Global Calibration Research Articles

Soil organic matter prediction with benchtop and implement‐mounted optical reflectance sensing approaches

AbstractProximal sensing technologies can densely quantify soil organic matter (OM) variability using visible and near infrared (VNIR) reflectance spectroscopy. However, issues with global calibrations and varying water content can reduce accuracy. Therefore, research was conducted to determine OM prediction accuracy across soil volumetric water content (VWC) with (a) a planter‐mounted optical sensor and (b) multiple combinations of reflectance bands within the VNIR spectrum. Ninety soils collected across Missouri and Illinois were used in the study. Data were collected at three VWC with the Precision Planting SmartFirmer and a benchtop spectrometer. SmartFirmer OM was derived through an internal algorithm, while spectral pre‐processing and machine learning were used for OM prediction in all approaches that used reflectance from the benchtop spectrometer. Results found that SmartFirmer OM predictions were affected by soil VWC, with predicted OM decreasing with increasing VWC. Findings from three modeling approaches using the benchtop spectrometer showed that a continuous spectrum (i.e., 400–1,500 nm) improved performance (RMSE = 5.25 g kg–1) over the discrete waveband approach (RMSE = 9.23 g kg–1). Furthermore, including the entire VNIR region (400–2,500 nm) resulted in the best predictive capability (RMSE = 1.42 g kg–1). However, because a full‐spectrum approach increases cost, using reflectance from 400 to 1,500 nm along with spectral pre‐processing and machine learning may be an acceptable method for estimating OM. These findings contribute to the improvement of commercially available proximal sensors that may be used to monitor soil carbon (C) stocks, assess soil health, or for other precision agriculture applications.

Planktic-benthic foraminifera ratio (%P) as a tool for the reconstruction of paleobathymetry and geohazard: A case study from Taiwan

The calcite tests of foraminifera are an important biogenic component of marine sediments. The abundance of foraminiferal tests in marine sediments broadly varies with bathymetry, thus has been used to reconstruct paleobathymetry. It is also promising as a tracer for downslope transport triggered by earthquakes and typhoons, especially if the displaced material from shallow locality contrasts strongly with the background autochthonous sediments in terms of foraminiferal abundance, such as the ratio of benthic and planktic foraminifera termed %P. However, its applicability in sediments off Taiwan has not been assessed. Taiwan is located in the path of typhoons and at tectonic plate margins, where typhoons and earthquakes may trigger submarine geohazards. This, combined with the fact that its seafloor spans a large bathymetric range, render this region an ideal natural laboratory to evaluate the applicability of %P as a proxy for tracing submarine geohazards and bathymetry. Here we report foraminiferal abundance, %P, grain size and elemental data from 148 surface sediment samples off 6 sectors off Taiwan, namely Southern Okinawa Trough, Hoping-Nanao-Hateruma Basins, Taitung-Hualien, Hengchun Ridge, Gaoping, and Changyun Sand Ridge. Of all the hydrographic and sedimentological parameters assessed, seafloor bathymetry is the major driver of foraminiferal abundance and %P in these regions. Notably, several data points deviate from the regional %P-water depth relationship. Based on sedimentological parameters and previous studies, we posit that these outliers may have to do with local sedimentation setting. These processes include earthquake-induced sediment transport via submarine canyon in the Southern Okinawa Trough, typhoon-triggered sediment flushing in Gaoping Canyon, cross-shelf and northward advection of planktic foraminifera on the Gaoping shelf, and carbonate dissolution in the deep Hateruma Basin. Off Taiwan, the %P value in sediments increases exponentially with bathymetry (R2 = 0.72, n = 81), and agrees well with the global calibration obtained by combining data from several regions of the global ocean (R2 = 0.86, n = 1004). The regional %P-water depth relationship may be useful for reconstructing paleobathymetry here, albeit with an uncertainty in the range of 14–1600 m. The uncertainty increases with water depth. Our results also highlight the potential of the %P index as a tracer for downslope transport and lateral advection in the water column. In conclusion, the downcore application of %P has the potential to reconstruct past geohazard events while also identifying autochthonous sediment sequences that are suitable for paleoceanographic reconstruction.

