External Calibration Research Articles

Unlocking the electrochemical signature of Naringenin: Implications for screening in pharmaceutical formulations

Naringenin (NAR) is a versatile herbal flavonoid known for its broad pharmacological benefits, including antioxidant, anti-inflammatory, and anticancer properties. These properties underscore its potential and necessity for industrial applications within the pharmaceutical sector, making its detection and quantification crucial for ensuring the quality and efficacy of pharmaceutical products. Addressing the imperative for a sensitive and sustainable screening approach, this research leverages electrochemical analysis techniques including electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and differential pulse voltammetry (DPV). Complementary to experimental investigations, the electronic structure of NAR was probed using B3LYP/Def2-TZVPPD calculations alongside Fukui and dual descriptor analyses. Our findings delineate the dissociation kinetics of the hydroxyl groups within NAR, with particular emphasis on the phenolic group’s proton as the primary dissociation site, elucidating pivotal insights into the oxidation mechanism of NAR. Noteworthy is the irreversibility of the electrochemical oxidation process observed on both non-modified glassy carbon electrode (GCE) and gold electrode (GE), indicating a one-electron-one-proton mechanism. Utilizing the external calibration method and GCE, a linear range from 1.36 to 19.1 mg L-1 in Britton-Robinson (BR) supporting electrolyte was established, exhibiting a limit of detection (LOD) of 0.11 mg L-1 (R2 = 0.9945). For GE, the standard addition calibration approach revealed one linear range, from 0.27 to 0.48 mg L-1 (LOD of 0.04 mg L-1, R2 = 0.9960). Importantly, the proposed methodologies demonstrate satisfactory selectivity against potential interferences, with intra- and inter-day precision exhibiting low relative standard deviation (RSD) values. This study introduces a simple, sensitive, stable, selective, and sustainable electroanalytical method for the detection of NAR in pharmaceutical formulations, harnessing the advantages of unmodified electrodes and rigorous experimental validation.

Enhanced HPLC Method for Boar Taint Quantification.

Boar taint is an unpleasant odour found in the carcasses of entire male pigs, resulting from androstenone and skatole accumulation during pubertal development, and impacting pork quality. This study proposes the validation of an adapted chromatographic method for quantifying skatole and androstenone in the pigs' liquid fat using fluorescence detection. A good chromatographic separation was achieved, with skatole (SKA) and androstenone (AND) elution at 4.4 and 9.9 min., respectively. An external calibration method was applied, with calibration curves correlation coefficient of 0.9999 for both analytes. Detection limit values were 1.53 and 16.02 ng/g for SKA and AND, respectively. SKA recovery was 99.72±2.34 % (2.34 % RSD) and 102.84±1.62 % (1.57 % RSD) for AND. Results showed good precision values (repeatability <2.46 % RSD for SKA, <6.85 % RSD for AND; intermediate precision <2.87 % RSD for SKA, <6.98 % RSD for AND). The method's robustness was tested and the values were within the reference ranges. The validation results proved that the adaptation of an existing method resulted in good assessments of robustness, reliability and accuracy.

An edge feature-based external calibration of monocular camera and LiDAR using in-orbit data

Multimodal perception represents a vital approach for observing non-cooperative targets in space. The accurate determination of the extrinsic parameters for monocular camera and LiDAR systems serves as the cornerstone for achieving effective space multimodal perception. However, due to factors like launch and operation, these extrinsic parameters can experience drift, thereby compromising the overall performance of the multimodal perception system. To tackle this challenge, this study introduces a novel approach: an edge feature-based external calibration method for monocular camera and LiDAR using in-orbit data. The proposed methodology involves several steps. Firstly, edges are extracted from the image, followed by distance transformation based on the edge image. The subsequent integration of the edge image and distance image yields the search scene image. Then, edge feature is extracted from the point cloud data. Finally, after constructing the objective function, genetic algorithms are used to determine precise external parameters. Simulation results underscore the effectiveness of the proposed algorithm, affirming its ability to achieve high-precision calibration results for external parameters even within intricate spatial environments.

Comparing penalization methods for linear models on large observational health data.

