Calibration Transfer Techniques Research Articles

Supervised Factor Analysis Transfer: Calibration transfer with noise modeling and response variable integration

Multivariate calibration models often encounter challenges in extrapolating beyond the calibration instruments due to variations in hardware configurations, signal processing algorithms, or environmental conditions. Calibration transfer techniques have been developed to mitigate this issue. In this study, we introduce a novel methodology known as Supervised Factor Analysis Transfer (SFAT) aimed at achieving robust and interpretable calibration transfer. SFAT operates from a probabilistic framework and integrates response variables into its transfer process to effectively align data from the target instrument to that of the source instrument. Within the SFAT model, the data from the source instrument, the target instrument, and the response variables are collectively projected onto a shared set of latent variables. These latent variables serve as the conduit for information transfer between the three distinct domains, thereby facilitating effective spectra transfer. Moreover, SFAT explicitly models the noise variances associated with each variable, thereby minimizing the transfer of non-informative noise. Furthermore, we provide empirical evidence showcasing the efficacy of SFAT across three real-world datasets, demonstrating its superior performance in calibration transfer scenarios.

Transferability of a large mid‐infrared soil spectral library between two Fourier‐transform infrared spectrometers

AbstractLarge and publicly available soil spectral libraries, such as the USDA National Soil Survey Center–Kellogg Soil Survey Laboratory (NSSC‐KSSL) mid‐infrared (MIR) spectral library, are enormously valuable resources enabling laboratories around the world to make rapid low‐cost estimates of a number of soil properties. A limitation to widespread sharing of soil spectral data is the need to ensure that spectra collected on a secondary spectrometer are compatible with the spectra in the primary or reference library. Various spectral preprocessing and calibration transfer techniques have been proposed to overcome this limitation. We tested the transferability of models developed using the USDA NSSC‐KSSL MIR library to a secondary instrument. For the soil properties, total carbon (TC), pH, and clay content, we found that good performance (ratio of performance to deviation [RPD] = 4.9, 2.0, and 3.6, respectively) could be achieved on an independent test set with Savitzky‐Golay smoothing and first derivative preprocessing of the secondary spectra using a memory‐based learning chemometric approach. We tested three calibration transfer techniques (direct standardization [DS], piecewise direct standardization [PDS], and spectral space transformation [SST]) using different size transfer sets selected to be representative of the entire NSSC‐KSSL library. Among the transfer methods, SST consistently outperformed DS and PDS with 50 transfer samples being an optimal number for transfer model development. For TC and pH, performance was improved using the SST transfer (RPD = 7.7 and 2.2, respectively) primarily through the elimination of bias. Calibration transfer could not improve predictions for clay. These findings suggest that calibration transfer may not always be necessary, but users should test to confirm this assumption using a small set of representative samples scanned at both laboratories.

Modular Breath Analyzer (MBA): Introduction of a Breath Analyzer Platform Based on an Innovative and Unique, Modular eNose Concept for Breath Diagnostics and Utilization of Calibration Transfer Methods in Breath Analysis Studies.

Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment consists of classifying breath samples from 15 subjects before and after eating a specific meal using three instruments. We investigate the possibility to transfer calibration models across instruments using transfer samples from the experiment under study, since representative samples of human breath at some conditions are difficult to simulate in a laboratory. For example, exhaled breath from subjects suffering from a disease for which the biomarkers are mostly unknown. Results show that many transfer samples of all the classes under study (in our case meal/no meal) are needed, although some CT methods present reasonably good results with only one class.

(Invited) Strategies for Calibration Cost Reduction in Heterogeneous Chemical Sensor Arrays