Deep Learning Based Calibration Time Reduction for MOS Gas Sensors with Transfer Learning

In this study, methods from the field of deep learning are used to calibrate a metal oxide semiconductor (MOS) gas sensor in a complex environment in order to be able to predict a specific gas concentration. Specifically, we want to tackle the problem of long calibration times and the problem of transferring calibrations between sensors, which is a severe challenge for the widespread use of MOS gas sensor systems. Therefore, this contribution aims to significantly diminish those problems by applying transfer learning from the field of deep learning. Within the field of deep learning, transfer learning has become more and more popular. Nowadays, building a model (calibrating a sensor) based on pre-trained models instead of training from scratch is a standard routine. This allows the model to train with inherent information and reach a suitable solution much faster or more accurately. For predicting the gas concentration with a MOS gas sensor operated dynamically using temperature cycling, the calibration time can be significantly reduced for all nine target gases at the ppb level (seven volatile organic compounds plus carbon monoxide and hydrogen). It was possible to reduce the calibration time by up to 93% and still obtain root-mean-squared error (RMSE) values only double the best achieved RMSEs. In order to obtain the best possible transferability, different transfer methods and the influence of different transfer data sets for training were investigated. Finally, transfer learning based on neural networks is compared to a global calibration model based on feature extraction, selection, and regression to place the results in the context of already existing work.

Extrapolating distortion correction with local measurements for space-based multi-module splicing large-format infrared cameras

Conventional distortion correction methods with the classical models, including radial, decentering, and thin prism distortions and with the interpolation template, depend heavily on the evenly distributed measurement data on the entire focal plane. However, owing to the restricted cubage of the vacuum tank and the large size of the assembled camera, there is no more extra space for the amounted large-format camera to adjust with the 2D turntable during laboratory vacuum experiment, which, accordingly, makes the collected measurement points gathered in just one module of the focal plane and eventually results in poor correction accuracy of the mentioned approaches. Here, in terms of the problems above, an extrapolating distortion correction method with local measurements for space-based multi-module splicing large-format infrared cameras was proposed in this paper. Benefiting from the polynomial model not being affected by the distribution of data, a third-order polynomial model adopted for distortion correction is solved by using local measurements and extrapolated reasonably, which guarantees the global camera calibration. Experimental results show that the mean distortion error can be corrected within 0.5 pixels. This method overcoming the deficiency of local test points can effectively improve the correction accuracy of the large-format camera and provide a new idea for global high-precision calibration of on-orbit payloads based on local measurements.

NIR Spectroscopy as an Online PAT Tool for a Narrow Therapeutic Index Drug: Toward a Platform Approach Across Lab and Pilot Scales for Development of a Powder Blending Monitoring Method and Endpoint Determination.

An online near-infrared (NIR) spectroscopy platform system for real-time powder blending monitoring and blend endpoint determination was tested for a phenytoin sodium formulation. The study utilized robust experimental design and multiple sensors to investigate multivariate data acquisition, model development, and model scale-up from lab to manufacturing. The impact of the selection of various blend endpoint algorithms on predicted blend endpoint (i.e., mixing time) was explored. Spectral data collected at two process scales using two NIR spectrometers was incorporated in a single (global) calibration model. Unique endpoints were obtained with different algorithms based on standard deviation, average, and distributions of concentration prediction for major components of the formulation. Control over phenytoin sodium's distribution was considered critical due to its narrow therapeutic index nature. It was found that algorithms sensitive to deviation from target concentration offered the simplest interpretation and consistent trends. In contrast, algorithms sensitive to global homogeneity of active and excipients yielded the longest mixing time to achieve blending endpoint. However, they were potentially more sensitive to subtle uniformity variations. Qualitative algorithms using principal component analysis (PCA) of spectral data yielded the prediction of shortest mixing time for blending endpoint. The hybrid approach of combining NIR data from different scales presents several advantages. It enables simplifying the chemometrics model building process and reduces the cost of model building compared to the approach of using data solely from commercial scale. Success of such a hybrid approach depends on the spectroscopic variability captured at different scales and their relative contributions in the final NIR model.

