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Sun X, Hu Y, Liu C, Zhang S, Yan S, Liu X, Zhao K. Characterizing Edible Oils by Oblique-Incidence Reflectivity Difference Combined with Machine Learning Algorithms. Foods 2024; 13:1420. [PMID: 38731791 PMCID: PMC11083255 DOI: 10.3390/foods13091420] [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: 03/30/2024] [Revised: 04/24/2024] [Accepted: 04/29/2024] [Indexed: 05/13/2024] Open
Abstract
Due to the significant price differences among different types of edible oils, expensive oils like olive oil are often blended with cheaper edible oils. This practice of adulteration in edible oils, aimed at increasing profits for producers, poses a major concern for consumers. Furthermore, adulteration in edible oils can lead to various health issues impacting consumer well-being. In order to meet the requirements of fast, non-destructive, universal, accurate, and reliable quality testing for edible oil, the oblique-incidence reflectivity difference (OIRD) method combined with machine learning algorithms was introduced to detect a variety of edible oils. The prediction accuracy of Gradient Boosting, K-Nearest Neighbor, and Random Forest models all exceeded 95%. Moreover, the contribution rates of the OIRD signal, DC signal, and fundamental frequency signal to the classification results were 45.7%, 34.1%, and 20.2%, respectively. In a quality evaluation experiment on olive oil, the feature importance scores of three signals reached 63.4%, 18.9%, and 17.6%. The results suggested that the feature importance score of the OIRD signal was significantly higher than that of the DC and fundamental frequency signals. The experimental results indicate that the OIRD method can serve as a powerful tool for detecting edible oils.
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Affiliation(s)
- Xiaorong Sun
- College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; (X.S.); (Y.H.); (S.Z.)
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Yiran Hu
- College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; (X.S.); (Y.H.); (S.Z.)
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Cuiling Liu
- College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; (X.S.); (Y.H.); (S.Z.)
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Shanzhe Zhang
- College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; (X.S.); (Y.H.); (S.Z.)
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Sining Yan
- College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; (X.S.); (Y.H.); (S.Z.)
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Xuecong Liu
- College of Information Science and Engineering/College of Artificial Intelligence, China University of Petroleum, Beijing 102249, China;
| | - Kun Zhao
- College of New Energy and Materials, China University of Petroleum, Beijing 102249, China
- Key Laboratory of Oil and Gas Terahertz Spectroscopy and Photoelectric Detection, Petroleum and Chemical Industry Federation, China University of Petroleum, Beijing 102249, China
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2
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Vladev V, Brazkova M, Bozhkov S, Angelova G, Blazheva D, Minkova S, Nikolova K, Eftimov T. Light-Emitting-Diode-Induced Fluorescence from Organic Dyes for Application in Excitation-Emission Fluorescence Spectroscopy for Food System Analysis. Foods 2024; 13:1329. [PMID: 38731700 PMCID: PMC11083508 DOI: 10.3390/foods13091329] [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: 03/26/2024] [Revised: 04/12/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024] Open
Abstract
An experimental study is presented on the possibility of using the fluorescence from organic dyes as a broadband light source together with a monochromator for applications in excitation-emission matrix (EEM) fluorescence spectroscopy. A high-power single-chip light-emitting diode (LED) was chosen as an excitation source with a central output wavelength at 365 nm to excite a fluorescent solution of Coumarin 1 dye dissolved in ethanol. Two excitation configurations were investigated: direct excitation from the LED and excitation through an optical-fiber-coupled LED. A Czerny-Turner monochromator with a diffraction grating was used for the spectral tuning of the fluorescence. A simple method was investigated for increasing the efficiency of the excitation as well as the fluorescence signal collection by using a diffuse reflector composed of barium sulfate (BaSO4) and polyvinyl alcohol (PVA). As research objects, extra-virgin olive oil (EVOO), Coumarin 6 dye, and Perylene, a polycyclic aromatic hydrocarbon (PAH), were used. The results showed that the light-emitting-diode-induced fluorescence was sufficient to cover the losses on the optical path to the monochromator output, where a detectable signal could be obtained. The obtained results reveal the practical possibility of applying the fluorescence from dyes as a light source for food system analysis by EEM fluorescence spectroscopy.
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Affiliation(s)
- Veselin Vladev
- Department of Mathematics, Physics and Information Technologies, Faculty of Economics, University of Food Technologies, 26 Maritsa Blvd., 4002 Plovdiv, Bulgaria; (V.V.); (S.B.); (K.N.)
