1
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Kanwal N, Musharraf SG. Analytical approaches for the determination of adulterated animal fats and vegetable oils in food and non-food samples. Food Chem 2024; 460:140786. [PMID: 39142208 DOI: 10.1016/j.foodchem.2024.140786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 08/01/2024] [Accepted: 08/05/2024] [Indexed: 08/16/2024]
Abstract
Edible oils and fats are crucial components of everyday cooking and the production of food products, but their purity has been a major issue for a long time. High-quality edible oils are contaminated with low- and cheap-quality edible oils to increase profits. The adulteration of edible oils and fats also produces many health risks. Detection of main and minor components can identify adulterations using various techniques, such as GC, HPLC, TLC, FTIR, NIR, NMR, direct mass spectrometry, PCR, E-Nose, and DSC. Each detection technique has its advantages and disadvantages. For example, chromatography offers high precision but requires extensive sample preparation, while spectroscopy is rapid and non-destructive but may lack resolution. Direct mass spectrometry is faster and simpler than chromatography-based MS, eliminating complex preparation steps. DNA-based oil authentication is effective but hindered by laborious extraction processes. E-Nose only distinguishes odours, and DSC directly studies lipid thermal properties without derivatization or solvents. Mass spectrometry-based techniques, particularly GC-MS is found to be highly effective for detecting adulteration of oils and fats in food and non-food samples. This review summarizes the benefits and drawbacks of these analytical approaches and their use in conjunction with chemometric tools to detect the adulteration of animal fats and vegetable oils. This combination provides a powerful technique with enormous chemotaxonomic potential that includes the detection of adulterations, quality assurance, assessment of geographical origin, assessment of the process, and classification of the product in complex matrices from food and non-food samples.
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Affiliation(s)
- Nayab Kanwal
- H. E. J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi 75270, Pakistan
| | - Syed Ghulam Musharraf
- H. E. J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi 75270, Pakistan; Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi 75270, Pakistan..
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2
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He HJ, da Silva Ferreira MV, Wu Q, Karami H, Kamruzzaman M. Portable and miniature sensors in supply chain for food authentication: a review. Crit Rev Food Sci Nutr 2024:1-21. [PMID: 39066550 DOI: 10.1080/10408398.2024.2380837] [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: 07/28/2024]
Abstract
Food fraud, a pervasive issue in the global food industry, poses significant challenges to consumer health, trust, and economic stability, costing an estimated $10-15 billion annually. Therefore, there is a rising demand for developing portable and miniature sensors that facilitate food authentication throughout the supply chain. This review explores the recent advancements and applications of portable and miniature sensors, including portable/miniature near-infrared (NIR) spectroscopy, e-nose and colorimetric sensors based on nanozyme for food authentication within the supply chain. After briefly presenting the architecture and mechanism, this review discusses the application of these portable and miniature sensors in food authentication, addressing the challenges and opportunities in integrating and deploying these sensors to ensure authenticity. This review reveals the enhanced utility of portable/miniature NIR spectroscopy, e-nose, and nanozyme-based colorimetric sensors in ensuring food authenticity and enabling informed decision-making throughout the food supply chain.
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Affiliation(s)
- Hong-Ju He
- School of Food Science, Henan Institute of Science and Technology, Xinxiang, China
| | | | - Qianyi Wu
- Department of Agriculture and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Hamed Karami
- Department of Petroleum Engineering, Collage of Engineering, Knowledge University, Erbil, Iraq
| | - Mohammed Kamruzzaman
- School of Food Science, Henan Institute of Science and Technology, Xinxiang, China
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3
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Foli LP, Hespanhol MC, Cruz KAML, Pasquini C. Miniaturized Near-Infrared spectrophotometers in forensic analytical science - a critical review. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 315:124297. [PMID: 38640625 DOI: 10.1016/j.saa.2024.124297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/13/2024] [Accepted: 04/14/2024] [Indexed: 04/21/2024]
Abstract
The advent of miniaturized NIR instruments, also known as compact, portable, or handheld, is revolutionizing how technology can be employed in forensics. In-field analysis becomes feasible and affordable with these new instruments, and a series of methods has been developed to provide the police and official agents with objective, easy-to-use, tailored, and accurate qualitative and quantitative forensic results. This work discusses the main aspects and presents a comprehensive and critical review of compact NIR spectrophotometers associated with analytical protocols to produce information on forensic matters.
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Affiliation(s)
- Letícia P Foli
- Grupo de Análise e Educação para a Sustentabilidade, Departamento de Química, Centro de Ciências Exatas e Tecnológicas, Universidade Federal de Viçosa, Av. P. H. Rolfs, s/n, Viçosa, MG, 36570-900, Brazil
| | - Maria C Hespanhol
- Grupo de Análise e Educação para a Sustentabilidade, Departamento de Química, Centro de Ciências Exatas e Tecnológicas, Universidade Federal de Viçosa, Av. P. H. Rolfs, s/n, Viçosa, MG, 36570-900, Brazil
| | - Kaíque A M L Cruz
- Grupo de Análise e Educação para a Sustentabilidade, Departamento de Química, Centro de Ciências Exatas e Tecnológicas, Universidade Federal de Viçosa, Av. P. H. Rolfs, s/n, Viçosa, MG, 36570-900, Brazil
| | - Celio Pasquini
- Instituto de Química, Universidade Estadual de Campinas (UNICAMP), Rua Monteiro Lobato, 290, Campinas, SP 13083-862, Brazil.