Optimisation of the predictive ability of NIR models to estimate nutritional parameters in elephant grass through LOCAL algorithms

Elephant grass is a tropical forage widely used for livestock feed. The analytical techniques traditionally used for its nutritional evaluation are costly and time consuming. Alternatively, Near Infrared Spectroscopy (NIRS) technology has been used as a rapid analysis technique. However, in crops with high variability due to genetic improvement, predictive models quickly lose accuracy and must be recalibrated. The use of non-linear models such as LOCAL calibrations could mitigate these issues, although a number of parameters need to be optimized to obtain accurate results. The objective of this work was to compare the predictive results obtained with global NIRS calibrations and with LOCAL calibrations, paying special attention to the configuration parameters of the models.The results obtained showed that the prediction errors with the LOCAL models were between 1.6 and 17.5 % lower. The best results were obtained in most cases with a low number of selected samples (n = 100–250) and a high number of PLS terms (n = 20). This configuration allows a reduced computation time with high accuracy, becoming a valuable alternative for analytical determinations that require ruminal fluid, which would improve the welfare of the animals by avoiding the need to surgically prepare animals to estimate the nutritional value of the feeds.

Interlaboratory performance and quantitative PCR data acceptance metrics for NIST SRM® 2917

Surface water quality quantitative polymerase chain reaction (qPCR) technologies are expanding from a subject of research to routine environmental and public health laboratory testing. Readily available, reliable reference material is needed to interpret qPCR measurements, particularly across laboratories. Standard Reference Material® 2917 (NIST SRM® 2917) is a DNA plasmid construct that functions with multiple water quality qPCR assays allowing for estimation of total fecal pollution and identification of key fecal sources. This study investigates SRM 2917 interlaboratory performance based on repeated measures of 12 qPCR assays by 14 laboratories (n = 1008 instrument runs). Using a Bayesian approach, single-instrument run data are combined to generate assay-specific global calibration models allowing for characterization of within- and between-lab variability. Comparable data sets generated by two additional laboratories are used to assess new SRM 2917 data acceptance metrics. SRM 2917 allows for reproducible single-instrument run calibration models across laboratories, regardless of qPCR assay. In addition, global models offer multiple data acceptance metric options that future users can employ to minimize variability, improve comparability of data across laboratories, and increase confidence in qPCR measurements.

A novel hierarchical iterative hypothesis strategy for intrinsic parameters calibration of laser structured-light weld vision sensor

In automatic welding, multi-line laser structured-light vision sensors provide more adequate information to compared with traditional single-line structured light and are gradually being widely designed. However, when calibrating the sensor, a global calibration approach should be adopted in order that the laser intrinsic parameters follow the inherent constraints. In this paper, a Lagrange solution is shown and its unsuitability for general cases is qualitatively analyzed. We then propose a novel hierarchical iterative hypothesis (HIH) strategy to deal with issues with the following conditions. First, the configuration and pose (including rotation and translation) of a 3D model need to be solved. Second, the model can be obtained by scaling a known model in several dimensions. Third, part of sampling points of the model can be acquired. The proposed hypothesis is introduced based on the coordinate transformation theory. Hierarchical iteration, including global and local iteration, is elaborated mathematically, which utilizes singular value decomposition (SVD) and gradient descent technology. The process of applying hierarchical iterative hypothesis strategy HIH is illustrated, and a reconfirmation for transformation is introduced. The steps in the intrinsic parameters calibration for weld vision sensor are described. A calibration experiment is witnessed that the ambiguity on the intersection issue is reduced to compared with ordinary least square method for structured light calibration. Several quality measures were introduced that allow us to judge the performance of our method. Two experiments for verifying calibration results reveal that the proposed strategy earns a great calibration performance that can meet weld seam tracking needs.