- Central Laboratory of Applied Physics, Bulgarian Academy of Sciences, 61 Sankt Peterburg Blvd., 4002 Plovdiv, Bulgaria;
| | - Mariya Brazkova
- Department of Biotechnology, Technological Faculty, University of Food Technologies, 26 Maritsa Blvd., 4002 Plovdiv, Bulgaria;
| | - Stefan Bozhkov
- Department of Mathematics, Physics and Information Technologies, Faculty of Economics, University of Food Technologies, 26 Maritsa Blvd., 4002 Plovdiv, Bulgaria; (V.V.); (S.B.); (K.N.)
| | - Galena Angelova
- Department of Biotechnology, Technological Faculty, University of Food Technologies, 26 Maritsa Blvd., 4002 Plovdiv, Bulgaria;
| | - Denica Blazheva
- Department of Microbiology, Technological Faculty, University of Food Technologies, 26 Maritza Blvd., 4002 Plovdiv, Bulgaria;
| | - Stefka Minkova
- Department of Physics and Biophysics, Medical University—Varna, 84 Tzar Osvoboditel Blvd., 9000 Varna, Bulgaria;
| | - Krastena Nikolova
- Department of Mathematics, Physics and Information Technologies, Faculty of Economics, University of Food Technologies, 26 Maritsa Blvd., 4002 Plovdiv, Bulgaria; (V.V.); (S.B.); (K.N.)
- Department of Physics and Biophysics, Medical University—Varna, 84 Tzar Osvoboditel Blvd., 9000 Varna, Bulgaria;
| | - Tinko Eftimov
- Central Laboratory of Applied Physics, Bulgarian Academy of Sciences, 61 Sankt Peterburg Blvd., 4002 Plovdiv, Bulgaria;
- Centre de Recherche en Photonique, Université du Québec en Outaouais, 101 rue Saint-Jean-Bosco, Gatineau, QC J8Y 3G5, Canada
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3
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Saleem M, Ahmad N. Fluorescence Spectroscopy Based Characterization of Flaxseed Oil. J Fluoresc 2024:10.1007/s10895-024-03684-y. [PMID: 38602591 DOI: 10.1007/s10895-024-03684-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 03/22/2024] [Indexed: 04/12/2024]
Abstract
Fluorescence spectroscopy has been employed for the compositional analysis of flaxseed oil, detection of its adulteration and investigation of the thermal effects on its molecular composition. Excitation wavelengths from 320 to 420 nm have been used to explore the valued ingredients in flaxseed oil. The emission bands of flaxseed oil centred at 390, 414, 441, 475, 515 and 673/720 nm represent vitamin K, isomers of vitamin E, carotenoids and chlorophylls, which can be used as a marker for quality analysis. Due to its high quality, it is highly prone to adulteration and in this study, detection of its adulteration with canola oil is demonstrated by applying principal component analysis. Moreover, the effects of temperature on the molecular composition of cold pressed flaxseed oil has been explored by heating them at cooking temperatures of 100, 110, 120, 130, 140, 150, 160, 170 and 180 °C, each for 30 min. On heating, the deterioration of vitamin E, carotenoids and chlorophylls occurred with an increase in the oxidation products. However, it was found that up to 140 °C, flaxseed oil retains much of its natural composition whereas up to 180 oC, it loses much of its valuable ingredients along with increase of oxidized products.
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Affiliation(s)
- Muhammad Saleem
- National Institute of Lasers and Optronics College, Pakistan Institute of Engineering and Applied Sciences, 45650, Nilore, Islamabad, Pakistan.
| | - Naveed Ahmad
- Department of Physics, Mirpur University of Science and Technology (MUST), Azad Jammu & Kashmir, 10250, Mirpur, Pakistan.
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4
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Lujun Z, Nuo C, Xiaodong H, Xinmin F, Juanjuan G, Jin G, Sensen L, Yan W, Chunyan W. Adulteration Detection and Quantification in Olive Oil Using Excitation-Emission Matrix Fluorescence Spectroscopy and Chemometrics. J Fluoresc 2024:10.1007/s10895-024-03613-z. [PMID: 38457079 DOI: 10.1007/s10895-024-03613-z] [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: 10/24/2023] [Accepted: 02/08/2024] [Indexed: 03/09/2024]
Abstract
This research investigates the use of excitation-emission matrix fluorescence (EEMF) in conjunction with chemometric models to rapidly identify and quantify adulteration in olive oil, a critical concern where sample availability is limited. Adulteration is simulated by blending soybean, peanut, and linseed oils into olive oil, creating diverse adulterated samples. Principal component analysis (PCA) was applied to the EEMF spectral data as an initial exploratory measure to cluster and differentiate adulterated samples. Spatial clustering enabled vivid visualization of the variations and trends in the spectra. The novel application of parallel factor analysis (PARAFAC) for data decomposition in this paper focuses on unraveling correlations between the decomposed components and the actual adulterated components, which offers a novel perspective for accurately quantifying adulteration levels. Additionally, a comparative analysis was conducted between the PCA and PARAFAC methodologies. Our study not only unveils a new avenue for the quantitative analysis of adulterants in olive oil through spectral detection but also highlights the potential for applying these insights in practical, real-world scenarios, thereby enhancing detection capabilities for various edible oil samples. This promises to improve the detection of adulteration across a range of edible oil samples, offering significant contributions to food safety and quality assurance.
