Method For Quantification Research Articles

Quantification of fungal biomass in mycelium composites made from diverse biogenic side streams

AbstractMycelium composite materials are comprised of renewable organic substrates interconnected by fungal mycelium, allowing full biodegradability after use. Due to their promising material properties, adaptability, and sustainable nature, these biomaterials are investigated intensively. However, one crucial aspect that has hardly been covered so far is the proportion of fungal biomass in the composites, which would be necessary to assess its contribution to the material characteristics. Since a complete physical separation of mycelium and substrate is not feasible, we approached this issue by isolating the fungal DNA and relating it to the mass of mycelium with the help of quantitative PCR. Overall, 20 different combinations of fungi and biogenic side streams were evaluated for their handling stability, and growth observations were related to the quantification results. Ganoderma sessile was able to form stable composites with almost all substrates, and a positive correlation between mycelial biomass and composite stability could be found. However, the amount of mycelium required for fabricating firm materials strongly depends on the combination of substrate and fungal species used. Less than five mass percent of fungal biomass can suffice to achieve this, as for example when combining Trametes versicolor with sugar beet pulp, whereas a mass fraction of twenty percent leads to crumbly materials when using Pleurotus pulmonarius on green waste. These results indicate that the mycelial biomass is an important factor for the composite’s stability but that the properties of the fungal hyphae, as well as those of the substrate, are also relevant. The presented quantification method not only allows to estimate fungal growth during composite production but can also improve our understanding of how the mycelium influences the material.

Beyond Lux: methods for species and photoreceptor-specific quantification of ambient light for mammals.

Light is a key environmental regulator of physiology and behaviour. Mistimed or insufficient light disrupts circadian rhythms and is associated with impaired health and well-being across mammals. Appropriate lighting is therefore crucial for indoor housed mammals. Light is commonly measured in lux. However, this employs a spectral weighting function for human luminance and is not suitable for 'non-visual' effects of light or use across species. In humans, a photoreceptor-specific (α-opic) metrology system has been proposed as a more appropriate way of measuring light. Here we establish technology to allow this α-opic measurement approach to be readily extended across mammalian species, accounting for differences in photoreceptor types, photopigment spectral sensitivities, and eye anatomy. We develop a high-throughput method to derive spectral sensitivities for recombinantly expressed mammalian opsins and use it to establish the spectral sensitivity of melanopsin from 13 non-human mammals. We further address the need for simple measurement strategies for species-specific α-opic measures by developing an accessible online toolbox for calculating these units and validating an open hardware multichannel light sensor for 'point and click' measurement. We finally demonstrate that species-specific α-opic measurements are superior to photopic lux as predictors of physiological responses to light in mice and allow ecologically relevant comparisons of photosensitivity between species. Our study presents methods for measuring light in species-specific α-opic units that are superior to the existing unit of photopic lux and holds the promise of improvements to the health and welfare of animals, scientific research reproducibility, agricultural productivity, and energy usage.

FLIP: a novel method for patient-specific dose quantification in circulating blood in large vessels during proton or photon external beam radiotherapy treatments