This study evaluates regularization variants in logistic regression (L1, L2, ElasticNet, Adaptive L1, Adaptive ElasticNet, Broken adaptive ridge [BAR], and Iterative hard thresholding [IHT]) for discrimination and calibration performance, focusing on both internal and external validation. We use data from 5 US claims and electronic health record databases and develop models for various outcomes in a major depressive disorder patient population. We externally validate all models in the other databases. We use a train-test split of 75%/25% and evaluate performance with discrimination and calibration. Statistical analysis for difference in performance uses Friedman's test and critical difference diagrams. Of the 840 models we develop, L1 and ElasticNet emerge as superior in both internal and external discrimination, with a notable AUC difference. BAR and IHT show the best internal calibration, without a clear external calibration leader. ElasticNet typically has larger model sizes than L1. Methods like IHT and BAR, while slightly less discriminative, significantly reduce model complexity. L1 and ElasticNet offer the best discriminative performance in logistic regression for healthcare predictions, maintaining robustness across validations. For simpler, more interpretable models, L0-based methods (IHT and BAR) are advantageous, providing greater parsimony and calibration with fewer features. This study aids in selecting suitable regularization techniques for healthcare prediction models, balancing performance, complexity, and interpretability.

Rapid and reliable quantification of urinary malondialdehyde by HILIC-MS/MS: A derivatization-free breakthrough approach

BackgroundThe development of fast analytical methods is crucial for the research, discovery, and confirmation of crucial biomarkers. Furthermore, the implementation of fast analytical strategies contributes to efficient and time-effective procedures. In this sense, analysis of malondialdehyde (MDA) has become an important tool for understanding the role of oxidative stress in various diseases and for evaluating the efficacy of therapeutic interventions. ResultsA rapid and robust liquid chromatography tandem mass spectrometry method (HPLC-MS/MS) has been developed to determine endogenous amounts of malondialdehyde (MDA) in human urine without any associated derivatization reaction. MDA was separated in 4 min through a Urea-HILIC column and was analyzed using a triple quadrupole mass spectrometer in negative electrospray ionization mode. With a 50-fold dilution as the only sample pretreatment after alkaline hydrolysis, no matrix effect was present, which allowed for a fast and simple quantification by means of an external standard calibration with a limit of detection of 0.20 ng mL−1. The whole methodology was validated by analyzing unspiked and spiked urine samples from ten healthy individuals and comparing with the results obtained by the standard addition method. MDA was detected in all cases, with natural concentrations varying from 0.11 ± 0.03 to 0.31 ± 0.03 mg g−1 creatinine. Accuracies were found to be satisfactory, ranging from 95 % to 101 %. The proposed method also exhibited good repeatability and reproducibility (RSD<15 %) for four quality control levels. SignificanceThe main significance of this method is the avoidance of a derivatization reaction for the determination of urinary MDA, this constituting a step forward when compared with previous literature. This breakthrough not only streamlines time analysis to less than 5 min per sample but also results in a more robust procedure. Consequently, the method here developed could be applied to subsequent future research involving the determination of MDA as a lipid peroxidation biomarker, where simple, rapid, and reliable methods could represent a significant improvement.

Radiographical magnification of the shoulder region

Abstract Background Calibration of shoulder radiographs is required for accurate preoperative planning. Current practice mostly uses an empirical fixed calibration factor of 5%, and limited information is available about how the magnification of the glenohumeral region differs among patients. This retrospective observational study analyzed the patient-specific magnification factor in total shoulder arthroplasty. Methods Radiographs of 94 patients with unilateral total shoulder arthroplasty (SMR Reverse Shoulder System, Lima Ltd., San Daniele del Friuli, Italy) were obtained from archives. The reverse humeral body diameter was used as internal reference. The measured radiographical magnifications were correlated with the patients’ sex, weight, and height. Results The average magnification factor of the glenohumeral region was 11.9% (standard deviation: 3.2%, range: 5.7–20.3%). No statistically significant difference in radiographic magnification was found between the male and the female patients. The magnification factor was higher in patients with higher weight (p &lt; 0.05), but the explanatory power of the model was weak (R = 0.09). Conclusions The observed radiographic magnification was considerably higher than a commonly used fixed calibration factor of 5% and exhibited considerable variability among the patients. Therefore, standard radiographs might not be appropriate for accurate preoperative templating, and we recommend using either computer tomography data or calibrating radiographs through external calibration markers for each patient.