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Affiliation(s)
- Zhang Lujun
- Department of Physics and Electronic Information, Weifang University, Weifang, 261061, China
| | - Cai Nuo
- Department of Physics and Electronic Information, Weifang University, Weifang, 261061, China
| | - Huang Xiaodong
- Department of Physics and Electronic Information, Weifang University, Weifang, 261061, China
| | - Fan Xinmin
- Department of Physics and Electronic Information, Weifang University, Weifang, 261061, China
| | - Gao Juanjuan
- Department of Physics and Electronic Information, Weifang University, Weifang, 261061, China
| | - Gao Jin
- Department of Physics and Electronic Information, Weifang University, Weifang, 261061, China
| | - Li Sensen
- Science and Technology on Electro-Optical Information Security Control Laboratory, Tianjin, 300308, China
| | - Wang Yan
- Department of Physics and Electronic Information, Weifang University, Weifang, 261061, China.
| | - Wang Chunyan
- Department of Physics and Electronic Information, Weifang University, Weifang, 261061, China.
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5
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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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Gong B, Zhang H, Wang X, Lian K, Li X, Chen B, Wang H, Niu X. Ultraviolet-induced fluorescence of oil spill recognition using a semi-supervised algorithm based on thickness and mixing proportion-emission matrices. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:1649-1660. [PMID: 36917485 DOI: 10.1039/d2ay01776h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
In recent years, marine oil spill accidents have been occurring frequently during extraction and transportation, and seriously damage the ecological balance. Accurate monitoring of oil spills plays a vital role in estimating oil spill volume, determination of liability, and clean-up. The oil that leaks into natural environments is not a single type of oil, but a mixture of various oil products, and the oil film thickness on the sea surface is uneven under the influence of wind and waves. Increasing the mixed oil film thickness dimension and the mix proportion dimension has been proposed to weaken the effect of the detection environment on the fluorescence measurement results. To preserve the relationships between the data of oil films with different thicknesses and the relationships between the data of oil films with different mixing proportions, the three-dimensional fluorescence spectral data of mixed oil films on a seawater surface were measured in the laboratory, producing a thickness-fluorescence matrix and a proportion-fluorescence matrix. The nonlinear variation of the fluorescence spectra was investigated according to the fluorescence lidar equation. This work pre-processes the data by sum normalization and two-dimensional principal component analysis (2DPCA) and uses the dimensionality reduction results as two feature-point views. Then, semi-supervised classification of collaborative training (co-training) with K-nearest neighbors (KNN) and a decision tree (DT) is used to identify the samples. The results show that the average overall accuracy of this coupling model can reach 100%, which is 20.49% higher than that of the thickness-only view. Using unlabeled data can reduce the cost of data acquisition, improve the classification accuracy and generalization ability, and provide theoretical significance and application prospects for discrimination of spectrally similar oil species in natural marine environments.
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Affiliation(s)
- Bowen Gong
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, 130033, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China. @mails.ucas.ac.cn
| | - Hongji Zhang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, 130033, China.
| | - Xiaodong Wang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, 130033, China.
| | - Ke Lian
- Shanghai Institute of Spacecraft Equipment, Shanghai, 200240, China
| | - Xinkai Li
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, 130033, China.
| | - Bo Chen
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, 130033, China.
