographical origin. The aim of this work was to demonstrate the efficiency of an open source visible and near infra-red (VIS-NIR) spectrophotometer, relying on a specific app, in assessing olive oil geographical origin. Thus, 67 Italian and 25 foreign EVOO samples were analyzed and their spectral data were processed through an artificial intelligence algorithm. The multivariate analysis of variance (MANOVA) results reported significant differences (p < 0.001) between the Italian and foreign EVOO VIS-NIR matrices. The artificial neural network (ANN) model with an external test showed a correct classification percentage equal to 94.6%. Both the MANOVA and ANN tested methods showed the most important spectral wavelengths ranges for origin determination to be 308–373 nm and 594–605 nm. These are related to the absorption of phenolic components, carotenoids, chlorophylls, and anthocyanins. The proposed tool allows the assessment of EVOO samples’ origin and thus could help to preserve the “Made in Italy” from fraud and sophistication related to its commerce.
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Hayakawa T, Yanagawa M, Yamamoto A, Aizawa SI, Taga A, Mochizuki N, Itabashi Y, Uchida H, Ishihara Y, Kodama S. A Simple Screening Method for Extra Virgin Olive Oil Adulteration by Determining Squalene and Tyrosol. J Oleo Sci 2020; 69:677-684. [PMID: 32522947 DOI: 10.5650/jos.ess20033] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
A simple screening method for discrimination between commercial extra virgin olive oils and their blends with other vegetable oils was developed. Squalene, which was contained relatively high amounts in virgin olive oil, was determined by HPLC after a simple pretreatment that was carried out by dilution of oil samples with 2-propanol. Tyrosol, which was contained at relatively high concentration in virgin olive oil among phenolic compounds, was determined by HPLC after a simple liquid-liquid extraction. When using squalene and tyrosol contents as axes, extra virgin olive oils could be discriminated from pure olive oils, blended oils (extra virgin olive oils with sunflower oil or grapeseed oil) and other vegetable oils. These results suggest that determining squalene and tyrosol in seed oil samples could be useful in distinguishing between extra virgin olive oil and blended oils as a screening method.
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Affiliation(s)
| | | | - Atsushi Yamamoto
- Department of Biological Chemistry, College of Bioscience and Biotechnology, Chubu University
| | - Sen-Ichi Aizawa
- Graduate School of Science and Engineering, University of Toyama
| | | | | | | | - Hajime Uchida
- National Research Institute of Fisheries Science, Japan Fisheries Research and Education Agency
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Gertz C, Matthäus B, Willenberg I. Detection of Soft‐Deodorized Olive Oil and Refined Vegetable Oils in Virgin Olive Oil Using Near Infrared Spectroscopy and Traditional Analytical Parameters. EUR J LIPID SCI TECH 2020. [DOI: 10.1002/ejlt.201900355] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
| | - Bertrand Matthäus
- Max Rubner‐Institut (MRI)Department of Safety and Quality of CerealsWorking Group for Lipid Research Detmold 32756 Germany
| | - Ina Willenberg
- Max Rubner‐Institut (MRI)Department of Safety and Quality of CerealsWorking Group for Lipid Research Detmold 32756 Germany
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18
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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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Uncu O, Ozen B, Tokatli F. Mid-infrared spectroscopic detection of sunflower oil adulteration with safflower oil. GRASAS Y ACEITES 2019. [DOI: 10.3989/gya.0579181] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The oil industry is in need of rapid analysis techniques to differentiate mixtures of safflower-sunflower oils from pure oils. The current adulteration detection methods are generally cumbersome and detection limits are questionable. The aim of this study was to test the capability of a mid-infrared spectroscopic method to detect the adulteration of sunflower oil with safflower oil compared to fatty acid analysis. Mid-infrared spectra of pure oils and their mixtures at the 10–60% range were obtained at 4000–650 cm-1 wavenumber and fatty acid profiles were determined. Data were analyzed by multivariate statistical analysis techniques. The lowest level of detection was obtained with mid-infrared spectroscopy at 30% while the fatty acid profile could determine adulteration at around 60%. Adulteration levels were predicted successfully using PLS regression analysis of infrared data with R2 (calibration) = 0.96 and R2 (validation) = 0.93. As a rapid and minimum waste generating technique, mid-infrared spectroscopy could be a useful tool for the screening of raw material to detect safflower-sunflower oil mixtures.
