Calibration Samples Research Articles

Assessment of the possibility of modeling nonlinear multivariable calibration function using artificial neural networks

The use of artificial neural networks (ANNs) is considered justified when studying the problems that do not have a generally accepted solution algorithm. One of such problems in X-ray fluorescence analysis (XRF) is a control of the metal content in atmospheric air and air of the working area. Determination of the calibration characteristics is raveled by the lack of standard samples of the composition of aerosols collected on the filter. To solve this problem, synthetic calibration samples (CS) were manufactured as a thin organic film containing a powder material of the known chemical composition. The weight of the film samples varied within a range of 40 – 155 mg to simulate different aerosol loading of the filters and the content of components in them changed 20 – 200 times which corresponds to the samples of real aerosols. The possibility of modeling a nonlinear calibration multivariable function using artificial neural networks was evaluated in analysis of 38 film calibration samples (from 40 to 100 mg). The structure of the neural network, activation functions, learning algorithms have been investigated. Modeling was performed using an academic version of the BaseGroup Deductor analytical platform. It is shown that implementation of the back propagation of errors leads to much higher values of the error of analysis compared to the error of the regression calibration functions, whereas the Resilient Propagation algorithm provides the smallest values of the error of vanadium determination (Sr) in the calibration samples of aerosols. The range of low content of the elements in the training set is determined with a greater error compared to high content range, and therefore, the sigmoid activation function leads to unsatisfactory accuracy of the analysis results, and preference should be given to hyperbolic tangent (tanh).

Rapid determination of diesel fuel properties by near-infrared spectroscopy

The properties of middle distillates, which form diesel fuels, must be monitored during production according to the specific requirements established by standard analytical test methods. Although these test methods are accurate and well accepted, they are time-consuming and expensive. Hence, the best solution is to apply a method that reduces investment, maintenance costs, and time.The aim of this study was to determine seven properties of middle distillates (kinematic viscosity, cold filter plugging point, pour point, contents of sulfur, mono-, di-, and poly-aromatics) using rapid and inexpensive method, near-infrared (NIR) spectroscopy combined with chemometrics multivariate calibrations and the partial least squares (PLS) algorithm. The root mean square error of calibration (RMSEC) and root mean square error of cross-validation (RMSECV) were applied as basic chemometric diagnostic tools to optimize the NIR model predictions. We confidently predicted satisfactory results: the calibration samples were equally distributed along the line of the NIR models and observed a significant correlation between the reference values obtained by the standard methods and the calculated values by the NIR model. Therefore, NIR spectroscopy is an efficient tool for simultaneously determining several diesel fuel properties, which makes it an excellent option for online monitoring during fuel production.

Accuracy of stereolithography printed alumina with digital light processing

Digital light processing (DLP) stereolithography was used to prepare layers and samples for dimensional calibration from commercial alumina slurries. Single-layer squares were studied to understand the penetration depth and curing behavior, and samples with varying curing time and intensity were printed and sintered. Fourier-transform infrared spectroscopy (FTIR) of the squares was performed to measure the relative amount of curing based on the change of the bond transparency of the polymer during various printing conditions. X-ray computed tomography (XCT) scans were performed after printing of squares and parts as well as after sintering parts. The morphologies and structures of the squares and parts were studied after printing and after sintering. The dimensions were measured, and the differences before and after sintering are reported for the various printing conditions. The study shows how FTIR can monitor curing of printed parts, and dimensional accuracy of 0.20 mm can be achieved.

Multicomponent Measurement of Respirable Quartz, Kaolinite and Coal Dust using Fourier Transform Infrared Spectroscopy (FTIR): A Comparison Between Partial Least Squares and Principal Component Regressions.

