Spectroscopy Sensor Research Articles

The synergetic enhancement of the porous ZrO2@TiO2 heterostructure and the plasmonic silver nanoparticles for efficient surface‐enhanced Raman scattering

Rapid identification and analysis of pesticide residue on real‐world sample by a portable platform is of growing interest for monitoring food safety and safeguarding human health. Herein, we developed a robust surface‐enhanced Raman spectroscopy (SERS) sensor based on ZrO2@TiO2/Ag substrate for the analysis of pesticide residues. The synergetic effects of the metal organic framework (MOF)‐derived porous ZrO2@TiO2 heterostructure induced charge transfer enhancement and the electromagnetic enhancement from the plasmonic silver nanoparticles (AgNPs) hotspot generate significant SERS enhancement signal. The SERS substrate exhibits superior long‐term stability and signal homogeneity for SERS quantitative analysis. The platform has the capability to detect not only model molecule of 4‐ATP but also pesticides of triazophos and fonofos at the extremely low concentration of 10−8 M. Moreover, we simulate the quantitative detection of pesticide residue on real objects by spraying pesticides on dendrobium and chrysanthemum leaves, followed by transferring to the SERS platform to perform analysis. The nearly linear fitting curve with a wide concentration range suggests the superior qualitative detection capability. Remarkably, two components and three components of the pesticides are readily identified in a rapid way. The approach provides the rapid analysis, low‐cost device, and portable and quantitative pesticide identification and offers a clear path to multicomponent analysis at a trace level.

An adaptive soft-sensor for advanced real-time monitoring of an antibody-drug conjugation reaction.

In the production of antibody-drug conjugates (ADCs), the conjugation reaction is a central step defining the final product composition and, hence, directly affecting product safety and efficacy. To enable real-time monitoring, spectroscopic sensors in combination with multivariate regression models have gained popularity in recent years. The extended Kalman filter (EKF) can be used as so-called soft-sensor to fuse sensor predictions with long-horizon forecasts by process models. This enables the dynamic update of the current state and provides increased robustness against experimental noise or model errors. Due to the uncertainty associated with sensor and process models in biopharmaceutical applications, the deployment of such soft-sensors is challenging. In this study, we demonstrate the combination of an uncertainty-aware sensor model with a kinetic reaction model using an EKF to monitor a site-directed ADC conjugation reaction. As the sensor model, a Gaussian process regression model is presented to realize a time-variant determination of the sensor uncertainty. The EKF fuses the time-discrete predictions of the amount of conjugated drug from the sensor model with the time-continuous predictions from the kinetic model. While the ADC species are not distinguishable by on-line recorded UV/Vis spectra, the developed soft-sensor is able to dynamically update all relevant reaction species. It could be shown that the use of time-variant process and sensor noise computation approaches improved the performance of the EKF and achieved a reduction of the prediction error of up to 23% compared with the kinetic model. The developed framework proved to enhance robustness against noisy sensor measurements or wrong model initialization and was successfully transferred from batch to fed-batch mode. In future, this framework could be implemented for model-based process control and be adopted for other ADC conjugation reaction types.

Recent Advances in Sources, Migration, Public Health, and Surveillance of Bisphenol A and Its Structural Analogs in Canned Foods.

The occurrence of bisphenol A (BPA) and its structural analogs, known as endocrine disruptors is widely reported. Consumers could be exposed to these chemicals through canned foods, leading to health risks. Considerable advances have occurred in the pathogenic mechanism, migration law, and analytical methodologies for these compounds in canned foods. However, the confusion and controversies on sources, migration, and health impacts have plagued researchers. This review aimed to provide insights and perspectives on sources, migration, effects on human health, and surveillance of these chemicals in canned food products. Current trends in the determination of BPA and its structural analogs have focused on mass spectroscopy and electrochemical sensor techniques. Several factors, including pH, time, temperature, and volume of the headspace in canned foods, could affect the migration of the chemicals. Moreover, it is necessary to quantify the proportion of them originating from the can material used in canned product manufacturing. In addition, adverse reaction research about exposure to low doses and combined exposure with other food contaminants will be required. We strongly believe that the information presented in this paper will assist in highlighting the research needs on these chemicals in canned foods for future risk evaluations.

