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Abdel Halim AS, Abdel-Salam Z, Abdel-Harith M, Hamdy O. Investigating the effect of changing the substrate material analyzed by laser-induced breakdown spectroscopy on the antenna performance. Sci Rep 2024; 14:1964. [PMID: 38263437 PMCID: PMC10806075 DOI: 10.1038/s41598-024-52435-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 01/18/2024] [Indexed: 01/25/2024] Open
Abstract
Miniaturized microstrip antennas are efficiently utilized in MICS band wearable and implantable medical applications. However, the properties of the materials employed for antenna fabrication influence its resultant parameters and play a vital role in its performance. Rogers have been widely used as a substrate material in various antenna designs. In this work, a proof of concept study has been conducted to determine how altering the substrate used in antenna construction affects antenna performance. Using the laser-induced breakdown spectroscopy (LIBS) approach, the elements present in the two distinct substrate raw materials were compared to investigate potential effects on the antenna's performance. Given their accessibility and widespread use, two types of Rogers' substrates, RO 3210 and RO 4003, were selected. Furthermore, two identical antenna designs were modeled and fabricated using the two substrate materials. The reflection coefficient (S11) and other antenna parameters were determined and compared. Moreover, the recorded LIBS spectra were evaluated using principle component analysis and partial least square regression techniques. The LIBS spectra showed different copper and iron contents between the two Rogers (i.e., other dielectric properties), leading to a frequency shift. Additionally, impurities in the fabricated material increase the possible losses. Consequently, the elemental contents of the utilized Rogers control the antenna's performance and can ensure its safety in wearable and implant applications.
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Affiliation(s)
- Ashraf S Abdel Halim
- Department of Communication, Faculty of Engineering, Canadian International College (CIC), Cairo, Egypt
| | - Zienab Abdel-Salam
- Laser Applications in Metrology, Photochemistry, and Agriculture Department, National Institute of Laser Enhanced Science, Cairo University, Giza, Egypt
| | - Mohamed Abdel-Harith
- Laser Applications in Metrology, Photochemistry, and Agriculture Department, National Institute of Laser Enhanced Science, Cairo University, Giza, Egypt
| | - Omnia Hamdy
- Department of Engineering Applications of Lasers, National Institute of Laser Enhanced Sciences, Cairo University, Giza, 12613, Egypt.
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2
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Yuan Y, Chen X. Vegetable and fruit freshness detection based on deep features and principal component analysis. Curr Res Food Sci 2023; 8:100656. [PMID: 38188650 PMCID: PMC10767316 DOI: 10.1016/j.crfs.2023.100656] [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: 10/30/2023] [Revised: 12/05/2023] [Accepted: 12/07/2023] [Indexed: 01/09/2024] Open
Abstract
Vegetable and fruit freshness detecting can ensure that consumers get vegetables and fruits with good taste and rich nutrition, improve the health level of diet, and ensure that the agricultural and food industries provide high-quality products to meet consumer needs and increase sales and market share. At present, the freshness detection of vegetables and fruits mainly relies on manual observation and judgment, which has the problems of subjectivity and low accuracy, and it is difficult to meet the needs of large-scale, high-efficiency, and rapid detection. Although some studies have shown that large-scale detection of vegetable and fruit freshness can be carried out based on artificially extracted features, there is still the problem of poor adaptability of artificially extracted features, which leads to low efficiency of freshness detection. To solve this problem, this paper proposes a novel method for detecting the freshness of vegetables and fruits more objectively, accurately and efficiently using deep features extracted by pre-trained deep learning models of different architectures. First, resized images of vegetables and fruits are fed into a pre-trained deep learning model for deep feature extraction. Then, the deep features are fused and the fused deep features are dimensionally reduced to a representative low-dimensional feature space by principal component analysis. Finally, vegetable and fruit freshness are detected by three machine learning methods. The experimental results show that combining the deep features extracted by the three architecture pre-trained deep learning models GoogLeNet, DenseNet-201 and ResNeXt-101 combined with PCA dimensionality reduction processing has achieved the highest accuracy rate of 96.98% for vegetable and fruit freshness detection. This research concluded that the proposed method is promising to improve the efficiency of freshness detection of vegetables and fruits.
