Showing papers on "Spectrum analyzer published in 2022"

A calibration friendly approach to identify drugs of abuse mixtures with a portable near‐infrared analyzer

Abstract: Abstract Both the increasing number and diversity of illicit‐drug seizures complicate forensic drug identification. Traditionally, colorimetric tests are performed on‐site, followed by transport to a laboratory for confirmatory analysis. Higher caseloads increase laboratory workload and associated transport and chain‐of‐evidence assurance performed by police officers. Colorimetric tests are specific only for a small set of drugs. The rise of new psychoactive substances therefore introduces risks for erroneous results. Near‐infrared (NIR)‐based analyzers may overcome these encumbrances by their compound‐specific spectral selectivity and broad applicability. This work introduces a portable NIR analyzer that combines a broad wavelength range (1300–2600 nm) with a chemometric model developed specifically for forensic samples. The application requires only a limited set of reference spectra for time‐efficient model training. This calibration‐light approach thus eliminates the need of extensive training sets including mixtures. Performance was demonstrated with 520 casework samples resulting in a 99.6% true negative and 97.6% true positive rate for cocaine. Similar results were obtained for MDMA, methamphetamine, ketamine, and heroin. Additionally, 236 samples were analyzed by scanning directly through their plastic packaging. Also here, a >97% true positive rate was obtained. This allows for non‐invasive, operator‐safe chemical identification of potentially potent drugs of abuse. Our results demonstrate the applicability for multiple drug‐related substances. Ideally, the combination of this NIR approach with other portable techniques, such as Raman and IR spectroscopy and electrochemical tests, may eventually eliminate the need for subsequent laboratory analysis; therefore, saving tremendous resources in the overall forensic process of confirmatory illicit drug identification.

Unreliable Automated Complete Blood Count Results: Causes, Recognition, and Resolution

Abstract: Automated hematology analyzers generate accurate complete blood counts (CBC) results on nearly all specimens. However, every laboratory encounters, at times, some specimens that yield no or inaccurate result(s) for one or more CBC parameters even when the analyzer is functioning properly and the manufacturer’s instructions are followed to the letter. Inaccurate results, which may adversely affect patient care, are clinically unreliable and require the attention of laboratory professionals. Laboratory professionals must recognize unreliable results, determine the possible cause(s), and be acquainted with the ways to obtain reliable results on such specimens. We present a concise overview of the known causes of unreliable automated CBC results, ways to recognize them, and means commonly utilized to obtain reliable results. Some examples of unreliable automated CBC results are also illustrated. Pertinent analyzer-specific information can be found in the manufacturers’ operating manuals.

High sensitivity limited material proteomics empowered by data-independent acquisition on linear ion traps

Abstract: In recent years, the concept of cell heterogeneity in biology has gained increasing attention, concomitant with a push towards technologies capable of resolving such biological complexity at the molecular level. While RNA-based approaches have long been the method of choice, advances in mass spectrometry (MS)-based technologies have led to the ability to resolve cellular proteomes within minute sample quantities and, very recently, even down to a single cell. Current limitations are the incomplete proteome depth achieved and low sample throughput, and continued efforts are needed to push the envelope on instrument sensitivity, improved data acquisition methods, and chromatography. For single-cell proteomics using Mass Spectrometry (scMS) and low-input proteomics experiments, the sensitivity of an orbitrap mass analyzer can sometimes be limiting. Therefore, low-input proteomics and scMS could benefit from linear ion traps, which provide faster scanning speeds and higher sensitivity than an orbitrap mass analyzer, however, at the cost of resolution. We optimized and improved an acquisition method that combines the orbitrap and linear ion trap, as implemented on a tribrid instrument, while taking advantage of the high-field asymmetric waveform ion mobility spectrometry (FAIMS) pro interface, with a prime focus on low-input applications. First, we compared the performance of orbitrap-versus linear ion trap mass analyzers. Subsequently, we optimized critical method parameters for low-input measurement by data-independent acquisition (DIA) on the linear ion trap mass analyzer. We conclude that linear ion traps mass analyzers combined with FAIMS and Whisper™ flow chromatography are well-tailored for low-input proteomics experiments. They can simultaneously increase the throughput and sensitivity of large-scale proteomics experiments where limited material is available, such as clinical samples, cellular sub-populations, and eventually, scMS.