The effect of local samples in the accuracy of mid-infrared (MIR) and X-ray fluorescence (XRF) -based spectral prediction models

Within the soil spectroscopy community, there is an ongoing discussion addressing the comparison of the performance of prediction models built on a global calibration database, versus a local calibration database. In this study, this issue is addressed by spiking of global databases with local samples. The soil samples were analysed with MIR and XRF sensors. The samples were further measured using traditional wet chemistry methods to build the prediction models for seventeen major parameters. The prediction models applied by AgroCares, the company that assisted in this study, combine spectral information from MIR and XRF into a single ‘fused-spectrum’. The local dataset of 640 samples was split into 90% train and 10% test samples. To illustrate the benefits of using local calibration samples, three separate prediction models were built per element. For each model, 0%, 50% (randomly selected) and 100% of the local training samples were added to the global dataset. The remaining 10% local samples were used for validation. Seventeen soil parameters were selected to illustrate the differences in performance across a range of soil qualities, using the validation set to measure performance. The results showed that many models already exhibit an excellent level of performance (R2 ≥ 0.95) even without local samples. However, there was a clear trend that, as more local calibration samples were added, both R2 and ratio of performance to interquantile distance (RPIQ) increase.

Global calibration method for non-overlapping cameras based on rigidly connected stereo targets.

In complex field of view (FOV) environments, a single camera's FOV measurement range is limited and cannot cover the entire object under test for global calibration. Multiple cameras are used mostly for large FOV environment measurements, but the traditional one- and two-dimensional targets used for global calibration in large FOV environments are prone to overlapping FOV. Furthermore, other large-sized targets are difficult to produce and process, and the laser projection method and plane mirror calibration methods are easily affected by the outdoor environment. To solve this problem, a non-common FOV binocular calibration method based on rigidly connected stereo targets is proposed. The calibration process is as follows: First, the rigidly connected target, which is composed of two plane targets with a checkerboard, is placed in front of the two cameras, and the vision sensor captures the corresponding sub-target image; then, the target is moved multiple times, and the transformation relationship between multiple vision sensors is obtained according to the spatial constraint characteristics of the rigidly connected target. Hence, the method overcomes the limitation of the non-overlapping FOV calibration method that relies on large measuring instruments. The experimental results show that the RMS error of the 13 mm distance is 0.16 mm. The proposed method is effective, simpler to operate than other methods, and does not rely on the constraint of complex targets. More importantly, this measurement method solves the difficult problem of measurement in non-public FOV, meeting the requirements of large FOV measurement ranges.

Research on global calibration method of large-scene multi-vision sensors in wheel alignment

Wheel measurement and positioning technology is an important technology in vehicle production, and is of great importance for the safety of vehicles. At present, visual measurement and other methods are widely used in automotive production and inspection, but these methods are limited to the measurement of regular-sized vehicles. There is no effective solution to the problem of wheel alignment for large special-purpose vehicles with multiple tires. In order to solve the wheel positioning problem of large-size special-purpose vehicles, this paper designs a vision measurement system for wheel parameters in large scenes by combining vision sensors with linear motion guides to extend the vision measurement range and complete the global calibration of multiple-vision sensors with the help of laser trackers and self-researched stereo targets. This paper proposes an Elementary–Advance global calibration algorithm to improve the accuracy of coordinate system conversion during global calibration. This paper establishes a correction model for the measurement errors that exist in the initial global calibration process, and iterates the objective function to optimize the coordinate system conversion parameters between multiple vision sensors to improve the measurement accuracy of the system. The experimental results show that the goodness of fit and the accuracy of fit for the feature cylinder are 98.31% and 99.03% based on the global calibration method of this paper. The standard deviation of measurements for the standard ruler of the Inva alloy is less than 0.391 mm in the large dimensional range of 6050 mm × 3500 mm × 800 mm. Finally, by comparing the measurement results with the TrukCam four-wheel alignment instrument, the feasibility of the visual measurement system designed in this paper for large scenarios of wheel parameters is verified, and the measurement accuracy requirements for four-wheel alignment of special-purpose vehicles are met.

Multivariate methods with feature wavebands selection and stratified calibration for soil organic carbon content prediction by Vis‐NIR spectroscopy

AbstractVisible–near‐infrared (Vis‐NIR) spectroscopy is increasingly used to predict soil organic carbon (SOC) content. However, the prediction accuracy of this technology is dependent on model selection and study scale. This study explored the roles of spectral variable selection and stratified calibration based on soil type in Vis‐NIR spectroscopy for predicting SOC content at a provincial scale. A total of 490 samples, collected in Jiangxi Province (southeast China), were used for modeling with partial least squares regression, support vector machine, random forests, and back‐propagation neural network (BPNN). The feature wavebands of soil samples were selected by competitive adaptive reweighted sampling (CARS), and a stratified calibration was conducted based on soil type. The results showed that CARS‐based models outperformed models with full wavebands in predicting the SOC content. The CARS‐BPNN model combined with stratified calibration showed the best prediction performance for total soils (validation set R2 = .82, which was .21 higher than that of BPNN based on global calibration). This study established an accurate method to predict SOC content from provincial‐scale spectral data using the CARS‐BPNN model coupled with stratified calibration based on soil type.

