Set Of Wavelengths Research Articles

Using a Two-Stage Hybrid Dimensionality Reduction Method on Hyperspectral Data to Predict Chlorophyll Content of Camellia oleifera

Camellia oleifera is an oilseed crop that holds significant economic, ecological, and social value. In the realm of Camellia oleifera cultivation, utilizing hyperspectral analysis techniques to estimate chlorophyll content can enhance our understanding of its physiological parameters and response characteristics. However, hyperspectral datasets contain information from many wavelengths, resulting in high-dimensional data. Therefore, selecting effective wavelengths is crucial for processing hyperspectral data and modeling in retrieval studies. In this study, by using hyperspectral data and chlorophyll content from Camellia oleifera samples, three different dimensionality reduction methods (Taylor-CC, NCC, and PCC) are used in the first round of dimensionality reduction. Combined with these methods, various thresholds and dimensionality reduction methods (with/without further dimensionality reduction) are used in the second round of dimensionality reduction; different sets of core wavelengths with equal size are identified respectively. Using hyperspectral reflectance data at different sets of core wavelengths, multiple machine learning models (Lasso, ANN, and RF) are constructed to predict the chlorophyll content of Camellia oleifera. The purpose of this study is to compare the performance of various dimensionality reduction methods in conjunction with machine learning models for predicting the chlorophyll content of Camellia oleifera. Results show that (1) the Taylor-CC method can effectively select core wavelengths with high sensitivity to chlorophyll variation; (2) the two-stage hybrid dimensionality reduction methods demonstrate superiority in three models; (3) the Taylor-CC + NCC method combined with an ANN achieves the best predictive performance of chlorophyll content. The new two-stage dimensionality reduction method proposed in this study not only improves both the efficiency of hyperspectral data processing and the predictive accuracy of models, but can serve as a complement to the study of Camellia oleifera properties using the Taylor-CC method.

A CARS-SPA-GA Feature Wavelength Selection Method Based on Hyperspectral Imaging with Potato Leaf Disease Classification.

Early blight and ladybug beetle infestation are important factors threatening potato yields. The current research on disease classification using the spectral differences between the healthy and disease-stressed leaves of plants has achieved good progress in a variety of crops, but less research has been conducted on early blight in potato. This paper proposes a CARS-SPA-GA feature selection method. First, the raw spectral data of potato leaves in the visible/near-infrared light region were preprocessed. Then, the feature wavelengths were selected via competitive adaptive reweighted sampling (CARS) and the successive projection algorithm (SPA), respectively. Then, the two sets of wavelengths were reorganized and duplicates were removed, and secondary feature selection was conducted with genetic algorithm (GA). Finally, the feature wavelengths were fed into different classifiers and the parameters were optimized using a real-coded genetic algorithm (RCGA). The experimental results show that the feature wavelengths selected by the CARS-SPA-GA method accounted only for 9% of the full band, and the classification accuracy of the RCGA-optimized support vector machine (SVM) classification model reached 98.366%. These results show that it is feasible to classify early blight and ladybug beetle infestation in potato using visible/near-infrared spectral data, and the CARS-SPA-GA method can substantially improve the accuracy and detection efficiency of potato pest and disease classification.

Inline-delay Fourier transform imaging spectrometer for mid-IR ultrashort pulses

An inline-delay Fourier transform imaging spectrometer (iFTIS) is used to measure spatiospectral coupling in a mid-infrared (mid-IR) optical parametric amplifier (OPA). The method employs a compact inline delay line using a birefringent wedge pair and a microbolometer array as an imaging sensor, providing continuous spectral coverage from ∼0.4 to 4.5 µm in a single device. We find a spatial chirp that depends systematically on the OPA wavelength setting in the 3-4 µm range and also identify spatiospectral couplings beyond spatial chirp, highlighting the importance of advanced metrological techniques for this spectral region. Furthermore, we investigate the effect of depletion on the 2 µm pump beam and observe a complex spatiospectral reshaping. Our work opens the door to new applications of iFTIS to mid-IR laser science.