An automated system of soil sensor-based site-specific seeding for silage maize: A proof of concept

Despite recent studies of map-based site-specific seeding (SSS) revealing improved agronomic and economic outcomes over uniform rate seeding (URS), to our best knowledge no visible and near infrared spectroscopy (vis-NIRS) sensor-based SSS studies exist to date. This study aimed to develop and evaluate an automated sensor-based SSS technology for silage maize (Zea mays L.) production. An on-line visible and near-infrared reflectance spectroscopy (vis-NIRS) sensor was installed in front of a tractor to provide real-time input data to control the seed rate using a precision seeding machine mounted at the back of the tractor. A LabVIEW-based software was developed and used to predict soil fertility index using on-line vis-NIR spectra, which was then used to calculate the seed rate and transfer it to the controller of the seeding machine. The agronomic and economic benefits of SSS were compared with URS under a one-site-year experiment. Results showed that the proposed sensor-based SSS technology was 87.5% efficient in controlling the desired seed rates, according to the soil fertility status. In parallel with the observed spatial similarity between predicted soil fertility index and actual seed rates, a strong linear association (R2 = 0.80) was also achieved. As a result, SSS improved silage yield by 1.4 t ha−1 (4.4%), while sowing a lower seed rate (86.4 kSeeds ha−1) than the URS (90 kSeeds ha−1). This improved gross margin by 91 € ha−1, of which only 7 € ha−1 was attributed to savings on seed cost. The proposed sensor-based SSS technology was technically sound and thus transforms within-field fertility variations into agro-economic benefits effectively.

Controllable-Swelling Microneedle-Assisted Ultrasensitive Paper Sensing Platforms for Personal Health Monitoring.

Microneedle (MN) patches, which allow the extraction of skin interstitial fluid (ISF) without a pain sensation, are powerful tools for minimally invasive biofluid sampling. Herein, an MN-assisted paper-based sensing platform that enables rapid and painless biofluid analysis with ultrasensitive molecular recognition capacity is developed. First, a controllable-swelling MN patch is constructed through the engineering of a poly(ethylene glycol) diacrylate/methacrylated hyaluronic acid hydrogel; it combines rapid, sufficient extraction of ISF with excellent structural integrity. Notably, the analyte molecules in the needles can be recovered into a moist cellulose paper through spontaneous diffusion. More importantly, the paper can be functionalized with enzymatic colorimetric reagents or a plasmonic array, enabling a desired detection capacity-for example, the use of paper-based surface-enhanced Raman spectroscopy sensors leads to label-free, trace detection (sub-ppb level) of a diverse set of molecules (cefazolin, nicotine, paraquat, methylene blue). Finally, nicotine is selected as a model drug to evaluate the painless monitoring of three human volunteers. The changes in the nicotine levels could be tracked, with the levels varying significantly in response to metabolism of the drug in the different volunteers. This as-designed minimally invasive sensing system should open up new opportunities for precision medicine, especially for personal healthcare monitoring. This article is protected by copyright. All rights reserved.

Microbiological Quality Estimation of Meat Using Deep CNNs on Embedded Hardware Systems.

Spectroscopic sensor imaging of food samples meta-processed by deep machine learning models can be used to assess the quality of the sample. This article presents an architecture for estimating microbial populations in meat samples using multispectral imaging and deep convolutional neural networks. The deep learning models operate on embedded platforms and not offline on a separate computer or a cloud server. Different storage conditions of the meat samples were used, and various deep learning models and embedded platforms were evaluated. In addition, the hardware boards were evaluated in terms of latency, throughput, efficiency and value on different data pre-processing and imaging-type setups. The experimental results showed the advantage of the XavierNX platform in terms of latency and throughput and the advantage of Nano and RP4 in terms of efficiency and value, respectively.

