Baking releases microplastics from polyethylene terephthalate bakeware as detected by optical photothermal infrared and quantum cascade laser infrared.

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.

Exploring the identification of multiple bacteria on stainless steel using multi-scale spectral imaging from microscopic to macroscopic.

This work investigates non-contact reflectance spectral imaging techniques, i.e. microscopic Fourier transform infrared (FTIR) imaging, macroscopic visible-near infrared (VNIR), and shortwave infrared (SWIR) spectral imaging, for the identification of bacteria on stainless steel. Spectral images of two Gram-positive (GP) bacteria (Bacillus subtilis (BS) and Lactobacillus plantarum (LP)), and three Gram-negative (GN) bacteria (Escherichia coli (EC), Cronobacter sakazakii (CS), and Pseudomonas fluorescens (PF)), were collected from dried suspensions of bacterial cells dropped onto stainless steel surfaces. Through the use of multiple independent biological replicates for model validation and testing, FTIR reflectance spectral imaging was found to provide excellent GP/GN classification accuracy (> 96%), while the fused VNIR-SWIR data yielded classification accuracy exceeding 80% when applied to the independent test sets. However, classification within gram type was far less reliable, with lower accuracies for classification within the GP (< 75%) and GN (≤ 51%) species when calibration models were applied to the independent test sets, underlining the importance of independent model validation when dealing with samples of high biological variability.

A review of potential human health impacts of micro- and nanoplastics exposure.

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.

Characterisation and Classification of Foodborne Bacteria Using Reflectance FTIR Microscopic Imaging.

This work investigates the application of reflectance Fourier transform infrared (FTIR) microscopic imaging for rapid, and non-invasive detection and classification between Bacillus subtilis and Escherichia coli cell suspensions dried onto metallic substrates (stainless steel (STS) and aluminium (Al) slides) in the optical density (OD) concentration range of 0.001 to 10. Results showed that reflectance FTIR of samples with OD lower than 0.1 did not present an acceptable spectral signal to enable classification. Two modelling strategies were devised to evaluate model performance, transferability and consistency among concentration levels. Modelling strategy 1 involves training the model with half of the sample set, consisting of all concentrations, and applying it to the remaining half. Using this approach, for the STS substrate, the best model was achieved using support vector machine (SVM) classification, providing an accuracy of 96% and Matthews correlation coefficient (MCC) of 0.93 for the independent test set. For the Al substrate, the best SVM model produced an accuracy and MCC of 91% and 0.82, respectively. Furthermore, the aforementioned best model built from one substrate was transferred to predict the bacterial samples deposited on the other substrate. Results revealed an acceptable predictive ability when transferring the STS model to samples on Al (accuracy = 82%). However, the Al model could not be adapted to bacterial samples deposited on STS (accuracy = 57%). For modelling strategy 2, models were developed using one concentration level and tested on the other concentrations for each substrate. Results proved that models built from samples with moderate (1 OD) concentration can be adapted to other concentrations with good model generalization. Prediction maps revealed the heterogeneous distribution of biomolecules due to the coffee ring effect. This work demonstrated the feasibility of applying FTIR to characterise spectroscopic fingerprints of dry bacterial cells on substrates of relevance for food processing.

Spectral imaging for characterization and detection of plastic substances in branded teabags.

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.

Time series Fourier transform infrared spectroscopy for characterization of water vapor sorption in hydrophilic and hydrophobic polymeric films.

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.

Deep learning for classification of time series spectral images using combined multi-temporal and spectral features.

Time series spectral imaging facilitates a comprehensive understanding of the underlying dynamics of multi-component systems and processes. Most existing classification strategies focus exclusively on the spectral features and they tend to fail when spectra between classes closely resemble each other. This work proposes a hybrid approach of principal component analysis (PCA) and deep learning (i.e., long short-term memory (LSTM) model) for incorporating and utilizing the combined multi-temporal and spectral information from time series spectral imaging datasets. An example data, consisting of times series spectral images of casein-based biopolymers, was used to illustrate and evaluate the proposed hybrid approach. Compared to using partial least squares discriminant analysis (PLSDA), the proposed PCA-LSTM method applying the same spectral pretreatment achieved substantial improvement in the pixel-wise classification (i.e., accuracy increased from 59.97% of PLSDA to 85.73% of PCA-LSTM). When projecting the pixel-wise model to object-based classification, the PCA-LSTM approach produced an accuracy of 100%, correctly classifying the whole 21 film samples in the independent test set, while PLSDA only led to an accuracy of 80.95%. The proposed method is powerful and versatile in utilizing distinctive characteristics of time dependencies from multivariate time series dataset, which could be adapted to suit non-congruent images over time sequences as well as spectroscopic data.