Introduction Heterogeneous gas sensor arrays coupled with machine learning algorithms have been proposed for a wide range of applications. However, inherent sensor variability degrades the performance of calibration models when directly transferred to new replicates of the original system [1]. As a result, in order to fulfill industry requirements, very often, calibration needs to be performed individually for each unit, limiting mass-deployment [2]. Recently, calibration methodologies have been proposed to reduce calibration costs and extend lifetime of the models. Briefly, these strategies make use of calibration models built for a primary system, incorporate new information to the calibration model, or take advantage of nearby units. Calibration Transfer Calibration transfer relies on a primary unit, for which a calibration model is built using a full set of calibration samples. Then, the sensor space of the secondary unit is mapped to the space of the primary unit using a set of transfer samples. Finally, using the transfer function, the calibration model built for the primary can be used with the secondary instrument. Hence, the cost reduction can be estimated from the ratio between the full set of calibration measurements and the transfer samples.Different methodologies have been proposed to obtain the transfer function, with different assumptions. For example, Single Signal Standardization assumes that the differences between units are linear, with no shifts in the signal. On the contrary, Direct Standardization (DS) constructs the mapping between all signals and can capture shifts in the signal. DS, however, may incorporate unwanted sources of variability and may face highly underdetermined systems [3]. Piecewise Direct Standardization (PDS) operates locally and can overcome the computational limitations of DS. The mentioned techniques have been tested on heterogeneous MOX gas sensor arrays [4,5]. Results showed that variations in sensor operating temperature can be corrected by means of calibration transfer techniques. Moreover, aging and sensor drift can be also alleviated with calibration transfer methods [6]. Calibration Update Calibration update, instead, enriches the calibration dataset with new calibration examples (update samples). The model incorporates the update samples using a hyperparameter that controls the weight of the new samples. It can also be combined with system regularization, in which case the regularization parameter needs to be selected for each task. The method showed successful results when applied to potentiometric chemical sensors [7,8]. Nevertheless, the regularization parameter and the weight of the new samples significantly changed between calibration updates. After some time, new samples dominate the calibration model such that the original calibration dataset contributes scarcely to the model. Although calibration update was designed to adapt the calibration model of a sensing unit affected by drift, it can also be applied to other sensing units since, after some time, any sensing unit behaves like another unit. In-the-field calibration A background mismatch between laboratory calibration and in-the-field operation may render calibration models inefficient. In-the-field calibration takes advantage of on-site reference instrumentation to perform calibration and ensure same background during calibration phase and deployment. Usually, linear models are built, which can include additional terms to account for temperature, humidity and time effects [9]. Collocation, however, requires the installation of the sensing unit next to the reference instrumentation, time during which the system is not operational.Multi-hop calibration enables node-to-node calibration such that data stream is not interrupted (see Figure). This strategy is particularly appropriate in scenarios with moving units that encounter each other regularly. It provided higher accuracy than single collocation when implemented in public vehicles equipped with electrochemical sensors [10]. However, as the number of hops increases, the resulting calibration becomes less reliable. It has been suggested that a node redundancy of about 30% can keep calibration error contained [11]. Conclusions Very different methodologies have been proposed to reduce calibration costs and increase lifetime of heterogeneous chemical systems. However, the suitability of each method depends on the sensor technology and the required task. Further benchmark is still required to elucidate the methods that provide best results and under which conditions and environments. This work provides a discussion on the calibration methodologies that aim at reducing calibration costs and encourages researchers to share datasets for the benchmark of the different calibration strategies.

Transfer learning strategy for plastic pollution detection in soil: Calibration transfer from high-throughput HSI system to NIR sensor

Rapid detection tasks in soil environment are generally implemented by various spectrometers and chemometric models. To reduce costs for model construction, calibration transfer from laboratory spectral instruments to portable devices has recently received extensive attention. In different application cases of model transference, most conventional methods require extra time to tune hyperparameters and to select calibration transfer techniques. Based on the near-infrared (NIR) analytical technique, this work aimed at exploring a transfer learning strategy to detect plastic pollution levels in the soil by transferring the model from a high-throughput hyperspectral image (HSI) system to an ultra-portable NIR sensor. Transfer learning was explored to diagnose the proper calibration transfer algorithm and construct the transferable model. For transferable model construction, conventional calibration transfer algorithms (Direct Standardization (DS) or Repeatability file (Repfile)) served as a pre-processing step, and non-parametric transfer learning algorithm (Easy Transfer Learning (EasyTL)) was explored in the modeling step. Supporting vector machine (SVM) was carried out as a typical modeling algorithm for comparison. For transformation algorithms selection, a distance metric algorithm, maximum mean discrepancy (MMD), was performed on spectral feature matrices before and after DS or Repfile transformation. On three transfer tasks, the results indicated that the Repfile-EasyTL model was a promising solution with higher accuracy, much lower time costs, less parameters, and dependency on the increase of standard samples than other models (SVM, DS-SVM, Repfile-SVM, EasyTL, DS-EasyTL). Moreover, MMD distance presented the great potential to serve as an indicator to vote the optimal calibration transfer algorithm before the modeling step.

