Calibration Function Research Articles

Segmented Two-Dimensional Progressive Polynomial Calibration Method for Nonlinear Sensors.

Nonlinearity in sensor measurements reduces the sensor's accuracy. Therefore, accurate calibration is necessary for reliable sensor operation. This study proposes a segmented calibration method that divides the input range into multiple sections and calculates the optimized calibration functions for each one. This approach reduces the overall error rate and improves the calibration accuracy by isolating distinctive regions. The modified progressive polynomial calibration technique is used to calculate the calibration function. This algorithm addresses the computational complexity, allowing for reduced polynomial degrees and improving the accuracy. The segmented calibration method achieves a significantly lower error rate of 0.000006% compared to the original single calibration method, which has an error rate of 0.0823%, when using the same six calibration points and a fifth-degree polynomial function. This method maintains improved accuracy with fewer calibration points, and its ability to reduce the computational complexity and calculation time while using lower polynomial degrees is confirmed. Additionally, it can be extended to two dimensions to reduce the errors caused by cross-sensitivity. The results from a two-dimensional simulation show a reduction in the error rate ranging from 15.84% to 2.07% in an 8-bit signed fixed-point system. These results indicate that the segmented calibration method is an effective and scalable solution for various typical sensors.

CTRing: An R package to extract wood density profiles from computed tomography images of discs and logs

Accurately determining the position of pith and accessing tree-ring density profiles, including intra-ring variations, is important for both the forest industry and dendroclimatology. Although several available methods exist for acquiring this information, such as X-ray computed tomography (CT), micro-CT, and X-ray films, the availability of open-source programs for extracting data remains limited. The CTRing package in the R environment integrates a series of functions to detect precisely the pith and tree-ring boundaries and generate tree-ring density profiles using CT images of tree cross sections. Before processing, grey values are transformed into density using a calibration function. Pith position is then detected by combining an adapted Hough Transform method and a one-dimensional edge detector. Tree-ring profiles along the pith-to-bark path of interest are inspected visually, and tree-ring boundaries can be easily added or removed manually via a graphical user interface. After correcting for tree-ring boundaries, the inflection points of a 3rd-degree polynomial obtained from density profiles are used to delimit the earlywood–latewood transition. We tested this package using 60 CT-scanned images of white spruce (Picea glauca (Moench) Voss) discs collected at various tree heights (0%, 25%, 50% and 75% of the total tree height as well as at 1.3m). The pith detection function had an average mean error of 0.72mm with 95% of the automatically detected pith locations that differed by less than 2mm from their manually located positions. Error decreased toward the apex of the tree. The functions of the CTRing package are flexible and can be easily implemented or adapted. The package could also be used with simple images of discs to obtain ring-width time series; however, this use must be evaluated further. Future work with this package involves assessing the use of low-quality images and ring-porous species.

Double refractive particle tracking and sizing

We present a novel bifocal imaging method enabling three-dimensional particle tracking and size determination employing a single camera only. The method is based on double refraction causing a particle to be imaged twice, each particle image of different blur. From these double images, a linear calibration function can be derived allowing to determine the three-dimensional particle position unambiguously over the entire depth of measurement volume. As this calibration function is independent of the particle size used, the particle size can be determined simultaneously by relating size of the double images and depth position of the particle. To prove the applicability, a co-laminar flow of two particle suspensions with particles of 1.14 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}m and 2.47 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}m in diameter was measured in a Y-shaped microchannel. While the laminar flow field was measured with very low uncertainty and independent of the particle size, the particle size distributions determined reproduced reliably the size distributions expected for the co-laminar flow applied, with a precision of about 98.6 %\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} regarding the particle size discrimination. The progress for research is a new method readily to implement in common optical setups, promising, for example, valuable insights in polydisperse suspension flows—the vast majority of flows in fundamental research and applications.Graphical abstract

Raman Thermometry for Temperature Assessment of Inorganic Transformations During Microwave Heating