A Non-Invasive Optical Multimodal Photoplethysmography-Near Infrared Spectroscopy Sensor for Measuring Intracranial Pressure and Cerebral Oxygenation in Traumatic Brain Injury

(1) Background: Traumatic brain injuries (TBI) result in high fatality and lifelong disability rates. Two of the primary biomarkers in assessing TBI are intracranial pressure (ICP) and brain oxygenation. Both are assessed using standalone techniques, out of which ICP can only be assessed utilizing invasive techniques. The motivation of this research is the development of a non-invasive optical multimodal monitoring technology for ICP and brain oxygenation which will enable the effective management of TBI patients. (2) Methods: a multiwavelength optical sensor was designed and manufactured so as to assess both parameters based on the pulsatile and non-pulsatile signals detected from cerebral backscatter light. The probe consists of four LEDs and three photodetectors that measure photoplethysmography (PPG) and near-infrared spectroscopy (NIRS) signals from cerebral tissue. (3) Results: The instrumentation system designed to acquire these optical signals is described in detail along with a rigorous technical evaluation of both the sensor and instrumentation. Bench testing demonstrated the right performance of the electronic circuits while a signal quality assessment showed good indices across all wavelengths, with the signals from the distal photodetector being of highest quality. The system performed well within specifications and recorded good-quality pulsations from a head phantom and provided non-pulsatile signals as expected. (4) Conclusions: This development paves the way for a multimodal non-invasive tool for the effective assessment of TBI patients.

Quartz‐Enhanced Photoacoustic Sensors for Detection of Eight Air Pollutants

A quartz‐enhanced photoacoustic spectroscopy sensor architecture capable of detecting eight different air pollutants (CH4, NO2, CO2, N2O, CO, NO, SO2, and NH3) is reported. Each analyte is targeted using the same sensor components (acoustic detection module, quartz‐tuning fork, etc.) and a dedicated laser source. Both interband cascade lasers and quantum cascade lasers are employed to target a well‐resolved and isolated absorption feature from each investigated gas, in a wavelength ranging from 3.35 to 9.06 μm. The sensor is calibrated with certified concentrations of each gas species, in a wet nitrogen matrix. For each analyte, the optimal pressure is determined. Minimum detection limits of 40, 13, 800, 230, 450, 78, 18, and 5.8 ppb are obtained for CH4, NO2, CO2, N2O, CO, NO, SO2, and NH3, respectively, at 100 ms of integration time.

Mode-locking in anti-PT symmetric frequency lattices

Active mode-locking (ML) is an important technique in laser science, which greatly shortens the laser pulse. Here, we construct an anti-parity-time (anti-PT) symmetric Su–Schrieffer–Heeger frequency lattice by two ring resonators with antisymmetric amplitude (AM) modulations. We find that the temporal width of the generated pulse can be greatly shortened by the phase-mismatching of the AM modulations. In addition, the pulse shortening shows extremely high sensitivity to the phase transition point, at which the anti-PT symmetry of the system is completely broken. This work exploits the concept of anti-PT symmetry in a laser field to realize ML, and will have broad application prospects in ultrafast spectroscopy and ultra-high sensitive sensors.

Flexible SERS sensor using AuNTs-assembled PDMS film coupled chemometric algorithms for rapid detection of chloramphenicol in food

The misuse of chloramphenicol (CAP) has led to the development of drug-resistant strains that pose significant threats to public health. Here, we propose a universal flexible surface-enhanced Raman spectroscopy (SERS) sensor utilizing gold nanotriangles (AuNTs) and polydimethylsiloxane (PDMS) film for rapid detection of CAP in food samples. Initially, AuNTs@PDMS with unique optical and plasmonic properties were used to collect spectra of CAP. Afterward, four chemometric algorithms were executed and compared. Accordingly, random frog-partial least squares (RF-PLS) exhibited optimum results with correlation coefficient of prediction (Rp = 0.9802) and the lowest root-mean-square error of prediction (RMSEP = 0.348 µg/mL). Furthermore, the sensor's efficacy to detect CAP in milk samples was confirmed, and the findings were compatible with the conventional HPLC approach (P > 0.05). Therefore, the proposed flexible SERS sensor could effectively be used to monitor milk quality and safety.

