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.

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.

Real-Time Detection of Strawberry Ripeness Using Augmented Reality and Deep Learning.

Currently, strawberry harvesting relies heavily on human labour and subjective assessments of ripeness, resulting in inconsistent post-harvest quality. Therefore, the aim of this work is to automate this process and provide a more accurate and efficient way of assessing ripeness. We explored a unique combination of YOLOv7 object detection and augmented reality technology to detect and visualise the ripeness of strawberries. Our results showed that the proposed YOLOv7 object detection model, which employed transfer learning, fine-tuning and multi-scale training, accurately identified the level of ripeness of each strawberry with an mAP of 0.89 and an F1 score of 0.92. The tiny models have an average detection time of 18 ms per frame at a resolution of 1280 × 720 using a high-performance computer, thereby enabling real-time detection in the field. Our findings distinctly establish the superior performance of YOLOv7 when compared to other cutting-edge methodologies. We also suggest using Microsoft HoloLens 2 to overlay predicted ripeness labels onto each strawberry in the real world, providing a visual representation of the ripeness level. Despite some challenges, this work highlights the potential of augmented reality to assist farmers in harvesting support, which could have significant implications for current agricultural practices.

Microplastics in waste management systems: A review of analytical methods, challenges and prospects.

Numerous studies have reported the presence of microplastics (MPs) in waste collection and disposal systems. However, current scientific studies on measuring MP occurrence in a waste management context are not comparable due to a lack of standardized methodologies. Consequently, the impact of MPs on ecosystems and human health remains largely unclear. To address the inconsistencies, present in published studies, this review thoroughly examines sample preparation techniques for transfer stations, landfill leachate, recycling, compost, and incineration ash samples. Furthermore, various analytical approaches such as flotation, filtration, and organic matter digestion, as well as morphological categorization, identification, and quantification, are subsequently rigorously assessed. The benefits and limitations of each methodology are evaluated to facilitate the development of accurate and effective methods for detecting and characterizing nanoplastics. Recent research suggests that plastic recycling and composting facilities are the primary environmental sources of microplastic pollution among different waste treatment methods. The most prevalent microplastic types discovered in waste management were polyethylene (PE) and polypropylene (PP), with fragment and fiber being the most frequently reported morphologies. The review highlights a number of tactics that could be integrated into the methodology development for detecting microplastics in waste management systems (WMS), ultimately leading to better consistency and reliability of data across different studies. In essence, this will advance our comprehension of potential risks associated with microplastics.

Trends of Augmented Reality for Agri-Food Applications.

Recent years have witnessed an increasing interest in deploying state-of-the-art augmented reality (AR) head-mounted displays (HMDs) for agri-food applications. The benefits of AR HMDs to agri-food industry stakeholders (e.g., food suppliers, retail/food service) have received growing attention and recognition. AR HMDs enable users to make healthier dietary choices, experience novel changes in their perception of taste, enhance the cooking and food shopping experience, improve productivity at work and enhance the implementation of precision farming. Therefore, although development costs are still high, the case for integration of AR in food chains appears to be compelling. This review will present the most recent developments of AR HMDs for agri-food relevant applications. The summarized applications can be clustered into different themes: (1) dietary and food nutrition assessment; (2) food sensory science; (3) changing the eating environment; (4) retail food chain applications; (5) enhancing the cooking experience; (6) food-related training and learning; and (7) food production and precision farming. Limitations of current practices will be highlighted, along with some proposed applications.

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.

[Provenance of Groundwater Solute and Its Controlling Factors in Yancheng Area].

Affected by human activities and climate change, the deterioration of groundwater quality could continue to intensify, and it is difficult to repair after being polluted. In order to reduce the vulnerability of groundwater safety, it is urgent to strengthen research on groundwater quality. To analyze the characteristics of hydrochemical composition and control factors and to explore the impact of human activities, groundwater sampling was conducted in the alluvial and marine plains of Yancheng area, where industrial and agricultural activities were intense. The results showed that:① the TDS value of shallow groundwater was between 211 and 3790 mg·L-1, of which, brackish water samples accounted for 30.28% in Yancheng. The order of the concentration of anions and cations was:HCO3->Cl->SO42->NO3- and Na+>Ca2+>Mg2+>K+. However, from Type Ⅰ/Ⅱ water to Type Ⅴ water, with the increase in TDS value, the dominant anions and cations evolved from HCO3 type to Cl/SO4 type and from Ca type to Na type, respectively. ② In the initial Type Ⅰ/Ⅱ water bodies, Na+ should have mainly originated from the weathering erosion of albite, whereas Ca2+, Mg2+, and HCO3- should have had a common rock weathering source. In the subsequent water quality evolution process, the factors that led the TDS value to increase, or even to exceed the standard, were not limited to rock weathering, and the effects of evaporation-concentration and ion exchange were also very worthy of attention. ③ The enrichment factor analysis showed that the Cl- and SO42- were mainly from the input of sea salt sources, and their proportions were gradually increasing from the freshwater areas of Type Ⅰ/Ⅱ in the west to the brackish water areas in the east. However, Ca2+, K+, and HCO3- were mainly derived from crustal sources, and Ca2+ was precipitated due to saturation. ④ The NO3- derived from agricultural fertilizers was limited to water of category Ⅰ/Ⅱ and Ⅲ-1 (500 mg·L-1 < TDS ≤ 750 mg·L-1). Discharge of urban sewage mainly affected Type Ⅲ water bodies. The nitrates formed by agricultural organic fertilizers were mainly distributed in the water of Type Ⅳ and Ⅴ, which was the main rice-producing area of Sheyang, Jiangsu, and the SO42- formed by human activities in this area could be ignored. These should be one of the achievements in the development of regional ecological agriculture.

