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Lin X, Gowen AA, Chen S, Xu JL. Baking releases microplastics from polyethylene terephthalate bakeware as detected by optical photothermal infrared and quantum cascade laser infrared. Sci Total Environ 2024; 924:171408. [PMID: 38432360 DOI: 10.1016/j.scitotenv.2024.171408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 02/09/2024] [Accepted: 02/29/2024] [Indexed: 03/05/2024]
Abstract
The use of plastic bakeware is a potential source of human exposure to microplastics (MPs). However, characterizing MPs remains a challenge. This study aims to employ optical photothermal infrared (O-PTIR) and quantum cascade laser infrared (QCL-IR) technology to characterise polyethylene terephthalate (PET) MPs shed from PET bakeware during the baking process. The bakeware, filled with ultrapure water, underwent baking cycles at 220 °C for 20 min, 60 min, and three consecutive cycles of 60 min each. Subsequently, particles present in the ultrapure water were collected using an Al2O3 filter. O-PTIR and QCL-IR were used to characterise PET MPs collected from the filtration. Analysis revealed that QCL-IR spectra exhibited broader absorption peaks, compared to O-PTIR. Notably, MP spectra obtained from both techniques displayed common absorption peaks around 1119, 1623, 1341 and 1725 cm-1. The dominant size of PET MPs detected by O-PTIR and QCL-IR was 1-3 μm and 5-20 μm, respectively. The quantity of identified PET MPs using O-PTIR was 18 times greater than that with QCL-IR, which was attributed to variations in spatial resolution, sampling methods for spectra collection, and data analysis employed by the two methods. Importantly, findings from both techniques highlighted a notably large quantity of MPs released from PET bakeware, particularly evident after 3 cycles of 60 min of baking, suggesting a substantial increase in the potential ingestion of MPs, especially in scenarios involving extended baking durations. The research outcomes will guide consumers on minimizing the intake of microplastics by using PET bakeware for shorter baking time. Additionally, the study will yield valuable insights into the application of O-PTIR and QCL-IR for MPs detection, potentially inspiring advancements in MPs detection methodologies through cutting-edge technologies.
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Affiliation(s)
- Xiaohui Lin
- School of Biosystems and Food Engineering, University College Dublin (UCD), Belfield, Dublin 4, Ireland.
| | - Aoife A Gowen
- School of Biosystems and Food Engineering, University College Dublin (UCD), Belfield, Dublin 4, Ireland
| | - Shuai Chen
- Shanghai Polytechnic University 201209, China
| | - Jun-Li Xu
- School of Biosystems and Food Engineering, University College Dublin (UCD), Belfield, Dublin 4, Ireland
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Xu JL, Lin X, Wang JJ, Gowen AA. A review of potential human health impacts of micro- and nanoplastics exposure. Sci Total Environ 2022; 851:158111. [PMID: 35987230 DOI: 10.1016/j.scitotenv.2022.158111] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 08/12/2022] [Accepted: 08/14/2022] [Indexed: 06/15/2023]
Abstract
This systematic review aims to summarize the current knowledge on biological effects of micro- and nanoplastics (MNPs) on human health based on mammalian systems. An extensive search of the literature led to a total of 133 primary research articles on the health relevance of MNPs. Our findings revealed that although the study of MNP cytotoxicity and inflammatory response represents a major research theme, most studies (105 articles) focused on the effects of polystyrene MNPs due to their wide availability as a well characterised research material that can be manufactured with a large range of particle sizes, fluorescence labelling as well as various surface modifications. Among the 133 studies covered in this review, 117 articles reported adverse health effects after being exposed to MNPs. Mammalian in vitro studies identified multiple biological effects including cytotoxicity, oxidative stress, inflammatory response, genotoxicity, embryotoxicity, hepatotoxicity, neurotoxicity, renal toxicity and even carcinogenicity, while rodent in vivo models confirmed the bioaccumulation of MNPs in the liver, spleen, kidney, brain, lung and gut, presenting adverse effects at different levels including reproductive toxic effects and trans-generational toxicity. In contrast, the remaining 16 studies indicated an insignificant impact of MNPs on humans. A few studies attempted to investigate the mechanisms or factors driving the toxicity of MNPs and identified several determining factors including size, concentration, shape, surface charge, attached pollutants and weathering process, which, however, were not benchmarked or considered by most studies. This review demonstrates that there are still many inconsistencies in the evaluation of the potential health effects of MNPs due to the lack of comparability between studies. Current limitations hindering the attainment of reproducible conclusions as well as recommendations for future research directions are also presented.
