Nonlinear Calibration Method Research Articles

A Combined Sensor Design Applied to Large-Scale Measurement Systems.

The photoelectric sensing unit in a large-space measurement system primarily determines the measurement accuracy of the system. Aiming to resolve the problem whereby existing sensing units have difficulty accurately measuring the hidden points and free-form surfaces in large components, in this study, we designed a multi-node fusion of a combined sensor. Firstly, a multi-node fusion hidden-point measurement model and a solution model are established, and the measurement results converge after the number of nodes is simulated to be nine. Secondly, an adaptive front-end photoelectric conditioning circuit, including signal amplification, filtering, and adjustable level is designed, and the accuracy of the circuit function is verified. Then, a nonlinear least-squares calibration method is proposed by combining the constraints of the multi-position vector cones, and the internal parameters of the probe, in relation to the various detection nodes, are calibrated. Finally, a distributed system and laser tracking system are introduced to establish a fusion experimental validation platform, and the results show that the standard deviation and accuracy of the three-axis measurement of the test point of the combined sensor in the measurement area of 7000 mm × 7000 mm × 3000 mm are better than 0.026 mm and 0.24 mm, respectively, and the accuracy of the length measurement is within 0.28 mm. Further, the measurement accuracy of the hidden point of the aircraft hood and the free-form surface is better than 0.26 mm, which can meet most of the industrial measurement needs and expand the application field of large-space measurement systems.

Designing optimal sensor arrays: leveraging hard modeling for improved performance.

In a sensor array system with the ability to design multiple sensor elements, selecting the optimal sensor elements can maximize the efficiency of the sensor array in responding to various analytes. This paper proposes the application of hard chemical modeling as a means to identify the optimal subset of indicator displacement assay (IDA)-based sensors in the array, aiming to achieve maximum performance for detection or quantification. The model governing all reactions in the IDA sensor and the model of the pure spectrum of active species are first determined. Next, by applying the model of the pure spectrum of active species (including the indicator and indicator-receptor complex) to each sensor element and taking into account the system's nonlinearity, corrected concentration profiles of active species are derived using the generalized classical least square (G-CLS) method. These corrected concentration profiles are utilized as the output signal for each sensor element. Finally, the dynamic ranges (DR) of each sensor element and subsequently the DR for all possible sensor arrays are determined.To assess the effectiveness of the sensor array through dynamic range analysis, an IDA-based sensor system comprising five different elements was designed. It was observed that sensors with a larger dynamic range, when arranged together in an array, are more efficient for the quantitative identification of analytes. However, simply increasing the number of elements in the sensor array may not necessarily enhance its effectiveness; instead, it could amplify the noise within the system. Additionally, multivariate fitting regression with Gaussian function (MFRG), a nonlinear calibration method, was applied to assess the prediction ability of all possible designed sensor arrays.

Comparative study of linear and nonlinear calibration algorithm for extrapolation ability of near infrared spectroscopy quantitative analysis

The determination of the o-nitrotoluene (o-MNT) content in separation process of mononitrotoluene (MNT) is of interest, since it affects the purity of m-nitrotoluene (m-MNT) and p-nitrotoluene (p-MNT). In real-world applications, the calibration model inevitably requires dealing with complex extrapolation problems. Therefore, this study extracted the spectral features of the o-nitrotoluene based on the interval selection algorithm. The linear calibration method (partial least squares (PLS)) and nonlinear calibration methods (support vector machine (SVM), back propagation (BP), random forest (RF), extreme learning machine (ELM)) were used to build the calibration models based on o-nitrotoluene samples in different concentration ranges, and the prediction accuracy and robustness of the calibration model were compared. The results indicate that the effectiveness of different calibration methods is different when going from prediction accuracy to robustness. The prediction accuracy and robustness of RF models are not satisfactory. BP models, which are capable of producing very accurate results in terms of prediction accuracy, are not able to solve extrapolation problems. PLS model has more advantages in model prediction accuracy. ELM has shown the best behavior in terms of robustness of model, but is inferior to PLS in terms of prediction accuracy.

