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Deng P, Lin X, Yu Z, Huang Y, Yuan S, Jiang X, Niu M, Peng WK. Machine learning-enabled high-throughput industry screening of edible oils. Food Chem 2024; 447:139017. [PMID: 38531304 DOI: 10.1016/j.foodchem.2024.139017] [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: 11/15/2023] [Revised: 02/27/2024] [Accepted: 03/10/2024] [Indexed: 03/28/2024]
Abstract
Long-term consumption of mixed fraudulent edible oils increases the risk of developing of chronic diseases which has been a threat to the public health globally. The complicated global supply-chain is making the industry malpractices had often gone undetected. In order to restore the confidence of consumers, traceability (and accountability) of every level in the supply chain is vital. In this work, we shown that machine learning (ML) assisted windowed spectroscopy (e.g., visible-band, infra-red band) produces high-throughput, non-destructive, and label-free authentication of edible oils (e.g., olive oils, sunflower oils), offers the feasibility for rapid analysis of large-scale industrial screening. We report achieving high-level of discriminant (AUC > 0.96) in the large-scale (n ≈ 11,500) of adulteration in olive oils. Notably, high clustering fidelity of 'spectral fingerprints' achieved created opportunity for (hypothesis-free) self-sustaining large database compilation which was never possible without machine learning. (137 words).
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Affiliation(s)
- Peishan Deng
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523801, PR China.
| | - Xiaomin Lin
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523801, PR China.
| | - Zifan Yu
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523801, PR China; Guangdong Medical University, 523-808, China
| | - Yuanding Huang
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523801, PR China.
| | - Shijin Yuan
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523801, PR China.
| | - Xin Jiang
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523801, PR China.
| | - Meng Niu
- China Medical University, China.
| | - Weng Kung Peng
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523801, PR China.
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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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Sumara A, Stachniuk A, Trzpil A, Bartoszek A, Montowska M, Fornal E. LC-MS Metabolomic Profiling of Five Types of Unrefined, Cold-Pressed Seed Oils to Identify Markers to Determine Oil Authenticity and to Test for Oil Adulteration. Molecules 2023; 28:4754. [PMID: 37375308 DOI: 10.3390/molecules28124754] [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: 05/11/2023] [Revised: 06/08/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
The authenticity of food products marketed as health-promoting foods-especially unrefined, cold-pressed seed oils-should be controlled to ensure their quality and safeguard consumers and patients. Metabolomic profiling using liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (LC-QTOF) was employed to identify authenticity markers for five types of unrefined, cold-pressed seed oils: black seed oil (Nigella sativa L.), pumpkin seed oil (Cucurbita pepo L.), evening primrose oil (Oenothera biennis L.), hemp oil (Cannabis sativa L.) and milk thistle oil (Silybum marianum). Of the 36 oil-specific markers detected, 10 were established for black seed oil, 8 for evening primrose seed oil, 7 for hemp seed oil, 4 for milk thistle seed oil and 7 for pumpkin seed oil. In addition, the influence of matrix variability on the oil-specific metabolic markers was examined by studying binary oil mixtures containing varying volume percentages of each tested oil and each of three potential adulterants: sunflower, rapeseed and sesame oil. The presence of oil-specific markers was confirmed in 7 commercial oil mix products. The identified 36 oil-specific metabolic markers proved useful for confirming the authenticity of the five target seed oils. The ability to detect adulterations of these oils with sunflower, rapeseed and sesame oil was demonstrated.
