Quantitative Calibration Research Articles

Insights from the quantitative calibration of an elasto-plastic model from a Lennard-Jones atomic glass

We compare the macroscopic and the local plastic behavior of a model amorphous solid based on two radically different numerical descriptions. On the one hand, we simulate glass samples by atomistic simulations. On the other, we implement a mesoscale elasto-plastic model based on a solid-mechanics description. The latter is extended to consider the anisotropy of the yield surface via statistically distributed local and discrete weak planes on which shear transformations can be activated. To make the comparison as quantitative as possible, we consider the simple case of a quasistatically driven two-dimensional system in the stationary flow state and compare mechanical observables measured on both models over the same length scales. We show that the macroscale response, including its fluctuations, can be quantitatively recovered for a range of elasto-plastic mesoscale parameters. Using a newly developed method that makes it possible to probe the local yield stresses in atomistic simulations, we calibrate the local mechanical response of the elasto-plastic model at different coarse-graining scales. In this case, the calibration shows a qualitative agreement only for an optimized subset of mesoscale parameters and for sufficiently coarse probing length scales. This calibration allows us to establish a length scale for the mesoscopic elements that corresponds to an upper bound of the shear transformation size, a key physical parameter in elasto-plastic models. We find that certain properties naturally emerge from the elasto-plastic model. In particular, we show that the elasto-plastic model reproduces the Bauschinger effect, namely the plasticity-induced anisotropy in the stress-strain response. We discuss the successes and failures of our approach, the impact of different model ingredients and propose future research directions for quantitative multi-scale models of amorphous plasticity.

Deep learning aided quantitative analysis of anti-tuberculosis fixed-dose combinatorial formulation by terahertz spectroscopy

Anti-tuberculosis fixed-dose combinatorial formulation (FDCs) is an effective drug for the treatment of tuberculosis. As a compound medicine, its efficacy is based on the comprehensive action of multiple main ingredients. If the content of an active ingredient is insufficient, not only will it reduce the curative effect, but it also causes patients to develop drug resistance and leads to the evolution of drug-resistant strains of tuberculosis, which hamper the treatment of the disease. Thus accurate detection of the contents of active components in the anti-tuberculosis FDC is the key link of its quality control. For the first time, convolutional neural networks (CNN), one of the most popular deep learning methods, is adopted to develop a quantitative calibration model based on terahertz time-domain spectroscopy (THz-TDS) for the accurate detection of the content of each active component in the anti-tuberculosis FDCs. For comparison with CNN, partial least squares regression (PLSR) was also introduced to build a reference quantitative calibration model. For CNN modeling, the raw THz spectral is fed to the model directly; While for PLSR, prior to the spectrum feeding to the model, the raw spectral data are processed by multiple different combinations of preprocessing. Experimental and simulation results demonstrate that although preprocessing techniques can improve the prediction performance of PLSR, its prediction capabilities is still inferior to CNN based on raw spectrum. Therefore, for the quantitative analysis of the content of each active component in the anti-tuberculosis FDCs, CNN appears to be an ideal modeling method.

AUTENTIKASI CEPAT MADU HUTAN KALIMATAN TIMUR DENGAN ATR-FTIR SPEKTROSKOPI KOMBINASI ANALISIS KEMOMETRIKA

Honey adulteration is mostly conducted by the addition of sucrose. In this study, the authentication of honey was conducted using ATR-FTIR and chemometrics. Pure honey samples (MA) were collected from nine regions in East Kalimantan. The ATR-FTIR spectra of these samples were then compared to sucrose-adulterated honey (MS), which were prepared in the sucrose concentration from 2.5 to 50% (v / v).The data analysis was performed using chemometrics techniques: 1) Principle Component Analysis (PCA) method, 2) classification with Discriminant Analysis (DA), and 3) regression with (PCR) and (PLS). As a result, PCA was able to visualize the differences between MS and MA. DA analysis was able to distinguish MS and MA at wave numbers from 1200 to 800 cm-1 with 92.5% performance index. Quantitative calibration models of the sucrose-adulterated honey could be obtained from PLS and PCR, while the best calibration model was obtained with the PLS method from the 2nd derivative spectra. In summary, sucrose-adulterated honey from East Kalimantan can be authenticated using ATR-FTIR method in combination with chemometric analysis.

