Calibration Quality Research Articles

Sistem Layanan Metrologi Legal Terpusat Berbasis Web

The development of information technology, especially in data management, can have a significant impact on improving efficiency and effectiveness in legal metrology services. Technological transformation not only changes how information is obtained but also fundamentally alters service systems for the public. A centralized web-based Legal Metrology Service System is being developed to streamline administrative data management and enhance services for individuals applying for metrological calibration and re-verification. Therefore, this system is designed using the waterfall method, ensuring each development stage, from needs analysis to implementation and maintenance, is conducted in a structured and systematic manner. The primary objective of this research is to improve the quality of calibration and re-verification services in Legal Metrology at the Office of Trade and Industry in Jombang Regency. With this system in place, it is expected that administrative processes and data management in legal metrology will operate more effectively and efficiently, thereby delivering better services to the public.

Mitigating calibration errors from mutual coupling with time-domain filtering of 21 cm cosmological radio observations

Abstract The 21 cm transition from neutral Hydrogen promises to be the best observational probe of the Epoch of Reionisation (EoR). This has led to the construction of low-frequency radio interferometric arrays, such as the Hydrogen Epoch of Reionization Array (HERA), aimed at systematically mapping this emission for the first time. Precision calibration, however, is a requirement in 21 cm radio observations. Due to the spatial compactness of HERA, the array is prone to the effects of mutual coupling, which inevitably lead to non-smooth calibration errors that contaminate the data. When unsmooth gains are used in calibration, intrinsically spectrally-smooth foreground emission begins to contaminate the data in a way that can prohibit a clean detection of the cosmological EoR signal. In this paper, we show that the effects of mutual coupling on calibration quality can be reduced by applying custom time-domain filters to the data prior to calibration. We find that more robust calibration solutions are derived when filtering in this way, which reduces the observed foreground power leakage. Specifically, we find a reduction of foreground power leakage by 2 orders of magnitude at k∥ ≈ 0.5 h Mpc−1.

Sample Preparation for Pharmaceuticals using A Bioanalytical Method: A Review

The process of developing a method that will allow a compound of interest to be located and measured in a biological matrix is known as bioanalytical method development. A substance can frequently be measured using a variety of techniques, and selecting an analytical technique requires careful thought. Different extraction techniques, such as liquid-liquid extraction, solid phase extraction (SPE), and protein precipitation, are used to analyse drugs and their metabolites in a biological matrix. Samples from these extraction methods are spiked with calibration (reference) standards and quality control (QC) samples. The process of establishing if a quantitative analytical method is suitable for biomedical applications is known as bioanalytical method. includes all processes that will be presented measuring analytes quantitatively in particular biological samples such blood plasma, serum, or urine. To evaluate a drug's effectiveness and safety, clinical and non-clinical toxicokinetic and pharmacokinetic studies are used. Therefore, in order to produce accurate results, it is crucial that the applied bioanalytical procedures utilised are thoroughly defined, verified, and documented to a suitable quality. An overview of the development and validation of bioanalytical methods is given in this article, along with key considerations for each stage of method validation. The created procedure is then verified. Evaluation and interpretation of bioavailability, bioequivalence, pharmacokinetic, and pharmacodynamic investigations heavily rely on bioanalytical validations. where various parameters are carried out, including accuracy, precision, selectivity, sensitivity, reproducibility, and stability.

A review on quality management system in laboratory testing

A Laboratory Quality Management System (LQMS) refers to a set of policies, procedures, and practices implemented in a laboratory to ensure consistent quality and accuracy in its operations. Continuous improvement of laboratory quality service is vital to ensure accuracy, reliability, and timeliness of laboratory results. Implementation of the quality management system is an effective way of monitoring and assuring laboratory quality service. Various standards and regulations outline specific requirements for implementing a Quality Management System (QMS) in a laboratory environment, such as ISO 15189:2022, ISO 17025:2017, and FDA 42 CFR Part 493. These standards provide guidelines and criteria for laboratory processes, including testing, calibration, documentation, personnel competency, and overall quality control. This article will discuss the applicable standards and legislation, the 12 essential elements of laboratory QMS, and the role of QMS software in a laboratory environment. The objective of this study is to assess the impact of laboratory quality management system implementation on improving quality laboratory service.

