Advanced optical photothermal infrared spectroscopy for comprehensive characterization of microplastics from intravenous fluid delivery systems.

Growing attention is being directed towards exploring the potential harmful effects of microplastic (MP) particles on human health. Previous reports on human exposure to MPs have primarily focused on inhalation, ingestion, transdermal routes, and, potentially, transplacental transfer. The intravenous transfer of MP particles in routine healthcare settings has received limited exploration in existing literature. Standard hospital IV system set up with 0.9 % NaCl in a laminar flow hood with MP contamination precautions. Various volumes of 0.9 % NaCl passed through the system, some with a volumetric pump. Fluid filtered with Anodisc filters washed with isopropyl alcohol. The IV cannula was immersed in Mili-Q water for 72 h to simulate vein conditions. Subsequently, the water was filtered and washed. Optical photothermal infrared (O-PTIR) microspectroscopy is used to examine filters for MP particles. All filters examined from the IV infusion system contained MP particles, including MPs from the polymer materials used in the manufacture of the IV delivery systems (polydimethylsiloxane, polypropylene, polystyrene, and polyvinyl chloride) and MP particles arising from plastic resin additives (epoxy resin, polyamide resin, and polysiloxane-containing MPs). The geometric mean from the extrapolated result data indicated that approximately 0.90 MP particles per ml of 0.9 % NaCl solution can be administered through a conventional IV infusion system in the absence of a volumetric pump. However, with the implementation of a pump, this value may increase to 1.57 particles per ml. Notably, over 72 h, a single cannula was found to release approximately 558 MP particles including polydimethylsiloxane, polysiloxane-containing MPs, polyamide resin, and epoxy resin. Routine IV infusion systems release microplastics. MP particles are also released around IV cannulas, suggesting transfer into the circulatory system during standard IV procedures.

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.

Analysing micro- and nanoplastics with cutting-edge infrared spectroscopy techniques: a critical review.

The escalating prominence of micro- and nanoplastics (MNPs) as emerging anthropogenic pollutants has sparked widespread scientific and public interest. These minuscule particles pervade the global environment, permeating drinking water and food sources, prompting concerns regarding their environmental impacts and potential risks to human health. In recent years, the field of MNP research has witnessed the development and application of cutting-edge infrared (IR) spectroscopic instruments. This review focuses on the recent application of advanced IR spectroscopic techniques and relevant instrumentation to analyse MNPs. A comprehensive literature search was conducted, encompassing articles published within the past three years. The findings revealed that Fourier transform infrared (FTIR) spectroscopy stands as the most used technique, with focal plane array FTIR (FPA-FTIR) representing the cutting edge in FTIR spectroscopy. The second most popular technique is quantum cascade laser infrared (QCL-IR) spectroscopy, which has facilitated rapid analysis of plastic particles. Following closely is optical photothermal infrared (O-PTIR) spectroscopy, which can furnish submicron spatial resolution. Subsequently, there is atomic force microscopy-based infrared (AFM-IR) spectroscopy, which has made it feasible to analyse MNPs at the nanoscale level. The most advanced IR instruments identified in articles covered in this review were compared. Comparison metrics encompass substrates/filters, data quality, spatial resolution, data acquisition speed, data processing and cost. The limitations of these IR instruments were identified, and recommendations to address these limitations were proposed. The findings of this review offer valuable guidance to MNP researchers in selecting suitable instrumentation for their research experiments, thereby facilitating advancements in research aimed at enhancing our understanding of the environmental and human health risks associated with MNPs.

Assessing the inconsistency of microplastic measurements in foods and beverages.

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.

Machine learning driven methodology for enhanced nylon microplastic detection and characterization.

In recent years, the field of microplastic (MP) research has evolved significantly; however, the lack of a standardized detection methodology has led to incomparability across studies. Addressing this gap, our current study innovates a reliable MP detection system that synergizes sample processing, machine learning, and optical photothermal infrared (O-PTIR) spectroscopy. This approach includes examining high-temperature filtration and alcohol treatment for reducing non-MP particles and utilizing a support vector machine (SVM) classifier focused on key wavenumbers that could discriminate between nylon MPs and non-nylon MPs (1077, 1541, 1635, 1711 cm-1 were selected based on the feature importance of SVM-Full wavenumber model) for enhanced MP identification. The SVM model built from key wavenumbers demonstrates a high accuracy rate of 91.33%. Results show that alcohol treatment is effective in minimizing non-MP particles, while filtration at 70 °C has limited impact. Additionally, this method was applied to assess MPs released from commercial nylon teabags, revealing an average release of 106 particles per teabag. This research integrates machine learning with O-PTIR spectroscopy, paving the way for potential standardization in MP detection methodologies and providing vital insights into their environmental and health implications.

Vibrational Analysis of Hydration-Layer Water around Ubiquitin, Unpeeled Layer by Layer: Molecular-Dynamics Perceptions.

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.

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.

Detection of previously frozen poultry through plastic lidding film using portable visible spectral imaging (443-726 NM).

