Novel calibration design improves knowledge transfer across products for the characterization of pharmaceutical bioprocesses.

Modern machine learning has the potential to fundamentally change the way bioprocesses are developed. In particular, horizontal knowledge transfer methods, which seek to exploit data from historical processes to facilitate process development for a new product, provide an opportunity to rethink current workflows. In this work, we first assess the potential of two knowledge transfer approaches, meta learning and one-hot encoding, in combination with Gaussian process (GP) models. We compare their performance with GPs trained only on data of the new process, that is, local models. Using simulated mammalian cell culture data, we observe that both knowledge transfer approaches exhibit test set errors that are approximately halved compared to those of the local models when two, four, or eight experiments of the new product are used for training. Subsequently, we address the question whether experiments for a new product could be designed more effectively by exploiting existing knowledge. In particular, we suggest to specifically design a few runs for the novel product to calibrate knowledge transfer models, a task that we coin calibration design. We propose a customized objective function to identify a set of calibration design runs, which exploits differences in the process evolution of historical products. In two simulated case studies, we observed that training with calibration designs yields similar test set errors compared to common design of experiments approaches. However, the former requires approximately four times fewer experiments. Overall, the results suggest that process development could be significantly streamlined when systematically carrying knowledge from one product to the next.

Determination of Carbohydrate Composition in Lentils Using Near-Infrared Spectroscopy.

Carbohydrates are the main components of lentils, accounting for more than 60% of their composition. Their content is influenced by genetic factors, with different contents depending on the variety. These compounds have not only been linked to interesting health benefits, but they also have a significant influence on the techno-functional properties of lentil-derived products. In this study, the use of near-infrared spectroscopy (NIRS) to predict the concentration of total carbohydrate, fibre, starch, total sugars, fructose, sucrose and raffinose was investigated. For this purpose, six different cultivars of macrosperm (n = 37) and microsperm (n = 43) lentils have been analysed, the samples were recorded whole and ground and the suitability of both recording methods were compared. Different spectral and mathematical pre-treatments were evaluated before developing the calibration models using the Modified Partial Least Squares regression method, with a cross-validation and an external validation. The predictive models developed show excellent coefficients of determination (RSQ > 0.9) for the total sugars and fructose, sucrose, and raffinose. The recording of ground samples allowed for obtaining better models for the calibration of starch content (R > 0.8), total sugars and sucrose (R > 0.93), and raffinose (R > 0.91). The results obtained confirm that there is sufficient information in the NIRS spectral region for the development of predictive models for the quantification of the carbohydrate content in lentils.

Optimizing the feasibility of polyvinyl alcohol-potassium iodine gel for medical dosimeter.

This work aims to improve the post stabilty of reusable potassium iodide hydrogel dosimter. A reusable and low-cost radiochromic dosimeter containing a gel matrix of polyvinyl alcohol, potassium iodide dye, froctose as reducing agent and glutaraldehyde as cross-linking agent was developed for dose calibration in radiotherapy. The gel samples were exposed to different absorbed doses using a medical linear acceleration. UV-vis Spectrophotometry was utilized to investigate the changes in optical-properties of irradiated gels with regard to peak wavelength of 353 nm. The stability of the gel (one of the most limitation of using this dosimeter) was improved significantly by the addition of certain concentrations of dimethyl sulfoxide. The two-dimensional optical imaging system of charge-coupled-device (CCD) camera with a uniform RGB light-emitting-diode (LED) array source was used for diffusion coefficient purpose using two dimensional gel template. The value of diffusion coefficient reported is significant and highly reduced compared with other dosimeters reported in the literatures. Moreover, heating the improved gels to certain temperatures results in resetting their optical properties, which makes it possible to reuse for multiple times.

Inferring the natural history of HPV from global cancer registries: insights from a multi-country calibration.