Purpose.To provide a novel and personalized method (FLIP, FLowand Irradiation Personalized) using patient-specific circulating blood flows and individualized time-dependent irradiation distributions, to quantify the dose delivered to blood in large vessels during proton or photon external beam radiotherapy.Methods.Patient-specific data were obtained from ten cancer patients undergoing radiotherapy, including the blood velocity field in large vessels and the temporal irradiation scheme using photons or protons. The large vessels and the corresponding blood flow velocities are obtained from phase-contrast MRI sequences. The blood dose is obtained discretizing the fluid into individual blood particles (BPs). A Lagrangian approach was applied to simulate the BPs trajectories along the vascular velocity field flowlines. Beam delivery dynamics was obtained from beam delivery machine measurements. The whole IS is split into a sequence of successive IEs, each one with its constant dose rate, as well as its corresponding initial and final time. Calculating the dose rate and knowing the spatiotemporal distribution of BPs, the dose is computed by accumulating the energy received by each BP as the time-dependent irradiation beams take place during the treatment.Results.Blood dose volume histograms from proton therapy and photon radiotherapy patients were assessed. The irradiation times distribution is obtained for BPs in both modalities. Two dosimetric parameters are presented: (i)D3%, representing the minimum dose received by the 3% of BPs receiving the highest doses, and (ii)V0.5 Gy, denoting the blood volume percentage that has received at least 0.5 Gy.Conclusion.A novel methodology is proposed for quantifying the circulating blood dose along large vessels. This methodology involves the use of patient-specific vasculature, blood flow velocity field, and dose delivery dynamics recovered from the irradiation machine. Relevant parameters that affect the dose received, as the distance between large vessels and CTV, are identified.

Construction of inherent geometric pattern for equal-area hexagonal discrete global grid systems

ABSTRACT Discrete Global Grid Systems (DGGSs) are hierarchical frameworks that provide seamless global coverage and enable the efficient processing and analysis of heterogeneous geospatial data. As cell resolution becomes finer, similarities in cell attributes, such as cell size and shape, increase. Understanding these shared characteristics enhances our knowledge of DGGSs structures, improving their reliability and accuracy across a range of applications. However, existing methods for quantifying these characteristics are limited, often emphasizing overall analysis and visualization rather than detailed assessment. This paper introduces a geometric pattern quantification method for equal-area hexagonal DGGSs by calculating inter-level attribute similarities to identify a reference level, segmenting the spatial distribution image at this reference level, and fitting the boundaries of these sub-images to create iso-feature lines. The results show that this method improves the accuracy and efficiency of cell attribute calculations, with enhancements of at least 5.6 times for study regions and 13.8 times for sample points at lower levels. This pattern provides a robust basis for grid selection and optimization in regional applications, promoting the broader adoption of DGGSs across various fields.

Innovative Peptide-Based Plasmonic Optical Biosensor for the Determination of Cholesterol

Plasmonic-based biosensors have gained prominence as potent optical biosensing platforms in both scientific and medical research, attributable to their enhanced sensitivity and precision in detecting biomolecular and chemical interactions. However, the detection of low molecular weight analytes with high sensitivity and specificity remains a complex and unresolved issue, posing significant limitations for the advancement of clinical diagnostic tools and medical device technologies. Notably, abnormal cholesterol levels are a well-established indicator of various pathological conditions; yet, the quantitative detection of the free form of cholesterol is complicated by its small molecular size, pronounced hydrophobicity, and the necessity for mediator molecules to achieve efficient sensing. In the present study, a novel strategy for cholesterol quantification was developed, leveraging a plasmonic optical readout in conjunction with a highly specific cholesterol-binding peptide (C-pept) as a biorecognition element, anchored on a functionalized silica substrate. The resulting biosensor exhibited an exceptionally low detection limit of 21.95 µM and demonstrated a linear response in the 10–200 µM range. This peptide-integrated plasmonic sensor introduces a novel one-step competitive method for cholesterol quantification, positioning itself as a highly sensitive biosensing modality for implementation within the AVAC platform, which operates using reflective dark-field microscopy.

Age and sex effects of a validated LC-MS/MS method for the simultaneous quantification of testosterone, allopregnanolone, and its isomers in human serum.