Design Implementation of a Logger-Based Digital Thermometer

LM35 temperature sensor is a widely used 3-terminal sensor known for its precise temperature measurement capabilities without the need for external calibration circuitry. With a sensitivity of 10mV/°C, it operates within a temperature range of -55°C to 150°C, with an error margin of 0.5°C. This paper presents the development and testing of a logger-based digital thermometer using LM35 sensor, integrated with a dsPIC30F4013 digital signal processing controller, an LCD, a real-time clock (DS1307), and other essential components. The methodology involves the analysis of the sensor's sensitivity and its response under both stress and non-stress temperature conditions. The findings indicate that the sensor's output closely follows the theoretical sensitivity, with a negligible offset, ensuring accurate temperature measurements. Comparative analysis with a standard laboratory thermometer suggests that the digital sensor provides superior precision, with an error margin of ±0.01°C. The results confirm LM35's suitability for accurate ambient temperature monitoring and validation of environmental temperature fluctuations.

Prognostic nomogram to predict cancer-specific survival with small-cell carcinoma of the prostate: a multi-institutional study.

The aim of this study is to examine the predictive factors for cancer-specific survival (CSS) in patients diagnosed with Small-Cell Carcinoma of the Prostate (SCCP) and to construct a prognostic model. Cases were selected using the Surveillance, Epidemiology, and End Results (SEER) database. The Kaplan-Meier method was utilized to calculate survival rates, while Lasso and Cox regression were employed to analyze prognostic factors. An independent prognostic factor-based nomogram was created to forecast CSS at 12 and 24 months. The model's predictive efficacy was assessed using the consistency index (C-index), calibration curve, and decision curve analysis (DCA) in separate tests. Following the analysis of Cox and Lasso regression, age, race, Summary stage, and chemotherapy were determined to be significant risk factors (P < 0.05). In the group of participants who received training, the rate of 12-month CSS was 44.6%, the rate of 24-month CSS was 25.5%, and the median time for CSS was 10.5 months. The C-index for the training cohort was 0.7688 ± 0.024. As for the validation cohort, it was 0.661 ± 0.041. According to the nomogram, CSS was accurately predicted and demonstrated consistent and satisfactory predictive performance at both 12 months (87.3% compared to 71.2%) and 24 months (80.4% compared to 71.7%). As shown in the external validation calibration plot, the AUC for 12- and 24-month is 64.6% vs. 56.9% and 87.0% vs. 70.7%, respectively. Based on the calibration plot of the CSS nomogram at both the 12-month and 24-month marks, it can be observed that both the actual values and the nomogram predictions indicate a predominantly stable CSS. When compared to the AJCC staging system, DCA demonstrated a higher level of accuracy in predicting CSS through the use of a nomogram. Clinical prognostic factors can be utilized with nomograms to forecast CSS in Small-Cell Carcinoma of the Prostate (SCCP).

Determination of Phosphorus Content in Cola Drinks by 31P NMR Spectroscopy Using External Calibration and Standard Addition Approaches

Determination of Phosphorus Content in Cola Drinks by ³¹P NMR Spectroscopy Using External Calibration and Standard Addition Approaches

Determination of Bovine Lactoferrin in Powdered Infant Formula and Adult Nutritionals by Heparin Affinity Extraction and Reverse-Phase High-Performance Liquid Chromatography/Ultraviolet Detection (HPLC/UV): Single-Laboratory Validation, First Action 2021.10.

Infant formulas, and pediatric and adult nutritional products, are being fortified with bovine lactoferrin (bLF) due to its beneficial impacts on immune development and gut health. Lactoferrin supplementation into these products requires an analytical method to accurately quantify the concentrations of bLF to meet global regulatory and quality standards. To develop and validate a lactoferrin method capable of meeting the AOAC INTERNATIONAL Standard Method Performance Requirements (SMPR®) 2020.005. Powder formula samples are extracted using warm dibasic phosphate buffer, pH 8, then centrifuged at 4°C to remove insoluble proteins, fat, and other solids. The soluble fraction is further purified on a HiTrap heparin solid-phase extraction (SPE) column to isolate bLF from interferences. Samples are filtered, then analyzed by LC-UV using a protein BEH C4 analytical column and quantitated using an external calibrant. The LOQ (2 mg/100 g), repeatability (RSD: 2.0-4.8%), recovery (92.1-97.7%), and analytical range (4-193 mg/100 g) all meet the method requirements as stated in SMPR 2020.005 for lactoferrin. The reported single-laboratory validation (SLV) results demonstrate the ability of this lactoferrin method to meet or exceed the method performance requirements to measure soluble, intact, non-denatured bLF in infant and adult nutritional powder formulas. The use of a heparin affinity column to isolate lactoferrin from bovine milk products combined with a selective analytical chromatographic column provides suitable analyte specificity without requiring proprietary equipment or reagents.