Human papillomavirus (HPV) is the cause of almost all cases of cervical cancer, a disease that kills some 340,000 women per year. The timeline from initial infection with HPV to the onset of invasive cervical cancer spans decades, and observational studies of this process are limited to settings in which treatment of precancerous lesions was withheld or inadequate. Such studies have been critical for understanding the natural history of HPV. Modeling can shed additional insight on the natural history of HPV, especially across geographical settings with varying prevalence of factors known to affect the host-side immune response to HPV, such as HIV and tobacco use. In this study, we create models for the 30 most populous countries in Sub-Saharan Africa, each with country-specific demographic, and behavioral inputs. We found that it was not possible to fit the data if we assumed that the natural history parameters were exactly identical for all countries, even after accounting for demographic and behavioral differences, but that we could achieve a good fit with the addition of a single immunocompetence parameter for each country. Our results indicate that variation in host immune responses may play a role in explaining the differences in the burden of cervical cancer between countries, which in turn implies a greater need for more geographically diverse data collection to understand the natural history of HPV.

Avoidance of specific calibration sessions in motor intention recognition for exoskeleton-supported rehabilitation through transfer learning on EEG data.

Exoskeleton-based support for patients requires the learning of individual machine-learning models to recognize movement intentions of patients based on the electroencephalogram (EEG). A major issue in EEG-based movement intention recognition is the long calibration time required to train a model. In this paper, we propose a transfer learning approach that eliminates the need for a calibration session. This approach is validated on healthy subjects in this study. We will use the proposed approach in our future rehabilitation application, where the movement intention of the affected arm of a patient can be inferred from the EEG data recorded during bilateral arm movements enabled by the exoskeleton mirroring arm movements from the unaffected to the affected arm. For the initial evaluation, we compared two trained models for predicting unilateral and bilateral movement intentions without applying a classifier transfer. For the main evaluation, we predicted unilateral movement intentions without a calibration session by transferring the classifier trained on data from bilateral movement intentions. Our results showed that the classification performance for the transfer case was comparable to that in the non-transfer case, even with only 4 or 8 EEG channels. Our results contribute to robotic rehabilitation by eliminating the need for a calibration session, since EEG data for training is recorded during the rehabilitation session, and only a small number of EEG channels are required for model training.

Calib-RT: an open source python package for peptide retention time calibration in DIA mass spectrometry data.

MOTIVATION: The data independent acquisition (DIA) mass spectrometry (MS) method is increasingly popular in the field of proteomics. But the loss of the correspondence between peptide ions and their spectra in DIA makes the identification challenging. One effective approach to reduce false positive identification is to calculate the deviation between the peptide's estimated retention time (RT) and measured RT. During this process, scaling the spectral library RT into the estimated RT, known as the RT calibration, is a prerequisite for calculating the deviation. Currently, within the DIA algorithm ecosystem, there is a lack of engine-independent and readily usable RT calibration toolkits. RESULTS: In this work, we introduce Calib-RT, a RT calibration method tailored to the characteristics of RT data. This method can achieve the nonlinear calibration across various data scales and tolerate a certain level of noise interference. Calib-RT is expected to enrich the open source DIA algorithm toolchain and assist in the development of DIA identification algorithms. AVAILABILITY AND IMPLEMENTATION: Calib-RT is released as an open source software under the MIT license and can be installed from PyPi as a python module. The source code is available on GitHub at https://github.com/chenghui03/Calib_RT.

Determination of melon seed physical parameters and calibration of discrete element simulation parameters.

