Accuracy Of Calibration Models Research Articles

Comprehensive Anomaly Score Rank Based Unsupervised Sample Selection Method

ABSTRACTThe process of selecting representative samples is crucial for establishing an accurate calibration model. To enhance the representativeness of the samples, a method for sample selection, utilizing the degree of anomaly as the evaluation criterion, is proposed. Initially, anomaly scores corresponding to various detection methods are obtained to ensure a comprehensive evaluation. These scores are then normalized by the confidence lower limit to establish a consistent scoring criterion. Subsequently, the weights of different detection methods are determined through eigenvector centrality analysis of a graph, where the methods serve as nodes and the similarity acts as weighted edges. Finally, the comprehensive anomaly scores are computed as the sum of weighted scores and are subsequently sorted. Representative samples are selected using a uniformly spaced sampling approach, with the spacing determined by a predefined and provided sample number. The efficacy of the method is validated across different sample sets.

Open-source Raman spectra of chemical compounds for active pharmaceutical ingredient development

Raman spectroscopy is utilised extensively in pharmaceutical analysis for tasks such as drug discovery, quality control and active pharmaceutical ingredient (API) development. Despite this, access to open-source Raman spectral datasets for modelling and analysis is often a challenge. In laboratory settings, small spectral libraries are typically compiled for one-shot identification of intermediates or unknown chemicals, which restricts availability to comprehensive and high-quality reference data. In this work, we introduce a new open-source Raman dataset consisting of pure chemical compounds commonly employed in the development of APIs. By curating and publishing this dataset, we aim to provide the scientific community with access to high-quality, reusable data. Containing 3,510 samples spanning 32 compounds, this data can be utilised for referencing and can potentially facilitate in the development of more accurate and generalisable calibration models when access to reference data is limited.

Neural Network Methods in the Development of MEMS Sensors.

As a kind of long-term favorable device, the microelectromechanical system (MEMS) sensor has become a powerful dominator in the detection applications of commercial and industrial areas. There have been a series of mature solutions to address the possible issues in device design, optimization, fabrication, and output processing. The recent involvement of neural networks (NNs) has provided a new paradigm for the development of MEMS sensors and greatly accelerated the research cycle of high-performance devices. In this paper, we present an overview of the progress, applications, and prospects of NN methods in the development of MEMS sensors. The superiority of leveraging NN methods in structural design, device fabrication, and output compensation/calibration is reviewed and discussed to illustrate how NNs have reformed the development of MEMS sensors. Relevant issues in the usage of NNs, such as available models, dataset construction, and parameter optimization, are presented. Many application scenarios have demonstrated that NN methods can enhance the speed of predicting device performance, rapidly generate device-on-demand solutions, and establish more accurate calibration and compensation models. Along with the improvement in research efficiency, there are also several critical challenges that need further exploration in this area.

Can Soil Spectroscopy be a Strong Alternative to the Conventional Methods of Analysis?

Soil is considered as the source of life on the globe. Although, numerous research studies, the soil is not completely understood whereas it is dynamic complex matrix include many simultaneous processes. The conventional methods of soil testing are the most reliable for assessing the land productivity and management. Unfortunately, these methods are time consuming and laborious as well as costly and hazardous to the environment. Therefore, the soil spectroscopy technique provides the functions of detecting, characterizing, quantifying and mapping several soil properties based on the uses of different kinds of the sensors (ground-based, airborne-based, and satellite-based). An integration of soil spectroscopic data, data processing, and modelling is considered as an effective tool for estimating soil parameters. Although these advanced techniques able to predict the majority of soil properties, there are some of these properties require more experiments for building accurate calibration models for estimation. Moreover, creating accurate soil spectral libraries (SSLs) for specific areas or soil types is very crucial for saving time, effort, expenses of soil surveying, sampling, and analysis. The application of these SSLs has a big limitation of the continuous variability of the soil properties over time. Thus, establishing extensive spectral libraries is important and mandatory for covering the small scaled areas’ variabilities as well as available soil types. Till now, the soil spectroscopy is not entirely replacing the conventional testing methods of soils because these techniques need future applications to be trusted and guaranteed of their effectiveness.

Tracking the impact of heavy metals on human health and ecological environments in complex coastal aquifers using improved machine learning optimization.

