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Sensing Mechanisms of Rough Plasmonic Surfaces for Protein Binding of Surface Plasmon Resonance Detection
(2023-04-01)
Treebupachatsakul, Treesukon
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Shinnakerdchoke, Siratchakrit
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Pechprasarn, Suejit
Surface plasmon resonance (SPR) has been utilized in various optical applications, including biosensors. The SPR-based sensor is a gold standard for protein kinetic measurement due to its ultrasensitivity on the plasmonic metal surface. However, a slight change in the surface morphology, such as roughness or pattern, can significantly impact its performance. This study proposes a theoretical framework to explain sensing mechanisms and quantify sensing performance parameters of angular surface plasmon resonance detection for binding kinetic sensing at different levels of surface roughness. The theoretical investigation utilized two models, a protein layer coating on a rough plasmonic surface with and without sidewall coatings. The two models enable us to separate and quantify the enhancement factors due to the localized surface plasmon polaritons at sharp edges of the rough surfaces and the increased surface area for protein binding due to roughness. The Gaussian random surface technique was employed to create rough metal surfaces. Reflectance spectra and quantitative performance parameters were simulated and quantified using rigorous coupled-wave analysis and Monte Carlo simulation. These parameters include sensitivity, plasmonic dip position, intensity contrast, full width at half maximum, plasmonic angle, and figure of merit. Roughness can significantly impact the intensity measurement of binding kinetics, positively or negatively, depending on the roughness levels. Due to the increased scattering loss, a tradeoff between sensitivity and increased roughness leads to a widened plasmonic reflectance dip. Some roughness profiles can give a negative and enhanced sensitivity without broadening the SPR spectra. We also discuss how the improved sensitivity of rough surfaces is predominantly due to the localized surface wave, not the increased density of the binding domain.
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Machine Learning Models Using Data Mining for Biomass Production from Yarrowia lipolytica Fermentation
(2023-03-01)
Pensupa, Nattha
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Treebuppachartsakul, Treesukon
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Pechprasarn, Suejit
In this paper, a database of biomass production from Yarrowia lipolytica fermentation is prepared and constructed using machine learning and data mining approaches. The database is curated from 15 publications and consists of 301 rows of data with 25 predictors and 1 label. The predictors include inoculum size, temperature, pH, and time, while the label is the corresponding biomass production. The database is then divided into training, validation, and test datasets and analyzed as a supervised machine learning task for regression. Twenty-six regression models are employed and compared for their performance in predicting biomass production. The best-performing model is the Matern 5/2 Gaussian process regression model, which has the lowest root-mean-squared error of 0.75 g/L, the highest R squared of 0.90, and the lowest mean absolute error of 0.52 g/L. The t-test is used to identify the most important predictors, and 14 predictors are sufficient for creating an accurate model. These 14 predictors are fermentation time, peptone, temperature, total Kjeldahl nitrogen, shaking rate, total nitrogen, inoculum size, yeast extract, crude glycerol, glucose, oil and grease, media pH, ammonium sulfate, and olive oil. This research demonstrates the application of machine learning and data mining to estimate biomass production and gives insight into which parameters are essential for Yarrowia lipolytica fermentation.
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Measurement precision enhancement of surface plasmon resonance based angular scanning detection using deep learning
(2022-12-01)
Thadson, Kitsada
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Sasivimolkul, Suvicha
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Suvarnaphaet, Phitsini
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Visitsattapongse, Sarinporn
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Pechprasarn, Suejit
Angular scanning-based surface plasmon resonance measurement has been utilized in label-free sensing applications. However, the measurement accuracy and precision of the surface plasmon resonance measurements rely on an accurate measurement of the plasmonic angle. Several methods have been proposed and reported in the literature to measure the plasmonic angle, including polynomial curve fitting, image processing, and image averaging. For intensity detection, the precision limit of the SPR is around 10–5 RIU to 10–6 RIU. Here, we propose a deep learning-based method to locate the plasmonic angle to enhance plasmonic angle detection without needing sophisticated post-processing, optical instrumentation, and polynomial curve fitting methods. The proposed deep learning has been developed based on a simple convolutional neural network architecture and trained using simulated reflectance spectra with shot noise and speckle noise added to generalize the training dataset. The proposed network has been validated in an experimental setup measuring air and nitrogen gas refractive indices at different concentrations. The measurement precision recovered from the experimental reflectance images is 4.23 × 10–6 RIU for the proposed artificial intelligence-based method compared to 7.03 × 10–6 RIU for the cubic polynomial curve fitting and 5.59 × 10–6 RIU for 2-dimensional contour fitting using Horner's method.