Exposure to respirable crystalline silica (RCS) is potentially hazardous to the health of thousands of workers in Great Britain. Both X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy can be used to measure RCS to assess exposures. The current method outlined in the Health and Safety Executive's (HSE) Methods for the Determination of Hazardous Substances (MDHS) guidance series is 'MDHS 101 Crystalline silica in respirable airborne dust - Direct-on-filter analyses by infrared spectroscopy or x-ray'. This describes a procedure for the determination of time-weighted average concentrations of RCS either as quartz or cristobalite in airborne dust. FTIR is more commonly employed because it is less expensive, potentially portable and relatively easy to use. However, the FTIR analysis of RCS is affected by spectral interference from silicates. Chemometric techniques, known as Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR), are two computational processes that have the capability to remove spectral interference from FTIR spectra and correlate spectral features with constituent concentrations. These two common chemometric processes were tested on artificial mixtures of quartz and kaolinite in coal dust using the same commercially available software package. Calibration, validation and prediction samples were prepared by collecting aerosols of these dusts onto polyvinylchloride (PVC) filters using a Safety in Mines Personal Dust Sampler (SIMPEDS) respirable cyclone. PCR and PLSR analyses were compared when processing the same spectra. Good correlations between the target values, measured using XRD, were obtained for both the PCR and PLSR models e.g. 0.98-0.99 (quartz), 0.98-0.98 (kaolinite) and 0.96-0.97 (coal). The level of agreement between PCR and PLSR was within the 95% confidence value for each analyte. Slight differences observed between predicted PCR and PLSR values were due to the number of optimal principal components applied to each chemometric process. The presence of kaolinite in these samples caused an 18% overestimation of quartz, for the FTIR, when following MDHS 101 without a chemometric method. Chemometric methods are a useful approach to obtain interference-free results for the measurement of RCS from some workplace environments and to provide a multicomponent analysis to better characterise exposures of workers.

Development and validation of an HPLC-MS/MS method for the quantification of the anti-leishmanial drug miltefosine in human skin tissue

Miltefosine is the only oral drug approved for the treatment of various clinical presentations of the neglected parasitic disease leishmaniasis. In cutaneous leishmaniasis and post-kala-azar dermal leishmaniasis, Leishmania parasites reside and multiply in the dermis of the skin. As miltefosine is orally administered and this drug is currently studied for the treatment of these skin-related types of leishmaniasis, there is an urgent need for an accurate assay to determine actual miltefosine levels in human skin tissue to further optimize treatment regimens through target-site pharmacokinetic studies. We describe here the development and validation of a sensitive method to quantify miltefosine in 4-mm human skin biopsies utilizing high-performance liquid chromatography coupled to tandem mass spectrometry. After the skin tissues were homogenized overnight by enzymatic digestion using collagenase A, the skin homogenates were further processed by protein precipitation and phenyl-bonded solid phase extraction. Final extracts were injected onto a Gemini C18 column using alkaline eluent for separation and elution. Detection was performed by positive ion electrospray ionization followed by a quadrupole – linear ion trap mass spectrometer, using deuterated miltefosine as an internal standard. The method was validated over a linear calibration range of 4–1000 ng/mL (r2 ≥ 0.9996) using miltefosine spiked digestion solution for calibration and quality control samples. Validation parameters were all within internationally accepted criteria, including intra- and inter-assay accuracies and precisions within± 15% and ≤ 15% (within± 20% and ≤ 20% at the lower limit of quantitation). There was no significant matrix effect of the human skin tissue matrix and the recovery for miltefosine, and internal standard were comparable. Miltefosine in human skin tissue homogenates was stable during the homogenization incubation (37 °C,± 16 h) and after a minimum of 10 days of storage at − 20 °C after the homogenization process. With our assay we could successfully detect miltefosine in skin biopsies from patients with post-kala azar dermal leishmaniasis who were treated with this drug in Bangladesh.

Development and initial validation of ability levels to interpret pediatric spinal cord injury activity measure and pediatric measure of participation scores.

To develop ability levels and descriptions to interpret Pediatric Spinal Cord Injury Activity Measure (PEDI-SCI AM) and Pediatric Measure of Participation (PMoP) scores. Mixed-methods approach to 1.) create item maps using PEDI-SCI AM and PMoP calibration data; 2.) employ bookmarking methods and an iterative consensus process including semi-structured interviews, focus groups and surveys to establish PEDI-SCI AM and PMoP level score cut-points and descriptions; 3.) use calibration sample data to examine mean score differences across levels [analyses of variance (ANOVAs)] and assess the sample distribution (%) across levels; 4.) repeat in a separate validation sample; 5.) develop level descriptions. Throughout the mixed methods approach, the sample participants include children with spinal cord injury (SCI), parents of children with SCI, and professionals who work with children with SCI. Four or five ability levels were identified for each PEDI-SCI AM and PMoP domain along with descriptions for each level. ANOVA results revealed significant overall differences for level mean scores and pairwise comparisons (p < 0.05). Consensus (>80%) was achieved for all PEDI-SCI AM and some PMoP level descriptions. PEDI-SCI AM and PMoP score cut-points identify different levels of activity and participation among children with SCI. These levels and descriptions provide clinical relevance for PEDI-SCI and PMoP numeric scores.