1H NMR monitoring of dynamic microstructure evolution of HACP during adsorption of different fluids

In order to develop functional concrete suitable for sponge city construction, a high adsorbent cement paste (HACP) was prepared. The capillary water absorption performance of the HACP reached 70 kg/m2, and the compressive strength reached more than 30 MPa. Taking advantage of the relatively large proportion of capillary pores in HACP and combining the test results of MIP and BET, a non-linear calibration method for low-field NMR was proposed. The conversion of calibration results of different fluids such as diesel oil (DO), soybean oil (SO), and engine oil (EO) were realized. Through 1H NMR real-time monitoring of the adsorption process of water, DO, SO and EO in cement slurry, it was found that HACP is mainly capillary adsorption, and the adsorption rate of the fluid is negatively correlated with the viscosity. In addition, it was also found that fluid transmission is not always from small pores to large pores, but also from gel pores to interlayer pores. Finally, a dynamic microstructure evolution model of HACP was proposed.

On-line monitoring of total sugar during kombucha fermentation process by near-infrared spectroscopy: Comparison of linear and non-linear multiple calibration methods

Kombucha is widely recognized for its health benefits, and it facilitates high-quality transformation and utilization of tea during the fermentation process. Implementing on-line monitoring for the kombucha production process is crucial to promote the valuable utilization of low-quality tea residue. Near-infrared (NIR) spectroscopy, together with partial least squares (PLS), backpropagation neural network (BPANN), and their combination (PLS-BPANN), were utilized in this study to monitor the total sugar of kombucha. In all, 16 mathematical models were constructed and assessed. The results demonstrate that the PLS-BPANN model is superior to all others, with a determination coefficient (R2p) of 0.9437 and a root mean square error of prediction (RMSEP) of 0.8600 g/L and a good verification effect. The results suggest that NIR coupled with PLS-BPANN can be used as a non-destructive and on-line technique to monitor total sugar changes.

3D error calibration of spatial spots based on dual position-sensitive detectors.

In this paper, a dual position-sensitive detector-based vision measurement system camera is built instead of a traditional CCD camera. The 3D position information for the light point is calculated according to the 2D coordinate information of a certain light point in the space illuminated on the two position-sensitive detector (PSD) photosensitive surfaces, which is used for position detection of the spatial light point. In addition, the positioning model for 2D PSDs with different spot sizes in the Gaussian spot mode is derived by the mathematical model of Lucovsky's differential equation for a PSD. For the nonlinear distortion of the PSD, a nonlinear error calibration method using a particle swarm combined with a back propagation neural network is proposed to correct the errors in the measured values through the relationship between the input and output values, to obtain the predicted value that approximates the real coordinates. Then, by comparing the influence of different spot sizes on the positioning accuracy, we conclude that the smaller the spot formed by the convergence of the beam under the optical lens, the higher the positioning accuracy. We believe this conclusion can help improve the accuracy of PSD measurements. Finally, a red LED light spot is set up, and the 3D position measurement and error calibration of the light spot is done by dual PSD cameras, which better solves the position detection problem of a space light spot under close-range conditions because it is fast, reliable, and easy to implement. It also provides an effective method to detect the motion trajectory of a moving light spot in space.

Quantitation of Flavor Compounds in Refill Solutions for Electronic Cigarettes Using HS-GCxIMS and Internal Standards.