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Affiliation(s)
- Jun-Li Xu
- School of Biosystems and Food Engineering, University College of Dublin, Belfield, Dublin 4, Ireland; Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Ireland; Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland.
| | - Xiaohui Lin
- School of Biosystems and Food Engineering, University College of Dublin, Belfield, Dublin 4, Ireland
| | - Jing Jing Wang
- AMBER Research Centre and Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN), Trinity College Dublin, Dublin 2, Ireland
| | - Aoife A Gowen
- School of Biosystems and Food Engineering, University College of Dublin, Belfield, Dublin 4, Ireland; Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Ireland; Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland
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Xu JL, Lin X, Hugelier S, Herrero-Langreo A, Gowen AA. Spectral imaging for characterization and detection of plastic substances in branded teabags. J Hazard Mater 2021; 418:126328. [PMID: 34118538 DOI: 10.1016/j.jhazmat.2021.126328] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 06/01/2021] [Accepted: 06/02/2021] [Indexed: 06/12/2023]
Abstract
The addition of plastic substances in teabags is of increasing concern for conscious consumers due to the harmful effects on the environment and the potential threats to human health. This work introduces an innovative and cost-effective approach to detect and quantify plastic substances in teabags by applying near infrared hyperspectral imaging (951-2496 nm) coupled with multivariate analysis. Teabags from 6 popular brands were investigated and categorized into three classes based on spectral unmixing and target detection results: 1) the plastic teabag primarily made of nylon 6/6; 2) those made of a composite with various polypropylene and cellulose ratios; 3) biodegradable teabags free from any plastic traces. Results demonstrated the presence of numerous plastic particles in the beverage obtained after steeping nylon teabags, but the release of particles was further amplified after microwave treatment. Nevertheless, target detection results obtained from Fourier transform infrared imaging (4000-675 cm-1) dataset evidenced that a considerable proportion of particle residues detected were the contaminants obtained from tea granules that adsorbed on the teabag. This work highlights the significant importance of performing rigorous spectral analysis for chemical characterization, which is lacking in most published microplastic studies.
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Affiliation(s)
- Jun-Li Xu
- School of Biosystems and Food Engineering, University College of Dublin (UCD), Belfield, Dublin 4, Ireland.
| | - Xiaohui Lin
- School of Biosystems and Food Engineering, University College of Dublin (UCD), Belfield, Dublin 4, Ireland
| | - Siewert Hugelier
- Laboratory for Nanobiology, Department of Chemistry, KU Leuven, B-3001 Leuven, Belgium
| | - Ana Herrero-Langreo
- School of Biosystems and Food Engineering, University College of Dublin (UCD), Belfield, Dublin 4, Ireland
| | - Aoife A Gowen
- School of Biosystems and Food Engineering, University College of Dublin (UCD), Belfield, Dublin 4, Ireland
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Xu JL, Gowen AA. Time series Fourier transform infrared spectroscopy for characterization of water vapor sorption in hydrophilic and hydrophobic polymeric films. Spectrochim Acta A Mol Biomol Spectrosc 2021; 250:119371. [PMID: 33418477 DOI: 10.1016/j.saa.2020.119371] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 11/18/2020] [Accepted: 12/18/2020] [Indexed: 06/12/2023]
Abstract
This work investigates the nature of the molecular interactions between water vapor and polymers using time series Fourier transform infrared (FTIR) spectroscopy. A detailed analysis of the frequency shifts and relative peak intensities led to the conclusion that polyvinyl alcohol (PVOH) sorbed a large quantity of water vapor molecules, resulting in swelling and dissolving of polymer crystallites. Difference spectra were calculated to investigate spectral changes occurring upon sorption by dividing the spectra of polymers during the sorption time series by the spectrum of the dry sample and subsequently subtracting the water vapor spectrum. Based on the absorbance area of the OH stretching vibration region (4000-3000 cm-1) in difference spectra, the amount of water sorbed was significantly higher in poly-L-lactic acid (PLLA) and polyvinyl chloride (PVC) than in polyethylene (PE) and polytetrafluoroethylene (PTFE), increasing with the hydrophilicity of the surface. The OH stretching band of difference spectra shifted from 3499 cm-1 for PVC, to 3416 cm-1 for PE and finally to 3387 cm-1 for PTFE, indicating a more strengthened hydrogen-bonding network in the PTFE matrix upon water vapor sorption.