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Azizian H, Wang SC, Li X, Kramer JKG. Improvement of the Fourier Transform Near Infrared Method to Evaluate Extra Virgin Olive Oils by Analyzing 1,2-Diacylglycerols and 1,3-Diacylglycerols and Adding Unesterified Fatty Acids. Lipids 2019; 53:1097-1112. [DOI: 10.1002/lipd.12113] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2018] [Revised: 11/18/2018] [Accepted: 11/21/2018] [Indexed: 11/10/2022]
Affiliation(s)
- Hormoz Azizian
- NIR Technologies Inc.; 1312 Fairmeadow Trail, Oakville ON L6M 2M2, Canada
| | - Selina C. Wang
- Olive Center; University of California; 392 Old Davis Road, Davis CA 95616-8571
- Department of Food Science and Technology; University of California; 392 Old Davis Road, Davis CA 95616-8571
| | - Xueqi Li
- Olive Center; University of California; 392 Old Davis Road, Davis CA 95616-8571
| | - John K. G. Kramer
- Guelph Food Research Center; Agriculture and Agri-Food Canada; Guelph, 10 Eringate Walk, Cambridge, Ontario, N1S 4Y6 Canada (Retired)
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21
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Olive oil nutritional labeling by using Vis/NIR spectroscopy and compositional statistical methods. INNOV FOOD SCI EMERG 2019. [DOI: 10.1016/j.ifset.2018.05.018] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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22
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Vanstone N, Moore A, Martos P, Neethirajan S. Detection of the adulteration of extra virgin olive oil by near-infrared spectroscopy and chemometric techniques. FOOD QUALITY AND SAFETY 2018. [DOI: 10.1093/fqsafe/fyy018] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Nick Vanstone
- BioNano Laboratory, School of Engineering, University of Guelph, Guelph, Ontario, Canada
- Agriculture & Food Laboratory, Laboratory Services Division, University of Guelph, Guelph, Ontario, Canada
| | - Andrew Moore
- Agriculture & Food Laboratory, Laboratory Services Division, University of Guelph, Guelph, Ontario, Canada
| | - Perry Martos
- Agriculture & Food Laboratory, Laboratory Services Division, University of Guelph, Guelph, Ontario, Canada
| | - Suresh Neethirajan
- BioNano Laboratory, School of Engineering, University of Guelph, Guelph, Ontario, Canada
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23
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Picouet PA, Gou P, Hyypiö R, Castellari M. Implementation of NIR technology for at-line rapid detection of sunflower oil adulterated with mineral oil. J FOOD ENG 2018. [DOI: 10.1016/j.jfoodeng.2018.01.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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24
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Tan J, Li R, Jiang ZT, Shi M, Xiao YQ, Jia B, Lu TX, Wang H. Detection of Extra Virgin Olive Oil Adulteration With Edible Oils Using Front-Face Fluorescence and Visible Spectroscopies. J AM OIL CHEM SOC 2018. [DOI: 10.1002/aocs.12071] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Jin Tan
- Tianjin Key Laboratory of Food Biotechnology; College of Biotechnology and Food Science, Tianjin University of Commerce; 409 Guangrong Road, Beichen District Tianjin 300134 People's Republic of China
| | - Rong Li
- Tianjin Key Laboratory of Food Biotechnology; College of Biotechnology and Food Science, Tianjin University of Commerce; 409 Guangrong Road, Beichen District Tianjin 300134 People's Republic of China
| | - Zi-Tao Jiang
- Tianjin Key Laboratory of Food Biotechnology; College of Biotechnology and Food Science, Tianjin University of Commerce; 409 Guangrong Road, Beichen District Tianjin 300134 People's Republic of China
| | - Meng Shi
- Tianjin Key Laboratory of Food Biotechnology; College of Biotechnology and Food Science, Tianjin University of Commerce; 409 Guangrong Road, Beichen District Tianjin 300134 People's Republic of China
| | - Yi-Qian Xiao
- Tianjin Key Laboratory of Food Biotechnology; College of Biotechnology and Food Science, Tianjin University of Commerce; 409 Guangrong Road, Beichen District Tianjin 300134 People's Republic of China
| | - Bin Jia
- Tianjin Key Laboratory of Food Biotechnology; College of Biotechnology and Food Science, Tianjin University of Commerce; 409 Guangrong Road, Beichen District Tianjin 300134 People's Republic of China
| | - Tian-Xiang Lu