Despite the great relevance of the neurosteroid allopregnanolone and related isomers to various health conditions, quantification typically involves immunoassay, which suffers from serious issues with cross-reactivity of closely related molecules. This article describes the development and partial validation of a liquid chromatography coupled with tandem mass spectrometry assay for the simultaneous quantification of allopregnanolone, pregnanolone, isopregnanolone, epi-allopregnanolone, and testosterone in the human serum of healthy males and females aged 5-85 years. 1-amino-4-methylpiperazine (AMP) was used as a derivatisation reagent to enhance the ionisation signal. Linearity was calculated at 0.99 with a lower limit of quantification of 10.08 pg/mL for allopregnanolone, along with a linearity of 0.98 and a lower limit of quantification of 42.32 pg/mL for testosterone. Application of the method showed sex and age effects across the lifespan for both allopregnanolone and testosterone, whereas a comparative immunoassay for allopregnanolone was not able to detect differences in the same samples. Our partial validation of this method should provide a new tool for researchers to discover the role of allopregnanolone and its isomers in human health, and how it compares to testosterone and sex hormones.

PunctaFinder: an algorithm for automated spot detection in fluorescence microscopy images.

Fluorescence microscopy has revolutionised biological research by enabling the visualisation of subcellular structures at high resolution. With the increasing complexity and volume of microscopy data, there is a growing need for automated image analysis to ensure efficient and consistent interpretation. In this study, we introduce PunctaFinder, a novel Python-based algorithm designed to detect puncta, small bright spots, in raw fluorescence microscopy images without image denoising or signal enhancement steps. Furthermore, unlike other available spot detectors, PunctaFinder not only detects puncta, but also defines the cytoplasmic region, making it a valuable tool to quantify target molecule localisation in cellular contexts. PunctaFinder is a widely applicable punctum detector and size estimator, as evidenced by its successful detection of Atg9-positive vesicles, lipid droplets, aggregates of a destabilised luciferase mutant, and the nuclear pore complex. Notably, PunctaFinder excels in detecting puncta in images with a relatively low resolution and signal-to-noise ratio, demonstrating its capability to identify dim puncta and puncta of dynamic target molecules. PunctaFinder reliably detects puncta in fluorescence microscopy images where automated analysis was not possible before, providing researchers with an efficient and robust method for punctum quantification in fluorescence microscopy images.

LC-MS/MS method for quantification of 23 TKIs in Plasma: Assessing the relationship between anlotinib trough concentration and toxicities.

To develop a simple, rapid, and sensitive LC-MS/MS method for quantifying 23 tyrosine kinase inhibitors (TKIs) in plasma samples, and evaluate the relationship between the trough concentration of anlotinib(ANL) and its toxicities. The method was developed in Agilent 1290-6460 UHPLC-MS/MS system. This study prospectively enrolled 55 cancer patients undergoing ANL treatment. Plasma samples were collected at steady-state trough concentration and subsequently analyzed using the method. Patients were recorded for the occurrence of toxicities. Statistical analysis was performed to assess the association of the toxicities with ANL exposure level and patients' characteristics. The LC-MS/MS method was developed and validated for all items required by pharmacopoeia. The results revealed a positive association between the trough concentration of ANL and the incidence of toxicities. The exposure level 17.655 ng/mL (AUC 0.82, p = 0.010) was identified as a predictive threshold value for grade ≥ 3 overall toxicities. In addition, lower platelet count (PLT count < 179 × 109 g/L) was significantly associated with higher occurrence of grade ≥ 3 toxicities (AUC 0.75, p = 0.049). A logistic model incorporating these two factors demonstrated improved diagnostic capacity for predicting ≥ 3 overall toxicities (AUC = 0.90, p = 0.001). This study successfully developed and validated a simple, rapid, and sensitive LC-MS/MS method for quantifying 23 TKIs in plasma samples. Besides, this study found that both Ctrough of ANL and PLT count as independent predictors for ANL-induced ≥ 3 overall toxicities. Moreover, a logistic model including these two factors presents better prediction capacity for ≥ 3 overall toxicities.