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4
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Giussani B, Gorla G, Riu J. Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview. Crit Rev Anal Chem 2024; 54:11-43. [PMID: 35286178 DOI: 10.1080/10408347.2022.2047607] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.
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Affiliation(s)
- Barbara Giussani
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Giulia Gorla
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Jordi Riu
- Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Tarragona, Spain
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5
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Hwang J, Choi KO, Jeong S, Lee S. Machine learning identification of edible vegetable oils from fatty acid compositions and hyperspectral images. Curr Res Food Sci 2024; 8:100742. [PMID: 38708100 PMCID: PMC11066601 DOI: 10.1016/j.crfs.2024.100742] [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: 12/12/2023] [Revised: 04/05/2024] [Accepted: 04/17/2024] [Indexed: 05/07/2024] Open
Abstract
Hyperspectral imaging analysis combined with machine learning was applied to identify eight edible vegetable oils, and its classification performance was compared with the chemical method based on fatty acid compositions. Furthermore, the degree of adulteration in vegetable oils was quantitatively investigated using machine learning-enabled hyperspectral approaches. The hyperspectral absorbance spectra of palm oil with a high degree of saturation were distinctly different from those of the other liquid oils. The flaxseed and olive oils exhibited the dominant hyperspectral intensities at 1170/1671 and 1212/1415 nm, respectively. Linear discriminant analysis demonstrated that two linear discriminants could explain a significant portion of the total variability, accounting for 96.0% (fatty acid compositions) and 98.9% (hyperspectral images). When the hyperspectral results were used as datasets for three machine learning models (decision tree, random forest, and k-nearest neighbor), several instances to incorrectly classify grapeseed and sunflower oils were detected, while olive, palm, and flaxseed oils were successfully identified. The machine learning models showed a great classification performance that exceeded 98.9% from the hyperspectral images of the vegetable oils, which was comparable to the fatty acid composition-based chemical method in identifying edible vegetable oils. In addition, the random forest model was the most effective in ascertaining adulteration levels in binary oil blends (R2 > 0.992 and RMSE < 2.75).
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Affiliation(s)
- Jeongin Hwang
- Department of Food Science and Biotechnology, Seoul, 05006, South Korea
| | - Kyeong-Ok Choi
- Department of Food Science and Technology, Chungnam National University, Daejeon, 34134, South Korea
| | - Sungmin Jeong
- Carbohydrate Bioproduct Research Center, Sejong University, Seoul, 05006, South Korea
| | - Suyong Lee
- Department of Food Science and Biotechnology, Seoul, 05006, South Korea
- Carbohydrate Bioproduct Research Center, Sejong University, Seoul, 05006, South Korea
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6
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Suhag R, Ferrentino G, Morozova K, Zatelli D, Scampicchio M, Amorati R. Antioxidant efficiency and oxidizability of mayonnaise by oximetry and isothermal calorimetry. Food Chem 2024; 433:137274. [PMID: 37666126 DOI: 10.1016/j.foodchem.2023.137274] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 08/21/2023] [Accepted: 08/22/2023] [Indexed: 09/06/2023]
Abstract
This study aimed to introduce a new method based on isothermal calorimetry (IC) for measuring the autoxidation rate in mayonnaise samples. Mayonnaise samples were prepared by homogenizing an aqueous phase, consisting of vinegar and egg yolk, with various oil phases, including sunflower, corn, extra virgin olive, grape seed, and apple seed oils at 60 °C. The rate of free radical formation (Ri) was controlled by adding AIBN (Ri = 4.4±0.1×10-9 M/s). The autoxidation rate determined by IC was highly correlated with the one measured using the oxygen uptake method (R2 = 0.99). The IC method accurately indicated the antioxidant capacity and rates of both inhibited and uninhibited periods, together with the oxidizability of mayonnaise samples. The mayonnaise made with extra virgin olive oil exhibited the lowest oxidizability, while sunflower oil showed maximum antioxidant efficiency. A significant advantage of the IC method was its ability to simultaneously measure up to 24 samples with minimal effort.
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Affiliation(s)
- Rajat Suhag
- Faculty of Agricultural, Environmental and Food Sciences, Free University of Bolzano, Piazza Università, 1, Bolzano 39100, Italy
| | - Giovanna Ferrentino
- Faculty of Agricultural, Environmental and Food Sciences, Free University of Bolzano, Piazza Università, 1, Bolzano 39100, Italy
| | - Ksenia Morozova
- Faculty of Agricultural, Environmental and Food Sciences, Free University of Bolzano, Piazza Università, 1, Bolzano 39100, Italy
| | | | - Matteo Scampicchio
- Faculty of Agricultural, Environmental and Food Sciences, Free University of Bolzano, Piazza Università, 1, Bolzano 39100, Italy.