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Affiliation(s)
- Jun-Li Xu
- School of Biosystems and Food Engineering, University College Dublin, Belfield, Ireland
| | - Aoife A Gowen
- School of Biosystems and Food Engineering, University College Dublin, Belfield, Ireland.
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Martinez-Gonzalez JA, English NJ, Gowen AA. Molecular simulation of water adsorption on hydrophilic and hydrophobic surfaces of silicon: IR-spectral explorations. Molecular Simulation 2021. [DOI: 10.1080/08927022.2021.1899173] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Jose A. Martinez-Gonzalez
- School of Biosystems and Food Engineering, University College Dublin, Dublin 4, Ireland
- School of Chemical and Bioprocess Engineering, University College Dublin, Dublin 4, Ireland
- ISIS Pulsed Neutron and Muon Source, Rutherford Appleton Laboratory, Didcot, UK
| | - Niall J. English
- School of Chemical and Bioprocess Engineering, University College Dublin, Dublin 4, Ireland
| | - Aoife A. Gowen
- School of Biosystems and Food Engineering, University College Dublin, Dublin 4, Ireland
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Xu JL, Hugelier S, Zhu H, Gowen AA. Deep learning for classification of time series spectral images using combined multi-temporal and spectral features. Anal Chim Acta 2021; 1143:9-20. [DOI: 10.1016/j.aca.2020.11.018] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 11/07/2020] [Accepted: 11/17/2020] [Indexed: 01/19/2023]
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Martínez-González JA, Nandi PK, English NJ, Gowen AA. Infrared spectra and density of states at the interface between water and protein: Insights from classical molecular dynamics. Chem Phys Lett 2020. [DOI: 10.1016/j.cplett.2020.137867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Manaloto E, Gowen AA, Lesniak A, He Z, Casey A, Cullen PJ, Curtin JF. Cold atmospheric plasma induces silver nanoparticle uptake, oxidative dissolution and enhanced cytotoxicity in glioblastoma multiforme cells. Arch Biochem Biophys 2020; 689:108462. [PMID: 32590068 DOI: 10.1016/j.abb.2020.108462] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 05/28/2020] [Accepted: 06/06/2020] [Indexed: 12/18/2022]
Abstract
Silver nanoparticles (AgNP) emerged as a promising reagent for cancer therapy with oxidative stress implicated in the toxicity. Meanwhile, studies reported cold atmospheric plasma (CAP) generation of reactive oxygen and nitrogen species has selectivity towards cancer cells. Gold nanoparticles display synergistic cytotoxicity when combined with CAP against cancer cells but there is a paucity of information using AgNP, prompting to investigate the combined effects of CAP using dielectric barrier discharge system (voltage of 75 kV, current is 62.5 mA, duty cycle of 7.5kVA and input frequency of 50-60Hz) and 10 nm PVA-coated AgNP using U373MG Glioblastoma Multiforme cells. Cytotoxicity in U373MG cells was >100-fold greater when treated with both CAP and PVA-AgNP compared with either therapy alone (IC50 of 4.30 μg/mL with PVA-AgNP alone compared with 0.07 μg/mL after 25s CAP and 0.01 μg/mL 40s CAP). Combined cytotoxicity was ROS-dependent and was prevented using N-Acetyl Cysteine. A novel darkfield spectral imaging method investigated and quantified AgNP uptake in cells determining significantly enhanced uptake, aggregation and subcellular accumulation following CAP treatment, which was confirmed and quantified using atomic absorption spectroscopy. The results indicate that CAP decreases nanoparticle size, decreases surface charge distribution of AgNP and induces uptake, aggregation and enhanced cytotoxicity in vitro.