Quantitative amplitude-modulation scanning Kelvin probe microscopy via the second eigenmode excitation

Amplitude modulation scanning Kelvin probe microscopy (AM-SKPM) is widely used to measure the contact potential difference (CPD) between probe and samples in ambient or dry inert atmosphere. However, AM-SKPM is generally considered quantitatively inaccurate due to crosstalk between the cantilever and the sample. Here we demonstrate that the accuracy of AM-SKPM-based CPD measurements is drastically improved by exciting the SKPM probe at its second eigenmode. In the second eigenmode of oscillation, there exists a stationary node at the cantilever towards its free end, across which the displacement bears opposite signs; therefore driving the SKPM probe at its second eigenmode helps to partially cancel the virtual work done by the cantilever and reduce the crosstalk effect. The improvement in accuracy is experimentally confirmed with interdigitating electrodes calibration samples as well as practical samples such as the cross-section of wafer-bonded GaAs/GaN heterojunction.

The Operational Inflight Radiometric Uniform Calibration of a Directional Polarimetric Camera

The directional polarimetric camera (DPC) on-board the GF-5A satellite is designed for atmospheric or water color detection, which requires high radiometric accuracy. Therefore, in-flight calibration is a prerequisite for its inversion application. For large field optical sensors, it is very challenging to ensure the consistency of radiation detection in the whole field of view in the space environment. Our work proposes a vicarious in-flight calibration method based on sea non-equipment sites (visible bands) and land non-equipment sites (all bands). Combined with environmental parameters and radiation transmission calculations, we evaluated the radiation detection accuracy of the 0° to 60° view zenith angle of the DPC in each band. Our calibration method is based on the single-day normalized radiance data measured by the DPC. Through data selection, enough calibration samples can be obtained in a single day (the number of desert samples is more than 5000, and the number of calibration samples of the ocean is more than 2.8×106). The measurements are compared with the simulation of 6SV VRT code or look-up tables. The massive amount of data averages the uncertainty of a single-point calculation. Although the uncertainty of a single sample is significant, the final fitting of the curve of the variation in the radiometric calibration coefficient with the observation angle can still keep the root mean squared error at approximately 2–3% or even lower, and for visible bands, the calibration results for both ocean sites and desert sites are in good agreement regarding the non-uniformity of the sensor.

Evaluation of protein and amino acid intake estimates from the EPIC dietary questionnaires and 24-h dietary recalls using different food composition databases

Background and aimsThis study aimed to expand the European Prospective Investigation into Cancer and Nutrition (EPIC) nutrient database (ENDB) by adding amino acid (AA) values, using the U.S. nutrient database (USNDB). Additionally, we aimed to evaluate these new protein and AA intake estimates from the EPIC dietary questionnaires (DQ) and 24-h dietary recalls (24-HDR) using different matching procedures. Methods and resultsDietary energy, protein and AA intakes were assessed via DQ and 24-HDR by matching with the USNDB food composition table. Energy and protein intakes calculated using USNDB matching were compared with those calculated using ENDB, that uses country specific food composition tables. Pearson correlations, Cohen's weighted kappa statistic and Bland–Altman plots were used to compare data resulting from USNDB matching with our reference from ENDB matching.Very high correlations were found when comparing daily energy (r = 0.99) and dietary protein intakes (r = 0.97) assessed via USNDB with those obtained via ENDB (matching for DQ and 24-HDR). Significant positive correlations were also found with energy and protein intakes acquired via 24-HDRs in the EPIC calibration sample. ConclusionVery high correlations between total energy and protein intake obtained via the USDA matching and those available in ENDB suggest accuracy in the food matching. Individual AA have been included in the extended EPIC Nutrient database that will allow important analyses on AA disease prospective associations in the EPIC study.