Strategy and Metrological Support for Indoor Radon Measurements Using Popular Low-Cost Active Monitors with High and Low Sensitivity.

Traditionally, for indoor radon testing, predominantly passive measurements have been used, typically applying the solid-state alpha track-etch method for long-term and the charcoal method for short-term measurements. However, increasingly, affordable consumer-grade active monitors have become available in the last few years, which can generate a concentration time series of an almost arbitrary duration. Firstly, we argue that consumer-grade monitors can well be used for quality-assured indoor radon assessment and consequent reliable decisions. Secondly, we discuss the requirements of quality assurance, which actually allow for reliable decision-making. In particular, as part of a rational strategy, we discuss how to interpret measurement results from low-cost active monitors with high and low sensitivity with respect to deciding on conformity with reference levels that are the annual average concentration of indoor radon. Rigorous analysis shows that temporal variations in radon are a major component of the uncertainty in decision-making, the reliability of which is practically independent of monitor sensitivity. Manufacturers of low-cost radon monitors already provide sufficient reliability and quality of calibration for their devices, which can be used by both professional inspectors and the general public. Therefore, within the suggested measurement strategy and metrologically assured criteria, we only propose to clarify the set and values of the key metrological characteristics of radon monitors as well as to upgrade user-friendly online tools. By implementing clear metrological requirements as well as the rational measurement strategy for the reliable conformity assessment of a room (building) with radon safety requirements, we anticipate significant reductions in testing costs, increased accessibility, and enhanced quality assurance and control (QA/QC) in indoor radon measurements.

HUST-Grace2024: a new GRACE-only gravity field time series based on more than 20 years of satellite geodesy data and a hybrid processing chain

Abstract. To improve the accuracy of monthly temporal gravity field models for the Gravity Recovery and Climate Experiment (GRACE) and the GRACE Follow-On (GRACE-FO) missions, a new series named HUST-Grace2024 is determined based on the updated L1B datasets (GRACE L1B RL03 and GRACE-FO L1B RL04) and the newest atmosphere and ocean de-aliasing product (AOD1B RL07). Compared to the previous HUST temporal gravity field model releases, we have made the following improvements related to updating the background models and the processing chain: (1) during the satellite onboard events, the inter-satellite pointing angles are calculated to pinpoint outliers in the K-band ranging (KBR) range-rate and accelerometer observations. To exclude outliers, the advisable threshold is 50 mrad for KBR range rates and 20 mrad for accelerations. (2) To relieve the impacts of KBR range-rate noise at different frequencies, a hybrid data-weighting method is proposed. Kinematic empirical parameters are used to reduce the low-frequency noise, while a stochastic model is designed to relieve the impacts of random noise above 10 mHz. (3) A fully populated scale factor matrix is used to improve the quality of accelerometer calibration. Analyses in the spectral and spatial domains are then implemented, which demonstrate that HUST-Grace2024 yields a noticeable reduction of 10 % to 30 % in noise level and retains consistent amplitudes of signal content over 48 river basins compared with the official GRACE and GRACE-FO solutions. These evaluations confirm that our aforementioned efforts lead to a better temporal gravity field series. This data set is identified with the following DOI: https://doi.org/10.5880/ICGEM.2024.001 (Zhou et al., 2024).

A nomogram and online calculator for predicting depression risk in obese Americans

BackgroundObese patients with depression face higher risks of adverse events. However, depression is often misdiagnosed and undertreated in this group. This study aimed to identify predictors of depression and create a nomogram and calculator to assess depression risk in obese Americans. MethodsThis cross-sectional study included 2674 patients from the National Health and Nutrition Examination Survey database (NHANES). These participants were randomly classified into the training and validation groups in a 7:3 ratio. Predictors were selected by LASSO and multivariate logistic regression analysis to create the nomogram. C-statistics, calibration plots, and decision curve analysis (DCA) were used to test the nomogram's discriminative ability, calibration quality, and clinical value. Internal validation with bootstrap resampling and external validation with the validation group were also conducted. ResultsThe training and validation group consists of 1871 and 803 participants. Depression was presented in 11.4 % (203/2674) of these participants. Seven predictors were found, including gender, hypertension, weekday sleep duration, poverty to income ratio, history of seeing mental health doctor, diabetes, and feeling sleepy during the day. The nomogram showed good discrimination, with the area under the receiver operating characteristic curve (AUC) of 0.817 (95 % CI: 0.786–0.848) (0.806 through internal validation and 0.772 through external validation) and good calibration (P = 0.536). The DCA further confirmed the nomogram's clinical usefulness. ConclusionThe nomogram and calculator effectively predict depression risk in obese Americans and can be used as auxiliary tools for early screening in primary care.