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Gelatin-Based Microfluidic Channel for Quantitative E. Coli Detection Using Blue Fluorescence of 4-Methyl-Umbelliferone Product and a Smartphone Camera
(2022-07-01)
Treebupachatsakul, Treesukon
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Lochotinunt, Chanin
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Teechot, Thitirat
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Pensupa, Nattha
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Pechprasarn, Suejit
Escherichia coli (E. coli) is a foodborne pathogen that can produce potent toxins, causing severe illnesses due to contaminated food and water consumption. This research has utilized a fluorescence measurement to quantify E. coli colonies from blue fluorescence emitted by 4-methyl-umbelliferone (4MU). The 4MU is the product of the catalytic reaction between beta-D-glucuronidase (GUD) secreted by multiple strains of Escherichia coli and its substrate 4-methylumbelliferyl-beta-D-glucuronide (MUG). Here, we apply the 4MU enzymatic reaction and propose simple instrumentation for label-free, real-time, in-situ, and quantitative E. coli measurement. The detection platform consists of a smartphone camera, an ultraviolet light source for fluorescence excitation, and MUG suspended microfluidic channels. The underlining mechanism for the proposed E. coli measurement is the passive diffusion process of the MUG secreted by E. coli and the GUD suspended in the gelatin, forming the blue fluorescence 4MU product in the channels. We have also proposed a cost-effective and eco-friendly fabrication method for preparing the MUG suspended gelatin microfluidic channels using a laser printer. Gelatin is an ultraviolet light-absorbing material in nature, providing an embedded optical filter. Here, we demonstrate that a smartphone camera can be utilized to image the fluorescence emission of the 4MU excited by the ultraviolet light in the gelatin film. The proposed E. coli detection technique allows the amount of E. coli colonies to be quantified without liquid sampling, cell-culturing, inoculation, and sophisticated equipment. Furthermore, the proposed method has a trade-off between response time and detection limit.
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Analysis of Deep Learning-Based Phase Retrieval Algorithm Performance for Quantitative Phase Imaging Microscopy
(2022-05-01)
Visitsattapongse, Sarinporn
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Thadson, Kitsada
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Pechprasarn, Suejit
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Thongpance, Nuntachai
Quantitative phase imaging has been of interest to the science and engineering community and has been applied in multiple research fields and applications. Recently, the data-driven approach of artificial intelligence has been utilized in several optical applications, including phase retrieval. However, phase images recovered from artificial intelligence are questionable in their correctness and reliability. Here, we propose a theoretical framework to analyze and quantify the performance of a deep learning-based phase retrieval algorithm for quantitative phase imaging microscopy by comparing recovered phase images to their theoretical phase profile in terms of their correctness. This study has employed both lossless and lossy samples, including uniform plasmonic gold sensors and dielectric layer samples; the plasmonic samples are lossy, whereas the dielectric layers are lossless. The uniform samples enable us to quantify the theoretical phase since they are established and well understood. In addition, a context aggregation network has been employed to demonstrate the phase image regression. Several imaging planes have been simulated serving as input and the label for network training, including a back focal plane image, an image at the image plane, and images when the microscope sample is axially defocused. The back focal plane image plays an essential role in phase retrieval for the plasmonic samples, whereas the dielectric layer requires both image plane and back focal plane information to retrieve the phase profile correctly. Here, we demonstrate that phase images recovered using deep learning can be robust and reliable depending on the sample and the input to the deep learning.
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Generalized figure of merit for plasmonic dip measurement-based surface plasmon resonance sensors
(2022-04-01)
Treebupachatsakul, Treesukon
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Boosamalee, Apivitch
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Chaithatwanitch, Kamejira
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Pechprasarn, Suejit
We propose a theoretical framework to analyze quantitative sensing performance parameters, including sensitivity, full width at half maximum, plasmonic dip position, and figure of merits for different surface plasmon operating conditions for a Kretschmann configuration. Several definitions and expressions of the figure of merit have been reported in the literature. Moreover, the optimal operating conditions for each figure of merit are, in fact, different. In addition, there is still no direct figure of merit comparison between different expressions and definitions to identify which definition provides a more accurate performance prediction. Here shot-noise model and Monte Carlo simulation mimicking the noise behavior in SPR experiments have been applied to quantify standard deviation in the SPR plasmonic dip measurements to evaluate the performance responses of the figure of merits. Here, we propose and formulate a generalized figure of merit definition providing a good performance estimation to the detection limit. The measurement parameters employed in the figure of merit formulation are identified by principal component analysis and machine learning. We also show that the proposed figure of merit can provide a good estimation for the surface plasmon resonance performance of plasmonic materials, including gold and aluminum, with no need for a resource-demanding computation.