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Affiliation(s)
- Yue Yuan
- School of Information Engineering, Shenyang University, Shenyang, 110042, China
| | - Xianlong Chen
- Liaoning Provincial Public Security Department, Shenyang, 110000, China
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3
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Ansar A, Ahmad N, Albqmi M, Saleem M, Ali H. Thermal Effects on the Quality Parameters of Extra Virgin Olive Oil Using Fluorescence Spectroscopy. J Fluoresc 2023; 33:1749-1760. [PMID: 36826729 DOI: 10.1007/s10895-023-03186-3] [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: 12/02/2022] [Accepted: 02/15/2023] [Indexed: 02/25/2023]
Abstract
Extra virgin olive oil is one of the superlative due to its health benefits. In this work, the Fluorescence spectra of extra virgin olive oil (EVOO) from different olive growing regions of Pakistan and Al-Jouf region from the Kingdom of Saudi Arabia (KSA) were obtained. The emission bands depicted relative intensity variations in all non-heated and heated EVOO samples. Prominent emission bands at 385, 400, 435 and 470 nm represent oxidized products of fatty acids, bands at 520 and 673 nm has been assigned to beta carotene and chlorophyll isomers respectively. All EVOO samples collected from Al-Jouf region, KSA and from Pakistan (Loralai Baluchistan, Barani Agricultural Research Institute, Chakwal and Morgha Biodiversity Park, Rawalpindi) regions showed thermal stability. Other EVOO samples from Chaman Baluchistan and one sample from wild specie (Baluchistan) bought directly from farmers showed denatured spectra even without heating. Chemical characteristics of all EVOO samples changed significantly at 200 °C. Relatively, EVOO samples from Al-Jouf showed more thermal stability which might be due to geographical distribution, environmental effects, genetic background and processing or storage conditions. These results demonstrated fluorescence spectroscopy as a quick, cost-effective and reliable approach to assess the quality and thermal stability of EVOO. These characteristics of fluorescence spectroscopy may lead to the development of portable device for the onsite monitoring of EVOO.
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Affiliation(s)
- Areeba Ansar
- National Institute of Lasers and Optronics College, Pakistan Institute of Engineering and Applied Sciences, Nilore, 45650, Islamabad, Pakistan
- Department of Physics, Mirpur University of Science and Technology (MUST), Mirpur, 10250, Azad Jammu and Kashmir, Pakistan
| | - Naveed Ahmad
- Department of Physics, Mirpur University of Science and Technology (MUST), Mirpur, 10250, Azad Jammu and Kashmir, Pakistan.
| | - Mha Albqmi
- Chemistry Department, College of Science and Arts, Jouf University, Alqurayyat, Saudi Arabia
| | - Muhammad Saleem
- National Institute of Lasers and Optronics College, Pakistan Institute of Engineering and Applied Sciences, Nilore, 45650, Islamabad, Pakistan
| | - Hina Ali
- National Institute of Lasers and Optronics College, Pakistan Institute of Engineering and Applied Sciences, Nilore, 45650, Islamabad, Pakistan
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Hamdy O, Mohammed HS. Post-heating Fluorescence-based Alteration and Adulteration Detection of Extra Virgin Olive Oil. J Fluoresc 2023; 33:1631-1639. [PMID: 36808529 PMCID: PMC10361879 DOI: 10.1007/s10895-023-03165-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 02/01/2023] [Indexed: 02/21/2023]
Abstract
Olive oils are more expensive compared with other vegetable oils. Therefore, adulterating such expensive oil is prevalent. The traditional methods for olive oil adulteration detection are complex and require pre-analysis sample preparation. Therefore, simple and precise alternative techniques are required. In the present study, the Laser-induced fluorescence (LIF) technique was implemented for detecting alteration and adulteration of olive oil mixed with sunflower or corn oil based on the post-heating emission characteristics. Diode-pumped solid-state laser (DPSS, λ = 405 nm) was employed for excitation and the fluorescence emission was detected via an optical fiber connected to a compact spectrometer. The obtained results revealed alterations in the recorded chlorophyll peak intensity due to olive oil heating and adulteration. The correlation of the experimental measurements was evaluated via partial least-squares regression (PLSR) with an R-squared value of 0.95. Moreover, the system performance was evaluated using receiver operating characteristics (ROC) with a maximum sensitivity of 93%.