Updated System of S i Levels Using Fourier Transform Infrared Spectroscopy in the Range from 800 to 11,000 cm−1

In this study we report on high-precision laboratory measurements of transition wavenumbers for 172 atomic sulfur lines in the infrared region 800–11,000 cm−1 using Fourier transform spectroscopy techniques. Our analysis includes 96 lines that have not been previously measured in the laboratory. We also correct several sulfur energy-level measurements reported in earlier studies. These refined measurements are important for a range of scientific disciplines, such as astrophysics, atmospheric chemistry, and combustion plasma physics. We have used the combined list of all observed lines to derive a refined set of sulfur energy levels. For about half of all nonautoionizing levels, the uncertainties have been reduced by a factor between 2 and 23. From the newly measured nonpenetrating (high-l) Rydberg levels we have also obtained the first ionization energy of the S atom, IE = 83,559.170(11) cm−1, which is more accurate than the currently recommended value by 2 orders of magnitude. Our analysis has led to a significantly more accurate result than the earlier set of Ritz wavelengths with observed intensities reduced to a common uniform scale and an extended list of recommended transition probabilities.

Design and development of a low-cost hyperspectral imaging setup via a machine-learningbased approach

This article presents a potential solution for developing a low-cost hyperspectral imaging (HSI) setup by preserving pertinent information acquired from a conventional hyperspectral imaging setup. Conventional hyperspectral images (HSI) of three different types of leaves, gongura (Hibiscus sabdariffa), amaranthus (Amaranthus viridis), and banana (Musa acuminata) were acquired with 204 wavelengths/bands. The spectra are processed using linear discriminant analysis (LDA) to find a set of signature wavelengths for leaf classification. Afterwards, 20 visible range wavelengths (440 to 700 nm) were found to be incorporated into a low-cost setup involving a monochromator, beam steering elements, and a smartphone camera with associated machine learning (ML) classifiers. For building the datasets, we extracted 90 spectra from the images captured using the conventional HSI setup under the full spectra range (397 nm to 1003 nm). Similarly, 90 spectra were extracted from the images captured from the low-cost setup under the 20 signature wavelengths. For further experimentation, we split the datasets into Dataset-A, containing 70% of the total spectra, and the remaining 30% in Dataset-B for HSI as well as low-cost setup. Dataset- B was reserved to evaluate the robustness of the classifiers on an unseen dataset. LDA surpasses the other classifiers in leaves classification for HSI as well as low-cost setup. For the HSI setup, LDA achieved a 100% average score for performance matrices (classification accuracy, precision, Recall, and F1-score) on dataset A as well as on dataset B. Moreover, for the low-cost setup, LDA achieved 98.33% ± 4.99% classification accuracy, 98.89% ± 3.33% precision, 98.33% ± 4.99% recall, and 98.22% ± 5.33% F1-score on dataset A. Additionally, LDA achieved 96.29% classification accuracy, 96.67% precision, 96.29% recall, and 96.28% F1-score for dataset B. The promising results indicated that the low-cost set-up closely emulates the HSI system’s performance in terms of performance metrics, delivering a low-cost HSI system for various applications in the future.

Working Voltage Switching the Photo-/Thermo-Electric Effect for Distinct Ultraviolet and Infrared Signal Detection.

The combination of sensing invisible ultraviolet photons and infrared radiation can significantly enhance target recognition by offsetting their own limit in a short sensing range and poor spatial resolution. However, the difference in their wavelength sets unique requirements for sensing materials and devices, which makes it hard to establish their implementation in a single detector. In this work, we present the design of a single detector with CH3NH3PbCl3 (MAPbCl3) for distinguishing ultraviolet and IR signals by switching its operating mode in the photo-/thermo-electric effect. The large optical band gap of ∼3.2 eV in MAPbCl3 ensures the response toward an ultraviolet photon, while its efficient thermoelectric effect allows the sensing of an IR signal. As a result, the detector exhibits a specific detectivity of 4.5 × 1012 Jones for 395 nm ultraviolet photons under 0 V, while under the working voltage of 2.5 V, it demonstrates a superior temperature coefficient of resistance of -3.7% K-1, a specific detectivity of 4.8 × 108 Jones, and a limit of detection of 0.58 mW/cm2 for 4 μm photons. The functionality of the switching response to ultraviolet or IR photons by working voltage allows background subtraction and enhances the target discrimination in the imaging.