The rising heavy metal (HM) pollution in coastal aquifers in rapidly urbanizing areas such as Dammam leads to significant risks to public health and environmental sustainability, challenging compliance with Environmental Protection Agency (EPA) guidelines, World Health Organization (WHO) standards, and Sustainable Development Goals (SDGs) related to clean water and life on land. This study developed the predictive-based monitoring of HM concentrations, including cadmium (Cd), chromium (Cr), and mercury (Hg) in the coastal aquifers of Dammam, influenced by industrial, agricultural, and urban activities. For this purpose, dynamic system identification and machine learning (ML) models integrated with three ensemble techniques, namely, simple averaging (SAE), weighted averaging (WAE), and neuro-ensemble (N-ESB), were employed to enhance the accuracy, reliability, and efficiency of environmental monitoring systems. The experimental data were calibrated and validated in addition to k-fold cross-validation to ensure the predictive skills of the models. The methodology integrates extensive data collection across varied land uses in Dammam and accurate model calibration and validation phases to develop highly accurate predictive models. The findings proved that the N-ESB and Hammerstein-Wiener (HW) models surpassed other models in predicting the concentrations of all HM. For Cd, the N-ESB model achieved a root mean square error (RMSE = 0.0010mg/kg). Similarly, Cr demonstrated superior performance (RMSE = 0.0179mg/kg). Further numerical results indicated that the HW algorithm proved the most effective for Hg, with RMSE = 0.0000mg/kg. The quantitative comparison suggested that the N-ESB model's consistently high performance and low error rates make it an optimal choice for real-time, precise monitoring and management of HM pollution in coastal aquifers. The outcomes of this research highlighted the importance of integrating advanced predictive modeling techniques in environmental science, providing significant and practical implications for policymaking and ecological management.

Near-Infrared Spectroscopy Analysis of the Phytic Acid Content in Fuzzy Cottonseed Based on Machine Learning Algorithms.

Cottonseed is rich in oil and protein. However, its antinutritional factor content, of phytic acid (PA), has limited its utilization. Near-infrared (NIR) spectroscopy, combined with chemometrics, is an efficient and eco-friendly analytical technique for crop quality analysis. Despite its potential, there are currently no established NIR models for measuring the PA content in fuzzy cottonseeds. In this research, a total of 456 samples of fuzzy cottonseed were used as the experimental materials. Spectral pre-treatments, including first derivative (1D) and standard normal variable transformation (SNV), were applied, and the linear partial least squares (PLS), nonlinear support vector machine (SVM), and random forest (RF) methods were utilized to develop accurate calibration models for predicting the content of PA in fuzzy cottonseed. The results showed that the spectral pre-treatment significantly improved the prediction performance of the models, with the RF model exhibiting the best prediction performance. The RF model had a coefficient of determination in prediction (R2p) of 0.9114, and its residual predictive deviation (RPD) was 3.9828, which indicates its high accuracy in measuring the PA content in fuzzy cottonseed. Additionally, this method avoids the costly and time-consuming delinting and crushing of cottonseeds, making it an economical and environmentally friendly alternative.

Resolving challenges of groundwater flow modelling for improved water resources management: a narrative review

Groundwater flow modelling is critical for managing groundwater resources, particularly amid climate change and rising water demand. This narrative review examines the role of groundwater flow models in sustainable water resource management, focusing on challenges and solutions to enhance model reliability. A key challenge is data limitation—especially in regions like sub-Saharan Africa and South Asia, where scarce hydrogeological data hinders accurate model calibration. The complexity of aquifer systems, such as karst aquifers in North America and fractured-rock aquifers in India, further complicates model development, requiring detailed geological data and complex simulations. Additionally, uncertainties arise from limited knowledge of aquifer properties, variable boundary conditions, and sparse monitoring networks, which can reduce model predictability. Despite these obstacles, groundwater flow models are essential for simulating groundwater behaviour in response to altered precipitation patterns, increasing extraction rates, and extreme events like droughts. For instance, predictive modelling has helped assess potential depletion risks in California’s Central Valley and contamination risks in industrial zones of East Asia, guiding sustainable extraction strategies and contamination assessments. To improve model reliability, this review emphasizes the need for enhanced data collection, integration of advanced technologies—such as artificial intelligence and machine learning for predictive accuracy—and the adoption of multidisciplinary modelling approaches. These advancements, improved sensor networks, and regional data-sharing initiatives are critical to reducing uncertainties and increasing model precision. Ultimately, such improvements will support climate adaptation efforts and promote the sustainable management of global groundwater resources, benefiting water managers and policy makers.