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.

[Spatial Hydrochemical Characteristics and Controlling Factors of Surface Water in the Yancheng Area].

Surface water resources are crucial to economic development in China's eastern coastal areas. Under the influence of intense human activities, problems such as abnormal water quality and pollution are very prominent. Here, the chemical composition of surface waters and their controlling factors were analyzed in the Yancheng area. The results showed that:① The surface water pH is low and the concentrations of total dissolved solids(TDS) are high in the study area. pH is likely controlled by the acidic organic pollutants discharged by human activities as well as acidic substances formed by anaerobic decomposition in soils. TDS data showed low values in the west and high values in the east, and low values in north and high south of the study region. ② Cation content was dominated by Na+, anions were dominated by HCO3- and Cl-, and, overall, hydrochemical composition was controlled by HCO3-(Ca+Na), although there was significant variability between the different regions. ③ Because the silt layer in the region is mainly composed of clay, subclay, and sandstone, the soluble matter in surface water is mainly derived from the decomposition of Na-feldspar in silicate debris via carbonation, supplemented by carbonation. Due to the low-lying terrain and high groundwater level in the tidal flat area, solutes are significantly affected by evaporation-driven concentration alongside evaporite weathering replenishment. ④ Although the surface waters in different regions were found to be affected by human activities to varying degrees, the impact was always strong. In particular, in areas of tidal wetland expansion, water quality is not only affected by urban sewage and agricultural irrigation but also industrial wastewater discharge.

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.

Authentication and Provenance of Walnut Combining Fourier Transform Mid-Infrared Spectroscopy with Machine Learning Algorithms.

Different varieties and geographical origins of walnut usually lead to different nutritional values, contributing to a big difference in the final price. The conventional analytical techniques have some unavoidable limitations, e.g., chemical analysis is usually time-expensive and labor-intensive. Therefore, this work aims to apply Fourier transform mid-infrared spectroscopy coupled with machine learning algorithms for the rapid and accurate classification of walnut species that originated from ten varieties produced from four provinces. Three types of models were developed by using five machine learning classifiers to (1) differentiate four geographical origins; (2) identify varieties produced from the same origin; and (3) classify all 10 varieties from four origins. Prior to modeling, the wavelet transform algorithm was used to smooth and denoise the spectrum. The results showed that the identification of varieties under the same origin performed the best (i.e., accuracy = 100% for some origins), followed by the classification of four different origins (i.e., accuracy = 96.97%), while the discrimination of all 10 varieties is the least desirable (i.e., accuracy = 87.88%). Our results implicated that using the full spectral range of 700-4350 cm-1 is inferior to using the subsets of the optimal spectral variables for some classifiers. Additionally, it is demonstrated that back propagation neural network (BPNN) delivered the best model performance, while random forests (RF) produced the worst outcome. Hence, this work showed that the authentication and provenance of walnut can be realized effectively based on Fourier transform mid-infrared spectroscopy combined with machine learning algorithms.

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.

Evaluating drying feature differences between ginger slices and splits during microwave-vacuum drying by hyperspectral imaging technique.

This study aimed to investigate the difference between ginger slices (vertically cut) and splits (horizontally cut) during microwave-vacuum drying (MVD) procedures. MVD ginger slices showed a higher shrinkage rate and a higher hardness value, with a more porous structure of the surface layer. MVD ginger splits had higher rehydration rates at the first 15 min of the rehydration. Nine optimal wavelengths were selected by regression coefficients (RC) from the partial least squares regression (PLSR) model based on the raw data. A simplified PLSR model based on optimal wavelengths showed a good performance with a coefficient of determination in prediction (Rp2) of 0.973 and a root mean square error in prediction (RMSEP) of 4.63%. Texture features of grey level co-occurrence matrix (GLCM) of moisture prediction maps demonstrated a more uniform moisture distribution in MVD ginger slices than that in splits in the original geometry.

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.