| | - Riccardo Amorati
- Department of Chemistry "G. Ciamician", University of Bologna, Via S. Giacomo 11, Bologna I-40126, Italy
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7
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Arroyo-Cerezo A, Yang X, Jiménez-Carvelo AM, Pellegrino M, Felicita Savino A, Berzaghi P. Assessment of extra virgin olive oil quality by miniaturized near infrared instruments in a rapid and non-destructive procedure. Food Chem 2024; 430:137043. [PMID: 37541043 DOI: 10.1016/j.foodchem.2023.137043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 07/25/2023] [Accepted: 07/26/2023] [Indexed: 08/06/2023]
Abstract
Food fraud in olive oil is a major concern for consumers and authorities due to the health risks and economic impacts. Common frauds include blending with other cheaper non-olive oils, or misleading labelling. The main issue is that legislation and methods presently used in routine laboratories are not always up to date with current fraudulent practices, making detection difficult, so new analytical methods development is required. This study focuses on developing an affordable and non-destructive analysis method based on NIR spectroscopy and chemometrics for EVOO quality assessment, specifically by monitoring 7 parameters of interest in EVOO measured by official methods and used to develop calibrations through NIR data. For this, two NIR low-cost portable instruments were employed, studied in-depth and compared with a NIR benchtop instrument. Calibration results enabled detection of atypical olive oils and excellent accuracy, especially for palmitic and oleic acid predictions, demonstrating the potential of the instruments.
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Affiliation(s)
- Alejandra Arroyo-Cerezo
- Department of Analytical Chemistry, University of Granada, C/ Fuentenueva s/n, 18071 Granada, Spain
| | - Xueping Yang
- Department of Animal Medicine, Production and Health, University of Padua, Via Dell'Università 16, 35020 Legnaro, Italy
| | - Ana M Jiménez-Carvelo
- Department of Analytical Chemistry, University of Granada, C/ Fuentenueva s/n, 18071 Granada, Spain.
| | - Marina Pellegrino
- Department of Animal Medicine, Production and Health, University of Padua, Via Dell'Università 16, 35020 Legnaro, Italy; Laboratorio di Perugia -ICQRF-MASAF, Via della Madonna Alta 138c/d, 06128 Perugia, Italy
| | - Angela Felicita Savino
- Laboratorio di Perugia -ICQRF-MASAF, Via della Madonna Alta 138c/d, 06128 Perugia, Italy
| | - Paolo Berzaghi
- Department of Animal Medicine, Production and Health, University of Padua, Via Dell'Università 16, 35020 Legnaro, Italy
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8
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Li L, Guo J, Wang Q, Wang J, Liu Y, Shi Y. Design and Experiment of a Portable Near-Infrared Spectroscopy Device for Convenient Prediction of Leaf Chlorophyll Content. SENSORS (BASEL, SWITZERLAND) 2023; 23:8585. [PMID: 37896678 PMCID: PMC10610571 DOI: 10.3390/s23208585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/10/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023]
Abstract
This study designs a spectrum data collection device and system based on the Internet of Things technology, aiming to solve the tedious process of chlorophyll collection and provide a more convenient and accurate method for predicting chlorophyll content. The device has the advantages of integrated design, portability, ease of operation, low power consumption, low cost, and low maintenance requirements, making it suitable for outdoor spectrum data collection and analysis in fields such as agriculture, environment, and geology. The core processor of the device uses the ESP8266-12F microcontroller to collect spectrum data by communicating with the spectrum sensor. The spectrum sensor used is the AS7341 model, but its limited number of spectral acquisition channels and low resolution may limit the exploration and analysis of spectral data. To verify the performance of the device and system, this experiment collected spectral data of Hami melon leaf samples and combined it with a chlorophyll meter for related measurements and analysis. In the experiment, twelve regression algorithms were tested, including linear regression, decision tree, and support vector regression. The results showed that in the original spectral data, the ETR method had the best prediction effect at a wavelength of 515 nm. In the training set, RMSEc was 0.3429, and Rc2 was 0.9905. In the prediction set, RMSEp was 1.5670, and Rp2 was 0.8035. In addition, eight preprocessing methods were used to denoise the original data, but the improvement in prediction accuracy was not significant. To further improve the accuracy of data analysis, principal component analysis and isolation forest algorithm were used to detect and remove outliers in the spectral data. After removing the outliers, the RFR model performed best in predicting all wavelength combinations of denoised spectral data using PBOR. In the training set, RMSEc was 0.8721, and Rc2 was 0.9429. In the prediction set, RMSEp was 1.1810, and Rp2 was 0.8683.
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Affiliation(s)
- Longjie Li
- College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China; (L.L.); (Q.W.); (J.W.); (Y.L.); (Y.S.)
| | - Junxian Guo
- Key Laboratory of Xinjiang Intelligent Agricultural Equipment, Urumqi 830052, China
| | - Qian Wang
- College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China; (L.L.); (Q.W.); (J.W.); (Y.L.); (Y.S.)
| | - Jun Wang
- College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China; (L.L.); (Q.W.); (J.W.); (Y.L.); (Y.S.)
| | - Ya Liu
- College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China; (L.L.); (Q.W.); (J.W.); (Y.L.); (Y.S.)
| | - Yong Shi
- College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China; (L.L.); (Q.W.); (J.W.); (Y.L.); (Y.S.)