ABSTRACTMicrowave heating is an intriguing method for the synthesis of inorganic solids offering a variety of advantages over conventional furnace heating, such as fast heating and cooling rates as well as volumetric and selective heating of precursors. However, there are many open questions regarding this “black‐box” process, and insights into the effect of microwave radiation on different types of solids are generally missing. In situ Raman spectroscopy is a powerful technique to unravel chemical transformations and identify intermediate species during microwave solid‐state syntheses. A major challenge is the temperature measurement under microwave conditions because (metallic) thermocouples cannot be used and optical pyrometry has significant drawbacks. In contrast, Raman thermometry is a viable method that relies on the temperature‐induced shift of Raman signals. Here, we use this method to estimate the temperature during microwave heating of a model system (titania) that undergoes a phase transition at temperatures >800°C. The estimation is derived from a flexible double exponential calibration function applied to Raman spectroscopic peak shifts in the temperature‐resolved furnace heating data, which was found to describe two titania modes (one anatase and one rutile) extremely well. Based on a detailed error and uncertainty analysis, we suggest options to further optimize Raman thermometry for use in high‐temperature microwave heating conditions.

Suppressing the gauge problem in local hybrid functionals without a calibration function: The choice of local mixing function.

Modern functionals based on the exact-exchange (EXX) energy density like local hybrid functionals (LHs) or range-separated LHs have recently received additional attention due to their advantages over established functionals when it comes to the local balance between self-interaction errors and static-correlation errors. A possible theoretical drawback of such functionals over the years has been the so-called gauge problem due to the inherent ambiguity of exchange-energy densities. Modern LHs like LH20t or more sophisticated functionals based thereon have been constructed using suitably optimized calibration functions (CFs) to minimize the mismatch of the semi-local and EXX energy densities. Here, we show that the unphysical contributions arising from the gauge problem may also be reduced significantly without a CF by tailoring the position-dependence of the EXX admixture (local mixing function, LMF) in a way to suppress spurious positive energy-density contributions locally in space. This is achieved by building the so-called x-LMFs upon the ratio between EXX and semi-local exchange-energy densities. The resulting LH24x functional provides similar accuracy, e.g., for the GMTKN55 test suite, as LH20t, but without introduction of a CF! We provide detailed comparative analyses of integrated energies and spatially resolved energy densities. The good performances of LHs for chemically relevant energy differences are to some extent due to the core nature of unphysical artifacts that cancel out efficiently.

Comparative Analysis and Optimal Selection of Calibration Functions in Pure Rotational Raman Lidar Technique

The pure rotational Raman (PRR) lidar technique relies on calibration functions (CFs) to extract temperature information from raw detection data. The choice of CF significantly impacts the accuracy of the retrieved temperature. In this study, we propose a method that combines multiple Monte Carlo simulation experiments with a statistical analysis, and we first conduct simulated comparisons of the calibration effects of different CFs while considering the impact of noise. We categorized ten common CFs into four groups based on their functional form and the number of calibration coefficients. Based on functional form, specifically, we defined 1/T = f(lnQ) as a forward calibration function (FCF) and lnQ = g(1/T) as a backward calibration function (BCF). Here, T denotes temperature, and Q denotes the signal intensity ratio. Their performance within and outside the calibration interval is compared across different integration times, smoothing methods, and reference temperature ranges. The results indicate that CFs of the same category exhibit similar calibration effects, while those of different categories exhibit notable differences. Within the calibration interval, the FCF performs better, especially with more coefficients. However, outside the calibration interval, the linear calibration function (which can be considered a two-coefficient FCF) has an obvious advantage. Conclusions based on the simulation results are validated with actual data, and the factors influencing calibration errors are discussed. Utilizing these findings to guide CF selection can enhance the accuracy and stability of PRR lidar detection.