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Affiliation(s)
- Omnia Hamdy
- Engineering Applications of Lasers Department, National Institute of Laser Enhanced Sciences, Cairo University, Giza, 12613, Egypt.
| | - Haitham S Mohammed
- Biophysics Department, Faculty of Science, Cairo University, Giza, 12613, Egypt
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5
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The Effect of Data Fusion on Improving the Accuracy of Olive Oil Quality Measurement. Food Chem X 2023; 18:100622. [DOI: 10.1016/j.fochx.2023.100622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 02/08/2023] [Accepted: 02/28/2023] [Indexed: 03/08/2023] Open
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6
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Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks: Application to olive oil. J FOOD ENG 2023. [DOI: 10.1016/j.jfoodeng.2022.111198] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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7
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Qualitative and Quantitative Detection of Acacia Honey Adulteration with Glucose Syrup Using Near-Infrared Spectroscopy. SEPARATIONS 2022. [DOI: 10.3390/separations9100312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Honey adulteration with cheap sweeteners such as corn syrup or invert syrup results in honey of lesser quality that can harm the objectives of both manufacturers and consumers. Therefore, there is a growing interest for the development of a fast and simple method for adulteration detection. In this work, near-infrared spectroscopy (NIR) was used for the detection of honey adulteration and changes in the physical and chemical properties of the prepared adulterations. Fifteen (15) acacia honey samples were adulterated with glucose syrup in a range from 10% to 90%. Raw and pre-processed NIR spectra of pure honey samples and prepared adulterations were subjected to Principal Component Analysis (PCA), Partial Least Squares (PLS) regression, and Artificial Neural Network (ANN) modeling. The results showed that PCA ensures distinct grouping of samples in pure honey samples, honey adulterations, and pure adulteration using NIR spectra after the Multiplicative Scatter Correction (MSC) method. Furthermore, PLS models developed for the prediction of the added adulterant amount, moisture content, and conductivity can be considered sufficient for screening based on RPD and RER values (1.7401 < RPD < 2.7601; 7.7128 < RER < 8.7157) (RPD of 2.7601; RER of 8.7157) and can be moderately used in practice. The R2validation of the developed ANN models was greater than 0.86 for all outputs examined. Based on the obtained results, it can be concluded that NIR coupled with ANN modeling can be considered an efficient tool for honey adulteration quantification.
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Zaroual H, Chèné C, Mestafa El Hadrami E, Karoui R. Comparison of four classification statistical methods for characterising virgin olive oil quality during storage up to 18 months. Food Chem 2022; 370:131009. [PMID: 34509151 DOI: 10.1016/j.foodchem.2021.131009] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 07/29/2021] [Accepted: 08/29/2021] [Indexed: 11/25/2022]
Abstract
This study examines the ability of fluorescence spectroscopy for monitoring the quality of 70 Moroccan virgin olive oils belonging to three varieties and originating from three regions of Morocco. By applying principal component analysis and factorial discriminant analysis to the emission spectra acquired after excitation wavelengths set at 270, 290, and 430 nm, a clear differentiation between samples according to their storage time was observed. The obtained results were confirmed following the application of four multivariate classification methods: partial least squares regression, principal component regression, support vector machine, and multiple linear regression on the emission spectra. The best prediction model of storage time was obtained by applying partial least squares regression since a coefficient of determination (R2) and a root mean square error of prediction (RMSEP) of 0.98 and 24.85 days were observed, respectively. The prediction of the chemical parameters allowed to obtain excellent validation models with R2 ranging between 0.98 and 0.99 for free acidity, peroxide value, chlorophyll level, k232, and k270.
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Affiliation(s)
- Hicham Zaroual
- Univ. Artois, Univ. Lille, Univ. Littoral Côte d'Opale, Univ. Picardie Jules Verne, Univ. de Liège, INRAE, Junia, UMR-T 1158, BioEcoAgro, F-62300, Lens, France; Univ. Sidi Mohamed Ben Abdellah, Faculty of Sciences and Technologies, Applied Organic Chemistry Laboratory, Fez M-30000, Morocco
| | | | - El Mestafa El Hadrami
- Univ. Sidi Mohamed Ben Abdellah, Faculty of Sciences and Technologies, Applied Organic Chemistry Laboratory, Fez M-30000, Morocco
| | - Romdhane Karoui
- Univ. Artois, Univ. Lille, Univ. Littoral Côte d'Opale, Univ. Picardie Jules Verne, Univ. de Liège, INRAE, Junia, UMR-T 1158, BioEcoAgro, F-62300, Lens, France.