Monitoring of functionally graded material during laser directed energy deposition by acoustic emission and optical emission spectroscopy using artificial intelligence

ABSTRACT Laser directed energy deposition (L-DED) allows the production of multi-materials and functionally graded material (FGM) parts. But for commercialisation, process and quality monitoring of parts is required. For the first time, a novel monitoring method for chemical composition and process regimes of FGMs is proposed using a cost-effective acoustic emission (AE) (microphone) and optical emission spectroscopy (OES) sensors. Four chemical compositions (100%Ti, 58%Ti42%Nb, 37%Ti63%Nb, and 100% Nb) and two process parameters (475 W – 1'400 mm/min and 175 W – 2'000 mm/min) were selected, leading to four regimes/quality (conduction mode, partial, minor, medium, and severe lack of fusion pores). The signals were classified using seven mainstream artificial intelligence algorithms. The main conclusions are twofold. First, microphones are unsuitable candidates for monitoring the laser-material interaction during L-DED. The acoustic waves generated by the laser-material interaction are shielded by high gas flow surrounding it and so are either highly disturbed or does not reach the microphone. Conversely, OES are suitable candidates as the classification accuracies are higher than 90% for most category and machine learning algorithms, even after drastic feature reduction. Considering the wide range of chemical composition and quality, our proposed methods using OES have high industrialised potentials for them during L-DED FGM.

Wavelength-modulated photoacoustic spectroscopy sensor for multi-gas measurement of acetone, methane, and water vapor based on a differential acoustic resonator

Precise assessment of breath acetone and methane is significant to the medical diagnosis process. A multi-gas sensing system based on wavelength-modulated differential photoacoustic spectroscopy was developed for simultaneous measurement of acetone and methane. A distributed feedback diode laser emitting in the range from 3363 to 3371 nm was employed to scan the absorption lines of acetone and methane. The cross sensitivities in terms of spectral interference among acetone, methane, and water vapor are effectively eliminated by using a linear combination method of reference spectra for accurately determining the concentration of acetone and methane. The positive effect of water vapor on photoacoustic signal resulting from the light absorption of acetone and methane was precisely evaluated. To improve the instrument performance, a differential PA cell companying with a differential amplifier circuit is experimentally demonstrated in providing a better performance of noise suppression compared with a single acoustic resonator. With a low detection limit down to 0.43 ppm and 12 ppb (integration time of 10 s) for acetone and methane, the sensor shows a great potential for medical diagnosis in simultaneous measurement of acetone and methane.

Using electrochemical impedance spectroscopy to study biofilm growth in a 3D-printed flow cell system

Biofilm contamination is a widespread issue that can occur anywhere when organisms attach to surfaces in the presence of water. In industrial environments, formation of biofilms can lead to component failure, material degradation, and biofouling or spoilage, which collectively come with significant economic costs. Microfabricated electrochemical impedance spectroscopy (EIS) sensors have emerged as a promising tool for monitoring biofilm as EIS sensors capture information about biofilm growth autonomously in real-time; however, sensors suffer from drift, and the technique lacks temporal interpretation of dynamic biofilm processes. In this work, microfabricated sensors featuring gold micro-interdigitated electrodes (μIDEs) were modified with an electrically conductive polymer layer resulting in EIS measurement variability that was significantly reduced compared to unmodified sensors, and enabled highly stable, time-resolved EIS measurements. EIS characterization of Pseudomonas aeruginosa biofilm in parallel with high-resolution confocal laser scanning microscopy (CLSM) was performed using a novel 3D-printed flow cell system, resulting in distinct changes to EIS data corresponding with consistent biofilm growth. We have shown that EIS microsensors can detect four stages of biofilm: (i) initial biofilm attachment to the sensor substrate, (ii) early-stage irreversible biofilm proliferation characterized by sparse biofilm coverage, (iii) mature biofilm detection characterized by uniform biofilm coverage, and (iv) changes due to detachment and regrowth of biofilm.

Dual-emission ratiometric fluorescent probe based on N-doped CQDs@UiO-66/PVA nanocomposite hydrogel for quantification of pethidine in human plasma.

Asynchronous fluorescence spectroscopy (SFS) sensor for pethidine detectionis describedbased on UiO-66 metal-organic frameworks (MOFs) modified with N-doped carbon quantum dots (N-CQDs) embedded in hydrogel nanocomposites. Benefitting from the inovative design of the doping method in the carbonaceous structure, N-CQDs were successfully deposited in the pores of the UiO-66 network. Then, N-CQDs were employed as a sensitive segment toward the target molecules. UiO-66 was used for sensitive and selective sensing ofthe bonding interactions between N-CQDs and pethidine so that the electron transfer process from UiO-66 to the pethidine-N-CQD complex results in quenching the SFS intensity of UiO-66. To embed the stable and suitable sensing interface for pethidine assessment, the designed nanomaterial was inserted into the hydrogel network. This nanocomposite hydrogel showed two well-resolved emission peaks at 300nm and 350nm under ∆λ = 70, which corresponded to N-CQDs and UiO-66, respectively. The SFS sensing platform was employed for ratiometric detection of pethidine with a low limit of detection of 0.002μgmL-1 over a wide concentration range from 0.005 to 1.0μgmL-1. The accurate monitoring of pethidine with a good recovery of 90.8-101.5% indicated their independency from matrix effects for pethidine detection in human plasmabeing a complicated biological matrix. Scheme 1. General procedure for synthesizing N-CQDs@UiO-66/PVA hydrogel-based nanoprobe and its application for pethidine determination.