| | - Hanlin Wang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, 130033, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China. @mails.ucas.ac.cn
| | - Xiaoqian Niu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, 130033, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China. @mails.ucas.ac.cn
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7
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Applications of UV–Visible, Fluorescence and Mid-Infrared Spectroscopic Methods Combined with Chemometrics for the Authentication of Apple Vinegar. Foods 2023; 12:foods12061139. [PMID: 36981065 PMCID: PMC10048337 DOI: 10.3390/foods12061139] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/02/2023] [Accepted: 03/06/2023] [Indexed: 03/11/2023] Open
Abstract
Spectroscopic techniques as untargeted methods have great potential in food authentication studies, and the evaluation of spectroscopic data with chemometric methods can provide accurate predictions of adulteration even for hard-to-identify cases such as the mixing of vinegar with adulterants having a very similar chemical nature. In this study, we aimed to compare the performances of three spectroscopic methods (fluorescence, UV–visible, mid-infrared) in the detection of acetic-acid/apple-vinegar and spirit-vinegar/apple-vinegar mixtures (1–50%). Data obtained with the three spectroscopic techniques were used in the generation of classification models with partial least square discriminant analysis (PLS-DA) and orthogonal partial least square discriminant analysis (OPLS-DA) to differentiate authentic and mixed samples. An improved classification approach was used in choosing the best models through a number of calibration and validation sets. Only the mid-infrared data provided robust and accurate classification models with a high classification rate (up to 96%), sensitivity (1) and specificity (up to 0.96) for the differentiation of the adulterated samples from authentic apple vinegars. Therefore, it was concluded that mid-infrared spectroscopy is a useful tool for the rapid authentication of apple vinegars and it is essential to test classification models with different datasets to obtain a robust model.
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8
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Wei K, Chen B, Li Z, Chen D, Liu G, Lin H, Zhang B. Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks. SENSORS (BASEL, SWITZERLAND) 2022; 22:7764. [PMID: 36298114 PMCID: PMC9609479 DOI: 10.3390/s22207764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/25/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
The development of the smartphone and computer vision technique provides customers with a convenient approach to identify tea species, as well as qualities. However, the prediction model may not behave robustly due to changes in illumination conditions. Fluorescence imaging can induce the fluorescence signal from typical components, and thus may improve the prediction accuracy. In this paper, a tea classification method based on fluorescence imaging and convolutional neural networks (CNN) is proposed. Ultra-violet (UV) LEDs with a central wavelength of 370 nm were utilized to induce the fluorescence of tea samples so that the fluorescence images could be captured. Five kinds of tea were included and pre-processed. Two CNN-based classification models, e.g., the VGG16 and ResNet-34, were utilized for model training. Images captured under the conventional fluorescent lamp were also tested for comparison. The results show that the accuracy of the classification model based on fluorescence images is better than those based on the white-light illumination images, and the performance of the VGG16 model is better than the ResNet-34 model in our case. The classification accuracy of fluorescence images reached 97.5%, which proves that the LED-induced fluorescence imaging technique is promising to use in our daily life.
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Affiliation(s)
- Kaihua Wei
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Bojian Chen
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Zejian Li
- Zhejiang Key Laboratory of Design and Intelligence and Digital Creativity, College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
| | - Dongmei Chen
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Guangyu Liu
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Hongze Lin
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China
- Shangyu Institute of Science and Engineering, Hangzhou Dianzi University, Shaoxing 312000, China
| | - Baihua Zhang
- School of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou 325000, China
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A Portable Battery-Operated Sensor System for Simple and Rapid Assessment of Virgin Olive Oil Quality Grade. CHEMOSENSORS 2022. [DOI: 10.3390/chemosensors10030102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Virgin olive oil quality is assessed by chemical as well as sensory analysis. Two of the most important parameters that define the quality of virgin olive oils are the free acidity and the peroxide index. These chemical parameters are usually determined by manual titration procedures that must be carried out in a laboratory by trained personnel. In this paper, a portable sensor system to support the quality grade assessment of virgin olive oil is presented. The system is battery operated and characterized by small dimensions, light weight and quick measurement response (about 30 s). The working principle is based on the measurement of the electrical conductance of an emulsion between a chemical reagent and the olive oil sample. Two different chemical reagents have been investigated: (1) a hydro-alcoholic solution (HAS), made of 60% ethanol and 40% distilled water; (2) 100% distilled water (DW). Tests have been carried out on a set of 40 olive oil samples. The results have shown how, for most of the fresh virgin olive oil samples (31 samples out of 40), the free acidity can be estimated with good accuracy from the electrical conductance of the emulsion using HAS as the reagent. In the case of the full set of samples, the emulsion electrical conductance, using HAS as the reagent, is a function of both the sample free acidity as well as the compounds produced by oil oxidation, and a compensation method based on the measured electrical conductance, using DW as the reagent, has been introduced to improve the accuracy in the estimated free acidity. Tests have also been carried out on the full set of samples, using a k-nearest neighbors algorithm, to demonstrate the feasibility of olive oil classification according to the quality grade. The results have shown how measurements carried out using only the HAS reagent provide better classification accuracy than measurements carried out using both the HAS and DW reagents. The proposed system can be a low-cost alternative to standard laboratory analyses to evaluate the quality grade of virgin olive oil.
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