Is Standardization Necessary for Sharing of a Large Mid-Infrared Soil Spectral Library?

Recent developments in diffuse reflectance soil spectroscopy have increasingly focused on building and using large soil spectral libraries with the purpose of supporting many activities relevant to monitoring, mapping and managing soil resources. A potential limitation of using a mid-infrared (MIR) spectral library developed by another laboratory is the need to account for inherent differences in the signal strength at each wavelength associated with different instrumental and environmental conditions. Here we apply predictive models built using the USDA National Soil Survey Center–Kellogg Soil Survey Laboratory (NSSC-KSSL) MIR spectral library (n = 56,155) to samples sets of European and US origin scanned on a secondary spectrometer to assess the need for calibration transfer using a piecewise direct standardization (PDS) approach in transforming spectra before predicting carbon cycle relevant soil properties (bulk density, CaCO3, organic carbon, clay and pH). The European soil samples were from the land use/cover area frame statistical survey (LUCAS) database available through the European Soil Data Center (ESDAC), while the US soil samples were from the National Ecological Observatory Network (NEON). Additionally, the performance of the predictive models on PDS transfer spectra was tested against the direct calibration models built using samples scanned on the secondary spectrometer. On independent test sets of European and US origin, PDS improved predictions for most but not all soil properties with memory based learning (MBL) models generally outperforming partial least squares regression and Cubist models. Our study suggests that while good-to-excellent results can be obtained without calibration transfer, for most of the cases presented in this study, PDS was necessary for unbiased predictions. The MBL models also outperformed the direct calibration models for most of the soil properties. For laboratories building new spectroscopy capacity utilizing existing spectral libraries, it appears necessary to develop calibration transfer using PDS or other calibration transfer techniques to obtain the least biased and most precise predictions of different soil properties.

Graph‐based calibration transfer

AbstractThe problem of transferring calibrations from a primary to a secondary instrument, that is, calibration transfer (CT), has been a matter of considerable research in chemometrics over the past decades. Current state‐of‐the‐art (SoA) methods like (piecewise) direct standardization perform well when suitable transfer standards are available. However, stable calibration standards that share similar (spectral) features with the calibration samples are not always available. Towards enabling CT with arbitrary calibration standards, we propose a novel CT technique that employs manifold regularization of the partial least squares (PLS) objective. In particular, our method enforces that calibration standards, measured on primary and secondary instruments, have (nearly) invariant projections in the latent variable space of the primary calibration model. Thereby, our approach implicitly removes interdevice variation in the predictive directions of X, which is in contrast to most SoA techniques that employ explicit preprocessing of the input data. We test our approach on the well‐known corn benchmark data set employing the National Bureau of Standards and Technology (NIST) glass standard spectra for instrument standardization and compare the results with current SoA methods.

(Invited) Strategies for Calibration Cost Reduction in Heterogeneous Chemical Sensor Arrays