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Affiliation(s)
- Eline Manaloto
- BioPlasma Research Group, School of Food Science and Environmental Health, Technological University Dublin, Ireland; FOCAS Research Institute, Technological University Dublin, Ireland.
| | - Aoife A Gowen
- UCD School of Biosystems and Food Engineering, UCD, Ireland
| | - Anna Lesniak
- UCD School of Biosystems and Food Engineering, UCD, Ireland
| | - Zhonglei He
- BioPlasma Research Group, School of Food Science and Environmental Health, Technological University Dublin, Ireland; FOCAS Research Institute, Technological University Dublin, Ireland; Environmental Sustainability and Health Institute, Technological University Dublin, Ireland
| | - Alan Casey
- FOCAS Research Institute, Technological University Dublin, Ireland; School of Physics and Clinical and Optometric Sciences, Technological University Dublin, Ireland
| | - Patrick J Cullen
- BioPlasma Research Group, School of Food Science and Environmental Health, Technological University Dublin, Ireland; School of Chemical and Biomolecular Engineering, University of Sydney, Australia
| | - James F Curtin
- BioPlasma Research Group, School of Food Science and Environmental Health, Technological University Dublin, Ireland; FOCAS Research Institute, Technological University Dublin, Ireland; Environmental Sustainability and Health Institute, Technological University Dublin, Ireland.
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Xu JL, Lesniak A, Gowen AA. Predictive Modeling of the In Vitro Responses of Preosteoblastic MC3T3-E1 Cells on Polymeric Surfaces Using Fourier Transform Infrared Spectroscopy. ACS Appl Mater Interfaces 2020; 12:24466-24478. [PMID: 32374584 DOI: 10.1021/acsami.0c04261] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Biomaterials' surface properties elicit diverse cellular responses in biomedical and biotechnological applications. Predicting the cell behavior on a polymeric surface is an ongoing challenge due to its complexity. This work proposes a novel modeling methodology based on attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy. Spectra were collected on wetted polymeric surfaces to incorporate both surface chemistry and information on water-polymer interactions. Results showed that predictive models built with spectra from wetted surfaces ("wet spectra") performed much better than models built using spectra acquired from dry surfaces ("dry spectra"), suggesting that the water-polymer interaction is critically important to the prediction of subsequent cell behavior. The best model was seen to predict total area of focal adhesions with coefficient of determination for prediction (R2P) of 0.94 and root-mean-square errors of prediction (RMSEP) of 4.03 μm2 when tested on an independent experimental set. This work offers new insights into our understanding of cell-biomaterial interactions. The presence of carboxyl groups in polymers promoted larger adhesion areas, yet the formation of carbonyl-to-water interaction decreased adhesion areas. Surface wettability, which was related to the water-polymer interaction, was proven to highly influence cell adhesion. The good predictive ability opens new possibilities for high throughput monitoring of cell attachment on polymeric substrates.