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9
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El Orche A, Elhamdaoui O, Cheikh A, Zoukeni B, El Karbane M, Mbarki M, Bouatia M. Comparative study of three fingerprint analytical approaches based on spectroscopic sensors and chemometrics for the detection and quantification of argan oil adulteration. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2022; 102:95-104. [PMID: 34032291 DOI: 10.1002/jsfa.11335] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 04/18/2021] [Accepted: 05/25/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Argan oil is one of the purest and rarest oils in the world, so that the addition of any further product is strictly prohibited by international regulations. Consequently, it is necessary to establish reliable analytical methods to ensure its authenticity. In this study, three multivariate approaches have been developed and validated using fluorescence, UV-visible, and attenuated total reflectance Fourier transform mid-infrared (FT-MIR) spectroscopies. RESULTS The application of a partial least squares discriminant analysis model showed an accuracy of 100%. The quantification of adulteration have been evaluated using partial least squares (PLS) regression. The PLS model developed from fluorescence spectroscopy provided the best results for the calibration and cross-validation sets, as it showed the highest R2 (0.99) and the lowest root mean square error of calibration and cross-validation (0.55, 0.79). The external validation of the three multivariate approaches by the accuracy profile shows that these approaches guarantee reliable and valid results of 0.5-32%, 7-32%, and 10-32% using fluorescence, FT-MIR and UV-visible spectroscopies respectively. CONCLUSION This study confirmed the feasibility of using spectroscopic sensors (routine technique) for rapid determination of argan oil falsification. © 2021 Society of Chemical Industry.
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Affiliation(s)
- Aimen El Orche
- Laboratory of Organic and Analytical Chemistry, University of Sultan Moulay Slimane, Beni-Mellal, Morocco
| | - Omar Elhamdaoui
- Laboratory of Analytical Chemistry, Faculty of Medicine and Pharmacy, Mohammed V University, Rabat, Morocco
| | - Amine Cheikh
- Faculty of Medicine, Abulcasis University, Rabat, Morocco
| | - Brahim Zoukeni
- Laboratory of Organic and Analytical Chemistry, University of Sultan Moulay Slimane, Beni-Mellal, Morocco
| | - Miloud El Karbane
- Laboratory of Analytical Chemistry, Faculty of Medicine and Pharmacy, Mohammed V University, Rabat, Morocco
| | - Mohamed Mbarki
- Laboratory of Organic and Analytical Chemistry, University of Sultan Moulay Slimane, Beni-Mellal, Morocco
| | - Mustapha Bouatia
- Laboratory of Analytical Chemistry, Faculty of Medicine and Pharmacy, Mohammed V University, Rabat, Morocco
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10
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Botosoa EP, Chèné C, Karoui R. Front Face Fluorescence Spectroscopy Combined with PLS‐DA Allows to Monitor Chemical Changes of Edible Vegetable Oils during Storage at 60 °C. EUR J LIPID SCI TECH 2021. [DOI: 10.1002/ejlt.202000088] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Eliot Patrick Botosoa
- Univ. Artois, EA 7394, ICV‐Institut Charles VIOLLETTE Lens F‐62300 France
- INRA, USC 1281 Lille F‐59000 France
- Ulco F‐62200 Boulogne‐sur‐Mer France
- Univ. Lille Lille F‐59000 France
- YNCREA Lille F‐59000 France
| | | | - Romdhane Karoui
- Univ. Artois, EA 7394, ICV‐Institut Charles VIOLLETTE Lens F‐62300 France
- INRA, USC 1281 Lille F‐59000 France
- Ulco F‐62200 Boulogne‐sur‐Mer France
- Univ. Lille Lille F‐59000 France
- YNCREA Lille F‐59000 France
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11
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El Orche A, Adade CA, Mefetah H, Cheikh A, Karrouchi K, El Karbane M, Bouatia M. Chemometric Analysis of UV-Visible Spectral Fingerprints for the Discrimination and Quantification of Clinical Anthracycline Drug Preparation Used in Oncology. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5580102. [PMID: 34041297 PMCID: PMC8121585 DOI: 10.1155/2021/5580102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 04/12/2021] [Accepted: 04/29/2021] [Indexed: 11/17/2022]
Abstract
In clinical treatment, the analytical quality assessment of the delivery of chemotherapeutic preparations is required to guarantee the patient's safety regarding the dose and most importantly the appropriate anticancer drug. On its own, the development of rapid analytical methods allowing both qualitative and quantitative control of the formulation of prepared solutions could significantly enhance the hospital's workflow, reducing costs, and potentially providing optimal patient care. UV-visible spectroscopy is a nondestructive, fast, and economical technique for molecular characterization of samples. A discrimination and quantification study of three chemotherapeutic drugs doxorubicin, daunorubicin, and epirubicin was conducted, using clinically relevant concentration ranges prepared in 0.9% NaCl solutions. The application of the partial least square discriminant analysis PLS-DA method on the UV-visible spectral data shows a perfect discrimination of the three drugs with a sensitivity and specificity of 100%. The use of partial least square regression PLS shows high quantification performance of these molecules in solution represented by the low value of root mean square error of calibration (RMSEC) and root mean square error of cross validation (RMSCECV) on the one hand and the high value of R-square on the other hand. This study demonstrated the viability of UV-visible fingerprinting (routine approach) coupled with chemometric tools for the classification and quantification of chemotherapeutic drugs during clinical preparation.