The objective of this study is to use a portable visible spectral imaging system (443-726 nm) to detect poultry thawed from frozen at the pixel level using multivariate analysis methods commonly used in machine learning (decision tree, logistic regression, linear discriminant analysis [LDA], k-nearest neighbors [KNN], support vector machines [SVM]). The selection of the most suitable method is based on the amount of data required to build an accurate model, computational speed, and the robustness of the model. The training set consists of pixel spectra from packages of chicken thighs without plastic lidding to evaluate the robustness of the models when implemented on the test set with and without plastic lidding. Data subsets were created by randomly selecting 1, 5, 10, 20, and 50% of the pixel spectra of each sample for both the training and test data sets. The subsets of pixel spectra and the full training set were used to train the machine learning algorithms to evaluate how the amount of data influences computational time. Logistic regression was found to be the best algorithm for detecting poultry thawed from frozen with and without plastic lidding film. Although logistic regression and SVM both performed with the same high accuracy and sensitivity for all training subset sizes, the computational time needed to implement SVM makes it the less suitable algorithm for detecting poultry thawed from frozen with and without plastic lidding film.

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.

Feature fusion of Raman chemical imaging and digital histopathology using machine learning for prostate cancer detection.

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.

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.

Microbial detection and identification methods: Bench top assays to omics approaches.

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.

Deep Spectral-Spatial Features of Near Infrared Hyperspectral Images for Pixel-Wise Classification of Food Products.

Hyperspectral imaging (HSI) emerges as a non-destructive and rapid analytical tool for assessing food quality, safety, and authenticity. This work aims to investigate the potential of combining the spectral and spatial features of HSI data with the aid of deep learning approach for the pixel-wise classification of food products. We applied two strategies for extracting spatial-spectral features: (1) directly applying three-dimensional convolution neural network (3-D CNN) model; (2) first performing principal component analysis (PCA) and then developing 2-D CNN model from the first few PCs. These two methods were compared in terms of efficiency and accuracy, exemplified through two case studies, i.e., classification of four sweet products and differentiation between white stripe ("myocommata") and red muscle ("myotome") pixels on salmon fillets. Results showed that combining spectral-spatial features significantly enhanced the overall accuracy for sweet dataset, compared to partial least square discriminant analysis (PLSDA) and support vector machine (SVM). Results also demonstrated that spectral pre-processing techniques prior to CNN model development can enhance the classification performance. This work will open the door for more research in the area of practical applications in food industry.

Literature review: spectral imaging applied to poultry products.

Consumption of poultry products is increasing worldwide, leading to an increased demand for safe, fresh, high-quality products. To ensure consumer safety and meet quality standards, poultry products must be routinely checked for fecal matter, food fraud, microbiological contamination, physical defects, and product quality. However, traditional screening methods are insufficient in providing real-time, nondestructive, chemical and spatial information about poultry products. Novel techniques, such as hyperspectral imaging (HSI), are being developed to acquire real-time chemical and spatial information about products without destruction of samples to ensure safety of products and prevent economic losses. This literature review provides a comprehensive overview of HSI applications to poultry products. The studies used for this review were found using the Google Scholar database by searching the following terms and their synonyms: "poultry" and "hyperspectral imaging". A total of 67 studies were found to meet the criteria. After all relevant literature was compiled, studies were grouped into categories based on the specific material or characteristic of interest to be detected, identified, predicted, or quantified by HSI. Studies were found for each of the following categories: food fraud, fecal matter detection, microbiological contamination, physical defects, and product quality. Key findings and technological advancements were briefly summarized and presented for each category. Since the first application to poultry products 20 yr ago, HSI has been shown to be a successful alternative to traditional screening methods.

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.

Using spatial information for evaluating the quality of prediction maps from hyperspectral images: A geostatistical approach.

Applying a calibration model onto hyperspectral (HS) images is of great interest because it produces images of chemical or physical properties. HS imaging is widely used in this way in food processing industries for monitoring product quality and process control. In this context, one of the main difficulties in the application of regression models to HS images is to evaluate the error of the obtained predictions, since in a proximal imaging set up, the size of the pixels is usually much smaller than the area required to obtain a wet chemical reference. Moreover, the selection of regression model parameters, such as the number of latent variables (LV) in a partial least squares (PLS) model, can modify the appearance of the prediction maps. The objective of this work is to propose an approach based on geostatistical indices to use spatial information of prediction maps for supporting the evaluation of regression models applied to HS images. This work stablishes a theoretical connection between linear regression model performance estimates and the spatial decomposition of variance in prediction maps, when the ground truth to estimate is spatially structured. This approach was tested in a simulated dataset and two real case studies. Geostatistical indices of the prediction maps were compared to model performance metrics for PLS models with increasing number of LV. The theoretical framework was proven by the results on the simulated dataset. In particular, the evolution of the nugget effect, C0, corresponded with the evolution of the random error of the model. Conversely, the error term of the model related with the slope of the model corresponded with the evolution of the structured variance observed in the prediction maps. On the real case studies, geostatistical indexes, extracted from the prediction maps, allowed to quantitatively evaluate the spatial structure of the estimations and complement the Root Mean Standard Error of Cross Validation (RMSECV) for the choice of optimal number of LV to consider in the model. The main advantage of this approach is that it does not require ground truth values. It could be used as a source of information for supporting the choice of optimum calibration parameters, such as the number of latent variables, or the choice of pre-treatments, complementing the traditional visual inspection of prediction maps with quantitative and objective metrics.