Calibrating low-cost rain gauge sensors for their applications in Internet of Things (IoT) infrastructures to densify environmental monitoring networks

Abstract. Environmental observations are crucial for understanding the state of the environment. However, current observation networks are limited in their spatial and temporal resolution due to high costs. For many applications, data acquisition with a higher resolution would be desirable. Recently, Internet of Things (IoT)-enabled low-cost sensor systems have offered a solution to this problem. While low-cost sensors may have lower quality than sensors in official measuring networks, they can still provide valuable data. This study describes the requirements for such a low-cost sensor system, presents two implementations, and evaluates the quality of the factory calibration for a widely used low-cost precipitation sensor. Here, 20 sensors have been tested for an 8-month period against three reference instruments at the meteorological site of the TU Dresden (Dresden University of Technology). Furthermore, the factory calibration of 66 rain gauges has been evaluated in the lab. Results show that the used sensor falls short for the desired out-of-the-box use case. Nevertheless, it could be shown that the accuracy could be improved by further calibration.

Applying the Machine Learning Method to Improve Calibration Quality of TDR Measuring Technique

Applying the Machine Learning Method to Improve Calibration Quality of TDR Measuring Technique

Novel Ransomware Detection Exploiting Uncertainty and Calibration Quality Measures Using Deep Learning

Ransomware poses a significant threat by encrypting files or systems demanding a ransom be paid. Early detection is essential to mitigate its impact. This paper presents an Uncertainty-Aware Dynamic Early Stopping (UA-DES) technique for optimizing Deep Belief Networks (DBNs) in ransomware detection. UA-DES leverages Bayesian methods, dropout techniques, and an active learning framework to dynamically adjust the number of epochs during the training of the detection model, preventing overfitting while enhancing model accuracy and reliability. Our solution takes a set of Application Programming Interfaces (APIs), representing ransomware behavior as input we call “UA-DES-DBN”. The method incorporates uncertainty and calibration quality measures, optimizing the training process for better more accurate ransomware detection. Experiments demonstrate the effectiveness of UA-DES-DBN compared to more conventional models. The proposed model improved accuracy from 94% to 98% across various input sizes, surpassing other models. UA-DES-DBN also decreased the false positive rate from 0.18 to 0.10, making it more useful in real-world cybersecurity applications.

Automatic multi-LiDAR calibration and refinement method

Sensor-based environmental perception is a crucial component of autonomous driving systems. To perceive the surrounding environment better, an intelligent system would utilize multiple LiDARs (3D Light Detection and Ranging). The accuracy of the perception largely depends on the quality of the sensor calibration. This research aims to develop a robust, fast, automatic, and accurate calibration strategy for multiple LiDAR systems. Our proposed multi-LiDAR calibration method consists of two stages: rough and refinement calibration. In the first stage, sensors are roughly calibrated from an arbitrary initial position using a deep neural network that does not rely on prior information or constraints on the initial sensor pose. In the second stage, we propose the octree-based refinement, an optimization method that considers sensor noise and prioritization. Our strategy is robust, fast, and not restricted to any environment. Additionally, we collected two datasets consisting of both real-world and simulated scenarios. Our experimental results from both datasets demonstrate the reliability and accuracy of our method. All the related datasets and codes are open-sourced on the GitHub website at https://github.com/OpenCalib/LiDAR2LiDAR.