GLOBAL-LOCAL: A new approach for local predictions of soil organic carbon content using large soil spectral libraries

There is much interest in the use of large visible near infrared (vis–NIR) soil spectral libraries for rapid soil analysis and to further soils research. However, the use of such large and diverse spectral libraries often provides biased predictions at local scales. Here, we present a new approach, GLOBAL-LOCAL, which aims to use large libraries to develop spectroscopic models that can fit locally. This is achieved by selecting the most common spectral neighbours of a small representative set (Lab) from the target site using a combination of principal component analysis (PCA) and k-nearest neighbours, followed by selection of the calibration with the lowest root mean square error (RMSE) in predicting the Lab, which is then added to the calibration to derive the GLOBAL-LOCAL. We evaluated this approach by predicting the soil organic carbon (SOC) content in two different target sites, one in Canada (Lévis, QC; N = 111) and the other in Finland (Maaninka; N = 101). Partial least squares regression (PLSR) was used for model development and validation. The soil spectral library used was a combination of that developed by the World Agroforestry Center and International Soil Reference Information Centre (N = 3875), and that developed at the Quebec Research and Development Centre (N = 1051). Based on a preliminary analysis, we selected 25 Lab samples from each target site using conditioned Latin hypercube sampling (cLHS). Prediction accuracy by GLOBAL-LOCAL for Lévis and Maaninka (RMSE = 1.41 and 3.08 g kg−1, RPIQ = 5.9 and 4.1, respectively) were better than using the global calibration (RMSE = 20.3 and 23.6 g kg−1, RPIQ = 0.41 and 0.53, respectively). The GLOBAL-LOCAL was better or similar to calibrations with site-specific samples (RMSE = 2.38 and 4.68 g kg−1, RPIQ = 3.5 and 2.7, respectively) and Lab samples only (RMSE = 1.41 and 2.54 g kg−1, RPIQ = 5.9 and 4.9, for Lévis and Maaninka, respectively). In addition, GLOBAL-LOCAL outperformed three other approaches, RS-LOCAL, the spectrum-based learner and LOCAL. While 25 Lab samples was regarded as optimum by cLHS, further examination revealed that as little as 10 Lab samples may be sufficient for the GLOBAL-LOCAL. The method uses the information in the large spectral library so that it can substantially reduce analytical cost and greatly improve predictions on local scales.

Branched glycerol dialkyl glycerol tetraethers as indicators for environmental parameters in a subtropical mountainous river, southern China