Cold atmospheric plasma induces silver nanoparticle uptake, oxidative dissolution and enhanced cytotoxicity in glioblastoma multiforme cells.

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.

Predictive Modeling of the In Vitro Responses of Preosteoblastic MC3T3-E1 Cells on Polymeric Surfaces Using Fourier Transform Infrared Spectroscopy.

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.

Investigation of plasticizer aggregation problem in casein based biopolymer using chemical imaging.

This work aims to investigate the emergence of aggregates caused by redundant plasticizers in the protein matrix of casein based biopolymers using chemical imaging techniques. Near infrared (NIR) images (950-1671 nm) were first acquired and the spatial variations on macroscale with a pixel size of 0.4 mm × 0.5 mm were visualized. The introduction of plasticizers resulted in a strong hydrogen bonding matrix in the protein polymeric film as evidenced by analysis of Fourier transform near infrared (FT-NIR) spectral profiles in the range of 7500-4000 cm-1. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) images (4000-650 cm-1) coupled with principal components analysis (PCA) and multivariate curve resolution alternating least squares (MCR-ALS) analysis suggested the existence of sorbitol re-crystallization after 5 months storage in the ambient condition. Raman images with a higher pixel size of 1.2 µm × 1.2 µm indicated an uneven film surface caused by sorbitol migration and re-crystallization. A partial least squares (PLS) regression model was developed to predict plasticizer concentration based on the mean spectra of FT-NIR hypercubes, producing coefficient of determination in calibration ( [Formula: see text] ) of 0.93, cross-validation ( [Formula: see text] ) of 0.92 and prediction ( [Formula: see text] ) of 0.89. Visualization of aggregates in the image field was obtained by applying the developed PLS model in a pixel-wise manner using single-element and array detectors. The combined information from NIR and FT-NIR evidenced the occurrence of high plasticizer-concentrated regions in the film sample, while the combined information from FT-NIR and ATR-FTIR further confirms the phenomenon of sorbitol re-crystallization.

Identification of Magnesium Oxychloride Cement Biomaterial Heterogeneity using Raman Chemical Mapping and NIR Hyperspectral Chemical Imaging.

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.

Hyperspectral chemical imaging reveals spatially varied degradation of polycarbonate urethane (PCU) biomaterials.

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.

Multivariate Chemical Image Fusion of Vibrational Spectroscopic Imaging Modalities.

Chemical image fusion refers to the combination of chemical images from different modalities for improved characterisation of a sample. Challenges associated with existing approaches include: difficulties with imaging the same sample area or having identical pixels across microscopic modalities, lack of prior knowledge of sample composition and lack of knowledge regarding correlation between modalities for a given sample. In addition, the multivariate structure of chemical images is often overlooked when fusion is carried out. We address these challenges by proposing a framework for multivariate chemical image fusion of vibrational spectroscopic imaging modalities, demonstrating the approach for image registration, fusion and resolution enhancement of chemical images obtained with IR and Raman microscopy.

Recent applications of hyperspectral imaging in microbiology.

Hyperspectral chemical imaging (HSI) is a broad term encompassing spatially resolved spectral data obtained through a variety of modalities (e.g. Raman scattering, Fourier transform infrared microscopy, fluorescence and near-infrared chemical imaging). It goes beyond the capabilities of conventional imaging and spectroscopy by obtaining spatially resolved spectra from objects at spatial resolutions varying from the level of single cells up to macroscopic objects (e.g. foods). In tandem with recent developments in instrumentation and sampling protocols, applications of HSI in microbiology have increased rapidly. This article gives a brief overview of the fundamentals of HSI and a comprehensive review of applications of HSI in microbiology over the past 10 years. Technical challenges and future perspectives for these techniques are also discussed.

Feasibility of conventional and Roundup Ready® soybeans discrimination by different near infrared reflectance technologies.

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%.

Use of Fourier transform infrared spectroscopy and chemometric data analysis to evaluate damage and age in mushrooms (Agaricus bisporus) grown in Ireland.

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.

Prediction of polyphenol oxidase activity using visible near-infrared hyperspectral imaging on mushroom (Agaricus bisporus) caps.

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.

Initial studies on the quantitation of bruise damage and freshness in mushrooms using visible-near-infrared spectroscopy.

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.