Portable multispectral fluorometer for determination of formalin in food samples using nitrogen-doped carbon dots as the fluorescence probe

In this work, we developed a portable multispectral fluorometer for determining formalin (FA). We used nitrogen-doped carbon dots (N-CDs) as the fluorescence probe, based on right-angle fluorescence spectrometry with twin excitation high-power light emitting diode sources. The multispectral spectroscopy sensor was used as a detector for fluorescence intensity. The fluorescence intensity values were displayed from 0 to 65535 a.u. The FA determination results show a linear relationship in the FA concentration range of 10-75 mg L−1 with r2 = 0.9908. The limit of detection (LOD) was 2.09 mg L−1 (calculated from 3SDblank/slope (n = 3)). In addition, the percentage of relative errors compared with the standard method and standard instrument shows less than 10 percent. The performance of a portable multispectral fluorometer in actual samples exhibited no significant difference compared to the validation instrument results. Therefore, the development of a portable multispectral fluorometer can be used as a fluorometer, and the measurement performance is comparable to a standard fluorescence spectrometer.

Selecting plasmonic nanoshells for colorimetric sensors

In this work, the use of gold and silver nanoshells was evaluated as a starting point for the establishment of colorimetric sensor platforms. The sensitivity and linearity of the nanoplatforms (SiO2 core–metallic shell nanoparticles) were assessed under the influence of the nanoshell configuration, color space, and light source illuminant. A computational procedure for selecting high-performance plasmonic colorimetric sensor platforms is described. The evaluation methodology involves considering five different color spaces and 15 different color components. By exploring crucial figures of merit for sensing, the performance of the plasmonic nanoplatforms was evaluated, exploring Mie theory. We determined that gold nanoshells are highly efficient on colorimetric sensing, while silver nanoshells are a better choice for spectroscopic sensors. Plasmonic nanoplatforms based on nanoshells with 10 nm SiO2 core radii and 5 nm thick Au shells presented sensitivity values up to 4.70RIU−1, considering the hue angle of the HSV color space. Color variation of up to 40% was observed, due to the adsorption of a 10 nm thick molecular layer on the gold nanoshell surface. In the search for advances in colorimetric biosensors, the optimization approach used in this work can be extended to different nanostructures.

Zero‐Bias Long‐Wave Infrared Nanoantenna‐Mediated Graphene Photodetector for Polarimetric and Spectroscopic Sensing

AbstractGraphene has attracted great interest for integrated photonic platforms in the long‐wave infrared (LWIR) for spectroscopic and polarimetric sensing due to the capability of on‐chip integration, fast response, and broadband operation. However, graphene suffers from low photoresponsivity and thus poor sensing performance due to weak absorption. Polarization detection using graphene is hindered by its small in‐plane anisotropy. Here, nanoantenna‐mediated graphene photodetectors (NMGPDs) are proposed to enhance responsivity by tailoring the nanoantenna near‐field distribution to generate a strong photoresponse. The devices demonstrate a high responsivity of 6.3 V W−1 under zero bias at room temperature with low noise‐equivalent power of 1.6 nW Hz−1/2. Furthermore, polarization detection is enabled by artificial near‐field anisotropy enabled by double L‐shaped nanoantennas. The proposed LWIR NMGPDs achieve subtle polarization‐angle detection down to 0.05° thanks to the unusual negative polarization ratio of −1. To demonstrate the advances of LWIR NMGPDs for molecule detection, acetone is chosen as an analyte for spectroscopic sensing. The devices show a low limit of detection of 115 ppm, and fast dynamic gas sensing response of 6 s for real‐time monitoring. These results reveal the potential of the device as a multi‐functional on‐chip miniaturized optoelectronic platform for polarimetric and spectroscopic sensing toward real‐time environmental monitoring and biomedical screening.