Matrix overcompensation calibration: A new strategy to correct matrix effects of carbon origin in multielement analysis by inductively coupled plasma mass spectrometry

BackgroundElemental analysis by inductively coupled plasma mass spectrometry (ICP-MS) may suffer from matrix effects; those caused by organic matrices cannot be corrected by internal standardization. A new strategy, matrix overcompensation calibration (MOC), was developed to correct such matrix effects. ResultsClear fruit juices were diluted 1:50 in 1 % HNO3 (v/v)- 0.5 % HCl (v/v)- 5 % ethanol (v/v). A standard series was treated likewise to construct an external calibration curve. As, Se, Cd, and Pb in juices were determined by dilute-and-shoot ICP-MS based on this MOC strategy. The results agreed with those obtained by standard addition calibration and microwave-aided digestion; data accuracy was validated by spike-recovery studies. SignificanceUnlike standard addition calibration, a single external calibration curve established by MOC can be applicable to juices of diversified fruit, geographical, and manufacturer origins enhancing productivity.

MobiLipid: A Tool for Enhancing CCS Quality Control of Ion Mobility-Mass Spectrometry Lipidomics by Internal Standardization.

Ion mobility-mass spectrometry (IM-MS) offers benefits for lipidomics by obtaining IM-derived collision cross sections (CCS), a conditional property of an ion that can enhance lipid identification. While drift tube (DT) IM-MS retains a direct link to the primary experimental method to derive CCS values, other IM technologies rely solely on external CCS calibration, posing challenges due to dissimilar chemical properties between lipids and calibrants. To address this, we introduce MobiLipid, a novel tool facilitating the CCS quality control of IM-MS lipidomics workflows by internal standardization. MobiLipid utilizes a newly established DTCCSN2 library for uniformly (U)13C-labeled lipids, derived from a U13C-labeled yeast extract, containing 377 DTCCSN2 values. This automated open-source R Markdown tool enables internal monitoring and straightforward compensation for CCSN2 biases. It supports lipid class- and adduct-specific CCS corrections, requiring only three U13C-labeled lipids per lipid class-adduct combination across 10 lipid classes without requiring additional external measurements. The applicability of MobiLipid is demonstrated for trapped IM (TIM)-MS measurements of an unlabeled yeast extract spiked with U13C-labeled lipids. Monitoring the CCSN2 biases of TIMCCSN2 values compared to DTCCSN2 library entries utilizing MobiLipid resulted in mean absolute biases of 0.78% and 0.33% in positive and negative ionization mode, respectively. By applying the CCS correction integrated into the tool for the exemplary data set, the mean absolute CCSN2 biases of 10 lipid classes could be reduced to approximately 0%.

Accuracy of single and mix calibration as method to determine ruminant feedstuffs nutrient utilization using Near-Infrared Reflectance Spectroscopy

Including real-time data nutrient utilization from quick and accurate methods such as NIRS in a ration formulation improves feed efficiency. However, the homogeneity of the feedstuff sample set used in calibration influences its prediction accuracy and application. This study compares the calibration accuracy of ruminant feed nutrient utilization using a single or mixed calibration. Feedstuffs of different origins (Padang (24), Bandung (24), and Bogor (24)) consisting of forages (30) and concentrate (30) were used separately for single calibration or together for mixed calibration. 12 samples were used for external calibration. The spectrum of each sample was collected using FT-NIRS. The calibration used the PLS regression model. Parallel to the NIRS spectrum collection, nutrient utilization was observed using in vitro methods including degradation of dry matter, organic matter, crude protein, rumen pH, VFA, and NH3 concentration. The results showed accuracy of nutrient utilization prediction varied between the parameters observed. Except for VFA, mixed calibration produced better accuracy (R2>0.83 and RPD>2.21) than single calibration. External calibration proved that NIRS prediction was not significantly different from the in vitro results (P > 0.05). It is concluded mixed ruminant feedstuffs database can be used accurately and broadly to estimate nutrient utilization using NIRS.