A non-coplanar high-precision calibration method for cameras based on an affine coordinate correction model

Traditional non-coplanar calibration methods such as Tsai’s method have many problems, such as insufficient calibration accuracy, inconvenient operation, inaccurate models, etc. This paper proposes a new high-precision non-coplanar calibration method that aims to solve these problems. Like Tsai’s method, the proposed calibration method utilizes a one-dimensional displacement stage and a two-dimensional plane target to generate a virtual 3D feature point sequence. As an improvement, an affine coordinate correction model is applied to ensure the accuracy and orthogonality of the obtained virtual 3D coordinates. A novel and accurate camera calibration model is further established. Compared with Tsai’s model, which uses a radial alignment constraint and ignores the orthonormal constraint of the rotation matrix, the proposed calibration model fully considers the degrees of freedom of the camera’s parameters to be calibrated, as well as the lens’s nonlinear distortion parameters. More accurate analytical solutions of intrinsic and extrinsic parameters can be obtained with the proposed calibration model. Finally, a novel high-precision non-coplanar calibration method is proposed based on the proposed calibration model. The reprojection experiment proves that the calibration accuracy of this calibration method is better than that of Tsai’s and Zhang’s calibration methods under the same calibration conditions. As a supplement, a novel binocular camera system extrinsic parameter calibration method with known intrinsic parameters is proposed. With accurate intrinsic and extrinsic parameters, the binocular camera system’s relative measurement accuracy could be within 1/10 000. Overall, this method can be used in experimental and industrial applications that require high-precision calibration parameters.

A review of machine learning models and influential factors for estimating evapotranspiration using remote sensing and ground-based data

A review of machine learning models and influential factors for estimating evapotranspiration using remote sensing and ground-based data

Improving spatially-resolved lithium quantification in drill core samples of spodumene pegmatite by using laser-induced breakdown spectroscopy and pixel-matched reference areas

Improving spatially-resolved lithium quantification in drill core samples of spodumene pegmatite by using laser-induced breakdown spectroscopy and pixel-matched reference areas

Avocado fruit sorting by hyperspectral images

The paper shows the use of the methods of hyperspectral imaging (HSI) in a range of 400–1000 nm and multivariate analysis for sorting Hass avocado fruits. The decomposition of the data matrix of HSIs of avocado fruits was carried out using the principle component analysis. The reflection bands in the visible and near-infrared spectral regions interrelated with the process of maturation and the moisture content of avocado fruits were revealed. It has been established that visualization upon avocado inline sorting by moisture is possible when using factor loadings as pseudo-color. Calibration models for determination of moisture and dry matter of avocado fruits were built based on the data of moisture measurement and hyperspectral images. The matrix of spectral data was formed by two methods: random selection of spectral signatures of HSIs from the whole surface of fruits or the image surface of HSIs of fruits (initial HSIs) as a region of interest (ROI). Based on the data of moisture measurement and selection of spectral signatures of hyperspectral images, calibration models were built for detection of moisture and dry matter of avocado fruits. Using sequential simulation by the projection to latent structures (PLS) method, accurate calibration models were developed to detect moisture (RP2 = 0.89) and dry matter (RP2 = 0.92) in the composition of avocado fruits. When building calibration models by the initial HSIs, models were obtained to predict moisture (RС2 = 0.99) and dry matter (RС2 = 0.99) in the composition of avocado fruits. It is proposed to use calibration models by the initial HSIs to determine moisture and dry matter in the intervals of the acceptable values according to the acting standard UNECE STANDARD FFV-42:2019.

Interspecies and in vitro-in vivo scaling for quantitative modeling of whole-body drug pharmacokinetics in patients: Application to the anticancer drug oxaliplatin.

Quantitative systems pharmacology holds the promises of integrating results from laboratory animals or in vitro human systems into the design of human pharmacokinetic/pharmacodynamic (PK/PD) models allowing for precision and personalized medicine. However, reliable and general in vitro-to-in vivo extrapolation and interspecies scaling methods are still lacking. Here, we developed a translational strategy for the anticancer drug oxaliplatin. Using ex vivo PK data in the whole blood of the mouse, rat, and human, a model representing the amount of platinum (Pt) in the plasma and in the red blood cells was designed and could faithfully fit each dataset independently. A "purely physiologically-based (PB)" scaling approach solely based on preclinical data failed to reproduce human observations, which were then included in the calibration. Investigating approaches in which one parameter was set as species-specific, whereas the others were computed by PB scaling laws, we concluded that allowing the Pt binding rate to plasma proteins to be species-specific permitted to closely fit all data, and guaranteed parameter identifiability. Such a strategy presenting the drawback of including all clinical datasets, we further identified a minimal subset of human data ensuring accurate model calibration. Next, a "whole body" model of oxaliplatin human PK was inferred from the ex vivo study. Its three remaining parameters were estimated, using one third of the available patient data. Remarkably, the model achieved a good fit to the training dataset and successfully reproduced the unseen observations. Such validation endorsed the legitimacy of our scaling methodology calling for its testing with other drugs.