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9
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da Silva BSF, Ferreira NR, Alamar PD, de Melo e Silva T, Pinheiro WBDS, dos Santos LN, Alves CN. FT-MIR-ATR Associated with Chemometrics Methods: A Preliminary Analysis of Deterioration State of Brazil Nut Oil. Molecules 2023; 28:6878. [PMID: 37836721 PMCID: PMC10574611 DOI: 10.3390/molecules28196878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/07/2023] [Accepted: 09/13/2023] [Indexed: 10/15/2023] Open
Abstract
Brazil nut oil is highly valued in the food, cosmetic, chemical, and pharmaceutical industries, as well as other sectors of the economy. This work aims to use the Fourier transform infrared (FTIR) technique associated with partial least squares regression (PLSR) and principal component analysis (PCA) to demonstrate that these methods can be used in a prior and rapid analysis in quality control. Natural oils were extracted and stored for chemical analysis. PCA presented two groups regarding the state of degradation, subdivided into super-degraded and partially degraded groups in 99.88% of the explained variance. The applied PLS reported an acidity index (AI) prediction model with root mean square error of calibration (RMSEC) = 1.8564, root mean square error of cross-validation (REMSECV) = 4.2641, root mean square error of prediction (RMSEP) = 2.1491, R2cal (calibration correlation coefficient) equal to 0.9679, R2val (validation correlation coefficient) equal to 0.8474, and R2pred (prediction correlation coefficient) equal to 0, 8468. The peroxide index (PI) prediction model showed RMSEC = 0.0005, REMSECV = 0.0016, RMSEP = 0.00079, calibration R2 equal to 0.9670, cross-validation R2 equal to 0.7149, and R2 of prediction equal to 0.9099. The physical-chemical analyses identified that five samples fit in the food sector and the others fit in other sectors of the economy. In this way, the preliminary monitoring of the state of degradation was reported, and the prediction models of the peroxide and acidity indexes in Brazil nut oil for quality control were determined.
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Affiliation(s)
- Braian Saimon Frota da Silva
- Graduate Program in Chemistry, Federal University of Pará (PPGQ), Belém 66075-110, Brazil; (T.d.M.e.S.); (W.B.d.S.P.); (C.N.A.)
| | - Nelson Rosa Ferreira
- Faculty of Food Engineering, Institute of Technology, Federal University of Pará (UFPA), Belém 66075-110, Brazil;
- Laboratory of Biotechnological Processes (LABIOTEC), Graduate Program in Food Science and Technology (PPGCTA), Institute of Technology (ITEC), Federal University of Pará (UFPA), Belém 66075-110, Brazil; (P.D.A.); (L.N.d.S.)
| | - Priscila Domingues Alamar
- Laboratory of Biotechnological Processes (LABIOTEC), Graduate Program in Food Science and Technology (PPGCTA), Institute of Technology (ITEC), Federal University of Pará (UFPA), Belém 66075-110, Brazil; (P.D.A.); (L.N.d.S.)
| | - Thiago de Melo e Silva
- Graduate Program in Chemistry, Federal University of Pará (PPGQ), Belém 66075-110, Brazil; (T.d.M.e.S.); (W.B.d.S.P.); (C.N.A.)
| | | | - Lucely Nogueira dos Santos
- Laboratory of Biotechnological Processes (LABIOTEC), Graduate Program in Food Science and Technology (PPGCTA), Institute of Technology (ITEC), Federal University of Pará (UFPA), Belém 66075-110, Brazil; (P.D.A.); (L.N.d.S.)
| | - Cláudio Nahum Alves
- Graduate Program in Chemistry, Federal University of Pará (PPGQ), Belém 66075-110, Brazil; (T.d.M.e.S.); (W.B.d.S.P.); (C.N.A.)
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10
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Gullifa G, Barone L, Papa E, Giuffrida A, Materazzi S, Risoluti R. Portable NIR spectroscopy: the route to green analytical chemistry. Front Chem 2023; 11:1214825. [PMID: 37818482 PMCID: PMC10561305 DOI: 10.3389/fchem.2023.1214825] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/07/2023] [Indexed: 10/12/2023] Open
Abstract
There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019-2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the "true green analytical chemistry" which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.