An uncertainty sampling strategy based model updating method for soluble solid content and firmness prediction of apples from different years

Visible/near-infrared spectroscopy combined with chemometric methods has been widely used in fruit quality detection. In order to ensure the performance of the prediction model, the calibration samples used to train model should cover the range of variability anticipated in prediction samples. However, this requirement is difficult to meet in practice, especially, for quality detection of fruit from cross-years with variational cultivation conditions, climate conditions, as well as growing management. In this study, a model updating method based on uncertainty sampling strategy was proposed to accommodate the sample from different periods. The proposed method firstly selected feature wavebands to form new feature space by using PLS projection analysis (PLS-P) algorithm. The representative samples which have high feature similarity to updating samples were then selected from original calibration set and used to train the initial partial least squares regression (PLSR) model. The uncertainty sampling strategy combined spectral similarity and predicted value from initial PLSR model was applied to obtain the samples with most informative in updating set, these samples were added to the training set. Finally, the PLSR model was updated iteratively until the demand is met. Three cultivars of apples, namely, ‘Jonagold’, ‘Golden Delicious’ and ‘Red Delicious’, harvested in 2009 and 2010 were used for evaluating the performance of the proposed method. Compared with random sampling (RS), traditional Kennard-Stone (KS) and joint x-y distances (SPXY), the proposed method achieved the best performance. It demonstrated that the proposed updating method is an effective way to improve the prediction accuracy of samples from cross-years.

Quantification via X‐ray fluorescence analysis of oxygen in the surface layer of a Si‐sphere used as a new mass standard

The distribution of oxygen present in the surface layer of the Si‐sphere used as new mass standard is measured and quantified using the combined X‐ray fluorescence (XRF)/X‐ray photoelectron spectroscopy (XPS) surface analysis system in the Center for Measurement Standards, Industrial Technology Research Institute (CMS/ITRI). A radiometric calibration of the X‐ray source is not possible because the spectral distribution of the X‐ray tube used is complex and not stable over the long term. Hence, the quantification of oxygen on the Si‐sphere is based on a calibration curve that establishes a correlation between the mass deposition of oxygen from the calibration samples qualified by PTB and the ratio of the oxygen fluorescence to silicon RRS (resonant Raman scattering) intensities in the in‐house system. This paper presents the methodology for and the results of an oxygen quantification performed using the combined XRF/XPS surface analysis system. With a relative uncertainty of less than 10%, the average mass deposition of oxygen on the Si‐sphere was 133 ± 12 ng/cm2. The oxygen quantified via XRF is treated as a reference for the quantification of other elements on the surface layer. The quantification of carbon mass deposition in the surface layer in relation to the oxygen mass deposition is also described in this paper. The surface analysis system is part of our contribution to the realization and dissemination of the unit of the kilogram (based on its new definition) via the XRCD method.

Development and testing of the shared decision-making attitudes scale among nurses in Taiwan: a cross-sectional scale development study

ObjectiveShared decision-making (SDM) enhances medical care, but an appropriate tool for evaluating nursing staff’s attitudes towards SDM in clinical practice is lacking. The objective of this study is to develop...

Variable Selection for Referenceless Multivariate Calibration: A Case Study on Nicotine Determination in Flue-Cured Tobacco Powder by Near-Infrared (NIR) Spectroscopy

Variable (wavelength) selection is critical in the multivariate calibration of spectra that improves prediction performance and provides clearer interpretation. Most variable selection methods use Y information (chemical reference of interest) to evaluating the importance of variables. In this work, a novel variable selection is proposed that requires no reference information. Replicate spectra of a sample under various acquisition conditions were used with an index to evaluate the importance of variables. The results show that the proposed method achieves better results than the original partial least squares model with a few available calibration samples. When a large number of calibration samples are available, this method provided comparable results to the state-of-the-art variable selection methods while reduced the risk of model over-fitting.

Calibrated automated thrombogram II: removing barriers for thrombin generation measurements