Y and Al Codoped ZnO-nanopowder Based Ultrasensitive Trace Ethanol Sensor: A Potential Breath Analyzer for Fatty Liver Disease and Drunken Driving Detection

Abstract: Excess ethanol in exhaled breath can be an indicator of intoxication and a biomarker for fatty liver disease. Herein, we report for the first time a highly sensitive and selective Al and Y co-doped ZnO nanopowder sensor for the detection of trace ethanol in exhaled breath. The nanopowder was synthesized by a facile sol-gel method and characterized by multiple sophisticated techniques, viz. XRD, XPS, FTIR, FESEM, EDX, BET surface area analysis, UV-Vis spectroscopy, photoluminescence, infrared imaging, and current-voltage (I-V) measurement. The developed sensors, especially 5% Y and 1% Al co-doped ZnO exhibited excellent n-type response to 1 ppm ethanol (62.8%). Further, appreciable selectivity to trace ethanol with respect to other interfering gases, viz. acetone, ammonia, CO, NO, NO2, formalin, acetylene, and saturated moisture was observed. Additionally, ultrafast response (0.77 s) and recovery (8.1 s) time, good repeatability, and long-term stability for at least 10 months were observed. Satisfactory resolution between healthy breath, and simulated breath with ethanol vapor excess was obtained. The optimized sensor could be very suitable for both the detection of liver problem as well as commercial breath ethanol analyzer for drunken driving detection.

Raman Natural Gas Analyzer: Effects of Composition on Measurement Precision

Abstract: Raman spectroscopy is a promising method for analyzing natural gas due to its high measurement speed and the potential to monitor all molecular components simultaneously. This paper discusses the features of measurements of samples whose composition varies over a wide range (0.005–100%). Analysis of the concentrations obtained during three weeks of experiments showed that their variation is within the error caused by spectral noise. This result confirms that Raman gas analyzers can operate without frequent calibrations, unlike gas chromatographs. It was found that a variation in the gas composition can change the widths of the spectral lines of methane. As a result, the measurement error of oxygen concentration can reach 200 ppm. It is also shown that neglecting the measurement of pentanes and n-hexane leads to an increase in the calculated concentrations of other alkanes and to errors in the density and heating value of natural gas.

High Sensitivity Limited Material Proteomics Empowered by Data-Independent Acquisition on Linear Ion Traps

Abstract: In recent years, the concept of cell heterogeneity in biology has gained increasing attention, concomitant with a push toward technologies capable of resolving such biological complexity at the molecular level. For single-cell proteomics using Mass Spectrometry (scMS) and low-input proteomics experiments, the sensitivity of an orbitrap mass analyzer can sometimes be limiting. Therefore, low-input proteomics and scMS could benefit from linear ion traps, which provide faster scanning speeds and higher sensitivity than an orbitrap mass analyzer, however at the cost of resolution. We optimized an acquisition method that combines the orbitrap and linear ion trap, as implemented on a tribrid instrument, while taking advantage of the high-field asymmetric waveform ion mobility spectrometry (FAIMS) pro interface, with a prime focus on low-input applications. First, we compared the performance of orbitrap- versus linear ion trap mass analyzers. Subsequently, we optimized critical method parameters for low-input measurement by data-independent acquisition on the linear ion trap mass analyzer. We conclude that linear ion traps mass analyzers combined with FAIMS and Whisper flow chromatography are well-tailored for low-input proteomics experiments, and can simultaneously increase the throughput and sensitivity of large-scale proteomics experiments where limited material is available, such as clinical samples and cellular subpopulations.

An empirical study on the effectiveness of static C code analyzers for vulnerability detection