Indices based on branched glycerol dialkyl glycerol tetraethers (brGDGTs) have been useful in paleo-environmental reconstructions, although their applicability in river-estuarine sediments remains unclear. This study aims to investigate the correlation between brGDGT-based indices and modern environmental parameters, and to discuss possible impact factors. In this study, we discussed the applicability of several brGDGT-based indices, with specific interests on the IRx’ (the relative abundance of 6- vs. 5-methylated brGDGTs) and CBT’ (cyclization index of branched tetraethers based on the separation of 5- and 6-methylated brGDGTs) for pH reconstruction, and MBT’5ME (the methylation index of branched tetraethers based on the separation of 5- and 6-methylated brGDGTs) for MAAT (mean annual air temperature) reconstruction. From the Jiulong River (JLR) catchment, we have analyzed both riverbank soil samples and riverbed surface sediment samples for brGDGTs, with the following main findings: Firstly, for pH reconstruction, the indices CBT’ and IRx’ are useful in soil and fluvial sedimentary settings. Secondly, for MAAT reconstruction, the MBT’5ME index is sensitive to the air temperature, and the BayMBT0 model performs relatively better than other global or regional calibrations in the study area; By employing the BayMBT0 model, MAAT reconstructed from soils are higher than the measured MAAT, but are generally lower than the measured MAAT when reconstructed from riverbed surface sediments. Thirdly, the disagreement between the reconstructed results from the two sedimentary archives suggests that: Signatures of brGDGTs in the aquatic environment are under strong impacts of in situ produced brGDGTs; Impacts of upper-reach brGDGTs input might have resulted in the increasingly negative offset for the reconstructed MAAT downstream; And influences of complex aquatic biogeochemical conditions on sedimentary brGDGT signals are site-dependent in this area. Finally, brGDGT-based indices are sensitive to the source of brGDGTs, e.g., soil vs aquatic in situ brGDGTs; And terrigenous brGDGTs have been exposed to a range of complex biogeochemical processes before their deposition in the estuary. Based on these findings, we suggest that at least the above impact factors need to be taken into consideration when interpreting the brGDGT signals from estuarine archives, and thus, multi-proxy studies are encouraged. The findings of this study provide a better understanding of the correlation between brGDGT-based indices and modern environmental parameters in subtropical river systems, and provide useful information for interpreting the paleo-environmental signals based on brGDGTs from fluvial-estuarine archives.

Photometric calibration methods for wide-field photometric surveys

Uniform and accurate photometric calibration plays an important role in the current and next-generation wide-field imaging surveys. Herein, we review the modern photometric calibration methods, including the classic standard star method, “hardware/observation-driven" methods (such as the Ubercalibration, Hypercalibration, and Forward Global Calibration Methods), and “software/physics-driven" methods (e.g., the Stellar Locus Regression, Stellar Locus, and Stellar Color Regression Methods). Further, we discuss their advantages, limitations, and future developments toward millimagnitude precision calibration.

Near infrared spectroscopy calibration strategies to predict multiple nutritional parameters of pasture species from different functional groups

Near infrared reflectance (NIR) spectroscopy has been used by the agricultural industry as a rapid and inexpensive technique to quantify nutritional chemistry in plants. The aim of this study was to evaluate the performance of NIR calibrations in predicting the nutritional composition of ten pasture species that underpin livestock industries in many countries. The species comprised a range of functional diversity (C3 legumes; C3/C4 grasses; annuals/perennials) and origins (tropical/temperate; introduced/native) that grew under varied environmental conditions (control and experimentally induced warming and drought) over a period of more than two years ( n = 2622). A maximal calibration set including 391 samples was used to develop and evaluate calibrations for all ten pasture species (global calibrations), as well as for subsets comprised of the plant functional groups. This study found that the global calibrations were appropriate to predict the six key nutritional quality parameters for the studied pasture species, with the highest estimation quality found for ash (ASH), crude protein (CP), amylase-treated neutral detergent fibre (aNDF) and acid detergent fibre (ADF), and the lowest for ether extract (EE) and acid detergent lignin (ADL) parameters. The plant functional group calibrations for C3 grasses performed better than the global calibrations for ASH, CP, ADF and EE parameters, whereas for C3 legumes and C4 grasses the functional group calibrations performed less well than the global calibrations for all nutritional parameters of these groups. Additionally, the calibrations were able to capture the range of variation in forage nutritional quality caused by future climate scenarios of warming and severe drought.

Assessment of watermelon maturity using portable new generation NIR spectrophotometers