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Affiliation(s)
- Jun-Li Xu
- School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland
| | - Anna Lesniak
- School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland
| | - Aoife A Gowen
- School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland
- UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland
- UCD Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Ireland
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Xu JL, Thomas KV, Luo Z, Gowen AA. FTIR and Raman imaging for microplastics analysis: State of the art, challenges and prospects. Trends Analyt Chem 2019. [DOI: 10.1016/j.trac.2019.115629] [Citation(s) in RCA: 100] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Dorrepaal RM, Gowen AA. Identification of Magnesium Oxychloride Cement Biomaterial Heterogeneity using Raman Chemical Mapping and NIR Hyperspectral Chemical Imaging. Sci Rep 2018; 8:13034. [PMID: 30158695 PMCID: PMC6115415 DOI: 10.1038/s41598-018-31379-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 08/14/2018] [Indexed: 11/10/2022] Open
Abstract
The present study investigated spatial heterogeneity in magnesium oxychloride cements within a model of a mould using hyperspectral chemical imaging (HCI). The ability to inspect cements within a mould allows for the assessment of material formation in real time in addition to factors affecting ultimate material formation. Both macro scale NIR HCI and micro scale pixel-wise Raman chemical mapping were employed to characterise the same specimens. NIR imaging is rapid, however spectra are often convoluted through the overlapping of overtone peaks, which can make interpretation difficult. Raman spectra are more easily interpretable, however Raman imaging can suffer from slower acquisition times, particularly when the signal to noise ratio is relatively poor and the spatial resolution is high. To overcome the limitations of both, Raman/NIR data fusion techniques were explored and implemented. Spectra collected using both modalities were co-registered and intra and inter-modality peak correlations were investigated while k-means cluster patterns were compared. In addition, partial least squares regression models, built using NIR spectra, predicted chemical-identifying Raman peaks with an R2 of up to >0.98. As macro scale imaging presented greater data collection speeds, chemical prediction maps were built using NIR HCIs.
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Affiliation(s)
- Ronan M Dorrepaal
- UCD School of Biosystems and Food Engineering, University College Dublin, Dublin, Ireland.
| | - Aoife A Gowen
- UCD School of Biosystems and Food Engineering, University College Dublin, Dublin, Ireland
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Dorrepaal RM, Lawless BM, Burton HE, Espino DM, Shepherd DE, Gowen AA. Hyperspectral chemical imaging reveals spatially varied degradation of polycarbonate urethane (PCU) biomaterials. Acta Biomater 2018; 73:81-89. [PMID: 29626697 DOI: 10.1016/j.actbio.2018.03.045] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 03/12/2018] [Accepted: 03/28/2018] [Indexed: 11/26/2022]
Abstract
Hyperspectral chemical imaging (HCI) is an emerging technique which combines spectroscopy with imaging. Unlike traditional point spectroscopy, which is used in the majority of polymer biomaterial degradation studies, HCI enables the acquisition of spatially localised spectra across the surface of a material in an objective manner. Here, we demonstrate that attenuated total reflectance Fourier transform infra-red (ATR-FTIR) HCI reveals spatial variation in the degradation of implantable polycarbonate urethane (PCU) biomaterials. It is also shown that HCI can detect possible defects in biomaterial formulation or specimen production; these spatially resolved images reveal regional or scattered spatial heterogeneity. Further, we demonstrate a map sampling method, which can be used in time-sensitive scenarios, allowing for the investigation of degradation across a larger component or component area. Unlike imaging, mapping does not produce a contiguous image, yet grants an insight into the spatial heterogeneity of the biomaterial across a larger area. These novel applications of HCI demonstrate its ability to assist in the detection of defective manufacturing components and lead to a deeper understanding of how a biomaterial's chemical structure changes due to implantation. STATEMENT OF SIGNIFICANCE The human body is an aggressive environment for implantable devices and their biomaterial components. Polycarbonate urethane (PCU) biomaterials in particular were investigated in this study. Traditionally one or a few points on the PCU surface are analysed using ATR-FTIR spectroscopy. However the selection of acquisition points is susceptible to operator bias and critical information can be lost. This study utilises hyperspectral chemical imaging (HCI) to demonstrate that the degradation of a biomaterial varies spatially. Further, HCI revealed spatial variations of biomaterials that were not subjected to oxidative degradation leading to the possibility of HCI being used in the assessment of biomaterial formulation and/or component production.