Abstract: Static code analysis is often used to scan source code for security vulnerabilities. Given the wide range of existing solutions implementing different analysis techniques, it is very challenging to perform an objective comparison between static analysis tools to determine which ones are most effective at detecting vulnerabilities. Existing studies are thereby limited in that (1) they use synthetic datasets, whose vulnerabilities do not reflect the complexity of security bugs that can be found in practice and/or (2) they do not provide differentiated analyses w.r.t. the types of vulnerabilities output by the static analyzers. Hence, their conclusions about an analyzer's capability to detect vulnerabilities may not generalize to real-world programs. In this paper, we propose a methodology for automatically evaluating the effectiveness of static code analyzers based on CVE reports. We evaluate five free and open-source and one commercial static C code analyzer(s) against 27 software projects containing a total of 1.15 million lines of code and 192 vulnerabilities (ground truth). While static C analyzers have been shown to perform well in benchmarks with synthetic bugs, our results indicate that state-of-the-art tools miss in-between 47% and 80% of the vulnerabilities in a benchmark set of real-world programs. Moreover, our study finds that this false negative rate can be reduced to 30% to 69% when combining the results of static analyzers, at the cost of 15 percentage points more functions flagged. Many vulnerabilities hence remain undetected, especially those beyond the classical memory-related security bugs.

Tea Analyzer: A low-cost and portable tool for quality quantification of postharvest fresh tea leaves

Abstract: The quality components in postharvest tea leaves determine their quality characteristics and processing suitability. Currently, chemical analysis is widely used to determine these indicators. However, this method is time-consuming and laborious. In this study, we developed a NIR-based analytical tool on a smartphone for in situ and rapid detection of quality indicators in postharvest tea leaves of multiple varieties and harvesting standards. The smartphone and the miniature NIR spectrometer are used for data acquisition and transfer to the phone via Bluetooth. Partial least squares (PLS) modeling coupled with spectral pre-processing and selection of characteristic wavelengths were used to obtain the optimal predictive model. The results showed that the quality of various tea tree varieties and harvesting standards differed considerably. The PLS models with suitable wavelengths achieved acceptable predictive performance for tea polyphenols, amino acids, and polyphenol to amino acid ratio (P/A values). The determination coefficients of the prediction set for tea polyphenols, amino acids, and the P/A value were 0.90, 0.91, and 0.91, and residual predictive deviations were 2.24, 2.43, and 2.42, respectively. Based on the optimal model constructed, a smartphone based analytical software was developed to achieve low-cost and rapid quality quantification of postharvest fresh tea leaves. • Quality quantification of postharvest tea fresh leaf was achieved. • Polyphenols, amino acids, and their ratio were predicted with high accuracy. • The spectral band has been reduced, thus simplifying the model. • Smartphone-based application for quality quantification was developed.

Lab-in-a-Cup (LiC): An autonomous fluidic device for daily urinalysis using smartphone

Abstract: Since urine can be collected noninvasively, a wide range of biomarkers found in urine makes urinalysis well suited for continuous routine health screening. Although there has been amazing progress in automated urine analyzers, they are laboratory-centered and still suffer from labor-involved sample processing and analysis being infeasible for routine healthcare. To facilitate routine healthcare, this work presents a cost-effectively implemented lab-in-a-Cup (LiC) hybrid urinalyzer developed using a disposable plastic cup in association with conventional paper-based urine sensors. Since the sensor pad is precisely fixed with the bottom of the cup, the small gap between the sensor pad and cup surface leads to the limited diffusion of urine which results in the stable, reproducible, repeatable data and sample volume-irrelevant measurement. To characterize the urine sample in the cup, a smartphone application with an image recognition algorithm has been developed and applied that automatizes the entire analysis procedures enabling it for routine healthcare without relying on the manual color scheme. The device was successfully applied for investigating a number of urinary biomarkers such as glucose, pH, protein, and red blood cell (RBC) to a wider concentration covering their clinical ranges. While quantified, the proposed analyzer was found to significantly improve the reaction time and sample volume-dependent irregularities enhancing the colorimetric assessment of urine sensors as a major concern for biological samples. As a result, the user-friendly LiC device may suitably be applied as a near-patient urinalyzer.

A quantum radio frequency signal analyzer based on nitrogen vacancy centers in diamond

Abstract: The fast development of radio-frequency (RF) technologies increases the need for compact, low consumption and broadband real-time RF spectral analyser. To overcome the electronic bottleneck encountered by electronic solutions, which limits the real time bandwidth to hundreds of MHz, we propose a new approach exploiting the quantum properties of the nitrogen-vacancy (NV) center in diamond. Here we describe a Quantum Diamond Signal Analyser (Q-DiSA) platform and characterize its performances. We successfully detect RF signals over a large tunable frequency range (25 GHz), a wide instantaneous bandwidth (up to 4 GHz), a MHz frequency resolution (down to 1 MHz), a ms temporal resolution and a large dynamic range (40 dB).

Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection

Abstract: Background: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters). Methods: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These features were then used to train machine learning models using Naïve Bayes classifiers, Support Vector Machines and Random Forests. Results: The accuracy of the trained models reached 77.8% (sensitivity: up to 66.54%; specificity: up to 92.39%). The use of the proposed shape-based features improved the accuracy in most cases, especially the overall accuracy and sensitivity. Conclusions: The results show that this point-of-care breath analyzer and data analysis approach constitute a promising combination for the detection of gastric cancer-specific breath. The cluster taxonomy-based sensor reaction curve representation improved the results, and could be used in other similar applications.

Study of the fast electron behavior in electron cyclotron current driven plasma on J-TEXT

Abstract: In J-TEXT tokamak, fast electron bremsstrahlung diagnostic with 9 chords equipped with multi-channel analyzer enables detailed studies of the generation and transport of fast electrons. The spatial profiles and energy spectrum of the fast electrons have been measured in two ECCD cases with either on-axis or off-axis injection, and the profiles processed by Abel-inversion are consistent with the calculated power deposition locations. Moreover, it is observed that the energy of fast electrons increases rapidly after turning off the ECCD, which may be attributed to the acceleration by the recovered loop voltage at low electron density.

Accurate non-linear harmonic simulations at X-band using the ASM-HEMT model validated with NVNA measurements

Abstract: In this paper, we validate the industry standard ASM-HEMT model for non-linear large-signal modeling of 140 nm GaN HEMT at X-band. An accurate model has been developed for fundamental, second-, and third-order harmonic frequency. Time-domain waveforms and dynamic load-line simulations from ASM-HEMT model are also validated against non-linear vector analyzer measurements. This is the first validation of ASM-HEMT model for harmonics and NVNA data. A good model agreement with measurements has been obtained.

Establishment of metrological parameters of the method for measuring the protein mass fraction in fish food products by the Kjeldahl method

Abstract: In laboratory practice, there are many protein quantification methods, and all of them have their own advantages and disadvantages. The most common and widely used method for the protein analysis in food products, including fish, is the Kjeldahl method. However, the current standards for measurement methods for the determination of the protein content in fish food products do not provide for the use of devices that meet the modern level of technical development, and also do not contain metrological indicators that guarantee the reliability of the results obtained. The aim of the study was to substantiate the method for measuring the protein mass fraction in fish food products by the Kjeldahl method on an automatic analyzer and to establish metrological parameters. The assessment of the quality indicators of the Kjeldahl measuring method was carried out using a Kjeltec System 2300 Nitrogen Analyzer (Foss Analytical AB, Sweden) in the form of a characteristic of the measurement error and its components, which will provide results with the required accuracy.

Total integrated centrifugal genetic analyzer for point-of-care Covid-19 testing with automatic and high-throughput capability

Abstract: We propose a prototype portable genetic analyzer, which performs the entire processes including RNA purification, reverse transcription multiplex loop-mediated isothermal amplification (RT-LAMP), and real-time fluorescence detection. Ten units of integrated functional entity were incorporated on a centrifugal disc, so that 10 samples could be analyzed on single device in one run. The 10 aliquoting chambers were connected sidewards by the zigzag aliquoting channel and were linked to the glass filter column outwards via the passive valves for the RNA extraction. The lower sideward capillary pressure of the zigzag aliquoting channel than the outward capillary pressure of the passive values is capable of automatic division of solution into the 10 aliquoting chambers with one injection by a needle. The sequent loading and partitioning of the sample, the washing, and the elution solutions by centrifugal protocols rendered the RT-LAMP cocktails ready for amplification in the three reaction chambers for each sample, in which the RT-LAMP primers targeting orf1ab, N and S genes were dried. After blocking the reaction chambers by wax to prevent evaporation, RT-LAMP reaction proceeded at 63 °C, and all the positive fluorescent signals in the three chambers verifies the existence of Covid-19 in the sample. For point-of-care testing (POCT), we constructed a portable workstation that consists of a solution storage, a solution injection system, a spinning motor, and a fluorescence detector. Using the proposed platform, we could perform the Covid-19 molecular diagnostics for 10 samples per run on single device in an automatic and high-throughput manner in 1.5 h.