The non-destructive evaluation of internal maturity in watermelons, which are large fruits with a thick rind, during their development on the vine can be considered as a challenge for the growing sector. Near infrared spectroscopy (NIRS) was used to assess in situ soluble solid content (SSC), the main parameter to establish full maturity, in 249 intact watermelons, of which, 152 had a striped light green rind and 97 a solid dark green rind. Two handheld new generation spectrophotometers were compared for this purpose. Different pre-processing methods and the partial least squares (PLS) regression algorithm were used to build global calibration models and specific calibration models for each one of the two types of watermelon analysed. The results obtained for the global models showed that NIRS is a suitable technology for screening the fruit for maturity, and that the linear variable filters (LVF) sensor is the best equipment for this purpose (SECV = 1.02%; RPDp = 1.36). Moreover, the best results were obtained when different models were used depending on the type of watermelon. Additionally, near infrared (NIR) classification models were developed to discriminate the samples by stage of maturity for each type of watermelon available, using partial least squares discriminant analysis (PLS-DA). The optimum threshold values for the striped light green and solid dark green rind watermelons (0.82 and 0.65, respectively) were displaced from the mean value of the discriminant variable due to the differences in terms of number of samples per class used. A total of 66.4% and 82.2% of the striped light green and solid dark green rind watermelons, respectively, were correctly classified. The results of this study demonstrated the viability of using NIRS technology as a decision-making support tool to measure the maturity of watermelons and to establish the optimum harvest time of watermelons and therefore meet the consumers’ demand for sweet-tasting fruits. Further studies will be needed to improve calibration robustness, and to further interpret outdoor applications in fruits with thick rind.

Multivision Sensor Extrinsic Calibration Method With Non-Overlapping Fields of View Using Encoded 1D Target

Multiple vision sensors (multi-VSs) are difficult to calibrate in large fields of view vision measurement owing to the lack of overlapping areas. Thus, this study proposes a novel method for the external parameter calibration of multi-VSs by using an encoded 1D target. The quick response code is used in this study to uniquely identify the feature circle. The Hough transform method and ellipse center solving method are jointly applied to optimize the feature points and further improve the collinearity of the feature points. As the cross-ratio of the collinearity feature points involves a projection invariance, the rotation and translation vectors between the multi-VSs are solved by the inverse process of projection and rigid transformation. The accuracy and advantages of the proposed method are verified by simulation and real experiments. The proposed target has a simple structure, the operation is convenient and flexible, and the method can be applied to the global calibration of multi-VSs with non-overlapping fields of view.

An accurate calibration method of a combined measurement system for large-sized components

Large-sized components with numerous supports are essential in advanced manufacturing. Efficient product quality control requires accurate three-dimensional (3D) measurements and error control. For 3D shape and spatial position measurements of the key local features on the support mounting surface, we propose an accurate measurement method combining a global laser tracker, local scanning system, and mobile robot system comprising an industrial robot and a mobile platform. To enhance the measurement accuracy, an accurate calibration method was introduced based on prior knowledge which establishes correlation constraints between local and global calibration. The local calibration method, including rail orientation and extrinsic parameters calibration based on a multi-dimensional combined collaboration target, achieves accurate calibration of both rail orientation and extrinsic parameters. Additionally, the enhanced global calibration method based on multi-dimensional cooperative calibrators constructs correlation constraints among global control points and provides an accurate global measurement field. We constructed a combined measurement system and conducted large-scale measurement experiments. As shown by the accuracy verification results of the proposed system, the maximum and mean measurement errors decreased from 0.142 and 0.132 mm to 0.111 and 0.095 mm within 8 m, respectively, which is highly accurate. Moreover, the large-scale scanning instance of a large-sized spacecraft cabin has also demonstrated that the developed method can significantly expand the measurement range as well as improve the measurement flexibility.

Determination of aroma compounds in grape mash under conditions of tasting by on-line near-infrared spectroscopy

The production of high-quality wines requires the use of high-quality grapes. Tasting represents a widespread method for the determination of grape maturity and quality aspects such as the corresponding aroma profile. However, sensory analysis always remains subjective and it is not possible to judge only aroma compounds because the overall impression is also influenced by main components (e.g. sugars and acids). In contrast, the use of near-infrared (NIR) spectroscopy allows the simultaneous determination of various compounds without being affected by personal preferences. In this study, grape mash samples were examined under comparable conditions to those in the mouth. Differences between grape mashes with varying phytosanitary status of the corresponding grapes as well as for different grape varieties were detected. The quantified concentrations of the detected aroma compounds were used to develop calibration models for determination by NIR spectroscopy. Using global calibration models, the single aroma compounds could be determined by NIR spectroscopy with accuracies reaching from R2C = 0.365 to R2C = 0.976. Separate calibration models for cultivation region and grape colour improved the prediction accuracy. Instrumental analysis cannot totally replace sensory evaluation, however, NIR spectroscopy has the potential to be used as an objective, additional method for the evaluation of grape aroma quality.