BackgroundThrombin generation (TG) assessed by Calibrated Automated Thrombogram (CAT-I) reflects the overall capacity of plasma to generate thrombin, thus evaluating the balance between the anti- and procoagulant processes. However, with this method the calibrator curve is usually not measured until completion which has a severe impact on the calculation of the TG parameters, especially under conditions where almost all substrate is consumed. In addition, direct thrombin inhibitor (DTI) cannot be present in the calibration sample due to inhibition of the calibrator. We have developed a modified TG assay (CAT-II) and performed head-to-head comparison with the CAT-I method using the same fluorometer. Furthermore, we have compared our CAT-II method to a new automated TG instrument (ST®-Genesia) using the same calibration method.MethodsTG was assessed with CAT-I and CAT-II using the same fulorometer and with ST®-Genesia in control plasma and plasma containing different anticoagulants (dabigatran, rivaroxaban, apixaban) and plasmas to which common interfering substances, bilirubin, hemoglobin and lipids were added. In CAT-I, calibration was against the same plasma containing calibrator in the presence of fluorogenic substrate (Z-GGR-AMC). In contrast, CAT-II method and ST®-Genesia used a standard concentration of thrombin in buffer and 7-amino-4-methylcoumarin (AMC) in a separate plasma sample for calibration.ResultsTG obtained from CAT-I using anticoagulant-free plasmas was lower compared with TG from CAT-II but both methods demonstrated an intra-assay variation less than 5% on all measured parameters. When comparing the two different calibration methods in the presence of different anticoagulants, a high correlation was seen in the presence of rivaroxaban and apixaban (R2 > 0.97), but not with dabigatran, a direct thrombin inhibitor. CAT-II method showed dose-dependent inhibition of TG in the presence of dabigatran, while CAT-I was not able to detect it. Both methods were able to correct for the interfering substances.ConclusionsOur results showed high similarity between the results of CAT-I and CAT-II method when it is applied in control plasmas and plasmas not inhibited with a direct thrombin inhibitor. Furthermore, both the CAT-II method and ST-Genesia using the same calibration method were able to detect the effect of all oral anticoagulants. Taken together, applying a new calibration method is a significant improvement for monitoring patients on direct thrombin inhibitors while not introducing any bias to results obtained on other types of samples.

Evaluation of poly(styrene-d5) and poly(4-fluorostyrene) as internal standards for microplastics quantification by thermoanalytical methods

Thermoanalytical methods such as pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) are among the most promising techniques for the quantification of microplastics (MP) in environmental samples. However, methods still lack harmonization and standardization. The use of an internal standard (IS) can improve the quality of quantitative data and the robustness of the analytical method. Poly(styrene-d5) has frequently been used as IS for MP quantification, but suffers from H–D exchange during pyrolysis, which is catalyzed by inorganic components of the sample matrix, most evidently in aluminum oxide filter matrix and in sea sand. Poly(4-fluorostyrene) (PFS) is a promising alternative, which does not suffer from exchange reactions. Both standards yielded comparable results in calibrations for polypropylene, polyethylene, and polystyrene MP, but a larger concentration range could be covered if PFS was used. Only minor interferences of both polymers with natural organic matrix components were observed. The study of influences of various inorganic matrices on pyrolysis of the polymers revealed manifold effects that may interfere with MP quantification and underline the need for matrix-matched calibration or extensive sample clean-up.

Robustness of Adaptive Measurement of Change to Item Parameter Estimation Error.

Adaptive measurement of change (AMC) is a psychometric method for measuring intra-individual change on one or more latent traits across testing occasions. Three hypothesis tests-a Z test, likelihood ratio test, and score ratio index-have demonstrated desirable statistical properties in this context, including low false positive rates and high true positive rates. However, the extant AMC research has assumed that the item parameter values in the simulated item banks were devoid of estimation error. This assumption is unrealistic for applied testing settings, where item parameters are estimated from a calibration sample before test administration. Using Monte Carlo simulation, this study evaluated the robustness of the common AMC hypothesis tests to the presence of item parameter estimation error when measuring omnibus change across four testing occasions. Results indicated that item parameter estimation error had at most a small effect on false positive rates and latent trait change recovery, and these effects were largely explained by the computerized adaptive testing item bank information functions. Differences in AMC performance as a function of item parameter estimation error and choice of hypothesis test were generally limited to simulees with particularly low or high latent trait values, where the item bank provided relatively lower information. These simulations highlight how AMC can accurately measure intra-individual change in the presence of item parameter estimation error when paired with an informative item bank. Limitations and future directions for AMC research are discussed.

PLS-regression-model-assisted raman spectroscopy for vegetable oil classification and non-destructive analysis of alpha-tocopherol contents of vegetable oils

In this study, a method based on chemometrics and Raman spectroscopy was developed to classify vegetable oils and quantify alpha-tocopherol, the most common vitamin E source in vegetable oils. The Raman spectra of 108 oil samples, obtained from 18 commercial brands and six oil types, were recorded in the scattering mode. The results of classification models, partial least squares–discriminant analysis (PLS-DA), and soft independent modeling of class analogies (SIMCA) showed that all samples were accurately assigned the oil brands and vegetable types. Furthermore, the partial least squares regression (PLSR) model for the determination of the alpha-tocopherol content was established from the Raman spectra of 72 calibration samples modeled with reference values achieved from high-performance liquid chromatography (HPLC). Data from both methods were highly correlated (R2 > 0.95). For the optimum PLSR model, orthogonal signal correction was employed in the data (800–2000 cm−1). Thus, a highly efficient model with 2 latent factors and a good root mean square error of prediction (16.05), was obtained.