Introduction Heterogeneous gas sensor arrays coupled with machine learning algorithms have been proposed for a wide range of applications. However, inherent sensor variability degrades the performance of calibration models when directly transferred to new replicates of the original system [1]. As a result, in order to fulfill industry requirements, very often, calibration needs to be performed individually for each unit, limiting mass-deployment [2]. Recently, calibration methodologies have been proposed to reduce calibration costs and extend lifetime of the models. Briefly, these strategies make use of calibration models built for a primary system, incorporate new information to the calibration model, or take advantage of nearby units. Calibration Transfer Calibration transfer relies on a primary unit, for which a calibration model is built using a full set of calibration samples. Then, the sensor space of the secondary unit is mapped to the space of the primary unit using a set of transfer samples. Finally, using the transfer function, the calibration model built for the primary can be used with the secondary instrument. Hence, the cost reduction can be estimated from the ratio between the full set of calibration measurements and the transfer samples.Different methodologies have been proposed to obtain the transfer function, with different assumptions. For example, Single Signal Standardization assumes that the differences between units are linear, with no shifts in the signal. On the contrary, Direct Standardization (DS) constructs the mapping between all signals and can capture shifts in the signal. DS, however, may incorporate unwanted sources of variability and may face highly underdetermined systems [3]. Piecewise Direct Standardization (PDS) operates locally and can overcome the computational limitations of DS. The mentioned techniques have been tested on heterogeneous MOX gas sensor arrays [4,5]. Results showed that variations in sensor operating temperature can be corrected by means of calibration transfer techniques. Moreover, aging and sensor drift can be also alleviated with calibration transfer methods [6]. Calibration Update Calibration update, instead, enriches the calibration dataset with new calibration examples (update samples). The model incorporates the update samples using a hyperparameter that controls the weight of the new samples. It can also be combined with system regularization, in which case the regularization parameter needs to be selected for each task. The method showed successful results when applied to potentiometric chemical sensors [7,8]. Nevertheless, the regularization parameter and the weight of the new samples significantly changed between calibration updates. After some time, new samples dominate the calibration model such that the original calibration dataset contributes scarcely to the model. Although calibration update was designed to adapt the calibration model of a sensing unit affected by drift, it can also be applied to other sensing units since, after some time, any sensing unit behaves like another unit. In-the-field calibration A background mismatch between laboratory calibration and in-the-field operation may render calibration models inefficient. In-the-field calibration takes advantage of on-site reference instrumentation to perform calibration and ensure same background during calibration phase and deployment. Usually, linear models are built, which can include additional terms to account for temperature, humidity and time effects [9]. Collocation, however, requires the installation of the sensing unit next to the reference instrumentation, time during which the system is not operational. Multi-hop calibration enables node-to-node calibration such that data stream is not interrupted (see Figure). This strategy is particularly appropriate in scenarios with moving units that encounter each other regularly. It provided higher accuracy than single collocation when implemented in public vehicles equipped with electrochemical sensors [10]. However, as the number of hops increases, the resulting calibration becomes less reliable. It has been suggested that a node redundancy of about 30% can keep calibration error contained [11]. Conclusions Very different methodologies have been proposed to reduce calibration costs and increase lifetime of heterogeneous chemical systems. However, the suitability of each method depends on the sensor technology and the required task. Further benchmark is still required to elucidate the methods that provide best results and under which conditions and environments. This work provides a discussion on the calibration methodologies that aim at reducing calibration costs and encourages researchers to share datasets for the benchmark of the different calibration strategies.

Cloud Aerosol Transport System (CATS) 1064 nm Calibration and Validation.

The Cloud-Aerosol Transport System (CATS) lidar on board the International Space Station (ISS) operated from 10 February 2015 to 30 October 2017 providing range-resolved vertical backscatter profiles of Earth's atmosphere at 1064 and 532 nm. The CATS instrument design and ISS orbit lead to a higher 1064 nm signal-to-noise ratio than previous space-based lidars, allowing for direct atmospheric calibration of the 1064 nm signals. Nighttime CATS Version 3-00 data were calibrated by scaling the measured data to a model of the expected atmospheric backscatter between 22 and 26 km above mean sea level (AMSL). The CATS atmospheric model is constructed using molecular backscatter profiles derived from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) re-analysis data and aerosol scattering ratios measured by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The nighttime normalization altitude region was chosen to simultaneously minimize aerosol loading and variability within the CATS data frame, which extends from 28 km to -2 km AMSL. Daytime CATS Version 3-00 data were calibrated through comparisons with nighttime measurements of the layer integrated attenuated total backscatter (iATB) from strongly scattering, rapidly attenuating opaque cirrus clouds. The CATS nighttime 1064 nm attenuated total backscatter (ATB) uncertainties for clouds and aerosols are primarily related to the uncertainties in the CATS nighttime calibration technique, which are estimated to be ~9%. Median CATS V3-00 1064 nm ATB relative uncertainty at night within cloud and aerosol layers is 7%, slightly lower than these calibration uncertainty estimates. CATS median daytime 1064 nm ATB relative uncertainty is 21% in cloud and aerosol layers, similar to the estimated 16-18% uncertainty in the CATS daytime cirrus cloud calibration transfer technique. Coincident daytime comparisons between CATS and the Cloud Physics Lidar (CPL) during the CATS-CALIPSO Airborne Validation Experiment (CCAVE) project show good agreement in mean ATB profiles for clear-air regions. Eight nighttime comparisons between CATS and the PollyXT ground based lidars also show good agreement in clear-air regions between 3-12 km, with CATS having a mean ATB of 19.7 % lower than PollyXT. Agreement between the two instruments (~7%) is even better within an aerosol layer. Six-month comparisons of nighttime ATB values between CATS and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) also show that iATB comparisons of opaque cirrus clouds agree to within 19%. Overall, CATS has demonstrated that direct calibration of the 1064 nm channel is possible from a space based lidar using the atmospheric normalization technique.