- Tianjin Key Laboratory of Food Biotechnology; College of Biotechnology and Food Science, Tianjin University of Commerce; 409 Guangrong Road, Beichen District Tianjin 300134 People's Republic of China
| | - Hao Wang
- Tianjin Key Laboratory of Food Biotechnology; College of Biotechnology and Food Science, Tianjin University of Commerce; 409 Guangrong Road, Beichen District Tianjin 300134 People's Republic of China
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25
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Karunathilaka SR, Yakes BJ, Farris S, Michael TJ, He K, Chung JK, Shah R, Mossoba MM. Quantitation of Saccharin and Cyclamate in Tabletop Formulations by Portable Raman and NIR Spectrometers in Combination with Partial Least Squares Regression. FOOD ANAL METHOD 2017. [DOI: 10.1007/s12161-017-1057-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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26
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Bajoub A, Bendini A, Fernández-Gutiérrez A, Carrasco-Pancorbo A. Olive oil authentication: A comparative analysis of regulatory frameworks with especial emphasis on quality and authenticity indices, and recent analytical techniques developed for their assessment. A review. Crit Rev Food Sci Nutr 2017; 58:832-857. [PMID: 27657556 DOI: 10.1080/10408398.2016.1225666] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Over the last decades, olive oil quality and authenticity control has become an issue of great importance to consumers, suppliers, retailers, and regulators in both traditional and emerging olive oil producing countries, mainly due to the increasing worldwide popularity and the trade globalization of this product. Thus, in order to ensure olive oil authentication, various national and international laws and regulations have been adopted, although some of them are actually causing an enormous debate about the risk that they can represent for the harmonization of international olive oil trade standards. Within this context, this review was designed to provide a critical overview and comparative analysis of selected regulatory frameworks for olive oil authentication, with special emphasis on the quality and purity criteria considered by these regulation systems, their thresholds and the analytical methods employed for monitoring them. To complete the general overview, recent analytical advances to overcome drawbacks and limitations of the official methods to evaluate olive oil quality and to determine possible adulterations were reviewed. Furthermore, the latest trends on analytical approaches to assess the olive oil geographical and varietal origin traceability were also examined.
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Affiliation(s)
- Aadil Bajoub
- a Department of Analytical Chemistry, Faculty of Science , University of Granada , Granada , Spain
| | - Alessandra Bendini
- b Department of Agricultural and Food Sciences , University of Bologna , Cesena (FC) , Italy
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27
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Kramer JKG, Azizian H. FT-NIR the method of choice to verify the authenticity of extra virgin olive oils. ACTA ACUST UNITED AC 2017. [DOI: 10.1177/0960336017707869] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Fourier transform near infrared spectroscopy was recently demonstrated to be an excellent method to evaluate the authenticity and adulteration of extra virgin olive oil. Since this method is matrix dependent, it takes a chemical fingerprint of all the components which sets it apart from the targeted methods. Careful examinations of the Fourier transform near infrared spectra lead to the identification of a minor carbonyl overtone absorption at 5269 cm−1 associated with the volatile fraction in extra virgin olive oil that appears to be a reliable indicator of authenticity. The same spectra were used to identify the fatty acids present in the oil using models based on comparison to accurate GC data. Gravimetric mixtures of extra virgin olive oil with refined edible oils were then prepared to develop PLS1 calibration models to identify possible adulterants and by how much. The great varietal difference in olive oils made it necessary to develop four unique sets of PLS1 calibration models for each extra virgin olive oil variety. As a result, an extra virgin olive oil acceptance specification was established.