To improve the accuracy of the Hami melon discrete element model, the parameters of the Hami melon seed discrete element model were calibrated by combining practical experiments and simulation tests. The basic physical parameters of Hami melon seeds were obtained through physical experiments, including triaxial size, 100-grain mass, moisture content, density, Poisson's ratio, Young's modulus, shear modulus, angle of repose, suspension speed and various contact parameters. Taking the repose angle of seed simulation as an index, the parameters of each simulation model were significantly screened by the Plackett-Burman test. The results showed that the recovery coefficient, static friction coefficient and rolling friction coefficient of Hami melon seeds had significant effects on repose angle. Based on the steepest climbing test and quadratic regression orthogonal rotation combination test, it was determined that the significant order of the influence of various contact parameters on the angle of repose was static friction coefficient, collision recovery coefficient, and rolling friction coefficient. The optimal parameter combination was obtained through the mathematical regression model between the angle of repose and various contact parameters, namely, the collision recovery coefficient of Hami melon seeds was 0.518, the static friction coefficient of Hami melon seeds was 0.585 and the rolling friction coefficient of Hami melon seeds was 0.337. Under this condition, three static seed-dropping experiments and dynamic rolling accumulation experiments were carried out. The average simulated angle of repose was 31.93°, and the relative error with the actual value was only 1.71%. The average simulated rolling accumulation angle was 51.98°, and the relative error with the actual value was only 1.92%.

[Adequate assessment of linear function of calibration according to GOST R ISO 11095-2007 for ethanol mass concentration measurement]. / Otsenka adekvatnosti lineinoi funktsii kalibrovki po GOST R ISO 11095-2007 dlya izmereniya massovoi kontsentratsii etanola.

OBJECTIVE: To assess the adequacy of linear function of calibration according to GOST R ISO 11095-2007 for ethanol mass concentration measurement using internal reference materials (RMs). MATERIAL AND METHODS: An experiment on calibration in accordance with the GOST R ISO 11095-2007 National standard of the RF was carried out using internal RMs, namely aqueous solutions of ethanol at different concentrations. Measurements were performed for two subbands of ethanol concentrations at RMs: 0.15-1.05 and 1.0-7.0 mg/ml - according to the certified methodology. RESULTS: The graphs of the calibration's functions based on experimental data are consistent with the assumption of the calibration function's linearity, as well as the assumption of the standard deviation's constance of residues is equitable for two subbands of RMs. CONCLUSION: Proven linear models in the calibration experiment may be recommended for use in the ethanol mass concentration measurement.

Phantomless calibration of CT scans for hip fracture risk prediction in silico: Comparison with phantom-based calibration.

Finite element models built from quantitative computed tomography images rely on element-wise mapping of material properties starting from Hounsfield Units (HU), which can be converted into mineral densities upon calibration. While calibration is preferably carried out by scanning a phantom with known-density components, conducting phantom-based calibration may not always be possible. In such cases, a phantomless procedure, where the scanned subject's tissues are used as a phantom, is an interesting alternative. The aim of this study was to compare a phantom-based and a phantomless calibration method on 41 postmenopausal women. The proposed phantomless calibration utilized air, adipose, and muscle tissues, with reference equivalent mineral density values of -797, -95, and 38 mg/cm3, extracted from a previously performed phantom-based calibration. A 9-slice volume of interest (VOI) centred between the femoral head and knee rotation centres was chosen. Reference HU values for air, adipose, and muscle tissues were extracted by identifying HU distribution peaks within the VOI, and patient-specific calibration was performed using linear regression. Comparison of FE models calibrated with the two methods showed average relative differences of 1.99% for Young's modulus1.30% for tensile and 1.34% for compressive principal strains. Excellent correlations (R2 > 0.99) were identified for superficial maximum tensile and minimum compressive strains. Maximum normalised root mean square relative error (RMSRE) values settled at 4.02% for Young's modulus, 2.99% for tensile, and 3.22% for compressive principal strains, respectively. The good agreement found between the two methods supports the adoption of the proposed methodology when phantomless calibration is needed.

Evaluation of a Reference-Free Collision Cross Section Calibration Strategy for Proteomics Using SLIM-Based High-Resolution Ion Mobility Spectrometry-Mass Spectrometry.