Evaluation of gases’ molecular abundance ratio by fiber-optic laser-induced breakdown spectroscopy with a metal-assisted method

A metal-assisted method is proposed for the evaluation of gases’ molecular abundance ratio in fiber-optic laser-induced breakdown spectroscopy (FO-LIBS). This method can reduce the laser ablation energy and make gas composition identification possible. The principle comes from the collision between the detected gases and the plasma produced by the laser ablation of the metal substrate. The interparticle collision in the plasma plume leads to gas molecules dissociating and sparking, which can be used to determine the gas composition. The quantitative relationship between spectral line intensity and molecular abundance ratio was developed over a large molecular abundance ratio range. The influence of laser ablation energy and substrate material on gas quantitative calibration measurement is also analyzed. The proposed metal-assisted method makes the measurement of gases’ molecular abundance ratios possible with an FO-LIBS system.

Identifying Internal Stresses during Mechanophore Activation

Mechanophores (MPs) undergo chemical reactions to become fluorescent in response to a mechanical stimulus that reflects the magnitude and distribution of applied stress. MPs are an emerging technology for self‐reporting damage sensing applications in polymeric materials in the aeronautical, energy generation, and automotive industries. However, quantitative calibration of the MP response to local stresses remains an outstanding challenge. Herein, a method to calibrate the intensity of the MP fluorescent activation (I) with local hydrostatic stresses (σh) is presented. Uniaxial tension is applied to a simple composite comprised of a rigid sphere (silica) embedded in a MP‐functionalized elastomeric matrix (spiropyran (SPN) functionalized polydimethylsiloxane (PDMS)). By monitoring the fluorescence intensity with a confocal microscope while a quasi‐static deformation is applied, in situ observations of MP activation as a function of applied uniaxial strain are obtained. To calculate the associated stress fields, a finite element analysis (FEA) with cohesive zone elements is employed. By comparing σh, calculated through FEA with the I of the PDMS/SPN system, a linear relationship between I and σh is directly determined. The technique presented can be employed for many MP‐containing materials systems to calibrate I to σh.

Chromatographic Applications in the Multi-Way Calibration Field.

In this review, recent advances and applications using multi-way calibration protocols based on the processing of multi-dimensional chromatographic data are discussed. We first describe the various modes in which multi-way chromatographic data sets can be generated, including some important characteristics that should be taken into account for the selection of an adequate data processing model. We then discuss the different manners in which the collected instrumental data can be arranged, and the most usually applied models and algorithms for the decomposition of the data arrays. The latter activity leads to the estimation of surrogate variables (scores), useful for analyte quantitation in the presence of uncalibrated interferences, achieving the second-order advantage. Recent experimental reports based on multi-way liquid and gas chromatographic data are then reviewed. Finally, analytical figures of merit that should always accompany quantitative calibration reports are described.

Partial least squares regression residual extreme learning machine (PLSRR-ELM) calibration algorithm applied in fast determination of gasoline octane number with near-infrared spectroscopy

Based on near-infrared (NIR) spectroscopy, a new quantitative calibration algorithm, called “Partial Least Squares Regression Residual Extreme Learning Machine (PLSRR-ELM)”, was proposed for fast determination of research octane number (RON) for blended gasoline. In this algorithm, partial least square (PLS) cooperates with non-linear extreme learning machine (ELM) to separate the relationship information suitable for each other from the raw relationship information (between NIR spectrum and corresponding property) with the unknown degree of non-linearity, with aim of calibrating them respectively. Since the advantages of both PLS and ELM are fully utilized, it is expected that PLSRR-ELM can address the relationship information more effectively and leads to improved calibration performance over PLS and ELM alone. The calibration performance of PLSRR-ELM was evaluated by a set of on-line gasoline blending sample data from a refinery. As a result, it showed an enhanced prediction performance, e.g., about 13% or 11% decrease in the root mean squared error of test (RMSE-T) over PLS or ELM alone, respectively. In method comparison, the model performance of PLRR-ELM exceeds all other methods including PLS, Poly-PLS, KPLS, ELM, and ANN, demonstrating its superiority for fast prediction of gasoline RON.