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Affiliation(s)
- Aimen El Orche
- Laboratory of Chemical Processes and Applied Materials, Faculty of Science and Technology, Sultan Moulay Slimane University, Beni-Mellal, Morocco
| | - Casimir Adade Adade
- Team of Formulation and Quality Control of Health Products, Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Morocco
| | - Hafid Mefetah
- Rabat Pediatrics Hospital, Ibn Sina University Hospital Center, Rabat, Morocco
| | - Amine Cheikh
- Departement of Pharmacy, Faculty of Pharmacy, Abulcasis University, Rabat, Morocco
| | - Khalid Karrouchi
- Team of Formulation and Quality Control of Health Products, Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Morocco
| | - Miloud El Karbane
- Team of Formulation and Quality Control of Health Products, Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Morocco
| | - Mustapha Bouatia
- Team of Formulation and Quality Control of Health Products, Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Morocco
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Venturini F, Sperti M, Michelucci U, Herzig I, Baumgartner M, Caballero JP, Jimenez A, Deriu MA. Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques. Foods 2021; 10:foods10051010. [PMID: 34066453 PMCID: PMC8148140 DOI: 10.3390/foods10051010] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 04/29/2021] [Accepted: 04/30/2021] [Indexed: 02/07/2023] Open
Abstract
Extra virgin olive oil (EVOO) is the highest quality of olive oil and is characterized by highly beneficial nutritional properties. The large increase in both consumption and fraud, for example through adulteration, creates new challenges and an increasing demand for developing new quality assessment methodologies that are easier and cheaper to perform. As of today, the determination of olive oil quality is performed by producers through chemical analysis and organoleptic evaluation. The chemical analysis requires advanced equipment and chemical knowledge of certified laboratories, and has therefore limited accessibility. In this work a minimalist, portable, and low-cost sensor is presented, which can perform olive oil quality assessment using fluorescence spectroscopy. The potential of the proposed technology is explored by analyzing several olive oils of different quality levels, EVOO, virgin olive oil (VOO), and lampante olive oil (LOO). The spectral data were analyzed using a large number of machine learning methods, including artificial neural networks. The analysis performed in this work demonstrates the possibility of performing the classification of olive oil in the three mentioned classes with an accuracy of 100%. These results confirm that this minimalist low-cost sensor has the potential to substitute expensive and complex chemical analysis.
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Affiliation(s)
- Francesca Venturini
- Institute of Applied Mathematics and Physics, Zurich University of Applied Sciences, Technikumstrasse 9, 8401 Winterthur, Switzerland; (I.H.); (M.B.)
- TOELT LLC, Birchlenstr. 25, 8600 Dübendorf, Switzerland;
- Correspondence: (F.V.); (M.A.D.)
| | - Michela Sperti
- Polito BIO Med Lab., Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy;
| | - Umberto Michelucci
- TOELT LLC, Birchlenstr. 25, 8600 Dübendorf, Switzerland;
- School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK
| | - Ivo Herzig
- Institute of Applied Mathematics and Physics, Zurich University of Applied Sciences, Technikumstrasse 9, 8401 Winterthur, Switzerland; (I.H.); (M.B.)
| | - Michael Baumgartner
- Institute of Applied Mathematics and Physics, Zurich University of Applied Sciences, Technikumstrasse 9, 8401 Winterthur, Switzerland; (I.H.); (M.B.)
| | - Josep Palau Caballero
- SCA San Sebastián Puente del Ventorro, s/n, 18566 Benalua de las Villas, Spain; (J.P.C.); (A.J.)
| | - Arturo Jimenez
- SCA San Sebastián Puente del Ventorro, s/n, 18566 Benalua de las Villas, Spain; (J.P.C.); (A.J.)
| | - Marco Agostino Deriu
- Polito BIO Med Lab., Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy;
- Correspondence: (F.V.); (M.A.D.)