Ion mobility spectrometry (IMS) is a gas-phase analytical technique that separates ions with different sizes and shapes and is compatible with mass spectrometry (MS) to provide an additional separation dimension. The rapid nature of the IMS separation combined with the high sensitivity of MS-based detection and the ability to derive structural information on analytes in the form of the property collision cross section (CCS) makes IMS particularly well-suited for characterizing complex samples in -omics applications. In such applications, the quality of CCS from IMS measurements is critical to confident annotation of the detected components in the complex -omics samples. However, most IMS instrumentation in mainstream use requires calibration to calculate CCS from measured arrival times, with the most notable exception being drift tube IMS measurements using multifield methods. The strategy for calibrating CCS values, particularly selection of appropriate calibrants, has important implications for CCS accuracy, reproducibility, and transferability between laboratories. The conventional approach to CCS calibration involves explicitly defining calibrants ahead of data acquisition and crucially relies upon availability of reference CCS values. In this work, we present a novel reference-free approach to CCS calibration which leverages trends among putatively identified features and computational CCS prediction to conduct calibrations post-data acquisition and without relying on explicitly defined calibrants. We demonstrated the utility of this reference-free CCS calibration strategy for proteomics application using high-resolution structures for lossless ion manipulations (SLIM)-based IMS-MS. We first validated the accuracy of CCS values using a set of synthetic peptides and then demonstrated using a complex peptide sample from cell lysate.

Evaluating the Improvement of Blend Potency Measurements in the Feed Frame of a Rotary Tablet Press Using Combined NIR and Raman Spectroscopy.

This study investigated the added value of combining both near-infrared (NIR) and Raman spectroscopy into a single NIRaman Combi Fiber Probe for in-line blend potency determination in the feed frame of a rotary tablet press. A five-component platform formulation was used, containing acetylsalicylic acid as the Active Pharmaceutical Ingredient (API). Calibration models for the determination of 1 and 5%w/w label claim tablets were developed using NIR and Raman spectra of powder blends ranging from 0.75 to 1.25%w/w and 3.75 to 6.25%w/w API, respectively. Step-change experiments with deliberate 10% deviation steps from the label claims were performed, from which the collected spectra were used for model validation. For model development and validation, low-level data fusion was explored through concatenation of preprocessed NIR and Raman spectra. Mid-level data fusion was also evaluated, based on extracted features of the preprocessed data. Herewith, score vectors were extracted by transforming preprocessed spectra through Principal Component Analysis, followed by critical feature selection through Elastic Net Regression. Partial Least Squares regression was applied to regress singular, low-level or mid-level fused data versus blend potency. It could be concluded that irrespective of the data fusion technique, an increase in Step-Change Sensitivity (SCS) and decrease in Root Mean Squared Error (RMSE) was observed when predicting the 5%w/w step-change experiment. For the prediction of the 1%w/w step-change experiment, no added benefit with regard to SCS and RMSE was observed due to the addition of the noisy NIR spectra.

A portable dry film FTIR instrument for industrial food and bioprocess applications.

The main objective of this study was to design, build, and test a compact, multi-well, portable dry film FTIR system for industrial food and bioprocess applications. The system features dry film sampling on a circular rotating disc comprising 31 wells, a design that was chosen to simplify potential automation and robotic sample handling at a later stage. Calibration models for average molecular weight (AMW, 200 samples) and collagen content (68 samples) were developed from the measurements of industrially produced protein hydrolysate samples in a controlled laboratory environment. Similarly, calibration models for the prediction of lactate content in samples from cultivation media (59 samples) were also developed. The portable dry film FTIR system showed reliable model characteristics which were benchmarked with a benchtop FTIR system. Subsequently, the portable dry film FTIR system was deployed in a bioprocessing plant, and protein hydrolysate samples were measured at-line in an industrial environment. This industrial testing involved building a calibration model for predicting AMW using 60 protein hydrolysate samples measured at-line using the portable dry film FTIR system and subsequent model validation using a test set of 26 samples. The industrial calibration in terms of coefficient of determination (R2 = 0.94), root mean square of cross-validation (RMSECV = 194 g mol-1), and root mean square of prediction (RMSEP = 162 g mol-1) demonstrated low prediction errors as compared to benchtop FTIR measurements, with no statistical difference between the calibration models of the two FTIR systems. This is to the authors' knowledge the first study for developing and employing a portable dry film FTIR system in the enzymatic protein hydrolysis industry for successful at-line measurements of protein hydrolysate samples. The study therefore suggests that the portable dry film FTIR instrument has huge potential for in/at-line applications in the food and bioprocessing industries.