One-Class Drift Compensation for an Electronic Nose

Drift compensation is an important issue in an electronic nose (E-nose) that hinders the development of E-nose’s model robustness and recognition stability. The model-based drift compensation is a typical and popular countermeasure solving the drift problem. However, traditional model-based drift compensation methods have faced “label dilemma” owing to high costs of obtaining kinds of prepared drift-calibration samples. In this study, we have proposed a calibration model for classification utilizing a single category of drift correction samples for more convenient and feasible operations. We constructed a multi-task learning model to achieve a calibrated classifier considering several demands. Accordingly, an associated solution process has been presented to gain a closed-form classifier representation. Moreover, two E-nose drift datasets have been introduced for method evaluation. From the experimental results, the proposed methodology reaches the highest recognition rate in most cases. On the other hand, the proposed methodology demonstrates excellent and steady performance in a wide range of adjustable parameters. Generally, the proposed method can conduct drift compensation with limited one-class calibration samples, accessing the top accuracy among all presented reference methods. It is a new choice for E-nose to counteract drift effect under cost-sensitive conditions.

Advances in CD-CAT: The General Nonparametric Item Selection Method.

Computerized adaptive testing (CAT) is characterized by its high estimation efficiency and accuracy, in contrast to the traditional paper-and-pencil format. CAT specifically for cognitive diagnosis (CD-CAT) carries the same advantages and has been seen as a tool for advancing the use of cognitive diagnosis (CD) assessment for educational practice. A powerful item selection method is the key to the success of a CD-CAT program, and to date, various parametric item selection methods have been proposed and well-researched. However, these parametric methods all require large samples, to secure high-precision calibration of the items in the item bank. Thus, at present, implementation of parametric methods in small-scale educational settings, such as classroom, remains challenging. In response to this issue, Chang, Chiu, and Tsai (Appl Psychol Meas 43:543-561, 2019) proposed the nonparametric item selection (NPS) method that does not require parameter calibration and outperforms the parametric methods for settings with only small or no calibration samples. Nevertheless, the NPS method is not without limitations; extra assumptions are required to guarantee a consistent estimator of the attribute profiles when data conform to complex models. To remedy this shortcoming, the general nonparametric item selection (GNPS) method that incorporates the newly developed general NPC (GNPC) method (Chiu et al. in Psychometrika 83:355-375, 2018) as the classification vehicle is proposed in this study. The inclusion of the GNPC method in the GNPS method relaxes the assumptions imposed on the NPS method. As a result, the GNPS method can be used with any model or multiple models without abandoning the advantage of being a small-sample technique. The legitimacy of using the GNPS method in the CD-CAT system is supported by Theorem 1 proposed in the study. The efficiency and effectiveness of the GNPS method are confirmed by the simulation study that shows the outperformance of the GNPS method over the compared parametric methods when the calibration samples are small.

Confirmatory factor analysis of imPACT cognitive tests in high school athletes.

ImPACT is the most commonly utilized computerized neurocognitive assessment for the clinical management of sport concussion. The cognitive composite scores that ImPACT currently reports include Verbal Memory, Visual Memory, Visual Motor Speed, Reaction Time, and Impulse Control. However, exploratory factor analytic studies report that two or more factors may better represent ImPACT's latent structure, suggesting that the current cognitive composites may not adequately represent the cognitive constructs ImPACT assesses. The latent structure of ImPACT cognitive baseline scores was examined using exploratory (EFA) and confirmatory factor analysis (CFA) of valid baseline ImPACT scores for 36,091 high school athletes. These athletes were randomly divided into two samples. The first sample was a calibration sample used for EFA and the second sample was a cross-validation sample used for CFA to estimate the best model identified in the calibration phase, along with other models that were reported in the literature or based on theoretical considerations, including hierarchical and bifactor models. EFA identified a first-order four-factor solution consisting of Visual Memory, Visual Reaction Time, Verbal Memory, and Working Memory constructs. CFA indicated that this four-factor model provided superior fit for the data, while the current five-composite structure of ImPACT provided a poor fit for the data. The latent constructs identified in this study using CFA do not map well onto the composite scores that are currently used to interpret ImPACT performance. Future research should investigate whether interpretation of ImPACT based on the constructs identified here will be more useful for clinical decision making than current approaches. (PsycInfo Database Record (c) 2021 APA, all rights reserved).