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Xu JL, Gowen AA, Sun DW. Time series hyperspectral chemical imaging (HCI) for investigation of spectral variations associated with water and plasticizers in casein based biopolymers. J FOOD ENG 2018. [DOI: 10.1016/j.jfoodeng.2017.09.006] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Esteve Agelet L, Gowen AA, Hurburgh CR, O'Donell CP. Feasibility of conventional and Roundup Ready® soybeans discrimination by different near infrared reflectance technologies. Food Chem 2012; 134:1165-72. [PMID: 23107744 DOI: 10.1016/j.foodchem.2012.02.144] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2011] [Accepted: 02/22/2012] [Indexed: 11/20/2022]
Abstract
Identification and proper labelling of genetically modified organisms is required and increasingly demanded by legislation and consumers worldwide. In this study, the feasibility of three near infrared reflectance technologies (a chemical imaging unit, a commercial diode array instrument, and a light tube non-commercial instrument) were compared for discriminating Roundup Ready® and not genetically modified soybean seeds. Over 200 seeds of each class (Roundup Ready® and conventional) were used. Principal Component Analysis with Artificial Neural Networks (PCA-ANN) and Locally Weighted Principal Component Regression (LW-PCR) were used for creating the discrimination models. Discrimination accuracies when new tested seeds belonged to samples included in the training sets achieved accuracies over 90% of correctly classified seeds for LW-PCR models. The light tube performed the best, while the imaging unit showed the worse accuracies overall. Models validated with new seeds from samples not included in the training set had accuracies of 72-79%.
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Affiliation(s)
- Lidia Esteve Agelet
- Department of Agriculture and Biosystems Engineering, Iowa State University, Ames, IA 50014, United States.
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Gowen AA, Tsuchisaka Y, O’Donnell C, Tsenkova R. Investigation of the Potential of Near Infrared Spectroscopy for the Detection and Quantification of Pesticides in Aqueous Solution. ACTA ACUST UNITED AC 2011. [DOI: 10.4236/ajac.2011.228124] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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O'Gorman A, Downey G, Gowen AA, Barry-Ryan C, Frias JM. Use of Fourier transform infrared spectroscopy and chemometric data analysis to evaluate damage and age in mushrooms (Agaricus bisporus) grown in Ireland. J Agric Food Chem 2010; 58:7770-7776. [PMID: 20518458 DOI: 10.1021/jf101123a] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
The aim of this research was to investigate whether the chemical changes induced by mechanical damage and aging of mushrooms can be (a) detected in the midinfrared absorption region and (b) identified using chemometric data analysis. Mushrooms grown under controlled conditions were bruise-damaged by vibration to simulate damage during normal transportation. Damaged and nondamaged mushrooms were stored for up to 7 days postharvest. Principal component analysis of Fourier transform infrared (FTIR) spectra showed evidence that physical damage had an effect on the tissue structure and the aging process. Random forest classification models were used to predict damage in mushrooms producing models with error rates of 5.9 and 9.8% with specific wavenumbers identified as important variables for identifying damage, and partial least-squares (PLS) models were developed producing models with low levels of misclassification. Modeling postharvest age in mushrooms using random forests and PLS resulted in high error rates and misclassification; however, random forest models had the ability to correctly classify 82% of day zero samples, which may be a useful tool in discriminating between "fresh" and old mushrooms. This study highlights the usefulness of FTIR spectroscopy coupled with chemometric data analysis in particular for evaluating damage in mushrooms and with the possibility of developing a monitoring system for damaged mushrooms using the FTIR "fingerprint" region.