Temporally Local Weighting-Based Phase-Locked Time-Shift Data Augmentation Method for Fast-Calibration SSVEP-BCI.

Various training-based spatial filtering methods have been proposed to decode steady-state visual evoked potentials (SSVEPs) efficiently. However, these methods require extensive calibration data to obtain valid spatial filters and temporal templates. The time-consuming data collection and calibration process would reduce the practicality of SSVEP-based brain-computer interfaces (BCIs). Therefore, we propose a temporally local weighting-based phase-locked time-shift (TLW-PLTS) data augmentation method to augment training data for calculating valid spatial filters and temporal templates. In this method, the sliding window strategy using the SSVEP response period as a time-shift step is to generate the augmented data, and the time filter which maximises the temporally local covariance between the original template signal and the sine-cosine reference signal is used to suppress the temporal noise in the augmented data. For the performance evaluation, the TLW-PLTS method was incorporated with state-of-the-art training-based spatial filtering methods to calculate classification accuracies and information transfer rates (ITRs) using three SSVEP datasets. Compared with state-of-the-art training-based spatial filtering methods and other data augmentation methods, the proposed TLW-PLTS method demonstrates superior decoding performance with fewer calibration data, which is promising for the development of fast-calibration BCIs.

A nano-level assay of tizanidine using the fluorogenic character of benzofurazan derivative: Application to plasma, tablets, and content homogeneity evaluation.

People frequently administer Tizanidine (TIZ) to treat spasticity resulting from diseases like multiple sclerosis or spinal cord injuries. It also helps prevent muscle spasms. It helps to relax and release tense and stiff muscles by inhibiting specific nerve signals in the brain and spinal cord. The technique employed in this study made use of the unique ability of benzofurazan to confer fluorescent character when reacted with TIZ at specific conditions. This fluorogenic property was harnessed to evolve a remarkably sensitive, affordable, and selective method to quantify TIZ. The resulting yellow fluorescent product was observedat a wavelength beam of 532.9 nm, and an excitation wavelength beam of 474.9 nm was applied. By looking at the response across the TIZ concentration, the calibration chart's linearity was assessed in the range of 40-500 ng/mL. By computation, the approach's detection level (LOD) was determined to be 11.9 ng/mL, while the quantitation level was approximated to be 36 ng/mL. All pertinent factors impacting the strategy's efficacy were thoroughly inspected and adjusted accordingly. The proposed strategy was validated following the guidelines outlined by the ICH. The outcomes confirmed the method's capability for the accurate quantifying of TIZ in tablets, spiked plasma, and pharmaceutical assessing content uniformity.

Quantification of antibiotics in food by octahedral gold-silver nanocages-based SERS sensor coupling multivariate calibration.

The abuse of antibiotics has caused gradually increases drug-resistant bacterial strains that pose health risks. Herein, a sensitive SERS sensor coupled multivariate calibration was proposed for quantification of antibiotics in milk. Initially, octahedral gold-silver nanocages (Au@Ag MCs) were synthesized by Cu2O template etching method as SERS substrates, which enhanced the plasmonic effect through sharp edges and hollow nanostructures. Afterwards, five chemometric algorithms, like partial least square (PLS), uninformative variable elimination-PLS (UVE-PLS), competitive adaptive reweighted sampling-PLS (CARS-PLS), random frog-PLS (RF-PLS), and convolutional neural network (CNN) were applied for TTC and CAP. RF-PLS performed optimally for TTC and CAP (Rc = 0.9686, Rp = 0.9648, RPD = 3.79 for TTC and Rc = 0.9893, Rp = 0.9878, RPD = 5.88 for CAP). Furthermore, the detection limit of 0.0001 µg/mL for both TTC and CAP was obtained. Finally, satisfactory (p > 0.05) results were obtained with the standard HPLC method. Therefore, SERS combined RF-PLS could be applied for fast, nondestructive sensing of TTC and CAP in milk.