Concept of a Satellite Cross-Calibration Radiometer for In-Orbit Calibration of Commercial Optical Satellites

The satellite Earth observation (EO) sector is burgeoning with hundreds of commercial satellites being launched each year, delivering a rich source of data that could be exploited for societal benefit. Data streams from the growing number of commercial satellites are of variable quality, limiting the potential for their combined use in science applications that need long time-series data from multiple sources. The quality of calibration performed on optical sensors onboard many satellite systems is highly variable due to calibration methods, sensor design, mission objective, budget, or other operational constraints. A small number of currently operating well-characterised satellite systems with onboard calibration, such as Landsat-8/9 and Sentinel-2, and planned future missions, like the NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder, the European Space Agency (ESA)’s Traceable Radiometry Underpinning Terrestrial and Helio Studies (TRUTHS), and LIBRA from China, are considered benchmarks for optical data quality due to their traceability to international measurement standards. This paper describes the concept of a space-based transfer calibration radiometer called the Satellite Cross-Calibration Radiometer (SCR) that would enable the calibration parameters from satellites such as Landsat-8/9, Sentinel-2, or other benchmark systems to be transferred to a range of commercial optical EO satellite systems while in orbit. A description of the key characteristics of the SCR to successfully operate in orbit and transfer calibration from reference systems to client systems is presented. A system like the SCR in orbit could complement SI-Traceable satellites (SITSats) to improve data quality and consistency and facilitate the interoperable use of data from multiple optical sensor systems for delivering higher returns on the global investment in EO.

CCTR: Calibrating Trajectory Prediction for Uncertainty-Aware Motion Planning in Autonomous Driving

Autonomous driving systems rely on precise trajectory prediction for safe and efficient motion planning. Despite considerable efforts to enhance prediction accuracy, inherent uncertainties persist due to data noise and incomplete observations. Many strategies entail formalizing prediction outcomes into distributions and utilizing variance to represent uncertainty. However, our experimental investigation reveals that existing trajectory prediction models yield unreliable uncertainty estimates, necessitating additional customized calibration processes. On the other hand, directly applying current calibration techniques to prediction outputs may yield sub-optimal results due to using a universal scaler for all predictions and neglecting informative data cues. In this paper, we propose Customized Calibration Temperature with Regularizer (CCTR), a generic framework that calibrates the output distribution. Specifically, CCTR 1) employs a calibration-based regularizer to align output variance with the discrepancy between prediction and ground truth and 2) generates a tailor-made temperature scaler for each prediction using a post-processing network guided by context and historical information. Extensive evaluation involving multiple prediction and planning methods demonstrates the superiority of CCTR over existing calibration algorithms and uncertainty-aware methods, with significant improvements of 11%-22% in calibration quality and 17%-46% in motion planning.

A multiplex UPLC-MS/MS method for the quantification of three PD-L1 checkpoint inhibitors, atezolizumab, avelumab, and durvalumab, in human serum

Background and aimTo support pharmacokinetic studies, a multiplex UPLC-MS/MS assay was developed and validated to quantify PD-L1 checkpoint inhibitors atezolizumab, avelumab, and durvalumab in serum. MethodsA bottom-up sample pre-treatment procedure was developed to determine atezolizumab, avelumab, and durvalumab in serum. This procedure consisted of (1) precipitation of the monoclonal antibody with ammonium sulfate, (2) reduction with dithiothreitol, (3) denaturation with methanol, and (4) tryptic digestion of the protein. The unique signature peptides resulting after sample pre-treatment of the antibodies were measured using UPLC-MS/MS with a total run time of 11 minutes. The clinical application was evaluated by analyzing 114 atezolizumab patient samples. ResultsThe developed method was found to be accurate and precise for all three analytes over a concentration range of 3.00–150 µg/mL. No endogenous interference was present in serum samples. Cross-interference experiments showed no cross-analyte interference and acceptable cross-internal standard interference. In addition, no substantial carry-over was observed. The stable isotopically labeled signature peptides were most effective in compensating for matrix effects. Recovery based on back-calculated concentrations of calibration standards and quality control samples was found to be high. The analytes were stable for at least three freeze-thaw cycles, for 42 hours at processing conditions, for at least two days at 2–8°C in the final extract, for five days before re-injection analysis at 4°C, and long-term for at least 11 months at −70°C. The assay was tested for its applicability in clinical practice. For this purpose, 114 atezolizumab patient samples were measured. ConclusionA multiplex UPLC-MS/MS assay was developed and validated to quantify atezolizumab, avelumab, and durvalumab in human serum. The applicability of this method was demonstrated by the analysis of clinical atezolizumab samples. The method is suitable to support clinical pharmacokinetic studies involving atezolizumab, avelumab, or durvalumab.