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Affiliation(s)
- G. Gullifa
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - L. Barone
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - E. Papa
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - A. Giuffrida
- Department of Chemical Sciences, University of Catania, Catania, Italy
| | - S. Materazzi
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - R. Risoluti
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
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11
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Melendreras C, Soldado A, Costa-Fernández JM, López A, Valledor M, Campo JC, Ferrero F. An Affordable NIR Spectroscopic System for Fraud Detection in Olive Oil. SENSORS (BASEL, SWITZERLAND) 2023; 23:1728. [PMID: 36772764 PMCID: PMC9920304 DOI: 10.3390/s23031728] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/29/2023] [Accepted: 02/01/2023] [Indexed: 06/18/2023]
Abstract
Adulterations of olive oil are performed by adding seed oils to this high-quality product, which are cheaper than olive oils. Food safety controls have been established by the European Union to avoid these episodes. Most of these methodologies require expensive equipment, time-consuming procedures, and expert personnel to execute. Near-infrared spectroscopy (NIRS) technology has many applications in the food processing industry. It analyzes food safety and quality parameters along the food chain. Using principal component analysis (PCA), the differences and similarities between olive oil and seed oils (sesame, sunflower, and flax oil) have been evaluated. To quantify the percentage of adulterated seed oil in olive oils, partial least squares (PLS) have been employed. A total of 96 samples of olive oil adulterated with seed oils were prepared. These samples were used to build a spectra library covering various mixtures containing seed oils and olive oil contents. Eighteen chemometric models were developed by combining the first and second derivatives with Standard Normal Variable (SNV) for scatter correction to classify and quantify seed oil adulteration and percentage. The results obtained for all seed oils show excellent coefficients of determination for calibration higher than 0.80. Because the instrumental aspects are not generally sufficiently addressed in the articles, we include a specific section on some key aspects of developing a high-performance and cost-effective NIR spectroscopy solution for fraud detection in olive oil. First, spectroscopy architectures are introduced, especially the Texas Instruments Digital Light Processing (DLP) technology for spectroscopy that has been used in this work. These results demonstrate that the portable prototype can be used as an effective tool to detect food fraud in liquid samples.
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Affiliation(s)
- Candela Melendreras
- Department of Physical and Analytical Chemistry, University of Oviedo, 33006 Oviedo, Spain
| | - Ana Soldado
- Department of Physical and Analytical Chemistry, University of Oviedo, 33006 Oviedo, Spain
| | | | - Alberto López
- Department of Electrical Engineering, University of Oviedo, 33204 Gijón, Spain
| | - Marta Valledor
- Department of Electrical Engineering, University of Oviedo, 33204 Gijón, Spain
| | - Juan Carlos Campo
- Department of Electrical Engineering, University of Oviedo, 33204 Gijón, Spain
| | - Francisco Ferrero
- Department of Electrical Engineering, University of Oviedo, 33204 Gijón, Spain
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12
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An Easy-to-Use and Cheap Analytical Approach Based on NIR and Chemometrics for Tomato and Sweet Pepper Authentication by Non-volatile Profile. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02439-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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13
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Fan D, Huang W, Liu TCY, Zhang X, Li W, Gao X, Meng Y. Quantitative analysis of blended oils by confocal Raman spectroscopy and chemometrics in situ. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109244] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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14
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Pastore TC, Braga LR, da C. Kunze DC, Soares LF, Pastore F, de O. Moreira AC, dos Anjos PV, Lara CS, Coradin VT, W. B. Braga J. A green and direct method for authentication of rosewood essential oil by handheld near infrared spectrometer and one-class classification modeling. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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15
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Sánchez-Rodríguez MI, Sánchez-López E, Marinas A, Caridad JM, Urbano FJ. Redundancy Analysis to Reduce the High-Dimensional Near-Infrared Spectral Information to Improve the Authentication of Olive Oil. J Chem Inf Model 2022; 62:4620-4628. [PMID: 36130074 PMCID: PMC9554901 DOI: 10.1021/acs.jcim.2c00964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
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The high price of
marketing of extra virgin olive oil
(EVOO) requires
the introduction of cost-effective and sustainable procedures that
facilitate its authentication, avoiding fraud in the sector. Contrary
to classical techniques (such as chromatography), near-infrared (NIR)
spectroscopy does not need derivatization of the sample with proper
integration of separated peaks and is more reliable, rapid, and cost-effective.
In this work, principal component analysis (PCA) and then redundancy
analysis (RDA)—which can be seen as a constrained version of
PCA—are used to summarize the high-dimensional NIR spectral
information. Then PCA and RDA factors are contemplated as explanatory
variables in models to authenticate oils from qualitative or quantitative
analysis, in particular, in the prediction of the percentage of EVOO
in blended oils or in the classification of EVOO or other vegetable
oils (sunflower, hazelnut, corn, or linseed oil) by the use of some
machine learning algorithms. As a conclusion, the results highlight
the potential of RDA factors in prediction and classification because
they appreciably improve the results obtained from PCA factors in
calibration and validation.