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Affiliation(s)
- Aoife O'Gorman
- School of Food Science & Environmental Health, Dublin Institute of Technology, Cathal Brugha Street, Dublin 1, Ireland
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Gaston E, Frías JM, Cullen PJ, O'Donnell CP, Gowen AA. Prediction of polyphenol oxidase activity using visible near-infrared hyperspectral imaging on mushroom (Agaricus bisporus) caps. J Agric Food Chem 2010; 58:6226-6233. [PMID: 20411944 DOI: 10.1021/jf100501q] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Physical stress (i.e., bruising) during harvesting, handling, and transportation triggers enzymatic discoloration of mushrooms, a common and detrimental phenomenon largely mediated by polyphenol oxidase (PPO) enzymes. Hyperspectral imaging (HSI) is a nondestructive technique that combines imaging and spectroscopy to obtain information from a sample. The objective of this study was to assess the ability of HSI to predict the activity of PPO on mushroom caps. Hyperspectral images of mushrooms subjected to various damage treatments were taken, followed by enzyme extraction and PPO activity measurement. Principal component regression (PCR) models (each with three PCs) built on raw reflectance and multiple scatter-corrected (MSC) reflectance data were found to be the best modeling approach. Prediction maps showed that the MSC model allowed for compensation of spectral differences due to sample curvature and surface irregularities. Results reveal the possibility of developing a sensor that could rapidly identify mushrooms with a higher likelihood to develop enzymatic browning, hence aiding produce management decision makers in the industry.
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Affiliation(s)
- Edurne Gaston
- School of Food Science and Environmental Health, Dublin Institute of Technology, Cathal Brugha Street, Dublin 1, Ireland
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Gowen AA, O'Donnell CP, Esquerre C, Downey G. Influence of polymer packaging films on hyperspectral imaging data in the visible-near-infrared (450-950 nm) wavelength range. Appl Spectrosc 2010; 64:304-312. [PMID: 20223066 DOI: 10.1366/000370210790918337] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Hyperspectral imaging (HSI) has recently emerged as a useful tool for quality analysis of consumer goods (e.g., food and pharmaceutical products). These products are typically packaged in polymeric film prior to distribution; however, HSI experiments are typically carried out on such samples ex-packaging (either prior to or after removal from packaging). This research examines the effects of polymer packaging films (polyvinyl chloride (PVC) and polyethylene terephthalate (PET)) on spectral and spatial features of HSI data in order to investigate the potential of HSI for quality evaluation of packaged goods. The effects of packaging film were studied for hyperspectral images of samples obtained in the visible-near-infrared (Vis-NIR, i.e., 450-950 nm) wavelength range, which is relevant to many food, agricultural, and pharmaceutical products. The dominant influence of the films tested in this wavelength range could be attributed to light scattering. Relative position of the light source, film, and detector were shown to be highly influential on the scattering effects observed. Detection of features on samples imaged through film was shown to be possible after some data preprocessing. This suggests that quality analysis of products packaged in polymer film is feasible using HSI. These findings would be useful in the development of quality monitoring tools for consumer products post-packaging using HSI.
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Affiliation(s)
- A A Gowen
- Biosystems Engineering, School of Agriculture, Food Science and Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland.
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Esquerre C, Gowen AA, O'Donnell CP, Downey G. Initial studies on the quantitation of bruise damage and freshness in mushrooms using visible-near-infrared spectroscopy. J Agric Food Chem 2009; 57:1903-1907. [PMID: 19215132 DOI: 10.1021/jf803090c] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Identification of mushrooms that have been physically damaged and the measurement of time elapsed from harvest are very important quality issues in industry. The purpose of this study was to assess whether the chemical changes induced by physical damage and the aging of mushrooms can: (a) be detected in the visible and near infrared absorption spectrum and (b) be modeled using multivariate data analysis. The effect of pre-treatment and the use of different spectral ranges to build PLS models were studied. A model that can identify damaged mushrooms with high sensitivity (0.98) and specificity (1.00), and models that allow estimation of the age (1.0-1.4 days root mean square error of cross-validation) were developed. Changes in water matrix and alterations caused by enzymatic browning were the factors that most influenced the models. The results reveal the possibility of developing an automated system for grading mushrooms based on reflectance in the visible and near infrared wavelength ranges.
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Affiliation(s)
- Carlos Esquerre
- Teagasc, Ashtown Food Research Centre, Ashtown, Dublin 15, Ireland, and Biosystems Engineering, School of Agriculture, Food Science and Veterinary Medicine, University College Dublin, Dublin 4, Ireland.
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