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Near UV-Vis and NMR Spectroscopic Methods for Rapid Screening of Antioxidant Molecules in Extra-Virgin Olive Oil. Antioxidants (Basel) 2020; 9:antiox9121245. [PMID: 33302468 PMCID: PMC7764626 DOI: 10.3390/antiox9121245] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 12/03/2020] [Accepted: 12/05/2020] [Indexed: 01/28/2023] Open
Abstract
Several spectroscopic techniques have been optimized to check extra-virgin olive oil quality and authenticity, as well as to detect eventual adulterations. These methods are usually complementary and can give information about different olive oil chemical components with bioactive and antioxidant properties. In the present work, a well-characterized set of extra-virgin olive oil (cultivar Frantoio) samples from a specific area of Tuscany (Italy) were investigated by combining near UV-Vis absorption spectroscopy, 1H and 13C nuclear magnetic resonance (NMR) to identify and quantify different chemical components, such as pigments, secoiridoids and squalene, related to the nutritional and quality properties of olive oils. Moreover, the pigmentation index of olives, organoleptic and sensory properties, total phenolic compound contents and the lipidic fractions of olive oils were investigated. The results obtained are, finally, compared and discussed in order to correlate several properties of both olives and olive oils with specific features of the cultivation area.
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14
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Elhamdaoui O, El Orche A, Cheikh A, Mojemmi B, Nejjari R, Bouatia M. Development of Fast Analytical Method for the Detection and Quantification of Honey Adulteration Using Vibrational Spectroscopy and Chemometrics Tools. JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY 2020; 2020:8816249. [PMID: 33425426 PMCID: PMC7773450 DOI: 10.1155/2020/8816249] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 11/15/2020] [Accepted: 12/10/2020] [Indexed: 05/07/2023]
Abstract
In this study, the Fourier transform mid-infrared (FT-MIR) spectroscopy technique combined with chemometrics methods was used to monitor adulteration of honey with sugar syrup. Spectral data were recorded from a wavenumber region of 4000-600 cm-1, with a spectral resolution of 4 cm-1. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used for qualitative analysis to discriminate between adulterated and nonadulterated honey. For quantitative analysis, we used partial least-squares regression (PLS-R) and the support vector machine (SVM) to develop optimal calibration models. The use of PCA shows that the first two principal components account for 96% of the total variability. PCA and HCA allow classifying the dataset into two groups: adulterated and unadulterated honey. The use of the PLS-R and SVM-R calibration models for the quantification of adulteration shows high-performance capabilities represented by a high value of correlation coefficients R 2 greater than 98% and 95% with lower values of root mean square error (RMSE) less than 1.12 and 1.85 using PLS-R and SVM-R, respectively. Our results indicate that FT-MIR spectroscopy combined with chemometrics techniques can be used successfully as a simple, rapid, and nondestructive method for the quantification and discrimination of adulterated honey.
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Affiliation(s)
- Omar Elhamdaoui
- Laboratory of Analytical Chemistry, Team of Formulation and Quality Control of Health Products, Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Rabat, Morocco
| | - Aimen El Orche
- Laboratory of Chemical Processes and Applied Materials, Faculty of Science and Technology, Sultan Moulay Slimane University, Beni-Mellal, Morocco
| | - Amine Cheikh
- Faculty of Pharmacy, Abulcasis University, Rabat, Morocco
| | - Brahim Mojemmi
- Laboratory of Analytical Chemistry, Team of Formulation and Quality Control of Health Products, Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Rabat, Morocco
| | - Rachid Nejjari
- Laboratory of Pharmacognosy, Team of Formulation and Quality Control of Health Products, Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Rabat, Morocco
| | - Mustapha Bouatia
- Laboratory of Analytical Chemistry, Team of Formulation and Quality Control of Health Products, Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Rabat, Morocco
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