Search

Despite the breadth of scientific literature on micro- and nanoplastics (MNPs), a standardized procedure for detecting MNPs is still lacking so far, leading to incomparable results between published studies. This work innovatively proposed the combination of machine learning with advanced optical photothermal infrared (O-PTIR) spectroscopy to develop an efficient and reliable detection framework for MNPs. Spectra of MPs and non-MPs were first collected and inputted to build a classification model, based on which four important wavenumbers were selected. A simplified support vector machine (SVM) model was subsequently developed using the selected four wavenumbers. Good predictive ability was evidenced by a high accuracy of 0.9133. The developed method can improve speed as well as the reliability of results, having a great potential for routine analysis of MNPs, ultimately leading to the standardization of detection methods.

The widespread occurrence of microplastics (MPs) in the food chain has gained substantial recognition as a pressing concern, highlighting the inevitability of human exposure through ingestion of foodborne MPs, coupled with the release of MPs from plastic packaging. However, there are notable disparities in the reported numbers of MPs in foods and beverages, warranting a thorough investigation into the factors contributing to these discrepancies. Table salt is one of the major sources of MPs, and there was an approximately hundred‐fold difference between the reviewed studies that reported the highest and lowest number of MPs. In addition, more noticeable discrepancies were discovered between studies on MPs released from teabags. One study reported that approximately 15 billion MPs were released into a cup of tea from a single teabag, whereas another research paper found only approximately 106.3 ± 14.6 MP/teabag after brewing. This comprehensive review focuses on the inconsistencies observed across studies examining MPs, shedding light on the plausible factors underlying these variations. Furthermore, the review outlines areas in analytical procedures that require enhancement and offers recommendations to promote accuracy and standardization in future research efforts, such as employing analytical methods capable of confirming the presence of MPs, using appropriate filter sizes, considering representative sample sizes when extrapolation is involved, and so on. By pinpointing the detection processes leading to the inconsistent results observed in MP studies, this comparative analysis will contribute to the development of reliable analytic methods for understanding the extent of microplastic contamination in the human food chain. [ABSTRACT FROM AUTHOR]

Classical molecular-dynamics simulations have been performed to examine the interplay between ubiquitin and its hydration-water sub-layers, chiefly from a vibrational-mode and IR viewpoint—where we analyse individual sub-layers characteristics. The vibrational Density of States (VDOS) revealed that the first solvation sub-shell indicates a confined character therein. For layers of increasing distance from the surface, the adoption of greater bulk-like spectral behaviour was evident, suggesting that vibrational harmonisation to bulk occurs within 6–7 Å of the surface. [ABSTRACT FROM AUTHOR]

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP–RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and mean and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization. [ABSTRACT FROM AUTHOR]

Molecular-dynamics simulations have been performed for full liquid water adsorbed onto two planar silicon surfaces, with varying hydrogen- and hydroxyl-termination (mimicking different extents of hydrophobicity and hydrophophilicity). It was found that there was water-density 'ordering' perpendicular to both surfaces – heavily dependent on the degree of hydrophobicity. The position and the width of the three solvation layers closest to the different surfaces depends, again, on the hydrophobicity of the surface. IR spectra of the first monolayer of adsorbed water indicate similarities to more confined-water dynamical behaviour, but without becoming ice-like. Moving away from the surface, the water behaviour converges on that of liquid water, albeit with some intermediate characteristics; this was seen for both hydro-phobic and –philic surfaces. [ABSTRACT FROM AUTHOR]

Rapid detection of foodborne pathogens, spoilage microbes, and other biological contaminants in complex food matrices is essential to maintain food quality and ensure consumer safety. Traditional methods involve culturing microbes using a range of nonselective and selective enrichment methods, followed by biochemical confirmation among others. The time‐to‐detection is a key limitation when testing foods, particularly those with short shelf lives, such as fresh meat, fish, dairy products, and vegetables. Some recent detection methods developed include the use of spectroscopic techniques, such as matrix‐assisted laser desorption ionization‐time of flight along with hyperspectral imaging protocols.This review presents a comprehensive overview comparing insights into the principles, characteristics, and applications of newer and emerging techniques methods applied to the detection and identification of microbes in food matrices, to more traditional benchtop approaches. The content has been developed to provide specialist scientists a broad view of bacterial identification methods available in terms of their benefits and limitations, which may be useful in the development of future experimental design. The case is also made for incorporating some of these emerging methods into the mainstream, for example, underutilized potential of spectroscopic techniques and hyperspectral imaging. [ABSTRACT FROM AUTHOR]