Spectrophotometric determination of celecoxib and tramadol in the new approved formulated dosage form using principle component regression assistive model.

Celecoxib and tramadol have been combined in a novel FDA-approved medication to address acute pain disorders requiring opioid treatment when other analgesics proved either intolerable or ineffective. The absorbance spectra of celecoxib and tramadol exhibit significant overlap, posing challenges for their individual quantification. This study introduces a spectrophotometric quantification approach for celecoxib and tramadol using a principle component regression assistive model to assist resolving the overlapped spectra and quantifying both drugs in their binary mixture. The model was constructed by establishing calibration and validation sets for the celecoxib and tramadol mixture, employing a five-level, two-factor experimental design, resulting in 25 samples. Spectral data from these mixtures were measured and preprocessed to eliminate noise in the 200-210 nm range and zero absorbance values in the 290-400 nm range. Consequently, the dataset was streamlined to 81 variables. The predicted concentrations were compared with the known concentrations of celecoxib and tramadol, and the errors in the predictions were evidenced calculating root mean square error of cross-validation and root mean square error of prediction. Validation results demonstrate the efficacy of the models in predicting outcomes; recovery rates approaching 100 % are demonstrated with relative root mean square error of prediction (RRMSEP) values of 0.052 and 0.164 for tramadol and celecoxib, respectively. The selectivity was further evaluated by quantifying celecoxib and tramadol in the presence of potentially interfering drugs. The model demonstrated success in quantifying celecoxib and tramadol in laboratory-prepared tablets, producing metrics consistent with those reported in previously established spectrophotometric methods.

Protocol for measuring labile cytosolic Zn2+ using an in situ calibration of a genetically encoded FRET sensor.

Zinc (Zn2+) plays roles in structure, catalysis, and signaling. The majority of cellular Zn2+ is bound by proteins, but a fraction of total Zn2+ exists in a labile form. Here, we present a protocol for measuring labile cytosolic Zn2+ using an in situ calibration of a genetically encoded Förster resonance energy transfer (FRET) sensor. We describe steps for producing buffered Zn2+ solutions for performing an imaging-based calibration and analyzing the imaging data generated to determine labile Zn2+ concentration in single cells. For complete details on the use and execution of this protocol, please refer to Rakshit and Holtzen et al.1.

Test Platform for Developing New Optical Position Tracking Technology towards Improved Head Motion Correction in Magnetic Resonance Imaging.

Optical tracking of head pose via fiducial markers has been proven to enable effective correction of motion artifacts in the brain during magnetic resonance imaging but remains difficult to implement in the clinic due to lengthy calibration and set up times. Advances in deep learning for markerless head pose estimation have yet to be applied to this problem because of the sub-millimetre spatial resolution required for motion correction. In the present work, two optical tracking systems are described for the development and training of a neural network: one marker-based system (a testing platform for measuring ground truth head pose) with high tracking fidelity to act as the training labels, and one markerless deep-learning-based system using images of the markerless head as input to the network. The markerless system has the potential to overcome issues of marker occlusion, insufficient rigid attachment of the marker, lengthy calibration times, and unequal performance across degrees of freedom (DOF), all of which hamper the adoption of marker-based solutions in the clinic. Detail is provided on the development of a custom moiré-enhanced fiducial marker for use as ground truth and on the calibration procedure for both optical tracking systems. Additionally, the development of a synthetic head pose dataset is described for the proof of concept and initial pre-training of a simple convolutional neural network. Results indicate that the ground truth system has been sufficiently calibrated and can track head pose with an error of <1 mm and <1°. Tracking data of a healthy, adult participant are shown. Pre-training results show that the average root-mean-squared error across the 6 DOF is 0.13 and 0.36 (mm or degrees) on a head model included and excluded from the training dataset, respectively. Overall, this work indicates excellent feasibility of the deep-learning-based approach and will enable future work in training and testing on a real dataset in the MRI environment.