1
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Cuff-Less Blood Pressure Prediction from ECG and PPG Signals Using Fourier Transformation and Amplitude Randomization Preprocessing for Context Aggregation Network Training
(2022-03-01)
Treebupachatsakul, Treesukon
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Boosamalee, Apivitch
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Shinnakerdchoke, Siratchakrit
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Pechprasarn, Suejit
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Thongpance, Nuntachai
This research proposes an algorithm to preprocess photoplethysmography (PPG) and electrocardiogram (ECG) signals and apply the processed signals to the context aggregation network-based deep learning to achieve higher accuracy of continuous systolic and diastolic blood pressure monitoring than other reported algorithms. The preprocessing method consists of the following steps: (1) acquiring the PPG and ECG signals for a two second window at a sampling rate of 125 Hz; (2) separating the signals into an array of 250 data points corresponding to a 2 s data window; (3) randomizing the amplitude of the PPG and ECG signals by multiplying the 2 s frames by a random amplitude constant to ensure that the neural network can only learn from the frequency information accommodating the signal fluctuation due to instrument attachment and installation; (4) Fourier transforming the windowed PPG and ECG signals obtaining both amplitude and phase data; (5) normalizing both the amplitude and the phase of PPG and ECG signals using z-score normalization; and (6) training the neural network using four input channels (the amplitude and the phase of PPG and the amplitude and the phase of ECG), and arterial blood pressure signal in time-domain as the label for supervised learning. As a result, the network can achieve a high continuous blood pressure monitoring accuracy, with the systolic blood pressure root mean square error of 7 mmHg and the diastolic root mean square error of 6 mmHg. These values are within the error range reported in the literature. Note that other methods rely only on mathematical models for the systolic and diastolic values, whereas the proposed method can predict the continuous signal without degrading the measurement performance and relying on a mathematical model.
1
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Performance Analysis of Machine Learning Models for Angular Interrogation of Surface Plasmon Resonance
(2022-01-01)
Shinnakerdchoke, Siratchakrit
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Thadson, Kitsada
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Pechprasarn, Suejit
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Treebupachatsakul, Treesukon
Surface plasmon resonance (SPR) paves the way for several cutting-edge sensing technologies well-known for being label-free and real-time monitoring. The angular scanning technique, one of the most common SPR applications, was performed by illuminating the SPR-based sensor with multiple incident angles of a single-wavelength laser beam. For refractive index sensing, the optical reflectance is absorbed in a specific angle, known as a plasmonic angle, which can be observed as a dark band when captured using a camera. Various methods have been proposed to locate the plasmonic position based on the detected image. This manuscript presented an analysis of the performance of machine learning on the identification of plasmonic angles based on the reflectance spectra for refractive index sensing. The reflectance curves are generated using Fresnel equations and the transfer matrix method with shot noise. After training and validating, the rational quadratic gaussian process regression model provides the most accurate model for predicting the plasmonic angle positions. The model can predict the plasmonic angles accurately for all studied refractive indices with a root mean square error of 3.83 \times 10^{\mathbf{-4}} RIU. Furthermore, the analysis of noise performance illustrated that a low number of photons could significantly degrade the model's accuracy and precision. The theoretical performance can be achieved at the photon energy level of 8.14 pJ.
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Classification model for predicting inflammation of the urinary bladder and acute nephritis of the renal pelvis
(2022-01-01)
Lochotinunt, Chanin
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Pechprasarn, Suejit
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Treebupachatsakul, Treesukon
Urinary tract diseases can occur in many organs of the urinary system, such as kidneys, urinary bladder, renal pelvis, ureters, and urethra. The most common disease in the urinary system is bladder inflammation, cystitis, and acute nephritis. In this research, the classification artificial intelligent model is applied to predict 2 symptoms of inflammation of the urinary bladder and acute nephritis of the renal pelvis from 6 parameters, including body temperature of patient, nausea, lumbar pain, urinary pushing, micturition pains, and burning of the urethra. Here, the principal components analysis or PCA are also applied to identify the critical parameters employed to train the machine learning model. Here, we propose to compare several machine learning classification models and show the proper model accurately diagnosing these two symptoms.
1
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Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application
(2021-12-01)
Thadson, Kitsada
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Visitsattapongse, Sarinporn
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Pechprasarn, Suejit
A deep learning algorithm for single-shot phase retrieval under a conventional microscope is proposed and investigated. The algorithm has been developed using the context aggregation network architecture; it requires a single input grayscale image to predict an output phase profile through deep learning-based pattern recognition. Surface plasmon resonance imaging has been employed as an example to demonstrate the capability of the deep learning-based method. The phase profiles of the surface plasmon resonance phenomena have been very well established and cover ranges of phase transitions from 0 to 2π rad. We demonstrate that deep learning can be developed and trained using simulated data. Experimental validation and a theoretical framework to characterize and quantify the performance of the deep learning-based phase retrieval method are reported. The proposed deep learning-based phase retrieval performance was verified through the shot noise model and Monte Carlo simulations. Refractive index sensing performance comparing the proposed deep learning algorithm and conventional surface plasmon resonance measurements are also discussed. Although the proposed phase retrieval-based algorithm cannot achieve a typical detection limit of 10–7 to 10–8 RIU for phase measurement in surface plasmon interferometer, the proposed artificial-intelligence-based approach can provide at least three times lower detection limit of 4.67 × 10–6 RIU compared to conventional intensity measurement methods of 1.73 × 10–5 RIU for the optical energy of 2500 pJ with no need for sophisticated optical interferometer instrumentation.

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