Transferability Assessment of OBD-Related Calibration and Validation Activities from the Vehicle to HiL Applications

With the Euro 7 pollutant emission legislation currently under discussion, advanced and more efficient exhaust aftertreatment systems are being developed. The technologies required for these are leading to an increase in the number of components and control systems requiring diagnoses strategies under the on-board diagnostics (OBD) legislation. With concurrent shorter development times and significant reductions in budgets allocated to conventional powertrain development, challenges in the field of OBD calibration and verification are already rising sharply. In response to these challenges, hardware-in-the-loop (HiL) approaches have been successfully introduced to support and replace conventional development methods. The use of complex simulation models significantly improves the quality of calibrations while minimizing the number of required prototype vehicles and test resources, thus reducing development costs. This paper presents a feasibility study for moving OBD-related calibration and validation tasks from the vehicle to a HiL platform. In this context, the calibration and verification process of an active diagnostic for monitoring the condition of the three-way catalyst (TWC) and the oxygen sensors in the exhaust aftertreatment system is presented. It is shown that all relevant signals are simulated with sufficient accuracy to ensure a robust transfer from the vehicle to a HiL test bench. Special attention is given to the simulation of aged components and their influence on the emission behavior of the system. Furthermore, it is discussed that transferring OBD tasks from the vehicle to the HiL test bench could result in significant savings in development time and a reduction in the number of physical prototype vehicles and test resources required.

How to make calibration less painful-A proposition for an automatic, reliable and time-efficient procedure.

In behavioral and neurophysiological pain studies, multiple types of calibration methods are used to quantify the individual pain sensation stimuli. Often, studies lack a detailed calibration procedure description, data linearity, and quality quantification and omit required control for sex pain differences. This hampers study repetition and interexperimental comparisons. Moreover, typical calibration procedures require a high number of stimulations, which may cause discomfort and stimuli habituation among participants. To overcome those shortcomings, we present an automatic calibration procedure with a novel stimuli estimation method for intraepidermal stimulation. We provide an in-depth data analysis of the collected self-reports from 70 healthy volunteers (37 males) and propose a method based on a dynamic truncated linear regression model (tLRM). We compare its estimates for the sensation (t) and pain (T) thresholds and mid-pain stimulation (MP), with those calculated using traditional estimation methods and standard linear regression models. Compared to the other methods, tLRM exhibits higher R2 and requires 36% fewer stimuli applications and has significantly higher t intensity and lower T and MP intensities. Regarding sex differences, t and T were found to be lower for females compared to males, regardless of the estimation method. The proposed tLRM method quantifies the calibration procedure quality, minimizes its duration and invasiveness, and provides validation of linearity between stimuli intensity and subjective scores, making it an enabling technique for further studies. Moreover, our results highlight the importance of control for sex in pain studies.

Multi-phase quantitative compositional mapping by LA-ICP-MS: Analytical approach and data reduction protocol implemented in XMapTools