Efficient Green Solvent Extraction and Bioassay Studies of Favipiravir in Rat Dried Blood Spots Using Ionic Liquid-Based Dispersive Liquid-Liquid Microextraction (IL-DLLME) Coupled with HPLC-PDA: Application to Bioequivalence Studies

Background: Favipiravir is an antiviral drug having pyrazine group moiety. It is a reliable and an efficient green solvent, and a highly recoverable bio-sampling method is required for it. It is widely used in COVID-19 treatment for the prevention of the spread of viral infections in the body Objective: This proposed green solvent (ionic liquid)-based bioassay study aimed to quantify favipiravir in rat-dried blood spots using Dispersive Liquid-liquid Microextraction (IL-DLLME). Methods: The proposed bioassay separation was achieved through an isocratic elution mode using a hybrid silica-based ODS column (250 × 4.6 mm; 5 μm), a column temperature of 25°C, an injection volume of 10 μL, a flow rate of 1.0 mL/min, and a detector wavelength set at 310 nm. The run time was less than 10 minutes. Mobile phase was delivered with acetonitrile, methanol, and 10 mM Na2HPO4 at pH 4.0 ± 0.5, (15: 20: 65, v/v/v). In a microtube, 50 mL of Ionic Liquid (IL), 500 μL of disperser ACN, 50 μL of 10% NaCl, and IS (20 ng mL-1) were added to perform the Dried Blood Spot (DBS) sample extraction methodology. The advantages of the proposed methodology have been found to include minimum hematocrit effect and an adequate blood volume for testing, easy transportation, and significant extraction recovery. The sample analysis has been carried out using HPLC-PDA with oseltamivir used as an internal standard. Results: We have investigated the important variables, i.e., salt concentration (10% NaCl, analyte recovery being higher) and disperser solvent [50μL of BMIHP plus 500 μL of ACN (v/v) yielding the highest recovery], in the extraction process. Extraction Recovery (ER) and Enrichment Factor (EF) were also evaluated using the IL-DLLME method. The calibration curve examined a range of 0.5-150 μg/mL, with a lower Limit of Quantification (LOQ) being 0.5 μg/mL in QC as well as calibration samples, respectively. Conclusion: Significant bioanalytical validation has been performed and all the parameters have been evaluated systematically as per bioanalytical method validation protocols, i.e., US FDA- 2018 guidelines. The following analytical parameters have been covered: standard curve, limit of quantification, range, recovery, stability, accuracy, precision, sensitivity, ER, EF, and selectivity. The developed bioassay method has been successfully applied in the pharmacokinetic studies of rats and successfully applied in bulk drugs.

Underwater Wavelength Attack on Discrete Modulated Continuous-Variable Quantum Key Distribution.

The wavelength attack utilizes the dependence of beam splitters (BSs) on wavelength to cause legitimate users Alice and Bob to underestimate their excess noise so that Eve can steal more secret keys without being detected. Recently, the wavelength attack on Gaussian-modulated continuous-variable quantum key distribution (CV-QKD) has been researched in both fiber and atmospheric channels. However, the wavelength attack may also pose a threat to the case of ocean turbulent channels, which are vital for the secure communication of both ocean sensor networks and submarines. In this work, we propose two wavelength attack schemes on underwater discrete modulated (DM) CV-QKD protocol, which is effective for the case with and without local oscillator (LO) intensity monitor, respectively. In terms of the transmittance properties of the fused biconical taper (FBT) BS, two sets of wavelengths are determined for Eve's pulse manipulation, which are all located in the so-called blue-green band. The derived successful criterion shows that both attack schemes can control the estimated excess noise of Alice and Bob close to zero by selecting the corresponding condition parameters based on channel transmittance. Additionally, our numerical analysis shows that Eve can steal more bits when the wavelength attack controls the value of the estimated excess noise closer to zero.

Selective protein quantification on continuous chromatography equipment with limited absorbance sensing: A partial least squares and statistical wavelength selection solution

AbstractReal‐time selective protein quantification is an integral component of operating continuous chromatography processes. Partial least squares models fit with spectroscopic UV‐Vis absorbance data have demonstrated the ability to selectively quantify proteins. With standard continuous chromatography equipment that is only capable of measuring absorbance at a few user‐defined wavelengths, the problem of selecting appropriate wavelengths that maximize the measurement capability of the instrument remains unaddressed. Therefore, we propose a method for selecting wavelengths for continuous chromatography equipment. We illustrate our method using sets of protein mixtures composed of bovine serum albumin and lysozyme. The first step is to refine the raw wavelength set with a statistical t‐test and an absorbance magnitude test. Then, the wavelengths within the refined spectroscopic range are ranked. Three existing techniques are evaluated – sequential forward search, variable importance to projection scores, and the least absolute shrinkage and selection operator. The best technique (in this case, sequential forward search) determines a subset of three wavelengths for further evaluation on the BioSMB PD. We use an exhaustive approach to determine the final wavelength set. We show that soft sensor models trained from the method's wavelength selections can quantify the two proteins more accurately than from the wavelength set of 230, 260 and 280 nm, by a factor of four. The method is shown to determine appropriate wavelengths for different path lengths and protein concentration ranges. Overall, we provide a tool that alleviates the analytical bottleneck for practitioners seeking to develop advanced monitoring and control methods on standard equipment.