TSMC-Net: Deep-Learning Multigas Classification Using THz Absorption Spectra.

The identification of gas mixture speciation from a complex multicomponent absorption spectrum is a problem in gas sensing that can be addressed using machine-learning approaches. Here, we report on a deep convolutional neural network for multigas classification using terahertz (THz) absorption spectra, THz spectra mixture classifier network or TSMC-Net. TSMC-Net has been developed to identify eight volatile organic compounds in mixtures based on their fingerprint rotational absorption spectra in the 220-330 GHz frequency range. A data set consisting of simulated absorption spectra for randomly generated mixtures, with absorption greater than thresholds representing detectable limits and annotated with multiple labels, was prepared for model development. The supervised multilabel classification problem, i.e., the identification of individual gases in a mixture, is converted to a supervised multiclass classification problem via label powerset conversion. The trained model is validated and tested against simulated spectra for gas mixtures, with and without white Gaussian noise. The trained model exhibits high precision, recall, and accuracy for each pure compound. Class activation maps illustrate the complex decision-making process of the model and highlight relevant frequency regions that are needed to identify unique mixtures. Finally, the model was demonstrated against measured THz absorption spectra for pure species and mixtures, acquired using a microelectronics-based THz absorption spectrometer. The data set generation strategy and deep convolutional neural network approach are generalized and can be extrapolated to other spectroscopy types, frequency ranges, and sensors.

Highly sensitive photoacoustic acetylene detection based on differential photoacoustic cell with retro-reflection-cavity

In this paper, a highly sensitive photoacoustic spectroscopy (PAS) sensor based on retro-reflection-cavity-enhanced differential photoacoustic cell (DPAC) is demonstrated for the first time. Acetylene (C2H2) was selected as the analyte. The DPAC was designed to effectively suppress noise and increase signal level. The retro-reflection-cavity consisted of two right-angle prisms was designed to reflect the incident light to realize four passes. The photoacoustic response of the DPAC was simulated and investigated based on the finite element method. Wavelength modulation and second harmonic demodulation technologies were applied for sensitive trace gas detection. The first-order resonant frequency of the DPAC was found to be 1310 Hz. The differential characteristics were investigated and the 2f signal amplitude for this C2H2-PAS sensor based on retro-reflection-cavity-enhanced DPAC had a 3.55 times improvement compared to the system without the retro-reflection-cavity. An Allan deviation analysis was performed to investigate the long-term stability of the system. The minimum detection limit (MDL) was measured to be 15.81 ppb with an integration time of 100 s

Evaluation of Airborne HySpex and Spaceborne PRISMA Hyperspectral Remote Sensing Data for Soil Organic Matter and Carbonates Estimation

Remote sensing and soil spectroscopy applications are valuable techniques for soil property estimation. Soil organic matter (SOM) and calcium carbonate are important factors in soil quality, and although organic matter is well studied, calcium carbonates require more investigation. In this study, we validated the performance of laboratory soil spectroscopy for estimating the aforementioned properties with referenced in situ data. We also examined the performance of imaging spectroscopy sensors, such as the airborne HySpex and the spaceborne PRISMA. For this purpose, we applied four commonly used machine learning algorithms and six preprocessing methods for the evaluation of the best fitting algorithm.. The study took place over crop areas of Amyntaio in Northern Greece, where extensive soil sampling was conducted. This is an area with a very variable mineralogical environment (from lignite mine to mountainous area). The SOM results were very good at the laboratory scale and for both remote sensing sensors with R2 = 0.79 for HySpex and R2 = 0.76 for PRISMA. Regarding the calcium carbonate estimations, the remote sensing accuracy was R2 = 0.82 for HySpex and R2 = 0.36 for PRISMA. PRISMA was still in the commissioning phase at the time of the study, and therefore, the acquired image did not cover the whole study area. Accuracies for calcium carbonates may be lower due to the smaller sample size used for the modeling procedure. The results show the potential for using quantitative predictions of SOM and the carbonate content based on soil and imaging spectroscopy at the air and spaceborne scales and for future applications using larger datasets.