Investigation of Direct Model Transferability Using Miniature Near-Infrared Spectrometers.

Recent developments in compact near infrared (NIR) instruments, including both handheld and process instruments, have enabled easy and affordable deployment of multiple instruments for various field and online or inline applications. However, historically, instrument-to-instrument variations could prohibit success when applying calibration models developed on one instrument to additional instruments. Despite the usefulness of calibration transfer techniques, they are difficult to apply when a large number of instruments and/or a large number of classes are involved. Direct model transferability was investigated in this study using miniature near-infrared (MicroNIR™) spectrometers for both classification and quantification problems. For polymer classification, high cross-unit prediction success rates were achieved with both conventional chemometric algorithms and machine learning algorithms. For active pharmaceutical ingredient quantification, low cross-unit prediction errors were achieved with the most commonly used partial least squares (PLS) regression method. This direct model transferability is enabled by the robust design of the MicroNIR™ hardware and will make deployment of multiple spectrometers for various applications more manageable.

Improved calibration transfer between near-Infrared (NIR) spectrometers using canonical correlation analysis

The application of mobile near-infrared (NIR) spectrometers in field measurements is growing. Calibration transfer techniques offer simple solutions for enabling models constructed on benchtop instruments for use on mobile spectrometers. Since different types of spectrometers with different components, scanning ranges and resolutions cause great differences in the spectral response, calibration transfer is difficult to apply. In this paper, we focus on calibration transfer among benchtop, portable and handheld spectrometers by a method of calibration transfer based on canonical correlation analysis (CTCCA). Its capability was illustrated by the example of a group of NIR spectra dataset for predicting reducing sugars, total sugar, and nicotine contents in tobacco leaves. The experimental results showed that the transferability of CTCCA was superior to other conventional calibration transfer methods, including piecewise direct standardization, spectral space transformation, calibration transfer based on independent component analysis, and calibration transfer based on the weight matrix. Moreover, the best transfer results were obtained in the three cases by canonical correlation analysis method executing transfer while the spectra were not interpolated, which shows that this approach has the advantage of easy implementation for calibration transfer. Therefore, CTCCA without interpolation calculation offers a new and simple solution for transferring the spectra acquired by mobile spectrometers to the optimized spectral models built on benchtop devices to improve the accuracy of the results. Additionally, the results show that the benchtop spectrometer is more suitable as the master instrument for calibration transfer with more accurate prediction than using a portable device as the master.

Progress and Applications of Multivariate Calibration Model Transfer Methods

Abstract The combination of multivariate calibration and spectroscopic analysis technology can achieve the rapid analysis of substance. However, when the external environmental conditions such as instruments or accessories are replaced or temperature changed, the current master calibration may be “invalid”. Recalibration usually takes a lot of time and financial resources. The development of calibration transfer techniques can solve this problem well, and the adaptability and robustness of the calibration are enhanced by correcting the spectra or selecting variables, therefore it is very important for popularizing the use of calibrations. This review describes a number of new calibration transfer methods and their applications in recent years, and introduces them according to traditional transfer methods, new methods and new strategies. The applications of each method as well as both its advantages and disadvantages are evaluated.

Joint and Unique Multiblock Analysis for Integration and Calibration Transfer of NIR Instruments.

In the present paper, we introduce an end-to-end workflow called joint and unique multiblock analysis (JUMBA), which allows multiple sources of data to be analyzed simultaneously to better understand how they complement each other. In near-infrared (NIR) spectroscopy, calibration models between NIR spectra and responses are used to replace wet-chemistry methods, and the models tend to be instrument-specific. Calibration-transfer techniques are used for standardization of NIR-instrumentation, enabling the use of one model on several instruments. The current paper investigates both the similarities and differences among a variety of NIR instruments using JUMBA. We demonstrate JUMBA on both a previously unpublished data set in which five NIR instruments measured mushroom substrate and a publicly available data set measured on corn samples. We found that NIR spectra from different instrumentation largely shared the same underlying structures, an insight we took advantage of to perform calibration transfer. The proposed JUMBA transfer displayed excellent calibration-transfer performance across the two analyzed data sets and outperformed existing methods in terms of both prediction accuracy and stability. When applied to a multi-instrument environment, JUMBA transfer can integrate all instruments in the same model and will ensure higher consistency among them compared with existing calibration-transfer methods.