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Affiliation(s)
- John KG Kramer
- Guelph Food Research Center, Agriculture and Agri-Food Canada, Guelph, Ontario, Canada (Retired)
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28
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Mossoba MM, Azizian H, Fardin-Kia AR, Karunathilaka SR, Kramer JKG. First Application of Newly Developed FT-NIR Spectroscopic Methodology to Predict Authenticity of Extra Virgin Olive Oil Retail Products in the USA. Lipids 2017; 52:443-455. [PMID: 28401382 DOI: 10.1007/s11745-017-4250-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2016] [Accepted: 03/28/2017] [Indexed: 11/25/2022]
Abstract
Economically motivated adulteration (EMA) of extra virgin olive oils (EVOO) has been a worldwide problem and a concern for government regulators for a long time. The US Food and Drug Administration (FDA) is mandated to protect the US public against intentional adulteration of foods and has jurisdiction over deceptive label declarations. To detect EMA of olive oil and address food safety vulnerabilities, we used a previously developed rapid screening methodology to authenticate EVOO. For the first time, a recently developed FT-NIR spectroscopic methodology in conjunction with partial least squares analysis was applied to commercial products labeled EVOO purchased in College Park, MD, USA to rapidly predict whether they are authentic, potentially mixed with refined olive oil (RO) or other vegetable oil(s), or are of lower quality. Of the 88 commercial products labeled EVOO that were assessed according to published specified ranges, 33 (37.5%) satisfied the three published FT-NIR requirements identified for authentic EVOO products which included the purity test. This test was based on limits established for the contents of three potential adulterants, oils high in linoleic acid (OH-LNA), oils high in oleic acid (OH-OLA), palm olein (PO), and/or RO. The remaining 55 samples (62.5%) did not meet one or more of the criteria established for authentic EVOO. The breakdown of the 55 products was EVOO potentially mixed with OH-LNA (25.5%), OH-OLA (10.9%), PO (5.4%), RO (25.5%), or a combination of any of these four (32.7%). If assessments had been based strictly on whether the fatty acid composition was within the established ranges set by the International Olive Council (IOC), less than 10% would have been identified as non-EVOO. These findings are significant not only because they were consistent with previously published data based on the results of two sensory panels that were accredited by IOC but more importantly each measurement/analysis was accomplished in less than 5 min.
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Affiliation(s)
- Magdi M Mossoba
- Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, MD, USA.
| | | | - Ali Reza Fardin-Kia
- Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, MD, USA
| | - Sanjeewa R Karunathilaka
- Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, MD, USA
| | - John K G Kramer
- Guelph Food Research Center, Agriculture and Agri-Food Canada, Guelph, ON, Canada
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29
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Wang L, Sun DW, Pu H, Cheng JH. Quality analysis, classification, and authentication of liquid foods by near-infrared spectroscopy: A review of recent research developments. Crit Rev Food Sci Nutr 2017; 57:1524-1538. [DOI: 10.1080/10408398.2015.1115954] [Citation(s) in RCA: 103] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Lu Wang
- School of Food Science and Engineering, South China University of Technology, Guangzhou, P. R. China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, P. R. China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou, P. R. China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, P. R. China
- Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou, P. R. China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, P. R. China
| | - Jun-Hu Cheng
- School of Food Science and Engineering, South China University of Technology, Guangzhou, P. R. China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, P. R. China
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30
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Developing FT-NIR and PLS1 Methodology for Predicting Adulteration in Representative Varieties/Blends of Extra Virgin Olive Oils. Lipids 2016; 51:1309-1321. [PMID: 27677754 DOI: 10.1007/s11745-016-4195-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Accepted: 08/24/2016] [Indexed: 10/20/2022]
Abstract
It was previously demonstrated that Fourier transform near infrared (FT-NIR) spectroscopy and partial least squares (PLS1) were successfully used to assess whether an olive oil was extra virgin, and if adulterated, with which type of vegetable oil and by how much using previously developed PLS1 calibration models. This last prediction required an initial set of four PLS1 calibration models that were based on gravimetrically prepared mixtures of a specific variety of extra virgin olive oil (EVOO) spiked with adulterants. The current study was undertaken after obtaining a range of EVOO varieties grown in different countries. It was found that all the different types of EVOO varieties investigated belonged to four distinct groups, and each required the development of additional sets of specific PLS1 calibration models to ensure that they can be used to predict low concentrations of vegetable oils high in linoleic, oleic, or palmitic acid, and/or refined olive oil. These four distinct sets of PLS1 calibration models were required to cover the range of EVOO varieties with a linoleic acid content from 1.3 to 15.5 % of total fatty acids. An FT-NIR library was established with 66 EVOO products obtained from California and Europe. The quality and/or purity of EVOO were assessed by determining the FT-NIR Index, a measure of the volatile content of EVOO. The use of these PLS1 calibration models made it possible to predict the authenticity of EVOO and the identity and quantity of potential adulterant oils in minutes.
