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The detection of ethanol-water solution concentration plays an important role in industries, medical care, food and other aspects, which has attracted much attention. In this paper, a 632.8 nm laser combined with the oblique-incidence reflectivity difference (OIRD) method was used to obtain a signal linearly related to the solution concentration and containing the information of the dielectric constant of the solution. Combined with a variety of deep learning algorithms, ethanol-water solutions with a volume concentration of 0-95 % are detected. Among them, the prediction accuracy of the MLP, CNN, LSTM, CNN + BiLSTM + Attention models were 93.65 %, 96.54 %, 97.12 %, 99.23 %, respectively. The experimental results indicate that the OIRD method can achieve rapid, non-destructive, accurate and reliable detection of ethanol-water solutions.

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Due to the significant price differences among different types of edible oils, expensive oils like olive oil are often blended with cheaper edible oils. This practice of adulteration in edible oils, aimed at increasing profits for producers, poses a major concern for consumers. Furthermore, adulteration in edible oils can lead to various health issues impacting consumer well-being. In order to meet the requirements of fast, non-destructive, universal, accurate, and reliable quality testing for edible oil, the oblique-incidence reflectivity difference (OIRD) method combined with machine learning algorithms was introduced to detect a variety of edible oils. The prediction accuracy of Gradient Boosting, K-Nearest Neighbor, and Random Forest models all exceeded 95%. Moreover, the contribution rates of the OIRD signal, DC signal, and fundamental frequency signal to the classification results were 45.7%, 34.1%, and 20.2%, respectively. In a quality evaluation experiment on olive oil, the feature importance scores of three signals reached 63.4%, 18.9%, and 17.6%. The results suggested that the feature importance score of the OIRD signal was significantly higher than that of the DC and fundamental frequency signals. The experimental results indicate that the OIRD method can serve as a powerful tool for detecting edible oils.

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Optical biosensors provide a platform for qualitatively and quantitatively analyzing various biomolecular interactions. In addition to advantages such as label-free and high-throughput detection, these devices are also capable of measuring real-time binding curves in response to changes in optical properties of biomolecules. These kinetic data may be fitted to models to extract binding affinities such as association rates, dissociation rates, and equilibrium dissociation constants. In these biosensors, one of the binding pair is usually immobilized on a solid substrate for capturing the other. Due to the nature of these surface-based methods, mass transport effects and immobilization heterogenetity may cause problems when fitting the kinetic curves with the simple one-to-one Langmuir model. Here real-time binding curves of various antibody-antigen reactions were obtained by using an ellipsometry-based biosensor, and the results were fitted to the simple one-to-one model as well as a more sophisticated approach. The results show that the one-to-two model fitted much better to the curves than the one-to-one model. The two-site model may be explained by assuming two immobilization configurations on the surface. In summary, in fitting real-time curves obtained from optical biosensors, more sophisticated models are usually required to take surface-related issues, such as immobilization heterogenetity and mass transport effects within targets, into account.

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Carbohydrates present on cell surfaces mediate cell behavior through interactions with other biomolecules. Due to their structural complexity, diversity, and heterogeneity, it is difficult to fully characterize a variety of carbohydrates and their binding partners. As a result, novel technologies for glycomics applications have been developed, including carbohydrate microarrays and label-free detection methods. In this paper, we report using the combination of oligosaccharide microarrays and the label-free oblique-incidence reflectivity difference (OI-RD) microscopy for real-time characterization of oligosaccharide binding proteins. Aminated human milk oligosaccharides were immobilized on epoxy-coated glass substrates as microarrays for reactions with Family 1 of solute binding proteins from Bifidobacterium longum subsp. infantis (B. infantis). Binding affinities of these protein-oligosaccharide interactions showed preferences of Family 1 of solute binding proteins to host glycans, which helps in characterizing the complex process of human milk oligosaccharides foraging by B. infantis.

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Microarrays of biological molecules such as DNAs, proteins, carbohydrates, and small molecules provide a high-throughput platform for screening tens of thousands of biomolecular interactions simultaneously, facilitating the functional characterization of these biomolecules in areas of genomics, proteomics, glycomics, and cytomics. Routinely, analysis of binding reactions between solution-phased probes and surface-immobilized targets involves some kinds of fluorescence-based detection methods. Even though these methods have advantages of high sensitivity and wide dynamic range, labeling probes and/or targets inevitably changes their innate properties and in turn affects probe-target interactions in often uncharacterized ways. Therefore, in recent years, various label-free sensing technologies have been developed for characterizing biomolecular interactions in microarray format. These biosensors, to a certain extent, take the place of fluorescent methods by providing a comparable sensitivity as well as retaining the conformational and functional integrality of biomolecules to be investigated. More importantly, some of these biosensors are capable of real-time monitoring probe-target interactions, providing the binding affinities of these reactions. Using label-free biosensors in microarrays has become a current trend in developing high-throughput screening platforms for drug discoveries and applications in all areas of "-omics." This article is aimed to provide principles and recent developments in label-free sensing technologies applicable to microarrays, with special attentions being paid to surface plasmon resonance microscopy and oblique-incidence reflectivity difference microscopy.