It is challenging to achieve consistent quantification of hydrogen‐bonded water species by fitting Gaussian bands on spectra collected from different instruments, especially when the spectral resolution is low. Therefore, this work aims to propose a near‐infrared (NIR) multivariate calibration model transfer method to obtain reliable, consistent, and reproducible results from different spectroscopic instruments. A composite method of Gaussian deconvolution, multivariate curve resolution‐alternating least squares (MCR‐ALS), and partial least squares (PLS) was developed to quantify five water species (labeled as C1, C2, C3, C4, and C5), respectively, assigned to non‐hydrogen‐bonded, singly hydrogen‐bonded, doubly and triply hydrogen‐bonded species, distorted ice‐like structures in the liquid phase, and fully hydrogen‐bonded species showing ice‐like structure. Primary instrument (iS50) was selected due to the highest spectral resolution and spectra (7500–5845 cm−1 with spectral resolution of 0.5 cm−1) were acquired from four types of salt solutions. The developed PLS model yielded a high prediction performance when applied on two independent prediction sets with R2P higher than 0.95 and RMSEP less than 0.009 for prediction of C1‐5. Model built with primary spectra was successfully transferred to spectra of secondary instrument (iN10), producing RMSEP less than 0.010. Transferred model applied on secondary instruments (NIRECO and MPA) showed that fully H‐bonded species in deionized water decreased as temperature increased, while non‐hydrogen‐bonded and singly hydrogen‐bonded demonstrated an opposite temperature dependence. Our results confirmed that it is promising to transfer the calibration model from a sophisticated spectrometer to a simple spectroscopic instrument in terms of quantification of hydrogen‐bonded water species. A composite method of Gaussian deconvolution, MCR‐ALS and PLS was proposed to quantify five hydrogen‐bonded water species. The developed model can be successfully transferred from a sophisticated spectrometer to a simple spectroscopic instrument. [ABSTRACT FROM AUTHOR]

Characterizing material strength in-situ for existing structures poses a major problem for a range of civil engineering applications including structural modelling for tunnelling-vulnerability assessment and pre-earthquake resiliency evaluation, especially for unreinforced masonry buildings. Present methods require expensive testing equipment often requiring access to the structure and possible destruction of historic material. This article introduces spectrometry as a non-destructive means for identifying different brick clays and their firing levels, both of which influence the masonry's mechanical behavior. The experiments herein considered bricks of 2 clay groups (red and yellow) fired at 3 kiln temperatures (700ºC, 950ºC, 1,060ºC). Samples were examined via spectrometry within the short-wave infrared range (1,300–2,200 nm). A Partial Least Square Discriminant Analysis (PLSDA) model was calibrated using 96 samples and tested on a set of 48 samples, resulting in a 98% success rate in classification of the two clay types and a 100% success rate for classification among the 3 firing levels. The ability of the PLSDA model to reliably distinguish well-fired bricks from other samples, irrespective of raw material configuration, shows the potential to use this approach as a new, non-destructive means for in-situ assessment of brick for architectural conservation, as well as for safety and serviceability assessments. [ABSTRACT FROM AUTHOR]

Abstract 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 ( R 2 Cal) of 0.93, cross-validation ( R 2 CV) of 0.92 and prediction ( R 2 P) 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. Graphical abstract fx1 Highlights • Plasticizer led to strong hydrogen bonding matrix as revealed by FT-NIR and ATR-FTIR; • ATR-FTIR and Raman imaging suggested the existence of sorbitol re-crystallization; • Visualization of aggregates was obtained by applying PLS model on FT-NIR images; • Combined NIR and FT-NIR information proves the emergence of plasticizer aggregates; • Combined FT-NIR and ATR-FTIR information confirms sorbitol re-crystallization. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: Array detectors improve data collection speed in hyperspectral chemical imaging, yet are prone to striping noise patterns in the image plane, which is difficult to remove. This type of noise affects spectral features and disturbs visual impression of the scene. We found that this type of noise depends on the material composition and setting parameters, ie, pixel size, and it also varies in accordance with the signal intensity of the observed wavelength. To address this, we proposed a new correction method on the basis of the application of chemometric techniques in the image plane of each wavelength. To verify the effectiveness of this method, infrared transmission images of the 2″ × 2″ positive, 1951 USAF Hi‐Resolution Target and biomaterial samples were obtained with a 16‐element ( 8 × 2) pixel array detector. Point detector images of some samples were also acquired and used as reference images. The proposed correction method produced substantial improvements in the visual impression of intensity images. Principal components analysis was performed to inspect spectral changes after preprocessing, and the results suggested that the major spectral features were not altered while the stripes on intensity images were removed. Spectral profiles and principal components analysis loadings inspection confirmed the smoothing ability of this correction method. As traditional preprocessing techniques, standard normal variate and derivative transformation were not able to remove line artifacts, especially on the biomaterial images. Overall, the proposed method was effective for removing striping noise patterns from infrared images with a minimal alteration of the valuable hyperspectral image information. [ABSTRACT FROM AUTHOR]

Calibration transfer from a handheld micro NIR spectrometer (NIR-point, 939-1602 nm, 6.2 nm) to a desktop hyperspectral imaging (NIR-HSI) for predicting soluble solids content (SSC) of bananito flesh was investigated in the study. Different spectral pre-processing and standardization methods were employed for correcting spectra so as to minimise spectral differences between NIR-point and NIR-HSI. Results show that application of standard normal variate (SNV) reduced spectral differences from 31.49 to 8.96%. The best standardization method was developed based on piecewise direct standardization (PDS) algorithm using ten transfer samples. The developed PLS model yielded a high prediction performance (R² p = 0.922 and RMSEP = 1.451%) for predicting SSC of validation samples using the NIR-point spectra. After SNV and standardization, the model was successfully transferred to NIR-HSI data, giving a comparable prediction accuracy of R² p = 0.925 and RMSEP = 1.592%. The results illustrated the potential of transferring calibration models from a simple and easy-available micro NIR spectrometer to a more expensive and sophisticated hyperspectral imaging system, when the spatial distribution of quality information is required. [ABSTRACT FROM AUTHOR]