Development of a novel and fast XRF instrument for large area heavy metal detection integrated with UAV.

Abstract: The disadvantages of the current chemical and instrumental analysis methods for soil heavy metal pollution are that they have a high detection cost, long cycle times, and may cause secondary pollution. The aims of this study were to improve the rapid detection of soil heavy metal pollution over large areas. This study combined aircraft technology, embedded development, computer software, electronic information, and other technical methods to create a novel solution to the problem, i.e., an integrated unmanned aerial vehicle (UAV) based soil heavy metal pollution rapid detection system (UAV-SHMPRDS) was built. The key technologies required for a rapid detection system were developed, including the development of a hardware system based on a UAV and an X-ray fluorescence spectrum (XRF) analyzer, the design and implementation of a control system software system, and the implementation of a data inversion processing algorithm. Finally, a prototype UAV-SHMPRDS was constructed. Testing showed that the system improved regionalized soil heavy metal pollution detection efficiency. This study provides new solutions for the current problems encountered in the actual rapid detection of soil heavy metal pollution.

Measuring serum sodium levels using blood gas analyzer and auto analyzer in heart and lung disease patients: A cross-sectional study

Abstract: Emergency services require precise and rapid measurement of electrolytes to initiate treatment. Blood gas analyzers (BGA) analyzes blood samples in seconds however, its accuracy is still debatable. The aim of this study was to compare the level of serum sodium measured through BGA and auto analyzer in the laboratory analyzers.This cross-sectional study was performed on 79 patients with heart and lung disease in the intensive care unit of the center. Patient information was recorded along with serum sodium levels measured through BGA and auto analyzer in the laboratory.The mean sodium level measured by BGA was 138.38 mEq/L and by auto analyzer was 137.42 mEq/L. The difference was statistically significant, p = 0.007. Among lung disease patients, the mean sodium levels from BGA and autoanalyzer did not differ significantly p = 0.052 where in patients with heart disease, these levels were 138.54 mEq/L and 137.23 mEq/L, respectively. The difference was significantly different, p = 0.015. Acidic pH measured using BGA and autoanalyzer also differed significantly, p = 0.006.Blood gas analyzer method has a high correlation with laboratory analyzer, but in cases of hypernatremia, the accuracy of blood gas analyzer method decreases and especially in acidosis and in patients with pulmonary problems, the difference with laboratory method increases.

Energy- and charge-state-resolved spectrometry of tin laser-produced plasma using a retarding field energy analyzer

Abstract: Abstract We present a method to obtain the individual charge-state-dependent kinetic-energy distributions of tin ions emanating from a laser-produced plasma from their joint overlapping energy distributions measured by means of a retarding field energy analyzer (RFA). The method of extracting charge state specific parameters from the ion signals is described mathematically, and reinforced with experimental results. The absolute charge-state-resolved ion energy distributions is obtained from ns-pulse Nd:YAG-laser-produced microdroplet tin plasmas in a setting relevant for state-of-the-art extreme ultraviolet nanolithography.

Point-of-care high-sensitivity troponin-I analysis in capillary blood for acute coronary syndrome diagnostics

Abstract: Abstract Objectives Patients with acute coronary syndrome (ACS) should be referred promptly to the hospital to reduce mortality and morbidity. Differentiating between low-risk and high-risk patients remains a diagnostic challenge. Point-of-care testing can contribute to earlier disposition decisions for patients excluded from ACS. This study describes the validation of the Atellica® VTLi. Patient-side Immunoassay Analyzer for high-sensitivity troponin point-of-care (POC) analysis. (The Atellica VTLi is not available for sale in the USA. The products/features (mentioned herein) are not commercially available in all countries. Their future availability cannot be guaranteed). Methods A total of 152 patients with acute chest pain admitted at the cardiac emergency department (ED) were included in the study. Capillary blood was compared with a whole blood and plasma sample obtained by venipuncture. All samples were analyzed using the Atellica VTLi Patient-side Immunoassay Analyzer; in addition, plasma was analyzed by a central lab immunoassay analyzer. Results No significant difference was observed between venous whole blood vs. plasma analyzed by the Atellica VTLi Patient-side Immunoassay Analyzer. The difference between capillary blood and venous blood showed a constant bias of 7.1%, for which a correction factor has been implemented. No clinically relevant differences were observed for the capillary POC results compared to plasma analyzed with a standard immunoassay analyzer. Conclusions The Atellica VTLi Patient-side Immunoassay Analyzer for high-sensitivity troponin analysis shows equivalent results for all sample types, including capillary blood. No clinically relevant discordances were observed between capillary POC and central laboratory results. With additional studies, this could pave the way towards rapid testing of high-sensitivity troponin in the ambulance or the general practitioner’s office without the need for hospitalization of patients with acute chest pain.