Quantifying uncertainty in neural network predictions of forced vibrations

AbstractThe prediction of forced vibrations in nonlinear systems is a common task in science and engineering, which can be tackled using various methodologies. A classical approach is based on solving differential (algebraic) equations derived from physical laws ('first principles'). Alternatively, Artificial Neural Networks (ANNs) may be applied, which rely on learning the dynamics of a system from given data. However, a fundamental limitation of ANNs is their lack of transparency, making it difficult to understand and trust the model's predictions. In this contribution, we follow a hybrid modelling approach combining a data‐based prediction using a stabilised Autoregressive Neural Network (s‐ARNN) with a priori knowledge from first principles. Moreover, aleatoric and epistemic uncertainty is quantified by a combination of mean‐variance estimation (MVE) and deep ensembles. Validating this approach for a classical Duffing oscillator suggests that the MVE ensemble is the most accurate and reliable method for prediction accuracy and uncertainty quantification. These findings underscore the significance of understanding uncertainties in deep ANNs and the potential of our method in improving the reliability of predictive nonlinear system modelling. We also demonstrate that including partially known dynamics can further increase accuracy, highlighting the importance of combining ANNs and physical laws.

Development and Validation of RP-HPLC Method for Quantification of Resveratrol in a Resveratrol-Solid Lipid Nanoparticles Loaded Microneedles Patch

Development and Validation of RP-HPLC Method for Quantification of Resveratrol in a Resveratrol-Solid Lipid Nanoparticles Loaded Microneedles Patch

A Bayesian-Based Credible and Reliable Control Method for Active Vibration Suppression of Structures with Uncertain Parameters

As a classical design method for addressing uncertainty in control systems, robust control is designed with the goal of complete reliability of the control system. Nevertheless, the pursuit of robustness can inadvertently give rise to considerable performance degradation, rendering the resultant closed-loop control system excessively conservative in its design. Reliability-based design methods serve as an effective approach for managing uncertainties, which will achieve a balance between the robustness and performance of the active control system by taking advantage of the characteristic of reliability that allows for a certain level of permissible failure probability. The accurate quantification of uncertainty is the key to achieving reliable control, but common uncertainty quantification methods cannot achieve credible quantification of uncertainty in small samples and update the distribution of new samples. In this paper, a nonprobabilistic Bayesian inference based interval uncertainty quantification method is proposed. This method updates the distribution of interval boundaries and radii by introducing a small number of samples, and obtains interval parameters under a given confidence level. On the basis of uncertainty quantification, the dynamic reliability of the vibration system is derived, and nonprobabilistic reliability is introduced into the design of active controllers through optimization strategies. The reliable control strategy aims to select the optimal controller parameters on a reliable path with a given credibility, in order to solve the problem of overly conservative robust control theory. Two numerical examples were used to verify the feasibility and applicability of this method.

TCP-UQ: two-color pyrometry uncertainty quantification using high speed color cameras

Abstract In this study, an uncertainty quantification method is developed for two-color pyrometry (TCP-UQ), using high-speed color cameras. A NIST traceable calibrated light source is used and the relative exposure is determined using spectrometer measurements and color camera response functions. The inherent digitization and noise uncertainty is estimated from a set of calibration images and characterized with probability distribution functions. These uncertainties are then propagated during TCP to two quantities of interest: the temperature and soot volume fraction measurements. TCP-UQ is applied to a polymethyl methacrylate slab burner experiment to determine the temperature and the soot volume fraction fields. Temperature measurements ranged from 2300 K to 2800 K , soot volume fractions are recorded between 0.5 and 10 ppm and are in good agreement with prior measurements reported in the literature. The newly developed TCP-UQ allows for uncertainty estimations in the temperature and the soot volume fraction measurements to be associated with variations in the pixel intensities. For the slab burner experiment, the average uncertainty in the temperature is between 120–140 K and the average uncertainty in the soot volume fraction is around 0.1 ppm. Generally, higher pixel intensities corresponded to lower uncertainty in the temperature measurement.