New regulations on the use of flavor compounds in tobaccoless electronic cigarettes require comprehensive analyses. Gas chromatography coupled ion mobility spectrometry is on the rise as an analytical technique for analyzing volatile organic compounds as it combines sensitivity, selectivity, and easy usage with a full-range screening. A current challenge is the quantitative GCxIMS-analysis. Non-linear calibration methods are predominantly used. This work presents a new calibration method using linearization and its corresponding fit based on the relation between the reactant and analyte ions from the chemical ionization. The analysis of e-liquids is used to compare the presented calibration with an established method based on a non-linear Boltzmann fit. Since e-liquids contain matrix compounds that have been shown to influence the analyte signals, the use of internal standards is introduced to reduce these effects in GCxIMS-analysis directly. Different matrix mixtures were evaluated in the matrix-matched calibration to improve the quantitation further. The system's detection and quantitation limits were determined using a separate linear calibration. A matrix-matched calibration series of 29 volatile compounds with 12 levels were used to determine the concentration of these substances in a spiked, flavorless e-liquid and a banana-flavored e-liquid, validating the quality of the different calibrations.

Multi-frequency imaging with non-linear calibration of magnetoresistance sensors for surface and buried defects inspection

Timely inspecting defects has both safety and economic significance in industry. Eddy current testing (ECT) probes with array sensors are widely used for metal structures inspection. However, the sensitivities of array sensors are typically not consistent, resulting in different responses when measuring the same defect signal. Therefore, the array sensors of an array probe are desired to be calibrated. This paper proposes an ECT probe with an array of tunnel magnetoresistance (TMR) sensors and two excitation coils for surface and buried defects inspection. Alternating currents with different frequencies and amplitudes are driven through the two coils simultaneously. The TMR sensors have non-linear resistance vs. magnetic field curves. A non-linear calibration method based on the non-linear characteristic is proposed to calibrate the high frequency signals of the sensors. This calibration method does not need any extra equipment and takes account of the effect of the non-uniformity of the excitation magnetic field to some extent. Experimental result shows that the signal becomes much flatter after the calibration. The relative flat index is reduced from 38.6% to 11% by the calibration process. To validate the detectability of the proposed probe for different kinds of defects, surface and buried defects in aluminum samples are tested by the probe. It is seen that a small surface defect with sizes 1 mm (length) x 0.5 mm (width) x 0.5 mm (height) can be clearly identified from the high-frequency image. Two defects buried 3 mm below the top surface are also inspected. The peak signal values of the two defects are 0.69 V and 0.97 V, respectively, demonstrating that the probe can distinguish the 0.1 mm height difference of the two defects. In addition, a defect buried 10 mm under the surface is imaged by the probe. Thanks to the special probe design and excellent sensitivity of the TMR sensors, the probe has good sensitivity for both surface and buried defects.

FBG-based strain monitoring and temperature compensation for composite tank

The composite tank has become the trend and focus for the power system of the new generation aerospace vehicles. However, the evaluation methods and monitoring technologies for composite tanks under cryogenic experiments are still challenging problems. In this paper, fiber Bragg grating (FBG) is used to measure the strain and temperature of the stiffened composite tank under room and cryogenic temperature pressurization experiments. Temperature compensation based on the thermal stratification phenomenon is proposed to increase the reliability and accuracy of strain monitoring in cryogenic environment. A non-linear fitting based calibration method of temperature sensitivity coefficient addresses the non-linear relationship between the FBG wavelength and temperature change from liquid nitrogen temperature to room temperature. The results show that the average absolute error of the nonlinear calibration method in the cryogenic test is less than 50 με. It is also illustrated that the structural features of the sensor location can be qualitatively determined according to the strain changes of different sensors. The comparison of strain increments in the cyclic pressurization illustrated that the different performance degradation phenomena of different regions could be observed. These results provide strong support for the high stability and reliability of FBG-based composite tank strain monitoring.