Verification on Molybdenum-99 (99Mo) Breakthrough from Non-Fission 99Mo/99mTc Generator to Produce Technetium-99m (99mTc) Medical Radionuclide

Abstract The medical radionuclide 99mTc is the most widely used type of radionuclide in the field of nuclear medicine. 99mTc is the decay product of 99Mo. One of the quality control parameters of 99mTc is the 99Mo breakthrough limit. It is important to know the 99Mo breakthrough so that the quality of 99mTc produced can be optimal for diagnosis in nuclear medicine. This study aims to verify the 99Mo breakthrough activity from a non-fission 99Mo/99mTc generator whether they meet the established international limit standard. This research used a non-fission 99Mo/99mTc generator produced in multipurpose reactor G.A. Siwabessy, Indonesia. Gamma Spectrometer measurement of 99Mo and two 5 mL liquid standard sources of mix 133Ba and 152Eu used the same geometry. First standard as a calibration and efficiency identification function while the second standard is used to verify the measurement each at 3, 5, 7, 9 and 11 cm source to detector distance respectively. The 99Mo breakthrough activity (µCi) then devided to the activity of 99mTc (mCi) for the six eluted product from each days elution process of the generator. The results obtained showed that the 99Mo elution in the first elution had the highest breakthrough of 0.233 uCi 99Mo /mCi 99mTc, which exceeded the set limit of 0.15 uCi 99Mo /mCi 99mTc. While in the second, third, fourth, and fifth elution still have the 99Mo breakthrough, but these breakthrough did not exceed the limit and met one of the requirements of the 99mTc medical radionuclide parameter.

Developing a European aquatic macrophyte transfer function for reconstructing past lake-water chemistry

Quantitative paleoecological reconstructions using biological proxies, such as diatoms, Cladocera, and chironomids, have revolutionized paleolimnology and have greatly contributed to the understanding of the past local and regional environmental changes, as well as to nature conservation. While macrophytes are good ecological indicators, they have rarely been used to reconstruct past lake-water chemistry. The present study investigates which environmental variable best explains aquatic plant community composition in Finnish, Polish, and Swedish lakes for its further use in quantitative paleoenvironmental reconstructions. The method involved the creation of a modern macrophyte-environment calibration dataset, calculation of modern calibration functions using simple averaging regression, and final reconstruction of past environmental conditions in Lake Linówek (NE Poland) from a fossil assemblage using weighted averaging calibration. The data demonstrate that conductivity and alkalinity best explained macrophyte community composition in our dataset. Species “optima” for alkalinity were influenced by the presence/absence of carbon concentrating mechanisms (CCMs), enabling the utilization of HCO3− as a carbon source. Quantitative paleoenvironmental reconstruction indicates that past water conductivity and alkalinity fluctuated depending on internal lake processes and the supply of basic ions to the lake from the catchment related to climate and soil development in the watershed during the late Glacial (∼14,500–11,700 calibrated years before the present; cal BP) and the Holocene (11,700 cal BP–recent). We conclude that macrophytes can be successfully used for past lake-water chemistry reconstruction. Furthermore, calculated modern calibration functions for conductivity and alkalinity can be used in nature conservation for determining habitat requirements of numerous endangered macrophyte species as a basis for successful (re)introductions.

A calibration method for a wireless real-time CMOS pixel medical dosimeter

This study introduces and validates a novel calibration function for wireless wearable dosimeters, crucial for real-time dosimetry in interventional radiology. Focusing on enhancing accuracy and safety, it compares data from threshold-based wireless dosimeters with a full-data-read detector. The research involves a central detector and four lateral wireless dosimeters, each equipped with an identically-typed CMOS Image Sensor (CIS), subjected to various photon dose rates and doses from a certified beam. Two key calibration functions were developed: one that correlates the pixel count over a threshold with the dose rate, and the other that links the cumulative pixel value above the threshold to the total absorbed dose. These functions are essential in converting raw CIS data into precise dosimetric information.The study findings reveal that the single-pixel sensitivity of wireless prototypes is approximately 8.5 ± 0.8 ADC/nGy. Following the application of calibration functions, the responses of these prototypes align consistently with the TLD dosimeter measurements. In particular, for three of the prototypes, this calibration ensures that the measurement uncertainty remains within 6% for the dose rate and within 8% for the dose.This research confirms the effectiveness and feasibility of pixel-based wireless dosimeters in interventional radiology, highlighting the importance of integrating offline data processing for enhanced measurement precision.