Vision-guided crack identification and size quantification framework for dam underwater concrete structures

Remotely operated vehicles with cameras provide a non-contact inspection solution for dam underwater structural defect detection. However, manual information extraction methods suffer from problems like labor costs and high misjudgment. This study proposes an integrated dam underwater crack identification and size quantification framework using machine vision and deep learning. First, a real-time automatic detection framework for dam underwater cracks is built via You Only Look Once v5 (YOLOv5), and four different backbone detectors are introduced to balance detection accuracy and speed. Then, the Swin-Transformer module is inserted into the YOLOv5 model to enhance its feature extraction capability and small object detection capability. Next, a method for measuring the true size of cracks was constructed based on deep learning and infrared laser rangefinders. In this study, physical model experiments and actual engineering projects are combined to validate the generalization capability of the proposed crack identification and size quantification method. Experimental results show that the Swin-Transformer-based YOLOv5 model effectively balances detection accuracy and speed with a precision of 0.986, a recall of 0.979, a mean average precision of 0.985, and a frame rate of 68 frames per second in detecting underwater crack images with 768 × 576 pixels. In addition, the proposed method can accurately identify and detect cracks in complex underwater scenes, including obstacle interference, tilt shooting angle, uneven illumination, and turbid water scenarios. Moreover, the method proposed in this paper can quantify the overall size and geometric parameters of underwater cracks with relatively small errors.

A novel approach to double-strand DNA break analysis through γ-H2AX confocal image quantification and bio-dosimetry.

DNA damage occurs in all living cells. γ-H2AX imaging by fluorescent microscopy is widely used across disciplines in the analysis of double-strand break (DSB) DNA damage. Here we demonstrate a method for the quantitative analysis of such DBSs. Ionising radiation, well known to induce DSBs, is used in this demonstration, and additional DBSs are induced if high-Z nanoparticles are present during irradiation. As a deliberate test of the methodology, cells are exposed to a spatially fractionated ionising radiation field, characterised by regions of high and low absorbed radiation dose that are only ever qualitatively verified biologically via γ-H2AX imaging. Here we validate our bio-dosimetric quantification method using γ-H2AX assays in the assessment of DSB enhancement. Our method reliably quantifies DSB enhancement in cells when exposed to either a spatially contiguous or fractionated irradiation fields. Using the γ-H2AX assay, we deduce the biological dose response, and for the first time, demonstrate equivalence to the independently measured physical absorbed dose. Using our novel method, we are also able quantify the nanoparticle DSB enhancement at the cellular level, which is not possible using physical dose measurement techniques. Our method therefore provides a new paradigm in γ-H2AX image quantification of DSBs, as well as an independently validated bio-dosimetry technique.

A Micro-Flow Liquid Chromatography-Mass Spectrometry Method for the Quantification of Oxylipins in Volume-Limited Human Plasma.

Oxylipins are well-known lipid mediators in various inflammatory conditions. Their endogenous concentrations range from low picomolar to nanomolar, and there are growing demands to determine their concentrations in low-volume matrices for pathological studies, including blood, cerebrospinal fluids from animal disease models, infants, and microsampling devices. Most of the published quantification methods for comprehensive profiling of oxylipins still require more than 50µL plasma as a starting volume to detect these low levels. The aim of our study is to develop a sensitive and reliable method for the quantification of oxylipins in volume-limited human plasma samples. We established and validated a micro-liquid chromatography (LC)-mass spectrometry (MS)/MS method that requires only 5µL of human plasma for the determination of 66 oxylipins. The optimized micro-LC-MS/MS method utilized a flow rate of 4µL/min with a 0.3-mm inner diameter column. With an injection volume of 3µL, our method provides limits of detection in the range from 0.1 to 91.9pM, and limits of quantification range from 0.3 to 306.2pM. The sensitivity enhancement compared to conventional flow ranged from 1.4 to 180.7 times for 51 compounds depending on their physical-chemical properties. After validation, the method was applied to analyze 40 plasma samples from a healthy aging study to demonstrate robustness and sensitivity.