NLoed: A Python Package for Nonlinear Optimal Experimental Design in Systems Biology.

Modeling in systems and synthetic biology relies on accurate parameter estimates and predictions. Accurate model calibration relies, in turn, on data and on how well suited the available data are to a particular modeling task. Optimal experimental design (OED) techniques can be used to identify experiments and data collection procedures that will most efficiently contribute to a given modeling objective. However, implementation of OED is limited by currently available software tools that are not well suited for the diversity of nonlinear models and non-normal data commonly encountered in biological research. Moreover, existing OED tools do not make use of the state-of-the-art numerical tools, resulting in inefficient computation. Here, we present the NLoed software package and demonstrate its use with in vivo data from an optogenetic system in Escherichia coli. NLoed is an open-source Python library providing convenient access to OED methods, with particular emphasis on experimental design for systems biology research. NLoed supports a wide variety of nonlinear, multi-input/output, and dynamic models and facilitates modeling and design of experiments over a wide variety of data types. To support OED investigations, the NLoed package implements maximum likelihood fitting and diagnostic tools, providing a comprehensive modeling workflow. NLoed offers an accessible, modular, and flexible OED tool set suited to the wide variety of experimental scenarios encountered in systems biology research. We demonstrate NLoed's capabilities by applying it to experimental design for characterization of a bacterial optogenetic system.

Non-destructive near infrared spectroscopy externally validated using large number sets for creation of robust calibration models enabling prediction of apple firmness

Non-invasive flesh firmness prediction using near infrared spectroscopy has been perfected and validated on three apple varieties. Three novel calibration models were developed following three year's of repeated large-scale sampling of stored commercial apple varieties ‘Gala’, ‘Red Jonaprince’ and ‘Jonagored’. The spectroscopic results were compared directly with those obtained using the invasive method. Increased accuracy of calibration models was achieved with the newly established data collection model. The results exhibited coefficient of determination for calibration, R2, and ratio of prediction to deviation (RPD) in excess of 0.91 and 2.3, respectively, thus enabling excellent prediction of flesh firmness via a non-invasive and fast spectroscopic approach. The highest R2 obtained was 0.94, RPD 2.6, root mean square error of calibration 5.87 N, and root mean square error of cross-validation (internal) 6.75 N for variety ‘Red Jonaprince’. Our complex long-term study provided excellent external validated calibration models and the approach can help developing calibration models for commercial sorting lines using near infrared spectroscopy.

Impedance matching to axion dark matter: considerations of the photon-electron interaction

We introduce the concept of impedance matching to axion dark matter by posing the question of why axion detection is difficult, even though there is enough power in each square meter of incident dark-matter flux to energize a LED light bulb. By quantifying backreaction on the axion field, we show that a small axion-photon coupling does not by itself prevent an order-unity fraction of the dark matter from being absorbed through optimal impedance match. We further show, in contrast, that the electromagnetic charges and the self-impedance of their coupling to photons provide the principal constraint on power absorption integrated across a search band. Using the equations of axion electrodynamics, we demonstrate stringent limitations on absorbed power in linear, time-invariant, passive receivers. Our results yield fundamental constraints, arising from the photon-electron interaction, on improving integrated power absorption beyond the cavity haloscope technique. The analysis also has significant practical implications, showing apparent tension with the sensitivity projections for a number of planned axion searches. We additionally provide a basis for more accurate signal power calculations and calibration models, especially for receivers using multi-wavelength open configurations such as dish antennas and dielectric haloscopes.

Prediction of bioaccessible lead in urban and suburban soils with Vis-NIR diffuse reflectance spectroscopy

Prediction of bioaccessible lead in urban and suburban soils with Vis-NIR diffuse reflectance spectroscopy

On-Line Real-Time Monitoring of a Rapid Enzymatic Oil Degumming Process: A Feasibility Study Using Free-Run Near-Infrared Spectroscopy.