Correcting bias in log-linear instrument calibrations in the context of chemical ionization mass spectrometry

Abstract. Quantitative calibration of analytes using chemical ionization mass spectrometers (CIMSs) has been hindered by the lack of commercially available standards of atmospheric oxidation products. To accurately calibrate analytes without standards, techniques have been recently developed to log-linearly correlate analyte sensitivity with instrument operating conditions. However, there is an inherent bias when applying log-linear calibration relationships that is typically ignored. In this study, we examine the bias in a log-linear-based calibration curve based on prior mathematical work. We quantify the potential bias within the context of a CIMS-relevant relationship between analyte sensitivity and instrument voltage differentials. Uncertainty in three parameters has the potential to contribute to the bias, specifically the inherent extent to which the nominal relationship can capture true sensitivity, the slope of the relationship, and the voltage differential below which maximum sensitivity is achieved. Using a prior published case study, we estimate an average bias of 30 %, with 1 order of magnitude for less sensitive compounds in some circumstances. A parameter-explicit solution is proposed in this work for completely removing the inherent bias generated in the log-linear calibration relationships. A simplified correction method is also suggested for cases where a comprehensive bias correction is not possible due to unknown uncertainties of calibration parameters, which is shown to eliminate the bias on average but not for each individual compound.

Planar laser-induced photofragmentation fluorescence for quantitative ammonia imaging in combustion environments

Ammonia is regarded as a potential carbon-free alternative fuel. To have a better understanding of its combustion characteristics, development of feasible techniques for ammonia detection in combustion environments is essential. In the present work, planar laser-induced photofragmentation fluorescence (LIPF) was developed for quantitative measurements of ammonia in hot gas flows. A 193 nm ArF excimer laser was used to photo-dissociate ammonia. As a fragment, NH* radicals were generated, and the fluorescence at 336 nm was used for ammonia detection. Quantitative calibration of the LIPF signal was performed in hot flows either from laminar flames or an electric heating-pipe with a known amount of ammonia and temperature. Ultraviolet absorption was used to obtain accurate concentrations of ammonia in the hot flows from laminar flames. The single-shot detection limit of the LIPF technique was estimated to be ∼50 ppm and ∼130 ppm in hot flue gas at 1140 and 1750 K, respectively, and ∼0.2 ppm in a room-temperature nitrogen flow. The technique was applied to detect the slip of ammonia in a premixed laminar ammonia-air flame with a fuel-air equivalence ratio of 1.2. Over 5000 ppm unburned ammonia was detected in the post-flame region and measurements showed good agreement with predictions of a chemical model.

Quantitative Detection of Components in Polymer-Bonded Explosives through Near-Infrared Spectroscopy with Partial Least Square Regression.

The components in polymer-bonded explosive X, including cyclotetramethylene-tetranitramine, paraffin, and polytetrafluoroethylene, were determined using near-infrared (NIR) spectroscopy. Using partial least squares as the multivariate calibration method, quantitative calibration models for components in X were verified internally and externally. The possible combinations of eight general spectral pretreatment methods and different bands of the scanning spectral region (12,500–4000 cm–1) were established. The models were analyzed, evaluated, and optimized via the fitting effect. The data were combined with the mathematical meaning of the model spectral pretreatment methods and the chemical significance of the modeling spectral bands. Prediction performance offered optimal quantitative calibration models. Paired bilateral Student’s t tests show that there is no significant difference between the values obtained by NIR and chemical analysis methods, and the NIR method has good accuracy. Moreover, the precision of the NIR method is better than that of the chemical method, and the analysis time is reduced from 2 days to a few minutes.

The impacts of elevated temperature and mNaCl for in situ Raman quantitative calibration of dissolved gas species