Selective active resonance tuning for multi-mode nonlinear photonic cavities

Resonant enhancement of nonlinear photonic processes is critical for the scalability of applications such as long-distance entanglement generation. To implement nonlinear resonant enhancement, multiple resonator modes must be individually tuned onto a precise set of process wavelengths, which requires multiple linearly-independent tuning methods. Using coupled auxiliary resonators to indirectly tune modes in a multi-resonant nonlinear cavity is particularly attractive because it allows the extension of a single physical tuning mechanism, such as thermal tuning, to provide the required independent controls. Here we model and simulate the performance and tradeoffs of a coupled-resonator tuning scheme which uses auxiliary resonators to tune specific modes of a multi-resonant nonlinear process. Our analysis determines the tuning bandwidth for steady-state mode field intensity can significantly exceed the inter-cavity coupling rate g if the total quality factor of the auxiliary resonator is higher than the multi-mode main resonator. Consequently, over-coupling a nonlinear resonator mode to improve the maximum efficiency of a frequency conversion process will simultaneously expand the auxiliary resonator tuning bandwidth for that mode, indicating a natural compatibility with this tuning scheme. We apply the model to an existing small-diameter triply-resonant ring resonator design and find that a tuning bandwidth of 136 GHz ≈ 1.1 nm can be attained for a mode in the telecom band while limiting excess scattering losses to a quality factor of 106. Such range would span the distribution of inhomogeneously broadened quantum emitter ensembles as well as resonator fabrication variations, indicating the potential for the auxiliary resonators to enable not only low-loss telecom conversion but also the generation of indistinguishable photons in a quantum network.

Multispectral Thermometry Method Based on Optimisation Ideas.

Multispectral thermometry is based on the law of blackbody radiation and is widely used in engineering practice today. Temperature values can be inferred from radiation intensity and multiple sets of wavelengths. Multispectral thermometry eliminates the requirements for single-spectral and spectral similarity, which are associated with two-colour thermometry. In the process of multispectral temperature inversion, the solution of spectral emissivity and multispectral data processing can be seen as the keys to accurate thermometry. At present, spectral emissivity is most commonly estimated using assumption models. When an assumption model closely matches an actual situation, the inversion of the temperature and the accuracy of spectral emissivity are both very high; however, when the two are not closely matched, the inversion result is very different from the actual situation. Assumption models of spectral emissivity exhibit drawbacks when used for thermometry of a complex material, or any material whose properties dynamically change during a combustion process. To address the above problems, in the present study, we developed a multispectral thermometry method based on optimisation ideas. This method involves analysing connections between measured temperatures of each channel in a multispectral temperature inversion process; it also makes use of correlations between multispectral signals at different temperatures. In short, we established a multivariate temperature difference correlation function based on the principles of multispectral radiometric thermometry, using information correlations between data for each channel in a temperature inversion process. We then established a high-precision thermometry model by optimising the correlation function and correcting any measurement errors. This method simplifies the modelling process so that it becomes an optimisation problem of the temperature difference function. This also removes the need to assume the relationships between spectral emissivity and other physical quantities, simplifying the process of multispectral thermometry. Finally, this involves correction of the spectral data so that any impact of measurement error on the thermometry is reduced. In order to verify the feasibility and reliability of the method, a simple eight-channel multispectral thermometry device was used for experimental validation, in which the temperature emitted from a blackbody furnace was identified as the standard value. In addition, spectral data from the 468-603 nm band were calibrated within a temperature range of 1923.15-2273.15 K, resulting in multispectral thermometry based on optimisation principles with an error rate of around 0.3% and a temperature calculation time of less than 3 s. The achieved level of inversion accuracy was better than that obtained using either a secondary measurement method (SMM) or a neural network method, and the calculation speed achieved was considerably faster than that obtained using the SMM method.