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Affiliation(s)
- Agata Sumara
- Department of Bioanalytics, Medical University of Lublin, ul. Jaczewskiego 8b, 20-090 Lublin, Poland
| | - Anna Stachniuk
- Department of Bioanalytics, Medical University of Lublin, ul. Jaczewskiego 8b, 20-090 Lublin, Poland
| | - Alicja Trzpil
- Department of Bioanalytics, Medical University of Lublin, ul. Jaczewskiego 8b, 20-090 Lublin, Poland
| | - Adrian Bartoszek
- Department of Bioanalytics, Medical University of Lublin, ul. Jaczewskiego 8b, 20-090 Lublin, Poland
| | - Magdalena Montowska
- Department of Meat Technology, Poznan University of Life Sciences, ul. Wojska Polskiego 31, 60-624 Poznan, Poland
| | - Emilia Fornal
- Department of Bioanalytics, Medical University of Lublin, ul. Jaczewskiego 8b, 20-090 Lublin, Poland
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Zniber M, Vahdatiyekta P, Huynh TP. Analysis of urine using electronic tongue towards non-invasive cancer diagnosis. Biosens Bioelectron 2023; 219:114810. [PMID: 36272349 DOI: 10.1016/j.bios.2022.114810] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 04/27/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022]
Abstract
Electronic tongues (e-tongues) have been broadly employed in monitoring the quality of food, beverage, cosmetics, and pharmaceutical products, and in diagnosis of diseases, as the e-tongues can discriminate samples of high complexity, reduce interference of the matrix, offer rapid response. Compared to other analytical approaches using expensive and complex instrumentation as well as required sample preparation, the e-tongue is non-destructive, miniaturizable and on-site method with little or no preparation of samples. Even though e-tongues are successfully commercialized, their application in cancer diagnosis from urine samples is underestimated. In this review, we would like to highlight the various analytical techniques such as Raman spectroscopy, infrared spectroscopy, fluorescence spectroscopy, and electrochemical methods (potentiometry and voltammetry) used as e-tongues for urine analysis towards non-invasive cancer diagnosis. Besides, different machine learning approaches, for instance, supervised and unsupervised learning algorithms are introduced to analyze extracted chemical data. Finally, capabilities of e-tongues in distinguishing between patients diagnosed with cancer and healthy controls are highlighted.
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Affiliation(s)
- Mohammed Zniber
- Laboratory of Molecular Science and Engineering, Åbo Akademi University, 20500, Turku, Finland
| | - Parastoo Vahdatiyekta
- Laboratory of Molecular Science and Engineering, Åbo Akademi University, 20500, Turku, Finland
| | - Tan-Phat Huynh
- Laboratory of Molecular Science and Engineering, Åbo Akademi University, 20500, Turku, Finland.
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Dodoo D, Adjei F, Tulashie SK, Awuku S, Amenakpor J, Megbenu HK. Postmarketing Surveillance for the Photosensitised Oxidation of Vegetable Oils in the Marketplace. J Oleo Sci 2022; 71:795-811. [PMID: 35584954 DOI: 10.5650/jos.ess21402] [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] [Indexed: 11/13/2022] Open
Abstract
This study conducts postmarketing surveillance for the photosensitised oxidation of vegetable oils (VOs) stored in different conditions in the marketplace during commercialisation. Coconut oil, palm kernel oil, soybean oil and sunflower oil were exposed to direct sunlight and kept in the dark for six weeks. The results showed a significant (p < 0.05) increase in PV and a severe decrease in the iodine value, chlorophyll, β-carotene, colour content, and the fatty acid compositions (oleic and linoleic acids mainly) in the light-exposed VOs. The FTIR analysis also identified the formation of the hydroperoxides (3444 cm-1), secondary oxidation products (1743 - 1723 cm-1) and the loss of the cis-disubstituted olefins (723 cm-1) bands in the light-exposed VOs. This indicated that oils exposed to light for an extended period of time could undergo photosensitised oxidation due to photosensitisers like chlorophyll. In contrast, the unexposed VOs showed no significant change (p > 0.05) in their chemical compositions. The photosensitised oxidation increased in the order: coconut oil < palm kernel oil < soybean oil < sunflower oil.
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Affiliation(s)
- Daniel Dodoo
- Industrial Chemistry Section, Department of Chemistry, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast
| | - Francis Adjei
- Industrial Chemistry Section, Department of Chemistry, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast.,Department of Chemical Engineering, College of Engineering, Kwame Nkrumah University of Science and Technology
| | - Samuel Kofi Tulashie
- Industrial Chemistry Section, Department of Chemistry, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast
| | - Stephen Awuku
- Department of Chemistry, College of Arts and Sciences, University of Saskatchewan
| | - Jacking Amenakpor
- Industrial Chemistry Section, Department of Chemistry, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast
| | - Harry Kwaku Megbenu
- Department of Chemical and Materials Engineering, School of Engineering and Digital Sciences, Nazarbayev University
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Pizzo JS, Cruz VH, Santos PD, Silva GR, Souza PM, Manin LP, Santos OO, Visentainer JV. Instantaneous characterization of crude vegetable oils via triacylglycerols fingerprint by atmospheric solids analysis probe tandem mass spectrometry with multiple neutral loss scans. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108710] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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Naji SZ, Tye CT, Abd AA. State of the art of vegetable oil transformation into biofuels using catalytic cracking technology: Recent trends and future perspectives. Process Biochem 2021. [DOI: 10.1016/j.procbio.2021.06.020] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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