In the past ten years, Raman spectroscopy analysis has been widely applied to the quantification of the components of hydrothermal fluids and fluid inclusions. However, the accuracy of the quantitative results of Raman spectroscopy is highly dependent on the quantitative calibration model. How temperature and salinity affect quantitative calibration models remains an unsolved question, which severely hinders the accurate geochemical characterization of hydrothermal fluids and fluid inclusions. In this study, the Raman spectra of the symmetric stretching mode of methane (~ 2917 cm−1) in homogeneous CH4-containing aqueous solutions were obtained at temperatures from 25 to 300 °C, pressures from 0.1 to 40 MPa, and mNaCl from 0 to 5.0 mol/kg to research the effects of elevated temperature and salinity on the Raman spectroscopy quantitative measurement of gas species, and two Raman calibration models of CH4 that reference OH bending and stretching bands were established. Both models show high dependence on salinity. Large measurement errors, causing underestimation of methane concentrations by up to 57.5%, occur if the salinity of the fluids is not taken into account during the calibration. The contributions of the Raman scattering cross section (RSCS) and molar density to the change of quantification factors (QFs) were quantified, and the results showed that the variation of the RSCS is the main factor determining QF values. The significant discrepancy between the measured and predicted QFs demonstrates that changes in the peak areas of both H2O and CH4 contribute to the dependence of quantitative calibration models on temperature and salinity.

In-line monitoring and endpoint determination of percolation process of herbal medicine using ultraviolet spectroscopy combined with convolutional neural network.

As a common step in the herbal medicine production process, percolation usually lacks effective process monitoring methods and is often conducted with fixed process parameters. In this study, an in-line ultraviolet (UV) spectroscopy was used for monitoring the Caulis Sinomenii percolation process. The spectra and concentration data of 156 percolation samples from five batches were collected. Convolutional neural networks (CNNs) were used to develop quantitative calibration models. The mean squared error (MSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) were compared to select the proper loss function for developing the CNN models. Meanwhile, partial least square regression (PLSR) was also used to develop calibration models for performance comparison. The CNN models with MAPE or MAE as the loss function could provide accurate predictions for all samples. However, CNN models adopting MSE as the loss function tended not to predict low-concentration samples accurately. The CNN models mostly achieved satisfactory results without any preprocessing techniques and surpassed PLSR models in all the performance metrics. An in-line UV spectroscopy system combining the CNN algorithm was implemented to monitor the percolation process of Caulis Sinomenii. The system can accurately determine the endpoint of the percolation process.

A tephra-based approach to calibrating relative geomagnetic paleointensity stacks to absolute values

Relative paleointensity (RPI) stacks provide a relative, global paleointensity variation, which reflects variations in the geocentric axial dipole moment. Previous calibrations of an RPI stack rely on adjusting a mean RPI to a mean of virtual axial dipole moments (VADMs), which are selected from absolute paleointensity (API) databases. To achieve more quantitative calibration of an RPI stack into absolute values, we propose a novel approach that compares (1) tephra-derived APIs and (2) the same tephras' horizons, in the oxygen isotope stratigraphy, tied RPIs (hereafter termed the “TA-TOR” approach). For the TA-TOR approach, APIs were newly determined for welded tuffs of 15 pyroclastic flow deposits in Japan. Eight of the 15 new APIs and 2 previously reported APIs have tephra horizons in the oxygen isotope stratigraphy. Based on the 10 data of the TA-TOR approach, combined with 4 data of the transitional field, VADMs calculated from the APIs (VADMAPI) were compared with isochronous RPIs from two RPI stacks, PISO-1500 and Sint-2000. The 14 data points in RPI vs. VADMAPI diagrams show a linear relationship with correlation coefficients of 0.83–0.89. Using the observed linear relationships, the two RPI stacks were reliably calibrated to VADM values (VADMRPI) for the period since 1.1 Ma. Compared with the VADMs calibrated by the previous studies, the present calibration provides a 12–13% smaller median/mean and a larger dispersion as represented by a 15–53% larger standard deviation for the two RPI stacks. The similarities between the cumulative distributions of VADMRPI from the two stacks and those of VADMs selected from the API database indicate the reliability of the present calibration. These results show that the present calibration, which relies on the TA-TOR approach, accurately converts a RPI stack into VADMRPI values and will contribute to studies on time variations in the geocentric axial dipole moment, sedimentary RPI, and cosmogenic radionuclides. Variations in VADMRPI for the past 1.1 Myr, obtained from PISO-1500 and Sint-2000 via the present calibration, can be used as standard geocentric axial dipole variations for this period.