Development and validation of a risk scoring system to identify patients with lupus nephritis in electronic health record data

ObjectiveAccurate identification of lupus nephritis (LN) cases is essential for patient management, research and public health initiatives. However, LN diagnosis codes in electronic health records (EHRs) are underused, hindering efficient...

Phase external calibration in interferometric microwave radiometer

The receiver error of the interferometric microwave radiometer can be calibrated by injecting the correlated noise into the receiver’s calibration port. However, in the antenna part without the internal calibration link, the residual phase error can be calibrated by an external noise source in an anechoic chamber and the significance of phase external calibration [1][2] is also verified by imaging the anechoic chamber.

农业环境高精度三维点云地图构建方法研究

In agricultural operation scenarios, the diversity of farmland terrain, crops and other forms, as well as uncertain factors such as weather changes and crop growth during agricultural operation, can have an impact on the construction of high-precision maps. To address these challenges and analyze operational scenarios based on the characteristics of agricultural scenarios, this paper proposes a point cloud map construction algorithm for plant point removal and locatability estimation. Based on the existing Simultaneous Localization and Mapping (SLAM) framework, plant point removal and locatability estimation are improved. Firstly, Red, Green, Blue (RGB) images and Near Infrared (NIR) images are fused to identify and remove plant point clouds, preserving effective inter frame matching information, reducing the impact of dynamic points on inter frame matching, and achieving high front-end motion estimation accuracy. Then, the localization estimation method based on learning is used to determine the motion estimation status and determine whether to execute the backend optimization algorithm. Finally, the back-end optimization algorithm based on Factor graph is designed, and the Factor graph, constraint relationship and optimization function are constructed to optimize the pose of all frames. The optimized map construction algorithm reduces the re projection errors between field roads, paths, and crop rows by 10.27%, 20.76%, and 14.36% compared to before optimization. To verify the actual operational effectiveness of the point cloud map construction algorithm, the hardware part of the multi-sensor information collection system was designed, and sensor internal and external parameter calibration were also carried out. A map information collection vehicle was built and field experiments were conducted. The results showed that the positioning error of the point cloud map construction method proposed in this paper is less than 0.5°, and the cumulative error of 30 m translation is less than 12 cm, which meets the actual operational requirements.

Determination of the Nanoscale Silica Mass Fraction by AF4/ICP-MS with Isotope Dilution Analysis Using 29Si-Enriched Silica Nanoparticles.

A methodology based on the use of asymmetrical flow field-flow fractionation (AF4) coupled to ICP-MS with size fraction-targeted isotope dilution analysis (IDA) has been developed, validated, and applied for the first time to determine the mass fraction of nanoscale silica (SiO2). For this purpose, 29Si-enriched SiO2 nanoparticles, to be used as an IDA spike/internal standard, were synthesized and characterized in-house. Double IDA was used to quantify an aqueous suspension of Stöber silica particles of similar characteristics to those of the 29SiO2 nanoparticle (NP) spike using a representative test material of natural Si isotopic composition as the calibrant. For fumed SiO2 NP in a highly complex food matrix, a methodology based on single IDA with AF4/ICP-MS using the same 29SiO2 NP spike was developed and validated. Relative expanded measurement uncertainties (k = 2) of 4% (double IDA) and 8% (single IDA) were achieved for nanoscale silica mass fractions of 5143 and 107 mg kg-1 in water suspension and food matrix, respectively. To assess the accuracy of AF4/ICP-IDMS for the characterization of SiO2 NP in a food matrix, standard addition measurements on samples spiked with Aerosil AF200, also in-house characterized for Si mass fraction, were undertaken, with an average recovery of 95.6 ± 4.1% (RSD, n = 3) obtained. The particle-specific IDA data obtained for both SiO2 NP-containing samples were also compared with that of post-AF4 channel external calibration using inorganic Si standards. The mass fractions obtained by IDA agreed well with those obtained by external calibration within their associated measurement uncertainties.