A revised radiocarbon calibration curve 350-250 BCE impacts high-precision dating of the Kyrenia Ship.

The Kyrenia Ship, found off the north coast of Cyprus, is a key vessel in the history of scientific underwater excavations and in the history of Greek shipbuilding. The first volume of the site's final publication appeared in 2023 and provides detailed archaeological information tightly constraining the dating of the ship. A very specific date range is proposed: ca. 294-290 BCE, but is based on a less than certain reading of one coin recovered from the ship. While there is clear benefit to finding high-precision dates for the Kyrenia Ship and its rich assemblage using independent scientific dating (combined with Bayesian chronological modeling), efforts to do so proved more challenging and complex than initially anticipated. Strikingly, extensive radiocarbon dating on both wooden materials from the ship and on short-lived contents from the final use of the ship fail to offer dates using the IntCal20 calibration curve-the current Northern Hemisphere radiocarbon calibration curve at the time of writing-that correspond with the archaeological constraints. The issue rests with a segment of IntCal20 ca. 350-250 BCE reliant on legacy pre-AMS radiocarbon data. We therefore measured new known-age tree-ring samples 350-250 BCE, and, integrating another series of new known-age tree-ring data, we obtained a redefined and more accurate calibration record for the period 433-250 BCE. These new data permit a satisfactory dating solution for the ship and may even indicate a date that is a (very) few years more recent than current estimations. These new data in addition confirm and only very slightly modify the dating recently published for the Mazotos ship, another Greek merchant ship from the southern coast of Cyprus. Our work further investigated whether ship wood samples impregnated with a common preservative, polyethylene glycol (PEG), can be cleaned successfully, including a known-age test.

[Verification of Response Uncertainty in Electrometers through Cross-Comparison with a Novel Current Source: A Comparative Study with Guidelines for Electrometers Used in Radiation Therapy Dosimeters].

BACKGROUND: A new quality assurance and control method for electrometers using a new current source, different from the method published in the guidelines for electrometers, has been reported. This current source uses dry batteries and exhibits excellent performance in terms of voltage, temperature, and time characteristics. The electrometer sensitivity coefficient can be calculated by comparing the sensitivity of one electrometer with that of another on the electrometer calibration coefficient that has been calibrated by a calibration laboratory in advance in both methods. The guideline method requires two or more sets of ionization chambers and electrometers in the facility. In contrast, our method does not use ionization chambers; therefore, the sensitivity ratio of the electrometer can be measured in any facility. This study compared the uncertainty of the electrometer sensitivity factor calculated using the new current source method (current method) with that calculated using a linear accelerator (LINAC) and ionization chambers (LINAC method) described in the electrometer guidelines. METHOD: In this study, we used a current source that we invented previously by Kawaguchi Electric Works in Japan. The sensitivity ratios of the electrometers were measured with three manufacture's electrometers. The electrometer sensitivity factor was calculated by multiplying the electrometer calibration coefficient. The ionization chamber was 30013 (PTW), and the current source was the current obtained from 10 MV TrueBeam X-rays under calibration conditions. The mean value, standard deviation, and coefficient of variation were calculated. The time required to set up the ionization chamber for calculating the sensitivity ratio of the electrometer was also measured. The accuracy was confirmed by calculating the expanded uncertainty of the electrometer sensitivity coefficients. RESULTS: The LINAC method had a maximum coefficient of variation of 0.072%. The gross time of the LINAC method was approximately 110 min. The current method had a maximum coefficient of variation of 0.0055% and took less than half the time taken by the LINAC method (35 min) because there was no waiting time for the ionization chamber to be set up and the applied voltage to stabilize under calibration conditions. The expanded uncertainties of the electrometer calibration coefficients were 0.36% and 0.36%, respectively. CONCLUSION: The new cross-comparison method for electrometer sensitivity factors using a current source is more efficient and useful than the linear accelerator method described in the guidelines; furthermore, this method ensured accuracy for quality assurance and control of electrometers.