Quantitative distribution characterization of gradient composition of additive-manufactured stainless steel using micro-beam X-ray fluorescence

The fabrication of the compositionally graded metals provides a new frontier in additive manufacturing and the characterization of the elemental distribution of graded materials is strongly desired for the quality control of powder mixing and improvement of additive manufacturing process parameters. In this study, Micro-beam X-ray fluorescence (μ-XRF) was used to characterize the elemental distribution of additive-manufactured compositionally graded stainless steel on a large area. A poly-capillary X-ray optics unit was used to obtain a high spatial resolution with the spot size of 20-μm. The quantitative distribution characterization of Ni, Mo, Cr, Mn, Si and Cu elements in the whole deposition direction have been realized through the improvement of μ-XRF quantitative calibration method by using certified reference materials with composition and microstructure similar to the graded sample. The composition distribution variation in different cladded layer was also investigated with the pixel distance decreased to 20 μm. Moreover, laser-induced breakdown spectroscopy was used to analyze the composition distribution of the composition-graded steels to verify the reliability of quantitative distribution characterization results by μ-XRF method. The elemental distribution results from two methods have good agreement, but the results determined by μ-XRF can give more details about the composition distribution in different cladded layers because of its higher spatial resolution, which is closely related to additive manufacturing process. The microstructure content varied linearly with the change of composition. The austenite volume fraction increased with an increase in Ni content and the evolution rule of the structure agreed with the calculated thermodynamics model results. Cracks appeared in the martensite and austenite transformation region with a large volume change when the Ni content ranged between 6.5% and 8.5% for the gradient stainless steels.

Variant curation expert panel recommendations for RYR1 pathogenicity classifications in malignant hyperthermia susceptibility

PurposeAs a ClinGen Expert Panel (EP) we set out to adapt the American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) pathogenicity criteria for classification of RYR1 variants as related to autosomal dominantly inherited malignant hyperthermia (MH). MethodsWe specified ACMG/AMP criteria for variant classification for RYR1 and MH. Proposed rules were piloted on 84 variants. We applied quantitative evidence calibration for several criteria using likelihood ratios based on the Bayesian framework. ResultsSeven ACMG/AMP criteria were adopted without changes, nine were adopted with RYR1-specific modifications, and ten were dropped. The in silico (PP3 and BP4) and hotspot criteria (PM1) were evaluated quantitatively. REVEL gave an odds ratio (OR) of 23:1 for PP3 and 14:1 for BP4 using trichotomized cutoffs of ≥0.85 (pathogenic) and ≤0.5 (benign). The PM1 hotspot criterion had an OR of 24:1. PP3 and PM1 were implemented at moderate strength. Applying the revised ACMG/AMP criteria to 44 recognized MH variants, 29 were classified as pathogenic, 13 as likely pathogenic, and 2 as variants of uncertain significance. ConclusionCuration of these variants will facilitate classification of RYR1/MH genomic testing results, which is especially important for secondary findings analyses. Our approach to quantitatively calibrating criteria is generalizable to other variant curation expert panels.

Artificial Intelligence Empowered Multispectral Vision Based System for Non-Contact Monitoring of Large Yellow Croaker (Larimichthys crocea) Fillets.

A non-contact method was proposed to monitor the freshness (based on TVB-N and TBA values) of large yellow croaker fillets (Larimichthys crocea) by using a visible and near-infrared hyperspectral imaging system (400–1000 nm). In this work, the quantitative calibration models were built by using feed-forward neural networks (FNN) and partial least squares regression (PLSR). In addition, it was established that using a regression coefficient on the data can be further compressed by selecting optimal wavelengths (35 for TVB-N and 18 for TBA). The results validated that FNN has higher prediction accuracies than PLSR for both cases using full and selected reflectance spectra. Moreover, our FNN based model has showcased excellent performance even with selected reflectance spectra with rp = 0.978, R2p = 0.981, and RMSEP = 2.292 for TVB-N, and rp = 0.957, R2p = 0.916, and RMSEP = 0.341 for TBA, respectively. This optimal FNN model was then utilized for pixel-wise visualization maps of TVB-N and TBA contents in fillets.

Relating Near-Infrared Light Path-Length Modifications to the Water Content of Scattering Media in Near-Infrared Spectroscopy: Toward a New Bouguer-Beer-Lambert Law.