Greens assessment of RP-UPLC method for estimating Triamcinolone Acetonide and its degraded products compared to Box-Behnken and Six Sigma designs

ABSTRACT A study on green analytical chemistry aims to develop eco-friendly alternatives to hazardous substances and minimize waste generation. The study thoroughly examined various tools to determine their greenness. A newly validated RP-UPLC method was then employed quantitatively to detect Triamcinolone Acetonide (TRA) in a creamy pharmaceutical formulation using UPLC BEH C18 (50 × 2.1 mm, 1.7μm) column for analysis, with a detection wavelength set at 240 nm. The Box-Behnken design was developed to efficiently determine the optimal chromatographic conditions while minimizing the required trials. The study effectively assessed the impact of three independent variables on various responses, including retention time, tailing factor, and theoretical plates. The variables examined ethanol ratio, pH levels, and flow rate were meticulously tested to optimize experimental conditions. The utilization of desirability and overlay plots, along with a mobile phase of ethanol and purified water in a volumetric ratio of 30:70 and pH adjustment to 5.0. Calibration curves were constructed to assess the linearity of TRA within the concentration range of 2.5-50 µg/mL, yielding correlation coefficients (r = 0.9999). The accuracy was validated through recovery studies with the acceptable range of 99.6-101.2%. The specificity of the method has been validated by conducting forced degradation studies per ICH guidelines.

Length measurement based on multi-wavelength interferometry using numerous stabilized frequency modes of an optical comb

Abstract The electro-optic comb (EO comb) with a relatively wide mode spacing of 25 GHz can be resolved into individual frequency modes with a commercially available high-resolution spectrometer. The EO comb has numerous discrete frequency modes, which can serve as a light source for a monochromatic laser interferometer to realize the meter. In this study, a method for measuring the absolute distances based on the multi-wavelength interferometer principle is proposed and demonstrated by simultaneously implementing 102 monochromatic laser interferometers using an EO comb. A phase shifting technique was used to determine the phase for each frequency mode by precisely translating a reference mirror with a constant interval. The phases of the interference signals for 102 stabilized individual frequency modes were measured by applying the model-based analysis on the phase-shifted interference signals. The absolute distance can be determined using phase values of a wavelength set corresponding to five to seven randomly selected frequency modes. In this study, the absolute distances for round-trip distances of 166 mm and 1316 mm were measured and the measurement uncertainty of each distance was evaluated. Through the uncertainty analysis of the distance measurement, the combined uncertainties of the measured distances in a short and long ranges were evaluated to be 30.1 nm and 211.1 nm, respectively. In addition, for each distance, the consistent measurement results of absolute distances were obtained through four different wavelength sets, which show the flexibility of wavelength selection in this work.

A LIBSVM quality assessment model for apple spoilage during storage based on hyperspectral data.

To assess the quality of apple samples during storage, this study proposes a spoilage benchmark based on hyperspectral data feature indicators and the Mahalanobis Distance (MD). Additionally, a quality assessment model was developed utilizing LIB Support Vector Machine (LIBSVM). Initially, a spoilage benchmark for apple samples was preliminarily established using hyperspectral data feature indicators, including the color feature, texture feature of sample hyperspectral images, and wavelet packet energy (WPE) of sample spectral information. Secondly, this study utilized the successive projection algorithm (SPA) to extract three wavelength sets sensitive to changes in the three indicators. This process resulted in the identification of 20 feature wavelengths based on the three sets. Subsequently, the spoilage benchmark for apple samples was verified using MD based on the spectral information of feature wavelengths. Ultimately, utilizing pre-processed spectral information enhanced by the sliding window algorithm and spoilage benchmark, the LIBSVM quality assessment model was developed, achieving a training set accuracy of 99.94% and a test set accuracy of 99.66%. Moreover, to assess the strength and applicability of the model, a verification experiment was conducted using a different set of apple samples. The training set accuracy was 100% and the test set accuracy was 99.83%. These findings indicate that the model can effectively indicate the level of spoilage in each sample during long-term storage. This also serves to demonstrate the robustness of the model and the effectiveness of the spoilage benchmark determination method during apple storage.