EMF Exposure in 5G Standalone mm-Wave Deployments: What Is the Impact of Downlink Traffic?

Abstract: The rolling-out of 5G networks is recently including 5G Base Stations (BSs) operating on millimeter-Wave (mm-Wave) frequencies. The goal of this work is to shed light on the exposure assessment from commercial 5G mm-Wave 5G BSs, by focusing on the impact of downlink traffic on the exposure levels. To this aim, we adopt an innovative measurement framework, based on hardware and software components, able to satisfy the challenging measurement requirements of mm-Wave frequencies. In addition, we design a completely softwarized algorithm, called M-Wave, in order to measure the mm-Wave exposure with a programmable spectrum analyzer. Results, obtained from a commercial 5G scenario, reveal that the exposure from the mm-Wave BS is directly proportional to the amount of traffic injected on the wireless link. However, the electric field is always lower than 0.08 V/m, while the downlink traffic is even larger than 800 Mbps.

TIA: A forward model and analyzer for Talbot interferometry experiments of dense plasmas

Abstract: Interferometry is one of the most sensitive and successful diagnostic methods for plasmas. However, owing to the design of most common interferometric systems, the wavelengths of operation and, therefore, the range of densities and temperatures that can be probed are severely limited. Talbot–Lau interferometry offers the possibility of extending interferometry measurements to x-ray wavelengths by means of the Talbot effect. While there have been several proof-of-concept experiments showing the efficacy of this method, it is only recently that experiments to probe High Energy Density (HED) plasmas using Talbot–Lau interferometry are starting to take place. To improve these experimental designs, we present here the Talbot-Interferometry Analyzer (TIA) tool, a forward model for generating and postprocessing synthetic x-ray interferometry images from a Talbot–Lau interferometer. Although TIA can work with any two-dimensional hydrodynamic code to study plasma conditions as close to reality as possible, this software has been designed to work by default with output files from the hydrodynamic code FLASH, making the tool user-friendly and accessible to the general plasma physics community. The model has been built into a standalone app, which can be installed by anyone with access to the MATLAB runtime installer and is available upon request to the authors.

Comparison of three hematocrit measurement methods in the southern white rhinoceros (Ceratotherium simum simum)

Abstract: Abstract Background Hematocrit (HCT) determination is an integral part of health and disease assessments in captive and wild white rhinoceroses. Several affordable automated hematology analyzers have been developed for in‐clinic and field use and have the advantage of being able to measure a large number of additional measurands. However, the accuracy of these analyzers for rhinoceros HCT measurements has not yet been investigated. Objectives We aimed to compare the HCT results generated by the EPOC portable analyzer system and the Abaxis VetScan HM5 with the gold standard of a manual packed cell volume (PCV) measured using the microhematocrit method. Methods Hematocrits were measured with the EPOC and the Abaxis VetScan HM5 (bovine setting) and compared with the PCVs of 69 white rhinoceros whole blood samples. Results were compared using Bland–Altman difference plots and Passing‐Bablok regression analysis. A total allowable analytical error of 10% was set as the performance goal. Results A significant positive bias, with a mean of 7.7% for the EPOC and 17.9% for the Abaxis, was found compared with the manual PCV method. Conclusions The allowable error goal of 10% was not exceeded with the EPOC analyzer. Although not analytically equivalent to the gold standard, the EPOC results could therefore be used as approximations in critical situations where manual measurements cannot be performed. The Abaxis exceeded this allowable error and overestimated HCTs in rhinoceroses. Therefore, method‐specific reference intervals should be used.

Performance evaluation of presepsin using a Sysmex HISCL‐5000 analyzer and determination of reference interval

Abstract: Analytical evaluation of newly developed presepsin by a Sysmex HISCL‐5000 (Sysmex, Japan) automated immune analyzer was performed.