Mapping of trace element signatures is an expanding tool in geoscience and material sciences, which allows the study of solid materials, and processes that may not be captured by major elements. Developments in laser-ablation inductively-coupled-plasma mass-spectrometry (LA-ICP-MS) capabilities in the last decade now provide the necessary spatial resolution for in situ element mapping. The acquisition of two-dimensional, fully quantitative and geologically meaningful data with LA-ICP-MS is still a challenging task, and a particular obstacle is the calibration of inhomogeneous phases, such as chemically zoned minerals. This work presents a novel approach to data reduction and image generation for multi-element mapping employing LA-ICP- quadrupole MS (LA-ICP-QMS), implemented in the free and open-source software XMapTools. Three geological applications are presented to illustrate the benefits of the procedures. Garnet from an eclogitic sample (Lato Hills, Togo) and plagioclase, K-feldspar, biotite from a migmatite sample (El Oro Complex, Ecuador) were mapped multiple times at different spatial resolutions to test the calibration quality and chemical detection capabilities. Rutile in a metapelite sample (Val Malenco, Italian Alps) was mapped, and Zr-in-rutile thermometry shows a temperature range of 510 to 550 °C within a single grain. The accuracy of the LA-ICP-MS method was verified by comparison with zoned major and minor element maps (garnet, plagioclase) and Ti-in-biotite geothermometry maps obtained by electron probe microanalysis (EPMA). A spatial resolution of up to 5 μm is achieved with LA-ICP-QMS, which is similar to the resolution reported for LA-ICP time-of-flight mass spectrometry (LA-ICP-TOFMS), albeit at significantly lower acquisition speed. Maps with lower spatial resolution offer better chemical detection power as demonstrated by lower per-pixel limit of detection (LOD) map calculation. Moreover, such maps are also recorded faster. The pixel allocation strategy and the instrumental conditions also have a direct impact on map quality. We recommend that maps are interpolated to square pixels, where a pixel consists of multiple sweeps to gain an improved detection power. Benchmarks using an emulated LA-ICP-MS mapping show that the spot size, together with scan direction, can lead to a shift in composition depending on the feature size of chemical patterns. This is verified by mapping a thin 10 μm annulus in garnet visible in REE and such compositional shifts can have a significant impact on e.g., diffusion modelling. The new software solution provides a multi-standard and variable composition calibration of LA-ICP-MS maps with single pixel LOD filtering at 95% confidence, allowing the user to quantify inhomogeneous materials of major and trace elements simultaneously with improved accuracy.

A Toxicokinetic-Toxicodynamic Modeling Workflow Assessing the Quality of Input Mortality Data.

Toxicokinetic-toxicodynamic (TKTD) models simulate organismal uptake and elimination of a substance (TK) and its effects on the organism (TD). The Reduced General Unified Threshold model of Survival (GUTS-RED) is a TKTD modeling framework that is well established for aquatic risk assessment to simulate effects on survival. The TKTD models are applied in three steps: parameterization based on experimental data (calibration), comparing predictions with independent data (validation), and prediction of endpoints under environmental scenarios. Despite a clear understanding of the sensitivity of GUTS-RED predictions to the model parameters, the influence of the input data on the quality of GUTS-RED calibration and validation has not been systematically explored. We analyzed the performance of GUTS-RED calibration and validation based on a unique, comprehensive data set, covering different types of substances, exposure patterns, and aquatic animal species taxa that are regularly used for risk assessment of plant protection products. We developed a software code to automatically calibrate and validate GUTS-RED against survival measurements from 59 toxicity tests and to calculate selected model evaluation metrics. To assess whether specific survival data sets were better suited for calibration or validation, we applied a design in which all possible combinations of studies for the same species-substance combination are used for calibration and validation. We found that uncertainty of calibrated parameters was lower when the full range of effects (i.e., from high survival to high mortality) was covered by input data. Increasing the number of toxicity studies used for calibration further decreased parameter uncertainty. Including data from both acute and chronic studies as well as studies under pulsed and constant exposure in model calibrations improved model predictions on different types of validation data. Using our results, we derived a workflow, including recommendations for the sequence of modeling steps from the selection of input data to a final judgment on the suitability of GUTS-RED for the data set. Environ Toxicol Chem 2024;43:197-210. © 2023 Bayer AG and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Improving UAV‐SfM photogrammetry for modelling high‐relief terrain: Image collection strategies and ground control quantity

AbstractImage collection strategies and ground control points (GCPs) are of particular importance for uncrewed aerial vehicle combined with Structure‐from‐Motion (UAV–SfM) photogrammetry, and the generalization of their effects has proved elusive. This study designed various photogrammetric scenarios to investigate the effects of image collection strategies, ground control quantity, and their interaction on digital elevation model (DEM) errors and their spatial structure in high‐relief terrain. The results of 1.77 × 105 UAV–SfM scenarios provide insights for improving UAV–SfM practices. A high image capture angle (20–40°) enhances camera calibration quality decreasing the magnitude and spatial correlation of errors. High camera inclination reduces the sensitivity of mean and standard deviation of error to flying height but not the spatial correlation of error. Including additional data (e.g. supplemented convergent images; images captured at multiple flying heights) has only a minor effect if imagery is highly inclined. GCPs provide more effective constraints than image collection strategies. The mean error and standard error decline quickly with a small number of GCPs and then become stable in all scenarios, but the spatial correlation of error can be further improved with increasing GCPs. However, the effects of GCP quantity do interact with image collection strategies. High camera inclination reduces requirements for GCPs, whilst strategies combining different flying heights and image orientations have little effect on necessary GCP quantity. The distribution of GCPs still affects the errors, but the effect of GCP distribution becomes less important with an increase in the number of GCPs. Finally, we show that UAV–SfM photogrammetric quality assessment should routinely assess the spatial dependence of error using a statistic like Moran's I.