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31
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Karunathilaka SR, Kia ARF, Srigley C, Chung JK, Mossoba MM. Nontargeted, Rapid Screening of Extra Virgin Olive Oil Products for Authenticity Using Near-Infrared Spectroscopy in Combination with Conformity Index and Multivariate Statistical Analyses. J Food Sci 2016; 81:C2390-C2397. [PMID: 27626761 DOI: 10.1111/1750-3841.13432] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Revised: 07/15/2016] [Accepted: 07/27/2016] [Indexed: 01/27/2023]
Abstract
A rapid tool for evaluating authenticity was developed and applied to the screening of extra virgin olive oil (EVOO) retail products by using Fourier-transform near infrared (FT-NIR) spectroscopy in combination with univariate and multivariate data analysis methods. Using disposable glass tubes, spectra for 62 reference EVOO, 10 edible oil adulterants, 20 blends consisting of EVOO spiked with adulterants, 88 retail EVOO products and other test samples were rapidly measured in the transmission mode without any sample preparation. The univariate conformity index (CI) and the multivariate supervised soft independent modeling of class analogy (SIMCA) classification tool were used to analyze the various olive oil products which were tested for authenticity against a library of reference EVOO. Better discrimination between the authentic EVOO and some commercial EVOO products was observed with SIMCA than with CI analysis. Approximately 61% of all EVOO commercial products were flagged by SIMCA analysis, suggesting that further analysis be performed to identify quality issues and/or potential adulterants. Due to its simplicity and speed, FT-NIR spectroscopy in combination with multivariate data analysis can be used as a complementary tool to conventional official methods of analysis to rapidly flag EVOO products that may not belong to the class of authentic EVOO.
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Affiliation(s)
- Sanjeewa R Karunathilaka
- Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, Md, U.S.A
| | - Ali-Reza Fardin Kia
- Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, Md, U.S.A
| | - Cynthia Srigley
- Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, Md, U.S.A
| | - Jin Kyu Chung
- Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, Md, U.S.A
| | - Magdi M Mossoba
- Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, Md, U.S.A.
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32
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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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33
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Sørensen KM, Khakimov B, Engelsen SB. The use of rapid spectroscopic screening methods to detect adulteration of food raw materials and ingredients. Curr Opin Food Sci 2016. [DOI: 10.1016/j.cofs.2016.08.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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34
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Karunathilaka SR, Farris S, Mossoba MM, Moore JC, Yakes BJ. Characterising variances of milk powder and instrumentation for the development of a non-targeted, Raman spectroscopy and chemometrics detection method for the evaluation of authenticity. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2016; 33:921-32. [PMID: 27167451 PMCID: PMC4918629 DOI: 10.1080/19440049.2016.1188437] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Accepted: 04/29/2016] [Indexed: 01/20/2023]
Abstract
There is a need to develop rapid tools to screen milk products for economically motivated adulteration. An understanding of the physiochemical variability within skim milk powder (SMP) and non-fat dry milk (NFDM) is the key to establishing the natural differences of these commodities prior to the development of non-targeted detection methods. This study explored the sources of variance in 71 commercial SMP and NFDM samples using Raman spectroscopy and principal component analysis (PCA) and characterised the largest number of commercial milk powders acquired from a broad number of international manufacturers. Spectral pre-processing using a gap-segment derivative transformation (gap size = 5, segment width = 9, fourth derivative) in combination with sample normalisation was necessary to reduce the fluorescence background of the milk powder samples. PC scores plots revealed no clear trends for various parameters, including day of analysis, powder type, supplier and processing temperatures, while the largest variance was due to irreproducibility in sample positioning. Significant chemical sources of variances were explained by using the spectral features in the PC loadings plots where four samples from the same manufacturer were determined to likely contain an additional component or lactose anomers, and one additional sample was identified as an outlier and likely containing an adulterant or differing quality components. The variance study discussed herein with this large, diverse set of milk powders holds promise for future use as a non-targeted screening method that could be applied to commercial milk powders.