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Affiliation(s)
- María Isabel Sánchez-Rodríguez
- Department of Statistics and Business, Faculty of Law and Business, University of Cordoba, Avda. Puerta Nueva, s/n., E-14071 Cordoba, Spain
| | - Elena Sánchez-López
- Department of Organic Chemistry, University of Cordoba, Campus de Rabanales, Marie Curie Building, E-14014 Cordoba, Spain
| | - Alberto Marinas
- Department of Organic Chemistry, University of Cordoba, Campus de Rabanales, Marie Curie Building, E-14014 Cordoba, Spain
| | - José María Caridad
- Department of Statistics and Business, Faculty of Law and Business, University of Cordoba, Avda. Puerta Nueva, s/n., E-14071 Cordoba, Spain
| | - Francisco José Urbano
- Department of Organic Chemistry, University of Cordoba, Campus de Rabanales, Marie Curie Building, E-14014 Cordoba, Spain
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16
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Violino S, Taiti C, Marone E, Pallottino F, Costa C. A statistical tool to determine the quality of extra virgin olive oil (EVOO). Eur Food Res Technol 2022. [DOI: 10.1007/s00217-022-04092-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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17
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Bian X, Wang Y, Wang S, Johnson JB, Sun H, Guo Y, Tan X. A Review of Advanced Methods for the Quantitative Analysis of Single Component Oil in Edible Oil Blends. Foods 2022; 11:foods11162436. [PMID: 36010436 PMCID: PMC9407567 DOI: 10.3390/foods11162436] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/04/2022] [Accepted: 08/11/2022] [Indexed: 12/21/2022] Open
Abstract
Edible oil blends are composed of two or more edible oils in varying proportions, which can ensure nutritional balance compared to oils comprising a single component oil. In view of their economical and nutritional benefits, quantitative analysis of the component oils in edible oil blends is necessary to ensure the rights and interests of consumers and maintain fairness in the edible oil market. Chemometrics combined with modern analytical instruments has become a main analytical technology for the quantitative analysis of edible oil blends. This review summarizes the different oil blend design methods, instrumental techniques and chemometric methods for conducting single component oil quantification in edible oil blends. The aim is to classify and compare the existing analytical techniques to highlight suitable and promising determination methods in this field.
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Affiliation(s)
- Xihui Bian
- School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
- Shandong Provincial Key Laboratory of Olefin Catalysis and Polymerization, Shandong Chambroad Holding Group Co., Ltd., Binzhou 256500, China
- Correspondence: ; Tel./Fax: +86-22-83955663
| | - Yao Wang
- School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
| | - Shuaishuai Wang
- Shandong Provincial Key Laboratory of Olefin Catalysis and Polymerization, Shandong Chambroad Holding Group Co., Ltd., Binzhou 256500, China
| | - Joel B. Johnson
- School of Health, Medical & Applied Sciences, Central Queensland University, Bruce Hwy, North Rockhampton, QLD 4701, Australia
| | - Hao Sun
- School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
| | - Yugao Guo
- School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
| | - Xiaoyao Tan
- School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
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18
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Digital Detection of Olive Oil Rancidity Levels and Aroma Profiles Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Machine Learning Modelling. CHEMOSENSORS 2022. [DOI: 10.3390/chemosensors10050159] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The success of the olive oil industry depends on provenance and quality-trait consistency affecting the consumers' acceptability/preference and purchase intention. Companies rely on laboratories to analyze samples to assess consistency within the production chain, which may be time-consuming, cost-restrictive, and untimely obtaining results, making the process more reactive than predictive. This study proposed implementing digital technologies using near-infrared spectroscopy (NIR) and a novel low-cost e-nose to assess the level of rancidity and aromas in commercial extra-virgin olive oil. Four different olive oils were spiked with three rancidity levels (N = 17). These samples were evaluated using gas-chromatography-mass-spectroscopy, NIR, and an e-nose. Four machine learning models were developed to classify olive oil types and rancidity (Model 1: NIR inputs; Model 2: e-nose inputs) and predict the peak area of 16 aromas (Model 3: NIR; Model 4: e-nose inputs). The results showed high accuracies (Models 1–2: 97% and 87%; Models 3–4: R = 0.96 and 0.93). These digital technologies may change companies from a reactive to a more predictive production of food/beverages to secure product quality and acceptability.
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19
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García Martín JF. Potential of Near-Infrared Spectroscopy for the Determination of Olive Oil Quality. SENSORS 2022; 22:s22082831. [PMID: 35458818 PMCID: PMC9031905 DOI: 10.3390/s22082831] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 12/10/2022]
Abstract
The analysis of the physico-chemical parameters of quality of olive oil is still carried out in laboratories using chemicals and generating waste, which is relatively costly and time-consuming. Among the various alternatives for the online or on-site measurement of these parameters, the available literature highlights the use of near-infrared spectroscopy (NIRS). This article intends to comprehensively review the state-of-the-art research and the actual potential of NIRS for the analysis of olive oil. A description of the features of the infrared spectrum of olive oil and a quick explanation of the fundamentals of NIRS and chemometrics are also included. From the results available in the literature, it can be concluded that the four most usual physico-chemical parameters that define the quality of olive oils, namely free acidity, peroxide value, K232, and K270, can be measured by NIRS with high precision. In addition, NIRS is suitable for the nutritional labeling of olive oil because of its great performance in predicting the contents in total fat, total saturated fatty acids, monounsaturated fatty acids, and polyunsaturated fatty acids in olive oils. Other parameters of interest have the potential to be analyzed by NIRS, but the improvement of the mathematical models for their determination is required, since the errors of prediction reported so far are a bit high for practical application.