Quantitative Analysis of Amorphous Form in Indomethacin by Near Infrared Spectroscopy Combined with Partial Least Squares Regression Analysis

Indomethacin (INDO) is a synthetic non-steroidal antipyretic, analgesic, and anti-inflammatory drug that commonly exists in both amorphous and crystalline states. Its amorphous state (A-INDO) is utilized by pharmaceutical companies as an active pharmaceutical ingredient (API) in the production of INDO drugs due to its higher apparent solubility and bioavailability. The crystal state also encompasses various crystal forms such as the α-crystal form (α-INDO) and γ-crystal form (γ-INDO), with the highly crystalline and insoluble γ-INDO being commercially available. A-INDO, existing in a thermodynamically high-energy state, is susceptible to several factors during the preparation, storage, and transportation of API leading to its conversion into γ-INDO, thus impacting the bioavailability and efficacy of INDO drugs. Therefore, quantitative analysis of the A-INDO/γ-INDO content in INDO API becomes essential for controlling the production quality of INDO. The primary objective of this study is to investigate the feasibility of NIR for the quantitative analysis of A-INDO in INDO API, and to further elucidate its quantitative analysis mechanism. The NIR spectral data were collected for A-INDO and γ-INDO binary mixture samples with different resolutions, and these spectra were then selected and reconstructed using the interval partial least square (iPLS) method. Different pretreatment methods were employed to enhance the reconstructed spectra by highlighting relevant eigen information while eliminating invalid information caused by environmental factors or physical characteristics of samples. The most suitable PLSR model for quantitative analysis of A-INDO within the range of 0.0000–100.0000% w/w% was established, screened, and validated. From various perspectives, including distribution of spectral effective information, impact of resolution on PLSR model performance, variance contribution/cumulative variance contribution of PLSR model principal components (PCs), PCI loadings, relationship between spectral scores, and A-INDO content, feasibility assessment was conducted for the quantitative analysis of A-INDO in INDO using NIR spectroscopy. Additionally, a detailed investigation on the quantitative analysis mechanism of the optimal PLSR model was undertaken including the correlation between the characteristic peaks of spectra and information regarding hydrogen groups or hydrogen bonds in A-INDO or γ-INDO molecules. This study aims to provide theoretical support for the quantitative analysis of A-INDO in INDO API as well as serve as a reliable reference method for API quantification and quality control in similar drugs.

A greener approach to assess bioactive compounds in tropical pumpkin (Cucurbita moschata) using colorimetry

Bioactive compounds have been shown to promote healthy human development and protect against certain diseases including some cancers. Tropical pumpkins bred for consumption, such as calabaza (Cucurbita moschata), possess these compounds, though concentrations vary considerably between varieties. Bioactive compound quantification methods can be expensive, complex, and generate considerable hazardous waste. A predictive tool utilizing the relationship between extrinsic properties and tristimulus colorimetry values of calabaza would allow for significantly higher throughput with lower requirements for equipment, training, and laboratory consumables. Therefore, this study examined correlations between flesh color and bioactive compound quantities of several calabaza germplasm lines to assess the utility of a sustainable and portable predictive tool. CIEL* a* b* values of pumpkin flesh were assessed using a colorimeter and compared to bioactive compound concentrations determined analytically. Redness was found to correlate with increased ascorbic acid and total soluble solids content. Yellowness correlated with β-carotene equivalent carotenoids and ascorbic acid content. These results suggest that colorimetry may be a viable pre-screening technique for quantifying bioactive compounds in C. moschata.

Proteome integral solubility alteration via label-free DIA approach (PISA-DIA), game changer in drug target deconvolution.