Liquid Chromatography with Orbitrap High-Resolution Mass Spectrometry for the Determination of Glucose in Saliva Following Derivatization with Dibenzylamine

As an important circulating metabolite in the body, the accurate determination of glucose in body fluids is very important for the evaluation of physiological status. However, liquid chromatography–mass spectrometry (LC–MS) methods present challenges for glucose due to poor ionization combined with weak chromatographic retention. Here, a method for determining salivary glucose using LC–MS after derivation by dibenzylamine is described in which 3-O-methyl-D-glucopyranose was used as internal standard. Saliva samples were prepared by protein precipitation, and the analytes were separated on a polar-copolymerized C18 reversed phase column. Detection took place by high-resolution orbitrap mass spectrometry after ionization in the positive mode. The calibration function was linear from 0.35 to 50 ng/mL. The recoveries were from 97.82 to 104.51% for intraday assays and 98.96 to 106.61% for interday assays. The intraday precision of salivary glucose was from 0.37% to 1.43% and the interday precision from 1.33% to 1.43%. The limits of detection and quantification in saliva were 0.2 and 0.6 ng/mL. The samples after derivatization were stored at 4 °C and 25 °C and analyzed every two days with five repeats. The relative standard deviations were 0.09% and 0.27% at 4 °C and 25 °C. This method was employed for the determination of the glucose in saliva for healthy male college students after exercise.

Diffraction order penalization to improve spectrometer calibrations

Wavelength calibration in diffraction spectroscopy typically depends on identifying strong, well-known lines in the recorded spectra and fitting a calibration function to them. In this paper, we outline a novel method (order penalization) for improving spectroscopic calibrations by extending non-linear least squares fitting of the calibration curve. The method introduces an extra term into the minimized quantity that penalizes disagreement in the positions of spectral lines observed in multiple diffraction orders. The primary advantage of this method is that the lines used do not have to be identified, except for establishing the fact that they are different orders of the same line. This increases the number of constraints on the calibration curve, potentially in spectral regions where no regular calibration lines are available. The mathematical basis of this method is described, and the performance of this method is evaluated on simulated data and experimental data from the National Institute of Standards and Technology (NIST) Electron Beam Ion Trap. We demonstrate the effectiveness of the method on the spectra of highly charged Ag-like Re28+ and nearby charge state ions.

Miniaturizing gas sensors combining mid-infrared interband cascade light emitting diodes with substrate-integrated hollow waveguides

In this study, we report a compact and versatile gas sensor system combining mid-infrared interband cascade light emitting diodes (MIR-ICLEDs) with substrate-integrated hollow waveguides (iHWGs) toward miniaturized gas sensors. Two readily exchangeable MIR-ICLEDs with center emission wavelengths of 3.4 µm (2941 cm−1) and 5.7 µm (1754 cm−1), respectively, were integrated into a modular sensor system using the iHWG simultaneously as a miniaturized gas cell. The performance of the sensor system for quantitative analysis was evaluated based on calibration functions established for four analytes (i.e., methane, isobutane, acetone, and acetaldehyde) within the respective spectral regions. The utility of such a sensor system as a non-dispersive infrared sensor was tested for various scenarios, including the application of spectral filters, the performance of the individual ICLEDs, and in the analysis of gas mixtures. Furthermore, we demonstrated that the modularity of the sensor design facilitates flexible adaptation to target gas species and available sample volumes, differentiating individual components within gas mixtures.