A Review of Calibration Transfer Practices and Instrument Differences in Spectroscopy.

Calibration transfer for use with spectroscopic instruments, particularly for near-infrared, infrared, and Raman analysis, has been the subject of multiple articles, research papers, book chapters, and technical reviews. There has been a myriad of approaches published and claims made for resolving the problems associated with transferring calibrations; however, the capability of attaining identical results over time from two or more instruments using an identical calibration still eludes technologists. Calibration transfer, in a precise definition, refers to a series of analytical approaches or chemometric techniques used to attempt to apply a single spectral database, and the calibration model developed using that database, for two or more instruments, with statistically retained accuracy and precision. Ideally, one would develop a single calibration for any particular application, and move it indiscriminately across instruments and achieve identical analysis or prediction results. There are many technical aspects involved in such precision calibration transfer, related to the measuring instrument reproducibility and repeatability, the reference chemical values used for the calibration, the multivariate mathematics used for calibration, and sample presentation repeatability and reproducibility. Ideally, a multivariate model developed on a single instrument would provide a statistically identical analysis when used on other instruments following transfer. This paper reviews common calibration transfer techniques, mostly related to instrument differences, and the mathematics of the uncertainty between instruments when making spectroscopic measurements of identical samples. It does not specifically address calibration maintenance or reference laboratory differences.

Multivariate Calibration Transfer between two Potentiometric Multisensor Systems

AbstractA multivariate calibration of multisensor systems is a very important stage in their application. Construction of reliable calibration model requires significant investment of time and money. Once established, calibration model can be applied normally only together with that particular multisensor system, which was employed for its’ construction – this can be a serious limitation for a wide adoption of multisensor systems in common laboratory practice. In order to address this issue we have studied the applicability of several calibration transfer techniques, such as direct standardization, single wavelength standardization and standardization with regularization coefficient for the data obtained from two potentiometric multisensor systems in analysis of complex lanthanide mixtures. It was found that mathematical correction of sensor array response using standardization with regularization coefficient allows for using the regression model derived for one sensor array together with the data obtained from another sensor array. The value of root mean squared error of prediction for total lanthanide concentration increased insignificantly (0.10 instead of 0.07 in log scale) compared with that provided by the first multisensor system.

Standardization from a benchtop to a handheld NIR spectrometer using mathematically mixed NIR spectra to determine fuel quality parameters

The interest in performing in field measures using portable instruments is growing increasingly. Calibration transfer techniques can be used to enable models, predicted values or spectra obtained in a benchtop instrument be used in portable instrument, saving money and time required for a complete recalibration. Most of the calibration transfer methods require a set of transfer samples which spectra have to be acquired in both spectrometers. The present work evaluates the use of virtual standards as transfer samples in the reverse standardization (RS) method in order to standardize very dissimilar spectral responses of fuel samples (gasoline and biodiesel blends) from a high-resolution benchtop Frontier FT-NIR (PerkinElmer) spectrometer and a handheld MicroNIR™1700 (JDSU). These virtual standards can be created by mathematically mixing spectra from the pure solvents present in gasoline or diesel/biodiesel (D/B) blends, to avoid volatilization and changes in the composition of the compounds during storage and/or transportation of the real transfer fuel samples. Virtual standards were created using ten and five pure solvents for gasoline and D/B blends, respectively. Partial least squares regression (PLS) models were built for five quality parameters of gasoline (distillation temperatures at 10%, 50%, 90% and final boiling point (FBP) volume recovered and density) and one of D/B blends (biodiesel content). The RMSEP values obtained after the standardization approaches were equivalent to the reproducibility of the reference methods, except for density and biodiesel content parameters obtained for the virtual samples standardization approach. RS procedure provided promising results showing that it is possible to transfer gasoline or D/B blend spectra acquired with a high-resolution benchtop instrument to the handheld MicroNIR using virtual standards as transfer samples.