Potential of visible and near infrared spectroscopy coupled with machine learning for predicting soil metal concentrations at the regional scale

Chemical analytical methods for metal analysis in soils are laborious, time-consuming and costly. This paper aims to evaluate the potential of short-range (SR) and full-range (FR) visible and infrared spectroscopy (vis-NIR) combined with linear and nonlinear calibration methods to estimate concentrations of nickel (Ni), cobalt (Co), cadmium (Cd), lead (Pb) and copper (Cu) in soils. A total of 435 soil samples were collected over agricultural sites, forest (7 %), pasture (5 %) and fallow land across a region in the northern part of Belgium. Generally, better predictions were obtained when using partial least squares regression (PLSR) and nonlinear calibration method [i.e., random forest (RF)] for processing of the spectral data, than when using support vector machine (SVM). FR generally outperformed SR and provided the best prediction results for Ni (R2p = 0.76), Co (R2p = 0.77), Cd (R2p = 0.64) and Pb (R2p = 0.65), when using PLSR and RF. SVM produced the best prediction result only for Pb (R2p = 0.57) using the SR spectra. The metals Ni, Co, Cd and Pb can be predicted successfully (good accuracy) from the FR vis-NIR spectra using PLSR for Co, and RF for Ni, Cd, Pb and Cu. Compared to the FR spectrophotometer, improvement in accuracy was obtained for Cd and Co, using the SR spectra when combined with PLSR and RF, respectively. It is concluded that the SR spectrometer can be used successfully for the prediction of Co with RF (R2p = 0.70), while it best predicted Cd with PLSR with an R2p value of 0.67, which is of value for regional survey.

In-situ quantitative monitoring the organic contaminants uptake onto suspended microplastics in aquatic environments

Microplastics act as a source of organic contaminants in aquatic environments and thus affect their environmental fate and toxicity. Because of the weak and reversible interactions between microplastics and organic species, the organic coronas vary with their surrounding environments. Thus, in order to evaluate the possible environmental risks of microplastics, methods for evaluating the dynamic uptake of organic contaminants onto suspended microplastics in aquatic environments are greatly desired. In this work, a UV–vis spectroscopy-based approach was developed for in-situ monitoring organic contaminants uptake onto suspended microplastics after correcting the light scattering interference from microplastics suspensions and establishing the nonlinear relationship between concentration and light absorbance of organic species. The inverse adding-doubling method based on radiative transfer theory was adopted to correct the light scattering effect of suspensions. Then, the resulting mixed absorption spectra were decomposed to calculate the concentrations of the aqueous and adsorbed organic species simultaneously with a nonlinear calibration method. The uptake processes of bisphenol A and p-nitrophenol onto nylon 66 microparticles were monitored with this approach and confirmed by high-performance liquid chromatography analysis. The approach was validated by applying it to natural water samples, and the equilibrium adsorption capacity was found to be interfered mainly by the protein-like substances. This approach has high accuracy, good reproducibility, remarkable universality, and ease of handling, and also provides a potential tool for characterizing the corona formation process on suspended particles both in natural and artificial environments, such as eco-corona formation and engineering surface modification on nano/micro-particles.