Drug protein-target identification in past decades required screening compound libraries against known proteins to determine drugs binding to specific protein. Protein targets used in drug-target screening were selected predominantly used laborious genetic manipulation assays. In 2013, a team led by Pär Nordlund from Karolinska Institutet(Stockholm, Sweden) developed Cellular Thermal Shift Assay (CETSA), a method which, for the first time, enabled the possibility of drug protein-target identification in the complex cellular proteome. High throughput, quantitative mass spectrometry (MS) proteomics appeared as a compatible analytical method of choice to complement CETSA, aka Thermal Protein Profiling assay (TPP). Since the seminal CETSA-MS/ TPP-MS publications, different protein-target deconvolution strategies emerged including Proteome Integral Solubility Alteration (PISA). The work of Emery-Corbin etal.(Proteomics2024,2300644), titled Proteome Integral Solubility Alteration via label-free DIA approach (PISA-DIA), introduces Data-Independent Acquisition (DIA) as a quantification method, opening new avenues in drug target-deconvolution field. Application of DIA for target deconvolution offers attractive alternative to widely used data dependent methodology.

Approaches for Behavior Intensity Estimation in Groups of Heterogeneous Individuals: Precision and Applicability for Data with Uncertainty

In socially oriented areas, there arises the problem of assessing the cumulative characteristics of behavior, such as intensity, that are realized in groups of individuals. All individuals vary in their behavior and the available data is limited and may be associated with significant uncertainty: only a few episodes may be known and only a few individualsi the group may be observed. Mathematical models of behavior are used for estimation of key characteristics of the behavior. One of them is based on the gamma–Poisson point process, that reflects the heterogeneity of individuals in a form of a mixing distribution. This general model allows to formulate several methods of frequency estimation: the Cox regression, estimation of the copula parameter, and a posteriori inference in Bayesian belief networks. The aim of the paper is to assess their determine the precision of these methods based on the Kantorovich–Rubinstein distance between estimates and the true distribution of the desired parameter. The analysis of assumptions of those methods allows to formulate rules, that allow to chose the appropriate method in various sutuations of data availability. It has been shown that the copula-based approach provides the most accurate estimates and has the mild assumptions for the number of observed objects, but it cannot take into account external factors that may influence the behavior. Among methods that can take into account process covariants, estimates based on a posteriori inference in hybrid Bayesian belief networks have the highest precision. The paper considers a method for quantification of a hybrid BBNs with the approximation of mixtures of truncated exponents, that is data-demanding at the stage of calculating a priori estimates. However, it is noted that there are other approaches to setting hybrid BSDs in which a priori estimates can be set completely expertly.

A Quantitative Method of Carbon Emission Reduction for Electrochemical Energy Storage Based on the Clean Development Mechanism

Electrochemical energy storage (EES) plays a crucial role in reducing the curtailed power from wind and solar PV power (WSP) generation and enhancing the decarbonization effects of power systems. However, research on quantifying the carbon emission reduction effects of EES methods in the engineering field is still insufficient, which constrains decision-makers from making intuitive assessments of the decarbonization effects of energy storage. Therefore, drawing on the principles of the clean development mechanism (CDM), this paper proposes a method for quantifying the carbon emission reductions of a standalone EES station. Firstly, based on the design principles of building marginal emission factor in the CDM, a method for calculating the BM weight of WSP is proposed. Secondly, three quantification methods for the carbon emission reductions of EES are presented based on the complexity of the calculations. Lastly, to analyze the impacts of different operational conditions and calculation methods on the carbon emission reduction of energy storage systems, a dispatch model is constructed for various operational scenarios. The results of the case study indicate that different calculation methods yield varying results in terms of the carbon emission reductions of energy storage systems, with the sharply value method yielding the smallest reduction and the output curve method yielding the largest reduction. Additionally, when considering the losses in the state of charge (SOC) of an energy storage system and reducing the overall output fluctuations of WSP-EES, the carbon emission reduction potential of the energy storage will decrease. This study establishes a theoretical basis for quantifying the carbon emission reductions of standalone electrochemical energy storage systems, aiding decision-makers in gaining a deeper understanding of the role of electrochemical energy storage in carbon reduction and operational value.