Fecal NIRS to estimate intake and diet composition in Sarda dairy ewes fed mixed diets or part-time grazing forage crops

Knowledge on the intake and diet composition is a key to efficiently and sustainably drive ruminant production systems. Near infra-red spectroscopy of fecal samples (F.NIRS) has been proposed as a tool to estimate intake, diet composition and digestibility of ruminants. This study was aimed at ascertaining if F.NIRS can precisely and accurately predict 1) individual dietary intake and composition in dairy sheep fed mixed diets in confinement; 2) group dietary intake and composition in grazing dairy ewes using spot sampled faeces. We used two main datasets consisting of pairs of NIRS fecal spectra and reference values for intake, nutrient digestibility and composition of the diet: STALL, based on the results of digestibility trials carried out on a total of 120 sheep individually fed mixed diets including fresh forages, hays and concentrates (29 diets) and GRAZE based on results of already published grazing experiments. GRAZE encompassed a total of 102 pairs of group fecal spectra and group reference values regarding 6 grazing treatments differing in forage species, supplementation type and access time to pasture, in which reference data were obtained by double weighing procedure for herbage intake and hand-plucking for herbage composition. Group fecal samples were collected the day after. Data of the two datasets were separately used to calibrate F.NIRS using partial least square regression analysis and subsequent cross-validation. The quality of calibrations was evaluated using the ratio performance deviation (RPDcv). The diet attributes showing RPDcv in a range good to excellent (RPD > 3) in cross-validation were submitted to a validation exercise, on a subset of data previously excluded from calibration. Validation performance were evaluated as RPDval. Under confined conditions, individual herbage intake was predicted with good precision R2val = 0.90 and accuracy, SEP = 11.6% (RDPval = 3.2). The intake of hay was also well predicted (R2val = 0.97, SEP = 9.9%, RPDval = 4.6), whereas that of concentrates was just fairly predicted with R2val = 0.88 but a lower accuracy (SEP = 26.7%, RPDval = 2.9). The estimation of individual diet composition (% ingredients) was excellent for herbage and hay and good for concentrate, with R2val = 0.97, SEP = 11.2% and RPDval = 4.9 for hay; R2val = 0.97, SEP = 6.7% and RPDval = 5.5 for herbage; and R2val = 0.95, SEP = 26.7% and RPDval = 4.5 for concentrates. Prediction of nutrient proportion in the diet (% DM) was good for NDF, ADF, EE (RPDval ≥ 3) but inadequate for NFC (RPDval = 1.5). Cross-validation models were rather imprecise for CP and OM. Individual digestibility coefficients were poorly estimated in cross-validation, being the best models those estimating the digestibility of NFC (R2cv = 0.83, SECV% = 2.3, RPDcv = 2.4) and ADF (R2cv = 0.81, SECV% = 9.3, RPDcv= 2.3). Under grazing conditions, after cross-validation all models showed RPD < 3. The group intake of herbage was estimated with a modest precision and accuracy (R2cv = 0.75, SECV% = 14.2, RPDcv = 2.0). The proportion of herbage in the diet and its NDF content showed a higher accuracy, and equal or better precision, although models were adequate only for rough screening (RPDcv = 2 and 2.3, respectively). To conclude, this research confirms for dairy sheep the usefulness of F.NIRS to estimate individual intake and individual diet composition in ruminants fed mixed diets in confinement. Moreover, this study shows a modest cross-validation performance of F.NIRS for estimating group average proportion of herbage and NDF in the diet of grazing ewes by spot-sampling their faeces. Further validation of these models is needed to assess the applicability of the above results to farm conditions.