T1w/FLAIR ratio standardization as a myelin marker in MS patients

IntroductionCalculation of a T1w/T2w ratio was introduced as a proxy for myelin integrity in the brain of multiple sclerosis (MS) patients. Since nowadays 3D FLAIR is commonly used for lesion detection instead of T2w images, we introduce a T1w/FLAIR ratio as an alternative for the T1w/T2w ratio. ObjectivesBias and intensity variation are widely present between different scanners, between subjects and within subjects over time in T1w, T2w and FLAIR images. We present a standardized method for calculating a histogram calibrated T1w/FLAIR ratio to reduce bias and intensity variation in MR sequences from different scanners and at different time-points. Material and methods207 Relapsing Remitting MS patients were scanned on 4 different 3 T scanners with a protocol including 3D T1w, 2D T2w and 3D FLAIR images. After bias correction, T1w/FLAIR ratio maps and T1w/T2w ratio maps were calculated in 4 different ways: without calibration, with linear histogram calibration as described by Ganzetti et al. (2014), and by using 2 methods of non-linear histogram calibration. The first nonlinear calibration uses a template of extra-cerebral tissue and cerebrospinal fluid (CSF) brought from Montreal Neurological Institute (MNI) space to subject space; for the second nonlinear method we used an extra-cerebral tissue and CSF template of our own subjects. Additionally, we segmented several brain structures such as Normal Appearing White Matter (NAWM), Normal Appearing Grey Matter (NAGM), corpus callosum, thalami and MS lesions using Freesurfer and Samseg. ResultsThe coefficient of variation of T1w/FLAIR ratio in NAWM for the no calibrated, linear, and 2 nonlinear calibration methods were respectively 24, 19.1, 9.5, 13.8. The nonlinear methods of calibration showed the best results for calculating the T1w/FLAIR ratio with a smaller dispersion of the data and a smaller overlap of T1w/FLAIR ratio in the different segmented brain structures. T1w/T2w and T1w/FLAIR ratios showed a wider range of values compared to MTR values. ConclusionsCalibration of T1w/T2w and T1w/FLAIR ratio maps is imperative to account for the sources of variation described above. The nonlinear calibration methods showed the best reduction of between-subject and within-subject variability. The T1w/T2w and T1w/FLAIR ratio seem to be more sensitive to smaller changes in tissue integrity than MTR. Future work is needed to determine the exact substrate of T1w/FLAIR ratio and to obtain correlations with clinical outcome.

Chemical model-based optimization of a sensor array for simultaneous determination of glucose and fructose

In order to present the general idea of model based optimization, the chemical system of interaction between boronic acids and diols was selected to implement the idea on a real chemical system for simultaneous determination of analytes. The reaction between diols and phenylboronic acids changes pH and the variation in pH depends on the diols concentrations.Glucose and fructose were determined using a sensor array including two sensor elements with different concentrations of phenylboronic acid in the presence of NaOH. Here, the sensor array denotes the different sensor solutions in which their responses to analytes are studied. Each sensor element solution contains phenylboronic acid (as a main part of the sensor) and a pH indicator for monitoring the pH change as a consequence of analytes and phenylboronic acid interaction. The optimum compositions of sensor elements were selected using the results of the calculation based on the chemical model and known equilibrium constants. Fructose and glucose were determined using the nonlinear calibration method (radial basis function artificial neural network) in concentration range 0.5–7 mM and 1–9 mM with root mean squares error prediction (RMSEP) 0.2 and 0.8 mM, respectively. The calculated figures of merit showed that the sensor is more sensitive to fructose.

AutoCCS: automated collision cross-section calculation software for ion mobility spectrometry-mass spectrometry.

MotivationIon mobility spectrometry (IMS) separations are increasingly used in conjunction with mass spectrometry (MS) for separation and characterization of ionized molecular species. Information obtained from IMS measurements includes the ion’s collision cross section (CCS), which reflects its size and structure and constitutes a descriptor for distinguishing similar species in mixtures that cannot be separated using conventional approaches. Incorporating CCS into MS-based workflows can improve the specificity and confidence of molecular identification. At present, there is no automated, open-source pipeline for determining CCS of analyte ions in both targeted and untargeted fashion, and intensive user-assisted processing with vendor software and manual evaluation is often required.ResultsWe present AutoCCS, an open-source software to rapidly determine CCS values from IMS-MS measurements. We conducted various IMS experiments in different formats to demonstrate the flexibility of AutoCCS for automated CCS calculation: (i) stepped-field methods for drift tube-based IMS (DTIMS), (ii) single-field methods for DTIMS (supporting two calibration methods: a standard and a new enhanced method) and (iii) linear calibration for Bruker timsTOF and non-linear calibration methods for traveling wave based-IMS in Waters Synapt and Structures for Lossless Ion Manipulations. We demonstrated that AutoCCS offers an accurate and reproducible determination of CCS for both standard and unknown analyte ions in various IMS-MS platforms, IMS-field methods, ionization modes and collision gases, without requiring manual processing. Availability and implementation https://github.com/PNNL-Comp-Mass-Spec/AutoCCS.Supplementary information Supplementary data are available at Bioinformatics online. Demo datasets are publicly available at MassIVE (Dataset ID: MSV000085979).