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Affiliation(s)
- Juan Francisco García Martín
- Departamento de Ingeniería Química, Facultad de Química, Universidad de Sevilla, 41012 Seville, Spain;
- University Institute of Research on Olive Groves and Olive Oils, GEOLIT Science and Technology Park, University of Jaén, 23620 Mengíbar, Spain
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20
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Zhang H, Hu X, Liu L, Wei J, Bian X. Near infrared spectroscopy combined with chemometrics for quantitative analysis of corn oil in edible blend oil. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 270:120841. [PMID: 35033805 DOI: 10.1016/j.saa.2021.120841] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 12/27/2021] [Accepted: 12/29/2021] [Indexed: 06/14/2023]
Abstract
In this study, near infrared (NIR) spectroscopy combined with chemometrics was used for the quantitative analysis of corn oil in binary to hexanary edible blend oil. Sesame oil, soybean oil, rice oil, sunflower oil and peanut oil were mixed with corn oil subsequently to form binary, ternary, quaternary, quinary and hexanary blend oil datasets. NIR spectra for the five order blend oil datasets were measured in a transmittance mode in the range of 12000-4000 cm-1. Partial least square (PLS) was used to build models for the five datasets. Six spectral preprocessing methods and their combinations were investigated to improve the prediction performance. Furthermore, the optimal preprocessing-PLS models were further optimized by uninformative variable elimination (UVE), Monte Carlo uninformative variable elimination (MCUVE) and randomization test (RT) variable selection methods. The optimal models acquire root mean square error of prediction (RMSEP) of 1.7299, 2.2089, 2.3742, 2.5608 and 2.6858 for binary, ternary, quaternary, quinary and hexanary blend oil datasets, respectively. The determination coefficients of prediction set (R2P) and residual predictive deviations (RPDs) for the five datasets are all above 0.93 and 3. Results show that the prediction accuracy is gradually decreased with the increasing of mixture order of blend oil. However, with proper spectral preprocessing and variable selection, the optimal models present good prediction accuracy even for the higher order blend oil. It demonstrates that NIR technology is feasible for determining the pure oil contents in binary to hexanary blend oil.
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Affiliation(s)
- Huan Zhang
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Environment Science and Engineering, Tiangong University, Tianjin 300387, China
| | - Xiaoyun Hu
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Environment Science and Engineering, Tiangong University, Tianjin 300387, China
| | - Limei Liu
- School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
| | - Junfu Wei
- School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
| | - Xihui Bian
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Environment Science and Engineering, Tiangong University, Tianjin 300387, China; School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China; Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, 644000, China; State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China.
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21
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Determination and multivariate evaluation of the mineral composition of red jambo (Syzygium malaccense (L.)). Food Chem 2022; 371:131381. [PMID: 34808774 DOI: 10.1016/j.foodchem.2021.131381] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 07/30/2021] [Accepted: 09/03/2021] [Indexed: 12/24/2022]
Abstract
This work aimed to evaluate the mineral composition of twelve samples of red jambo (Syzygium malaccensis) collected in 10 cities in the state of Bahia. The samples were digested in a digester block with a reflux system and cold finger, and the analytes were determined by optical emission spectrometry with inductively coupled plasma. The accuracy of the method was confirmed by analyzing NIST 1570a certified reference material (spinach leaves) at a 95% confidence level. The results were evaluated through Principal Component Analysis and Hierarchical Cluster Analysis, which allowed the identification of outliers in the results of the city of Jaguaquara. The analyte concentrations in the samples (mg 100 g -1) comprised a range of: Ca (3.0-28.9), Fe (0.035-0.125), K (134.8-197.5), Mg (2.7-19.8), Mn (0.012-0.131), Na (0.5-10.8), P (0.24-13.5), Sr (0.010-0.314), and Zn (0.026-0.129). This demonstrates that the fruit can be indicated as a potential nutritional supplement in human nutrition.
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22
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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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23
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24
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Data fusion of near-infrared diffuse reflectance spectra and transmittance spectra for the accurate determination of rice flour constituents. Anal Chim Acta 2022; 1193:339384. [DOI: 10.1016/j.aca.2021.339384] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 12/16/2021] [Accepted: 12/17/2021] [Indexed: 01/07/2023]
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25
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Surya V, Senthilselvi A. Identification of oil authenticity and adulteration using deep long short-term memory-based neural network with seagull optimization algorithm. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06829-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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26
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27
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Giussani B, Escalante-Quiceno AT, Boqué R, Riu J. Measurement Strategies for the Classification of Edible Oils Using Low-Cost Miniaturised Portable NIR Instruments. Foods 2021; 10:foods10112856. [PMID: 34829136 PMCID: PMC8618161 DOI: 10.3390/foods10112856] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/06/2021] [Accepted: 11/15/2021] [Indexed: 11/16/2022] Open
Abstract
Miniaturised near-infrared (NIR) instruments have been increasingly used in the last few years, and they have become useful tools for many applications on different types of samples. The market already offers a wide variety of these instruments, each one having specific requirements for the correct acquisition of the instrumental signal. This paper presents the development and optimisation of different measuring strategies for two miniaturised NIR instruments in order to find the best measuring conditions for the rapid and low-cost analysis of olive oils. The developed strategies have been applied to the classification of different samples of olive oils, obtaining good results in all cases.
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Affiliation(s)
- Barbara Giussani
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell’Insubria, Via Valleggio, 9, 22100 Como, Italy;
| | - Alix Tatiana Escalante-Quiceno
- Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Carrer Marcel·lí Domingo 1, 43007 Tarragona, Spain; (A.T.E.-Q.); (R.B.)
| | - Ricard Boqué
- Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Carrer Marcel·lí Domingo 1, 43007 Tarragona, Spain; (A.T.E.-Q.); (R.B.)
| | - Jordi Riu
- Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Carrer Marcel·lí Domingo 1, 43007 Tarragona, Spain; (A.T.E.-Q.); (R.B.)