A machine learning model to predict liver-related outcomes after the functional cure of chronic hepatitis B

The risk of hepatocellular carcinoma (HCC) and hepatic decompensation persists after hepatitis B surface antigen (HBsAg) seroclearance. This study aimed to develop and validate a machine learning model to predict the risk of liver-related outcomes (LROs) following HBsAg seroclearance. A total of 4,787 consecutive patients who achieved HBsAg seroclearance between 2000 and 2022 were enrolled from six centers in South Korea and a territory-wide database in Hong Kong, comprising the training (n= 944), internal validation (n= 1,102), and external validation (n= 2,741) cohorts. Three machine learning-based models were developed and compared in each cohort. The primary outcome was the development of any LRO, including HCC, decompensation, and liver-related death. During a median follow-up of 55.2 (IQR 30.1-92.3) months, 123 LROs were confirmed (1.1%/person-year) in the Korean cohort. The model with the best predictive performance in the training cohort was selected as the final model (designated as PLAN-B-CURE), which was constructed using a gradient boosting algorithm and seven variables (age, sex, diabetes, alcohol consumption, cirrhosis, albumin, and platelet count). Compared to previous HCC prediction models, PLAN-B-CURE showed significantly superior accuracy in the training cohort (c-index: 0.82 vs. 0.63-0.70, all p <0.001; area under the receiver-operating characteristic curve: 0.86 vs. 0.62-0.72, all p <0.01; area under the precision-recall curve: 0.53 vs. 0.13-0.29, all p <0.01). PLAN-B-CURE showed a reliable calibration function (Hosmer-Lemeshow test p >0.05) and these results were reproduced in the internal and external validation cohorts. This novel machine learning model consisting of seven variables provides reliable risk prediction of LROs after HBsAg seroclearance that can be used for personalized surveillance. Using large-scale multinational data, we developed a machine learning model to predict the risk of liver-related outcomes (i.e., hepatocellular carcinoma, decompensation, and liver-related death) after the functional cure of chronic hepatitis B (CHB). The new model named PLAN-B-CURE was constructed using seven variables (age, sex, alcohol consumption, diabetes, cirrhosis, serum albumin, and platelet count) and a gradient boosting machine algorithm, and it demonstrated significantly better predictive accuracy than previous models in both the training and validation cohorts. The inclusion of diabetes and significant alcohol intake as model inputs suggests the importance of metabolic risk factor management after the functional cure of CHB. Using seven readily available clinical factors, PLAN-B-CURE, the first machine learning-based model for risk prediction after the functional cure of CHB, may serve as a basis for individualized risk stratification.

A sensor platform based on SERS detection/janus textile for sweat glucose and lactate analysis toward portable monitoring of wellness status

Herein we report a wearable sweat sensor of a Janus fabric based on surface enhanced Raman scattering (SERS) technology, mainly detecting the two important metabolites glucose and lactate. Janus fabric is composed of electrospinning PU on a piece of medical gauze (cotton), working as the unidirectional moisture transport component (R = 1305%) to collect and transfer sweat efficiently. SERS tags with different structures act as the probe to recognize and detect the glucose and lactate in high sensitivity. Core-shell structured gold nanorods with DTNB inside (AuNRs@DTNB@Au) are used to detect lactate, while gold nanorods with MPBA (AuNRs@MPBA) are used to detect glucose. Through the characteristic SERS information, two calibration functions were established for the concentration determination of glucose and lactate. The concentrations of glucose and lactate in sweat of a 23 years volunteer during three-stage interval running are tested to be 95.5, 53.2, 30.5 μM and 4.9, 13.9, 10.8 mM, indicating the glucose (energy) consumption during exercise and the rapid accumulation of lactate at the early stage accompanied by the subsequent relief. As expected, this sensing system is able to provide a novel strategy for effective acquisition and rapid detection of essential biomarkers in sweat.