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Microarrays provide a platform for high-throughput characterization of biomolecular interactions. To increase the sensitivity and specificity of microarrays, surface blocking is required to minimize the nonspecific interactions between analytes and unprinted yet functionalized surfaces. To block amine- or epoxy-functionalized substrates, bovine serum albumin (BSA) is one of the most commonly used blocking reagents because it is cheap and easy to use. Based on standard protocols from microarray manufactories, a BSA concentration of 1% (10 mg/mL or 200 µM) and reaction time of at least 30 min are required to efficiently block epoxy-coated slides. In this paper, we used both fluorescent and label-free methods to characterize the BSA blocking efficiency on epoxy-functionalized substrates. The blocking efficiency of BSA was characterized using a fluorescent scanner and a label-free oblique-incidence reflectivity difference (OI-RD) microscope. We found that (1) a BSA concentration of 0.05% (0.5 mg/mL or 10 µM) could give a blocking efficiency of 98%, and (2) the BSA blocking step took only about 5 min to be complete. Also, from real-time and in situ measurements, we were able to calculate the conformational properties (thickness, mass density, and number density) of BSA molecules deposited on the epoxy surface.

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In recent years, various label-free biosensing technologies have been developed for studying the real-time kinetics of diverse biomolecular interactions. These biosensors partially take the place of fluorescence-based methods by providing a comparable sensitivity as well as retaining the conformational and functional integrality of biomolecules to be investigated. However, to completely eliminate the need of fluorescence, throughput is the next big consideration. Microarrays provide a high-throughput platform for screening tens of thousands of biomolecular interactions simultaneously, and many compatible fluorescent scanners have been commercially available. The combination of microarrays and label-free biosensors will be of great interest to researchers in related fields. Microarrays are fabricated by spotting, imprinting, or directly synthesizing biomolecules on solid supports such as glasses, silicon wafers, and other functionalized substrates, and they have been applied to detect DNAs, proteins, toxins, and so on in surface plasmon resonance (SPR) imaging systems and oblique-incidence reflectivity difference (OI-RD) microscopes. Current challenges include increasing sensitivity, reducing sampling time, improving surface chemistry, identifying captured molecules, and minimizing reagent consumption. Future research directions are to improve the instruments themselves, modify the microarray surface for more efficient analyte capture, and combine the systems with mass spectrometry and microfluidics.

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Monoclonal antibodies (mAbs) against human proteins are the primary protein capture reagents for basic research, diagnosis, and molecular therapeutics. The 2 most important attributes of mAbs used in all of these applications are their specificity and avidity. While specificity of a mAb raised against a human protein can be readily defined based on its binding profile on a human proteome microarray, it has been a challenge to determine avidity values for mAbs in a high-throughput and cost-effective fashion. To undertake this challenge, we employed the oblique-incidence reflectivity difference (OIRD) platform to characterize mAbs in a protein microarray format. We first systematically determined the Kon and Koff values of 50 mAbs measured with the OIRD method and deduced the avidity values. Second, we established a multiplexed approach that simultaneously measured avidity values of a mixture of 9 mono-specific mAbs that do not cross-react to the antigens. Third, we demonstrated that avidity values of a group of mAbs could be sequentially determined using a flow-cell device. Finally, we implemented a sequential competition assay that allowed us to bin multiple mAbs that recognize the same antigens. Our study demonstrated that OIRD offers a high-throughput and cost-effective platform for characterization of the binding kinetics of mAbs.

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Monoclonal antibodies (mAbs) are major reagents for research and clinical diagnosis. For their inherently high specificities to intended antigen targets and thus low toxicity in general, they are pursued as one of the major classes of new drugs. Yet binding properties of most monoclonal antibodies are not well characterized in terms of affinity constants and how they vary with presentations and/or conformational isomers of antigens, buffer compositions, and temperature. We here report a microarray-based label-free assay platform for high-throughput measurements of monoclonal antibody affinity constants to antigens immobilized on solid surfaces. Using this platform we measured affinity constants of over 1410 rabbit monoclonal antibodies and 46 mouse monoclonal antibodies to peptide targets that are immobilized through a terminal cysteine residue to a glass surface. The experimentally measured affinity constants vary from 10 pM to 200 pM with the median value at 66 pM. We compare the results obtained from the microarray-based platform with those from a benchmarking surface-plasmon-resonance-based (SPR) sensor (Biacore 3000).

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