Efficient prediction of SOC and aggregate OC components by continuous wavelet transform spectra under different feature selection methods

Soil Organic Carbon (SOC) fixation plays a vital role in reducing greenhouse gas emissions and the formation of soil aggregates is recognized as a crucial process in SOC fixation. In the current study, we characterized spectral information on SOC and aggregate Organic carbon (OC) components by using a rapid and robust method. Soil samples were collected from different layers to obtain the SOC and aggregate OC components to record corresponding spectra. The Continuous Wavelet Transform (CWT) spectra were then combined with various characteristic wavelength selection methods to build Multivariate Linear Regression (MLR), BP neural network (BP), and Random Forest (RF) models. Finally, the optimal combination of characteristic wavelengths based on the best-performing model for each index was determined. The results demonstrated that the model's accuracy, which was built using the set of characteristic wavelengths of the best model for all metrics, was higher than that of the best model for a single metric. This was due to the correlation between SOC and aggregate OC components. The best set of characteristic wavelengths were those selected by the Sequential Backward Selection method with wavelet decomposition scales of 23, 26, and 27, and those selected by the Minimum Redundancy Maximum Relevance method with wavelet decomposition scale 27. The RF model for Free silt and clay particles organic carbon (FSP-OC) showed the highest accuracy among the four SOC prediction models. In addition, it was discovered that as the wavelet decomposition scales become more similar, the extracted characteristic wavelengths also become more similar, resulting in similar model predictions. In summary, the accuracy of the predictive model for SOC and its constituents can be demonstrably improved by reasonable combination of characteristic wavelengths selection method and wavelet decomposition scale.

Three-dimensional simulation of green soybean infrared-assisted spouted bed drying.

The current study introduces a novel infrared-assisted spouted bed drying technique for the dehydration of green soybeans, which aims to enhance the drying quality and efficiency. The investigation involves an examination of the flow pattern in the spouted bed to obtain relevant data, followed by an optimization of the entire drying process. The drying process of green soybeans was simulated using SolidWorks and ANSYS Fluent software, based on the principles of computational fluid dynamics. The simulation test results showed that the simulation outcomes were consistent with the experimental data. The optimal conditions for the process of green soybean infrared-assisted spouted bed drying were found to be an inlet speed of 8 m/s and a temperature of 50 °C with the wavelength and power settings of the infrared board at 10 μm and 500 W, respectively. The simulation method selected in this article, based on gas-solid two-phase flow dynamics, is feasible for green soybean infrared-assisted spouted bed drying process. © 2023 Society of Chemical Industry.

Vitamin C deficiency in critically ill COVID-19 patients admitted to intensive care unit.

To determine vitamin C plasma kinetics, through the measurement of vitamin C plasma concentrations, in critically ill Coronavirus infectious disease 2019 (COVID-19) patients, identifying eventually the onset of vitamin C deficiency. Prospective, observational, single-center study. Intensive Care Unit (ICU), Vall d'Hebron University Hospital, Barcelona. Study period from November 12th, 2020, to February 24th, 2021. Patients who had a severe hypoxemic acute respiratory failure due to COVID-19 were included. Plasma vitamin C concentrations were measured on days 1, 5, and 10 of ICU admission. There were no vitamin C enteral nor parenteral supplementation. The supportive treatment was performed following the standard of care or acute respiratory distress syndrome (ARDS) patients. Plasma vitamin C concentrations were analyzed using an ultra-performance liquid chromatography (UPLC) system with a photodiode array detector (wavelength set to 245 nm). We categorized plasmatic levels of vitamin C as follows: undetectable: < 1,5 mg/L, deficiency: <2 mg/L. Low plasma concentrations: 2-5 mg/L; (normal plasma concentration: > 5 mg/L). Forty-three patients were included (65% men; mean age 62 ± 10 years). The median Sequential Organ Failure Assessment (SOFA) score was 3 (1-4), and the Acute Physiology and Chronic Health disease Classification System (APACHE II) score was 13 (10-22). Five patients had shock. Bacterial coinfection was documented in 7 patients (16%). Initially all patients required high-flow oxygen therapy, and 23 (53%) further needed invasive mechanical ventilation during 21 (± 10) days. The worst PaO2/FIO2 registered was 93 (± 29). ICU and hospital survival were 77 and 74%, respectively. Low or undetectable levels remained constant throughout the study period in the vast majority of patients. This observational study showed vitamin C plasma levels were undetectable on ICU admission in 86% of patients with acute respiratory failure due to COVID-19 pneumonia requiring respiratory support. This finding remained consistent throughout the study period.