Calibration update strategies for an array of potentiometric chemical sensors

One of the obstacles to the practical use of the multisensor systems – electronic tongues is a drift, i.e. gradual change of the sensor characteristics occurring in the process of their exploitation. Sensor drift leads to the deterioration of the performance of the calibration model that was calculated before changes in the sensor responses occurred. Two approaches can be employed to deal with this phenomenon. One consists of regular re-calibration of the sensor array, which is effective but time and labor consuming. Alternatively, multivariate statistical methods can be applied to the calibration model update using small subset of the standard samples. While significant efforts have been directed to the development of the calibration transfer and calibration update techniques, they were mostly applied to the near infrared spectroscopic instruments. Very few works addressed this issue for the potentiometric sensor arrays. In the present study, applicability of calibration update methods including slope and bias correction, direct standardization using PLS2 regression and auto-associative neural network, calibration update by weighting, Tikhonov regularization and Joint Y-PLS regression to the sensor array data was evaluated. Calibration models for copper and lead quantification in mixed solutions of transition metals using an array of 7 potentiometric sensors was used as an example. Of all studied methods, weighting and JYPLS regression were shown to be the most effective permitting to reduce prediction error for new data to the level of cross-validation error for the initial data.

Calibration transfer in temperature modulated gas sensor arrays

Shifts in working temperature are an important issue that prevents the successful transfer of calibration models from one chemical instrument to another. This effect is of special relevance when working with gas sensor arrays modulated in temperature. In this paper, we study the use of multivariate techniques to transfer the calibration model from a temperature modulated gas sensor array to another when a global change of temperature occurs. To do so, we built 12 identical master sensor arrays composed of three different types of commercial Figaro sensors and acquired a dataset of sensor responses to three pure substances (ethanol, acetone and butanone) dosed at 7 concentrations. The master arrays are then shifted in temperature (from −50 to 50°C, ΔT=10°C) and considered as slave arrays. Data correction is performed for an increasing number of transfer samples with 4 different calibration transfer techniques: Direct Standardization, Piece-wise Direct Standardization, Orthogonal Signal Correction and Generalized Least Squares Weighting. In order to evaluate the performance of the calibration transfer, we compare the Root Mean Square Error of Prediction (RMSEP) of master and slave arrays, for each instrument correction. Best results are obtained from Piece-wise Direct standardization, which exhibits the lower RMSEP values after correction for the smaller number of transfer samples.

Intercalibration of Microwave Radiometer Brightness Temperatures for the Global Precipitation Measurement Mission

A technique for comparing spaceborne microwave radiometer brightness temperatures (Tb) is described in the context of the upcoming National Aeronautics and Space Administration Global Precipitation Measurement (GPM) mission. The GPM mission strategy is to measure precipitation globally with high temporal resolution by using a constellation of satellite radiometers logically united by the GPM core satellite, which will be in a non-sun-synchronous medium inclination orbit. The usefulness of the combined product depends on the consistency of precipitation retrievals from the various microwave radiometers. The Tb calibration requirement to achieve such consistency demands first that Tb's from the individual radiometers be free of instrument and measurement artifacts and, second, that these self-consistent Tb's will be translated to a common standard (GPM core) for the unification of the precipitation retrieval. The intersatellite radiometric calibration technique described herein serves both the purposes by comparing individual radiometer observations to radiative transfer model (RTM) simulations (for “self-consistency” check) and by using a double-difference technique (to establish a linear calibration transfer function from one radiometer to another). This double-difference technique subtracts the RTM-simulated difference from the observed difference between a pair of radiometer Tb's. To establish a linear inter-radiometer calibration transfer function, comparisons at both the cold (ocean) and the warm (land) end of the Tb's are necessary so that, using these two points, slope and offset coefficients are determined. To this end, a simplified calibration transfer technique at the warm end (over the Amazon and Congo rain forest) is introduced. Finally, an error model is described that provides an estimate of the uncertainty of the radiometric bias estimate between comparison radiometer channels.

Use of synthetic wine for models transfer in wine analysis by HS-MS e-nose

A method to verify the feasibility of the calibration transfer technique in HS-MS wine analysis is proposed. PLS multivariate calibration models, whose elaboration and prediction quality are fully discussed, have been built for the quantitative determination of three of the most common volatiles found in wine aroma: ethyl hexanoate (EH), isoamyl acetate (IA) and 2-methyl butanol (MB). The method involves the use of a fortified synthetic wine to overcome the instability drawbacks related to the natural evolution of real wines. The results showed a decrease of the prediction error through the time for the three compounds when the calibration transfer is applied.