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Affiliation(s)
- Sanjeewa R. Karunathilaka
- US Food and Drug Administration (USFDA), Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, MD, USA
| | - Samantha Farris
- US Food and Drug Administration (USFDA), Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, MD, USA
| | - Magdi M. Mossoba
- US Food and Drug Administration (USFDA), Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, MD, USA
| | | | - Betsy Jean Yakes
- US Food and Drug Administration (USFDA), Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, MD, USA
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35
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Melucci D, Bendini A, Tesini F, Barbieri S, Zappi A, Vichi S, Conte L, Gallina Toschi T. Rapid direct analysis to discriminate geographic origin of extra virgin olive oils by flash gas chromatography electronic nose and chemometrics. Food Chem 2016; 204:263-273. [PMID: 26988501 DOI: 10.1016/j.foodchem.2016.02.131] [Citation(s) in RCA: 95] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2015] [Revised: 01/11/2016] [Accepted: 02/21/2016] [Indexed: 10/22/2022]
Abstract
At present, the geographical origin of extra virgin olive oils can be ensured by documented traceability, although chemical analysis may add information that is useful for possible confirmation. This preliminary study investigated the effectiveness of flash gas chromatography electronic nose and multivariate data analysis to perform rapid screening of commercial extra virgin olive oils characterized by a different geographical origin declared in the label. A comparison with solid phase micro extraction coupled to gas chromatography mass spectrometry was also performed. The new method is suitable to verify the geographic origin of extra virgin olive oils based on principal components analysis and discriminant analysis applied to the volatile profile of the headspace as a fingerprint. The selected variables were suitable in discriminating between "100% Italian" and "non-100% Italian" oils. Partial least squares discriminant analysis also allowed prediction of the degree of membership of unknown samples to the classes examined.
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Affiliation(s)
- Dora Melucci
- Department of Chemistry Ciamician, University of Bologna, Via Selmi, 2, 40126 Bologna, Italy.
| | - Alessandra Bendini
- Department of Agricultural and Food Sciences (DiSTAL), University of Bologna, P.zza Goidanich 60, 47521 Cesena, Italy.
| | - Federica Tesini
- Department of Agricultural and Food Sciences (DiSTAL), University of Bologna, P.zza Goidanich 60, 47521 Cesena, Italy.
| | - Sara Barbieri
- Department of Agricultural and Food Sciences (DiSTAL), University of Bologna, P.zza Goidanich 60, 47521 Cesena, Italy.
| | - Alessandro Zappi
- Department of Chemistry Ciamician, University of Bologna, Via Selmi, 2, 40126 Bologna, Italy.
| | - Stefania Vichi
- Department of Food Science and Nutrition, University of Barcelona, Food and Nutrition Torribera Campus, Av. Prat de la Riba, 171,, S.ta Coloma de Gramenet, Spain.
| | - Lanfranco Conte
- Department of Food Science, University of Udine, Via Sondrio 2/a, 33100 Udine, Italy.
| | - Tullia Gallina Toschi
- Department of Agricultural and Food Sciences (DiSTAL), University of Bologna, P.zza Goidanich 60, 47521 Cesena, Italy.
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