Radial basis function-artificial neural network (RBF-ANN) for simultaneous fluorescent determination of cysteine enantiomers in mixtures

The determination of chiral compounds is critically important in chemical and pharmaceutical sciences. Cysteine amino acid is one of the important chiral compounds where each enantiomer (L and D) has different effects on fundamental physiological processes. The unique optical properties of nanoparticles make them a suitable probe for the determination of different analytes. In this work, the water-soluble thioglycolic acid (TGA)-capped cadmium-telluride (CdTe) quantum dots (QDs) were applied as optical nanoprobe for the simultaneous determination of cysteine enantiomers. The difference in the kinetics of the interactions between L- and D-cysteine with CdTe QDs is used for multivariate quantitative analysis. Multivariate methods are superior to univariate methods in determining the concentration of each enantiomer in the mixture without the information about the total chiral analyte concentration. As a nonlinear calibration method the radial basis function -artificial neural network (RBF-ANN) model was more successful in predicting L-and D-cysteine concentrations than the linear partial least squares regression (PLS) model.

A nonlinear solar magnetic field calibration method for the filter-based magnetograph by the residual network

Context. The method of solar magnetic field calibration for the filter-based magnetograph is normally the linear calibration method under weak-field approximation that cannot generate the strong magnetic field region well due to the magnetic saturation effect. Aims. We try to provide a new method to carry out the nonlinear magnetic calibration with the help of neural networks to obtain more accurate magnetic fields. Methods. We employed the data from Hinode/SP to construct a training, validation and test dataset. The narrow-band Stokes I, Q, U, and V maps at one wavelength point were selected from all the 112 wavelength points observed by SP so as to simulate the single-wavelength observations of the filter-based magnetograph. We used the residual network to model the nonlinear relationship between the Stokes maps and the vector magnetic fields. Results. After an extensive performance analysis, it is found that the trained models could infer the longitudinal magnetic flux density, the transverse magnetic flux density, and the azimuth angle from the narrow-band Stokes maps with a precision comparable to the inversion results using 112 wavelength points. Moreover, the maps that were produced are much cleaner than the inversion results. The method can effectively overcome the magnetic saturation effect and infer the strong magnetic region much better than the linear calibration method. The residual errors of test samples to standard data are mostly about 50 G for both the longitudinal and transverse magnetic flux density. The values are about 100 G with our previous method of multilayer perceptron, indicating that the new method is more accurate in magnetic calibration.

On-field test and data calibration of a low-cost sensor for fine particles exposure assessment

BackgroundAccurate individual exposure assessment is crucial for evaluating the health effects of particulate matter (PM). Various portable monitors built upon low-cost optical sensors have emerged. However, the main challenge for their application is to guarantee accuracy of measurements. ObjectiveTo assess the performance of a newly developed PM sensor, and to develop methods for post-hoc data calibration to optimize its data quality. MethodWe conducted a series of laboratory experiments and field evaluations to quantify the reproducibility within Plantower PM sensors 7003 (PMS 7003) and the consistency between sensors and two established PM2.5 measurement methods [tapered element oscillating microbalances (TEOM) and gravimetric method (GM)]. Post-hoc data calibration methods for sensors were based on a multiple linear regression model (MLRM) and a random forest model (RFM). Ratios of raw and calibrated readings over the data of reference methods were calculated to examine the improvement after calibration. ResultsStrong correlations (≥0.82) and relatively small relative standard deviations (16–21%) between sensors were found during the laboratory and the field sampling. Compared with the reference methods, moderate to strong coefficients of determination (0.56–0.83) were observed; however, significant deviations were presented. After calibration, the ratios of PMS measurements over that of two reference methods both became convergent. ConclusionsOur study validated low-cost optical PM sensors under a wide range of PM2.5 concentrations (8–167 μg/m3). Our findings indicated potential applicability of PM sensors in PM2.5 exposure assessment, and confirmed a need of calibration. Linear calibration methods may be sufficient for ambient monitoring using TEOM as a reference, while nonlinear calibration methods may be more appropriate for indoor monitoring using GM as a reference.