- Correspondence: ; Tel.: +34-977-558-491
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28
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Ancora D, Milavec J, Gradišek A, Cifelli M, Sepe A, Apih T, Zalar B, Domenici V. Sensitivity of Proton NMR Relaxation and Proton NMR Diffusion Measurements to Olive Oil Adulterations with Vegetable Oils. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:12081-12088. [PMID: 34014664 PMCID: PMC8532151 DOI: 10.1021/acs.jafc.1c00914] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 04/11/2021] [Accepted: 05/07/2021] [Indexed: 05/27/2023]
Abstract
Olive oils and, in particular, extra-virgin olive oils (EVOOs) are one of the most frauded food. Among the different adulterations of EVOOs, the mixture of high-quality olive oils with vegetable oils is one of the most common in the market. The need for fast and cheap techniques able to detect extra-virgin olive oil adulterations was the main motivation for the present research work based on 1H NMR relaxation and diffusion measurements. In particular, the 1H NMR relaxation times, T1 and T2, measured at 2 and 100 MHz on about 60 EVOO samples produced in Italy are compared with those measured on four different vegetable oils, produced from macadamia nuts, linseeds, sunflower seeds, and soybeans. Self-diffusion coefficients on this set of olive oils and vegetable oil samples were measured by means of the 1H NMR diffusion ordered spectroscopy (DOSY) technique, showing that, except for the macadamia oil, other vegetable oils are characterized by an average diffusion coefficient sensibly different from extra-virgin olive oils. Preliminary tests based on both NMR relaxation and diffusometry methods indicate that eventual adulterations of EVOO with linseed oil and macadamia oil are the easiest and the most difficult frauds to be detected, respectively.
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Affiliation(s)
- Donatella Ancora
- Dipartimento
di Chimica e Chimica Industriale, Università
di Pisa, via Moruzzi, 3, 56124 Pisa, Italy
| | - Jerneja Milavec
- Department
of Condensed Matter Physics, Jožef
Stefan Institute, 39 Jamova Cesta, SI-1000, Ljubljana, Slovenia
| | - Anton Gradišek
- Department
of Condensed Matter Physics, Jožef
Stefan Institute, 39 Jamova Cesta, SI-1000, Ljubljana, Slovenia
| | - Mario Cifelli
- Dipartimento
di Chimica e Chimica Industriale, Università
di Pisa, via Moruzzi, 3, 56124 Pisa, Italy
| | - Ana Sepe
- Department
of Condensed Matter Physics, Jožef
Stefan Institute, 39 Jamova Cesta, SI-1000, Ljubljana, Slovenia
| | - Tomaž Apih
- Department
of Condensed Matter Physics, Jožef
Stefan Institute, 39 Jamova Cesta, SI-1000, Ljubljana, Slovenia
| | - Boštjan Zalar
- Department
of Condensed Matter Physics, Jožef
Stefan Institute, 39 Jamova Cesta, SI-1000, Ljubljana, Slovenia
| | - Valentina Domenici
- Dipartimento
di Chimica e Chimica Industriale, Università
di Pisa, via Moruzzi, 3, 56124 Pisa, Italy
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29
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Chemometric strategies for authenticating extra virgin olive oils from two geographically adjacent Catalan protected designations of origin. Microchem J 2021. [DOI: 10.1016/j.microc.2021.106611] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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30
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Study of the Evolution of Pigments from Freshly Pressed to 'On-the-Shelf' Extra-Virgin Olive Oils by Means of Near-UV Visible Spectroscopy. Foods 2021; 10:foods10081891. [PMID: 34441668 PMCID: PMC8394633 DOI: 10.3390/foods10081891] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 08/10/2021] [Accepted: 08/11/2021] [Indexed: 12/24/2022] Open
Abstract
Spectroscopic non-destructive methods have high potentialities as fast, cheap and easy-to-be-used approaches to address olive oil quality and authenticity. Based on previous research where near-UV Visible spectroscopy was used to investigate extra-virgin olive oils (EVOOs) and their main pigments’ content (i.e., β-carotene, lutein, pheophytin a and pheophytin b), we have implemented the spectral deconvolution method in order to follow the EVOO’s life, from ‘freshly pressed’ to ‘on-the-shelf’ EVOO samples at different storage time. In the first part of the manuscript, the new implemented deconvolution spectroscopic method aimed to quantify two additional pigments, namely chlorophyll a and chlorophyll b, is described and tested on ‘ad hoc’ samples with known concentrations of chlorophylls. The effect of light exposure and acidification was investigated to test the reliability and robustness of the spectral deconvolution. In the second part of the work, this approach was used to study the kinetic of pigments’ degradation in several monocultivar fresh EVOO samples under optimal storage’s conditions. The results here reported show that this spectroscopic deconvolution approach is a good method to study fresh EVOOs too; moreover, the proposed method revealed to be sensitive to detect eventual stresses of olive oil samples stored in not-good conditions.
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31
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32
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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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