Video velocity measurement: a two-stage flow velocity prediction method based on deep learning

Abstract Due to the uncertainty in output caused by environmental changes, significant discrepancies are expected between the surface flow velocities predicted using deep learning methods and the instantaneous flow velocities. In this paper, a two-stage deep learning flow velocity measurement algorithm is proposed. During the external calibration process, the upper and lower frames of the recorded water flow video are cyclically traversed to acquire predicted flow velocity values using the deep learning velocity measurement algorithm. Meanwhile, the pixel displacement is obtained using the sparse optical flow tracking method and then post-processed to derive the velocity calibration value and pixel calibration value. During the detection process, the deep learning-predicted flow velocity is internally calibrated using the velocity calibration value and the pixel calibration value to adapt to changes in water flows. Compared with the pre-improved algorithm, the method achieves the minimum root mean square error in five different flow velocity videos and maintains high accuracy when the flow velocity changes rapidly. The obtained results are very promising and can help improve the reliability of video flow rate assessment algorithms.

Wind Turbine Tower State Reconstruction Method Based on the Corner Cut Recursion Algorithm

This study introduces an innovative approach for the reconstruction of wind turbine tower states using a tangential recursion algorithm. The primary objective is to enable real-time monitoring of the operational condition of wind turbine towers. The proposed method is rooted in strain–load theory, which enables the accurate identification of tower load states. The tangential recursion algorithm is utilized to translate the strain data acquired from strategically placed sensors into reconstructed point positions. The subsequent refinement of these positions incorporates considerations of torsional loads and geometric deformations, culminating in the comprehensive and precise reconstruction of the tower’s deformation behavior. Through the use of the OpenFAST V8 simulation software, a thorough analysis is conducted to investigate the load and deformation characteristics of the NREL 5 MW wind turbine tower across diverse operational scenarios. Furthermore, the load conditions corresponding to rated operating circumstances are applied to a finite element model constructed with the lumped mass method. The identification of tower load states and the comprehensive reconstruction of deformation patterns are realized through the extraction of strain data from critical points in the finite element model. The credibility and accuracy of the proposed method are rigorously evaluated by juxtaposing the identification and reconstruction outcomes with the results derived from the OpenFAST simulations and finite element analyses. Notably, the proposed method circumvents the requirement for external auxiliary calibration equipment for the tower, rendering it adaptable to a broader spectrum of operational contexts and making it consistent with unfolding trajectories in wind power advancement.

SCALiR: A Web Application for Automating Absolute Quantification of Mass Spectrometry-Based Metabolomics Data.

Quantitative liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is becoming an important approach for studying complex biological systems but presents several technical challenges that limit its widespread use. Computing metabolite concentrations using standard curves generated from standard mixtures of known concentrations is a labor-intensive process that is often performed manually. Currently, there are few options for open-source software tools that can automatically calculate metabolite concentrations. Herein, we introduce SCALiR (standard curve application for determining linear ranges), a new web-based software tool specifically built for this task, which allows users to automatically transform LC-MS signals into absolute quantitative data (https://www.lewisresearchgroup.org/software). SCALiR uses an algorithm that automatically finds the equation of the line of best fit for each standard curve and uses this equation to calculate compound concentrations from the LC-MS signal. Using a standard mix containing 77 metabolites, we show a close correlation between the concentrations calculated by SCALiR and the expected concentrations of each compound (R2 = 0.99 for a y = x curve fitting). Moreover, we demonstrate that SCALiR reproducibly calculates concentrations of midrange standards across ten analytical batches (average coefficient of variation 0.091). SCALiR can be used to calculate metabolite concentrations either using external calibration curves or by using internal standards to correct for matrix effects. This open-source and vendor agnostic software offers users several advantages in that (1) it requires only 10 s of analysis time to compute concentrations of >75 compounds, (2) it facilitates automation of quantitative workflows, and (3) it performs deterministic evaluations of compound quantification limits. SCALiR therefore provides the metabolomics community with a simple and rapid tool that enables rigorous and reproducible quantitative metabolomics studies.