Strain Gauge Calibration for High Speed Weight-in-Motion Station.

The development of systems for weighing vehicles in motion aims to introduce systems allowing automatic enforcement of regulations. HSWIM (high speed weight-in-motion) systems enable measurement of a mass of vehicles passing through a measurement station without disturbing the traffic flow. This article focuses on the calibration of a weighing station for moving vehicles, where strain gauge sensors are used to measure pressures. A solution was proposed to replace the calibration coefficients with calibration functions. The analysis was performed for two methods of determining wheel loads: based on the maximum of the signal from strain gauge sensors and on a method using the field under the signal and the vehicle's speed. Calibration functions were determined jointly for all test vehicles and separately for each of them. The use of a calibration function for a specific vehicle type made it possible to determine wheel pressure and gross weight with a level of accuracy that allowed the weigh-in-motion station to be classified as a direct enforcement system. The achieved improvement in the accuracy of weighing in motion did not require any interference with the measurement station. The proposed change in the method of calibration and, ultimately, determination of wheel loads required only a change in the algorithm for determining wheel loads.

Estimation of site-specific amplification function from quarter-wavelength velocity profile and its application to obtain local amplification maps

Quarter-wavelength (QWL) velocity refers to the S-wave velocity at a depth equal to one-fourth of the wavelength of the seismic waves. It provides valuable information about the characteristics of the subsurface material properties affecting seismic waves propagation. The Swiss Seismological Service (SED) network, with over 200 stations across different lithologies, offers a rich dataset to implement correlation between site properties and site amplification factors. The current study is based on a subset of 113 selected SED seismic stations for which shear-wave velocity (Vs) profiles from geophysical measurements are available. We first meticulously analyzed and adjusted them to accurately determine the bedrock depth of the sites they are located on. Using empirical spectral modelling (ESM), we computed amplification functions from recorded earthquakes, referenced to the Swiss standard rock profile with a VS30 of 1100 m/s. Then we performed a bivariate analysis between empirical amplification functions, QWL velocity and QWL velocity contrast. The resulting coefficients are used to predict elastic amplification at frequencies between 0.5 and 10 Hz, with predictions generally being within 20–28% of observed values. We also developed an empirical equation relating VS30 (time-averaged Vs to 30 m depth) and κ0 (high-frequency attenuation parameter), to incorporate the anelastic term in the amplification. Applying our method to a 3D geophysical model of Visp, a high seismic hazard zone in Switzerland, we found it effective in predicting 1D site amplification, but noted caution in areas susceptible of 2D/3D resonance effects. A calibration function based on observed vs. predicted amplification and the frequencies of estimation normalized by the expected 2D resonance frequency improved predictions for Visp. Our study demonstrates that QWL velocity profiles offer a straightforward approach for characterizing site effects, which can be used in seismic hazard assessment to evaluate the potential amplification of ground motions.

A simple infiltrometer automated with a user‐friendly pressure datalogger

AbstractWe have constructed a new, simplified constant‐head infiltrometer automated with a self‐contained water level datalogger (HOBO U20L‐01) repurposed to measure changes in gas pressure inside an inverted bottle reservoir. Our field tests of six of these infiltrometers confirmed that recorded changes in gas pressure were strongly correlated with changes in water level in the infiltrometer reservoir (R2 = 0.9998). Further, by using the derived experimental calibration function, we were able to obtain accurate near‐steady‐state infiltration rates. This infiltrometer is cheaper and lighter than current commercially available infiltrometers. It can be easily assembled with materials readily available in most hardware stores, and its user‐friendly datalogger does not require any programming knowledge. This infiltrometer is compatible with various ponding infiltration methods, and its generic design allows for modifications with locally available materials to meet diverse research needs.