A signal‐switchable fluorescent probe for detection of HPO42− based on 5,10,15,20‐(4‐sulphonatophenyl)porphyrin (TPPS4)

AbstractIn this study, 5,10,15,20‐(4‐sulphonatophenyl)porphyrin (TPPS4) was selected as a fluorescent probe due to its excellent characteristics including high quantum yield, good water solubility, and exceptional biocompatibility. With an excitation wavelength set at 515 nm, the optimal fluorescence emission wavelength for TPPS4 was measured at 642 nm. At this moment, the fluorescence signal of TPPS4 pink solution was in the ‘ON’ state. The fluorescence intensity of TPPS4 was quenched when ascorbic acid (AA) was introduced, which was due to the electron transfer quenching effect between AA and TPPS4. The colour of the corresponding solution changed from pink to green, and the fluorescence signal was in the ‘OFF’ state. When HPO42− was further introduced into the TPPS4–AA system, the quenched fluorescence intensity of TPPS4 was recovered due to the unique interaction between HPO42− and AA. At this time, the colour of the corresponding solution changed from green to red, and the fluorescence signal was in the ‘ON’ state. Therefore, an ‘ON–OFF–ON’ signal‐switchable fluorescent probe was constructed based on TPPS4 to detect HPO42−. The results showed that the linear range of HPO42− was 4.0 × 10−9 to 1.7 × 10−6 M, and the detection limit was 1.3 × 10−9 M (S/N = 3). The sensing system exhibited high accuracy and sensitivity, and it could be used successfully to detect HPO42− in real samples.

Analytical Method Development and Validation of Lycopene Present in Multivitamin Tablets

Lycopene is an organic compound belonging to tetraterpene and a carotene category. Lycopene is a brick red carotenoid hydrocarbon found in tomatoes and other red fruits and vegetables. Lycopene is extracted from red fruit &amp; vegetables and found in two physical form Oily liquid &amp; granular solid powder form. Lycopene is also used as supplement with multivitamin formulated as tablets. An RP-HPLC method was developed and validated for the Lycopene (Powder form or water suspendable form) in Multivitamin Tablets. The chromatographic system was equipped with C18 a stainless steel column 30 cm x 4.0 mm, packed with octadecylsilane chemically bonded to porous silica or ceramic micro particles (5µm) and wavelength set at 475 nm, in conjunction with a mobile phase of Methanol, Water and Tetrahydrofuran in the ratio of 66:4:30 % v/v at a flow rate of 1.5 ml/min. The retention time of Lycopene was found to be 6 min ±1 min. The separation was performed at ambient temperature. Linearity was observed in the concentration range of 80-120% with correlation coefficient 0.9999 and slope 75145.63 Percentage recovery obtained 99.06-101.83 %. The percentage Assay was found to be 100.25 to 102.61 %.The proposed method is precise, accurate, selective and rapid for the determination of lycopene (Powder form) in Multivitamin tablet. The proposed method is optimized and validated as per guidelines of WHO TRS 937 &amp; the International Conference on Harmonization (ICH) guidelines.

Establishment and optimization of the three-band fluorometric indices for oil species identification: Implications on the optimal excitation wavelengths and the detection band combinations

BackgroundThe oil pollutants have become a major threat to the ocean environment with the rapid development of social economy and offshore production activities. The detection and identification of petroleum pollutants is the foundation and pre-requisite for controlling offshore oil pollution. Spectroscopic analysis using the ultraviolet–induced fluorescence of oil pollutants is a potential way for oil species identification. Nevertheless, the current fluorescence spectroscopic analysis methods, such as excitation-emission matrix, has high requirements on the detection equipment. There is a great need for rapid oil species identification method when it comes to practical applications. (93) ResultsThis study established the three-band fluorometric index (TBFI) method and examined its capability on the oil species identification. The fluorescence of five different types of oil samples under various excitation wavelengths were excited using a tuneable xenon lamp, and measured using a high-resolution spectrometer. The optimal excitation wavelengths and the corresponding detection band combinations were explored through the iterations of an optimal band combination algorithm through different excitation wavelengths. According to the results, three out of the four TBFIs tested in this study are suitable for oil species identification. They share the same sets of the excitation wavelengths under 365 nm, 375 nm, and 435 nm. The detection band combinations corresponding to each TBFI under each feasible excitation wavelength were also determined in this study. (125) SignificanceThe three-band fluorometric index method proposed in this study provides a feasible way for the rapid oil species identification using fluorescence spectroscopic analysis. The optimal excitation and detection wavelengths determined in this study also provide the theoretical basis for the designs of fluorescence sensors for rapid oil spill detection and identification. (51).