In near-infrared spectroscopy (NIRS), the linear relationship between absorbance and an absorbing compound concentration has been strictly defined by the Bouguer-Beer-Lambert law only for the case of transmission measurements of nonscattering media. However, various quantitative calibrations have been successfully built both on reflectance measurements and for scattering media. Although the lack of linearity for scattering media has been observed experimentally, the sound multivariate statistics and signal processing involved in chemometrics have allowed us to overcome this problem in most cases. However, in the case of samples with varying water content, important modifications of scattering levels still make calibrations difficult to build due to nonlinearities. Moreover, even when calibration procedures are successfully developed, many preprocessing methods used do not guarantee correct spectroscopic assignments (in the sense of a pure chemical absorbance). In particular, this may prevent correct modeling and interpretation of the structure of water. In this study, dynamic near-infrared spectra acquired during a drying process allow the study of the physical effects of water content variations, with a focus on the first overtone OH absorbance region. A model sample consisting of aluminum pellets mixed with water allowed us to study this specifically, without any other absorbing interaction terms related to the dry mass-absorbing constituents. A new formulation of the Bouguer-Beer-Lambert law is proposed, by expressing path length as a power function of water content. Through this new formulation, it is shown that a better and simpler prediction model of water content may be developed, with more precise and accurate identification of water absorbance bands.

Understanding the root cause(s) of nonlinearities in near infrared spectroscopy

NIR process monitoring and NIR hyperspectral video generates a deluge of non-selective spectral data, information-rich but per se useless. This paper demonstrates how interpretable data modelling can lead to simpler and better use of such NIR Big Data: A set of simple powder mixtures of the main constituents in wheat flour were measured by NIR transmission under different measurement conditions. Their absorbance spectra were submitted to multivariate calibration for predicting the protein content, by standard chemometric calibration by PLS regression. A reasonable calibration model was obtained, but it was unexpectedly complex and not robust. However, closer inspection the PLS regression subspace showed a surprising structure. This allowed us to identify the problem: Non-additive, strongly overlapping light scattering and light absorption effects in the NIR absorbance spectra. Based on this insight, a pragmatic, but causal preprocessing model was set up and iteratively optimized for predictive ability. This nonlinear optimized extended signal correction (OEMSC) separated and quantified the main physical and chemical sources of variation in the spectra. The preprocessing greatly simplified the NIR spectra and their quantitative calibration and prediction.

Determination of Chromium Based on Laser Ablation Inductively Coupled Plasma Mass Spectrometry

Abstract Chromium plays an irreplaceable role in the life system. The exploration of chromium is beneficial to reveal its physiological or pathological processes. In this study, a novel method was established for determination of chromium in gels by matrix-matched external standard method-laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The relative standard deviation of chromium signal intensity during ablation standard were less than 16% (n = 300), the correlation coefficient (R2) of LA-ICP-MS quantitative calibration curve was 0.9805, and the detection limit was 2.22 µg/g. The MCF-7 cells were used as an in vitro cell model, and two kinds of polyacrylamide gel electrophoresis (PAGE) method were used to separate the protein extract after chromium incubation. And then, the electrophoresis bands obtained by two electrophoresis methods were ablated respectively by LA-ICP-MS with line scan mode, and the weak chromium-containing protein bands were obtained in electrophoresis band. Finally, the LA-ICP-MS was used to measure and image the chromium in individual cell, and it was found that the cell element distribution map overlapped well with the cell contour map under the laser microscope.

Unveiling non-linear water effects in near infrared spectroscopy: A study on organic wastes during drying using chemometrics

In the context of organic waste management, near infrared spectroscopy (NIRS) is being used to offer a fast, non-destructive, and cost-effective characterization system. However, cumbersome freeze-drying steps of the samples are required to avoid water’s interference on near infrared spectra. In order to better understand these effects, spectral variations induced by dry matter content variations were obtained for a wide variety of organic substrates. This was made possible by the development of a customized near infrared acquisition system with dynamic highly-resolved simultaneous scanning of near infrared spectra and estimation of dry matter content during a drying process at ambient temperature. Using principal components analysis, the complex water effects on near infrared spectra are detailed. Water effects are shown to be a combination of both physical and chemical effects, and depend on both the characteristics of the samples (biochemical type and physical structure) and the moisture content level. This results in a non-linear relationship between the measured signal and the analytical characteristic of interest. A typology of substrates with respect to these water effects is provided and could further be efficiently used as a basis for the development of local quantitative calibration models and correction methods accounting for these water effects.