Synchronous Fluorescence Spectrometry (SFS) with Multivariate Calibration for the Determination of Polycyclic Aromatic Hydrocarbons (PAHs) and Their Oxygen Analogs in Printer Toner

Synchronous molecular fluorescence with multivariate calibration was investigated to assay seven polycyclic aromatic hydrocarbons (PAHs) and three oxygenated derivatives (O-PAHs) in printer toner. Great sensitivity and a good data compression capacity were achieved; therefore, a relatively small number of factors were used. Correlation coefficients above 0.95 for the calibration functions were obtained, and recoveries lie between 70 and 130% in analyzing synthetic samples (13 PCs). For the real samples assayed, recoveries between 81 and 125% were obtained when compared to the results obtained by gas chromatography – tandem mass spectrometry (GC-MS/MS). Despite a certain loss in accuracy when compared to chromatography, the proposed methodology is faster, simpler, and lower in cost. The multivariate tool proved to be an expeditious alternative for screening toner in relation to the presence and abundance of PAHs and derivatives.

Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors.

The use of low-cost environmental sensors has gained significant attention due to their affordability and potential to intensify environmental monitoring networks. These sensors enable real-time monitoring of various environmental parameters, which can help identify pollution hotspots and inform targeted mitigation strategies. Low-cost sensors also facilitate citizen science projects, providing more localized and granular data, and making environmental monitoring more accessible to communities. However, the accuracy and reliability of data generated by these sensors can be a concern, particularly without proper calibration. Calibration is challenging for low-cost sensors due to the variability in sensing materials, transducer designs, and environmental conditions. Therefore, standardized calibration protocols are necessary to ensure the accuracy and reliability of low-cost sensor data. This review article addresses four critical questions related to the calibration and accuracy of low-cost sensors. Firstly, it discusses why low-cost sensors are increasingly being used as an alternative to high-cost sensors. In addition, it discusses self-calibration techniques and how they outperform traditional techniques. Secondly, the review highlights the importance of selectivity and sensitivity of low-cost sensors in generating accurate data. Thirdly, it examines the impact of calibration functions on improved accuracies. Lastly, the review discusses various approaches that can be adopted to improve the accuracy of low-cost sensors, such as incorporating advanced data analysis techniques and enhancing the sensing material and transducer design. The use of reference-grade sensors for calibration and validation can also help improve the accuracy and reliability of low-cost sensor data. In conclusion, low-cost environmental sensors have the potential to revolutionize environmental monitoring, particularly in areas where traditional monitoring methods are not feasible. However, the accuracy and reliability of data generated by these sensors are critical for their successful implementation. Therefore, standardized calibration protocols and innovative approaches to enhance the sensing material and transducer design are necessary to ensure the accuracy and reliability of low-cost sensor data.

Stable isotope dilution assay and HS-SPME-GCMS quantification of key aroma volatiles of Australian pineapple (Ananas comosus) cultivars

Pineapple aroma is one of the most important sensory quality traits that influences consumer purchasing patterns. Reported in this paper is a high throughput method to quantify in a single analysis the key volatile organic compounds that contribute to the aroma of pineapple cultivars grown in Australia. The method constituted stable isotope dilution analysis in conjunction with headspace solid-phase microextraction coupled with gas-chromatography mass spectrometry. Deuterium labelled analogues of the target analytes purchased commercially were used as internal standards. Twenty-six volatile organic compounds were targeted for quantification and the resulting calibration functions of the matrix -matched validated method had determination coefficients (R2) ranging from 0.9772 to 0.9999. The method was applied to identify the key aroma volatile compounds produced by popular pineapple cultivars such as ‘Aus Carnival’, ‘Aus Festival’, ‘Aus Jubilee’, ‘Aus Smooth (Smooth Cayenne)’ and ‘Aussie Gold (73-50)’, grown in Queensland, Australia. Pineapple cultivars varied in its content and composition of free volatile components, which were predominantly comprised of esters, followed by terpenes, alcohols, aldehydes, and ketones.