Enzymatic degumming is a well established process in vegetable oil refinement, resulting in higher oil yield and a more stable downstream processing compared to traditional degumming methods using acid and water. During the reaction, phospholipids in the oil are hydrolyzed to free fatty acids and lyso-phospholipids. The process is typically monitored by off-line laboratory measurements of the free fatty acid content in the oil, and there is a demand for an automated on-line monitoring strategy to increase both yield and understanding of the process dynamics. This paper investigates the option of using Near-Infrared spectroscopy (NIRS) to monitor the enzymatic degumming reaction. A new method for balancing spectral noise and keeping the chemical information in the spectra obtained from a rapid changing chemical process is suggested. The effect of a varying measurement averaging window width (0 to 300 s), preprocessing method and variable selection algorithm is evaluated, aiming to obtain the most accurate and robust calibration model for prediction of the free fatty acid content (% (w/w)). The optimal Partial Least Squares (PLS) model includes eight wavelength variables, as found by rPLS (recursive PLS) calibration, and yields an RMSECV (Root Mean Square Error of Cross Validation) of 0.05% (w/w) free fatty acid using five latent variables.

Optimal sample size for calibrating DNA methylation age estimators.

Age is a fundamental parameter in wildlife management as it is used to determine the risk of extinction, manage invasive species, and regulate sustainable harvest. In a broad variety of vertebrates species, age can be determined by measuring DNA methylation. Animals with known ages are initially required during development, calibration, and validation of these epigenetic clocks. However, wild animals with known ages are frequently difficult to obtain. Here, we perform Monte‐Carlo simulations to determine the optimal sample size required to create an accurate calibration model for age estimation by elastic net regression modelling of cytosine‐phosphate‐guanine methylation data. Our results suggest a minimum calibration population size of 70, but ideally 134 individuals or more for accurate and precise models. We also provide estimates to the extent a model can be extrapolated beyond a distribution of ages that was used during calibration. The findings can assist researchers to better design age estimation models and decide if their model is adequate for determining key population attributes.

Validation Metrics for Fixed Effects and Mixed-Effects Calibration

AbstractThe modern scientific process often involves the development of a predictive computational model. To improve its accuracy, a computational model can be calibrated to a set of experimental data. A variety of validation metrics can be used to quantify this process. Some of these metrics have direct physical interpretations and a history of use, while others, especially those for probabilistic data, are more difficult to interpret. In this work, a variety of validation metrics are used to quantify the accuracy of different calibration methods. Frequentist and Bayesian perspectives are used with both fixed effects and mixed-effects statistical models. Through a quantitative comparison of the resulting distributions, the most accurate calibration method can be selected. Two examples are included which compare the results of various validation metrics for different calibration methods. It is quantitatively shown that, in the presence of significant laboratory biases, a fixed effects calibration is significantly less accurate than a mixed-effects calibration. This is because the mixed-effects statistical model better characterizes the underlying parameter distributions than the fixed effects model. The results suggest that validation metrics can be used to select the most accurate calibration model for a particular empirical model with corresponding experimental data.

Multi-Ion-Sensing Emulator and Multivariate Calibration Optimization by Machine Learning Models

One paramount challenge in multi-ion-sensing arises from ion interference that degrades the accuracy of sensor calibration. Machine learning models are here proposed to optimize such multivariate calibration. However, the acquisition of big experimental data is time and resource consuming in practice, necessitating new paradigms and efficient models for these data-limited frameworks. Therefore, a novel approach is presented in this work, where a multi-ion-sensing emulator is designed to explain the response of an ion-sensing array in a mixed-ion environment. A case study is performed emulating the concurrent monitoring of sodium, potassium, lithium, and lead ions, in a medium representative of sweat samples. These analytes are relevant examples of sweat ion-sensing applications for physiology, therapeutic drug monitoring, and heavy metal contamination. It is demonstrated that calibration datasets output by the emulator explain accurately the experimental response of polymeric solid-contact ion-selective electrodes, where root-mean-squared error of 1.37, 1.44, 1.78, 2 mV are obtained, respectively, for Na <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> , K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> , Li <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> , Pb <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2+</sup> sensor calibration in artificial sweat. Besides, synthetic datasets of custom size are generated to train, validate, and evaluate different types of multivariate regressors. A Multi-Output Support Vector Regressor (M-SVR) is proposed as a compact, accurate, robust, and efficient multivariate calibration model. It features 13.22% normalized root mean squares, and 20.29% mean root squares improvement compared to a simple linear regression model. It is an unbiased estimator for medium to large datasets, and its average generalization error is of 3.22%. Besides, M-SVR models have a lower computational complexity than single-output SVR or neural network models, making them a suitable solution for memory and energy-constrained edge devices used for continuous and real-time multi-ion monitoring.