A Nonlinear Calibration Method Based on Sinusoidal Excitation and DFT Transformation for High‐Precision Power Analyzers

For most high‐precision power analyzers, the measurement accuracy may be affected due to the nonlinear relationship between the input and output signal. Therefore, calibration before measurement is important to ensure accuracy. However, the traditional calibration methods usually have complicated structures, cumbersome calibration process, and difficult selection of calibration points, which is not suitable for situations with many measurement points. To solve these issues, a nonlinear calibration method based on sinusoidal excitation and DFT transformation is proposed in this paper. By obtaining the effective value data of the current sinusoidal excitation from the calibration source, the accurate calibration process can be done, and the calibration efficiency can be improved effectively. Firstly, through Fourier transform, the phase value at the initial moment of the fundamental frequency is calculated. Then, the mapping relationship between the sampling value and the theoretical calculation value is established according to the obtained theoretical discrete expression, and a cubic spline interpolation method is used to further reduce the calibration error. Simulations and experiments show that the calibration method presented in this paper achieves high calibration accuracy, and the results are compensation value after calibration with a deviation of ±3 × 10−4.

A Non-Linear Magnetic Field Calibration Method for Filter-Based Magnetographs by Multilayer Perceptron

For filter-based magnetographs, the linear calibration method under the weak-field assumption is usually adopted; this leads to magnetic saturation effect in the regions with strong magnetic field. This article explores a new method to overcome the above disadvantage using a multilayer perceptron network, which we call MagMLP, based on a back-propagation algorithm with one input layer, five hidden layers, and one output layer. We use the data from the \textit{Spectropolarimeter} (SP) on board \textit{Hinode} to simulate single-wavelength observations for the model training, and take into account the influence of the Doppler velocity field and the filling factor. The training results show that the linear fitting coefficient (LFC) of the transverse field reaches above 0.91, and that of the longitudinal field is above 0.98. The generalization of the models is good because the corresponding LFCs are above 0.9 for the test subsets. Compared with the linear calibration method, the MagMLP is much more effective on dealing with the magnetic saturation effect. Analyzing an active region, the results of the linear calibration present an evident magnetic saturation effect in the umbra regions; the corresponding systematic error reaches values greater than 1000 G in most areas, or even exceeds 2000 G at some pixels. However, the results of MagMLP at these locations are very close to the inversion results, and the systematic errors are basically within 300 G. In addition, we find that there are many "bright spots" and "dark spots" on the inclination angle images from the inversion results of \textit{Hinode}/SP with values of 180 and 0 degrees, respectively, where the inversion is not reliable and does not produce a good result; the MagMLP handles these points well.

UV-VIS-NIR spectroscopy and artificial neural networks for the cholesterol quantification in egg yolk

Artificial neural networks (ANN) are non-linear calibration methods that try to simulate the human nervous system with neurons, layers and transfer functions. In this study the input neurons were the UV-VIS-NIR spectra of egg yolks and the outputs were cholesterol content. Therefore, the aim of this work was to develop and assess a UV-VIS-NIR method coupled to chemometrics and ANN for cholesterol determination in egg yolks. A total of 57 samples of egg yolks were obtained from fresh shell eggs and pasteurised egg yolks. A total of 2311 variables (wavelengths) with four variable selection methods were analysed. An enzymatic method was used as reference method for cholesterol content. The effect of the solvent was also evaluated. The best results were obtained based on selected absorbance peaks. Calibration results showed r2cal over 0.90 and RMSEC below 0.65. Validation results showed r2val close to 0.7. The ANN model developed could be useful to determine cholesterol in egg yolk for routine quality control.