Manifold Parameters Research Articles

Real World Fuel Consumption and Emissions From LDVs and HDVs

Real world emission and energy consumption behavior from vehicles is a key element for meeting air quality and greenhouse gas (GHG) targets for any country. While CO2 fleet targets for vehicles are defined on basis of standardized test procedures, in real driving conditions manifold parameters show large variabilities. Main differences are Driving cycle Vehicle loading and driving resistances Ambient temperature levels, start conditions and trip length Gear shift behavior of the drivers Power demand from auxiliaries Fuel quality For the upcoming update of the Handbook Emission Factors for Road Transport (HBEFA 4.1) we have performed analysis, measurements and simulations to elaborate real world energy consumption values for 2-wheelers, passenger cars (PC), light commercial vehicles (LCVs) and heavy duty vehicles (HDVs), so called emission factors (EF). EFs show fuel consumption or emission level in [g/km] and [#/km] for fuel, gaseous exhaust gas components and also for the particle number (PN). EFs are provided for a lot of different traffic situations covering stop & go up to highway for different road gradient categories. EFs are different for each vehicle category and for each powertrain technology and emission standard (from EURO 0 gasoline PC to EURO VI HDV with CNG engine). To produce the EFs, vehicle tests from chassis dyno and from on-board measurements were collected in all European labs to set up models for all vehicle segments in the passenger cars and heavy duty emission model (PHEM). The models for PC and LCVs were based on weight and road load data available from the type approval test, the worldwide harmonized light vehicles test procedure (WLTP), and then calibrated in a stepwise approach to consider all influences in real world driving. Finally, the results for new vehicle fleet fuel consumption values were compared with data from the fuel consumption monitoring data base. For HDVs, the models are based on data from the development of the HDV CO2 determination method (Regulation (EU) 2017/2400, “VECTO”). The methods and also the results in terms of differences between real world and type approval for loading, road load, energy consumption and emission levels are described in this paper.

An exploratory analysis approach for understanding variation in stochastic textured surfaces

Stochastic textured surface data are increasingly common in industrial quality control and other settings. Although there are a number of recently developed methods for understanding variation (e.g., due to manufacturing inconsistency) across a set of profiles or other multivariate quality control data, these existing methods are not applicable to stochastic textured surfaces due to their stochastic nature. One challenge is that it is not straightforward how to define distances or dissimilarities between surface samples. An approach is developed for understanding variation in stochastic textured surfaces by deriving a new pairwise dissimilarity measure between surface samples and using these dissimilarities within a manifold learning framework to discover a low-dimensional parameterization of the surface variation patterns. Visualizing how the stochastic nature of the surfaces changes as the manifold parameters are varied helps build an understanding of the individual physical characteristic of each variation pattern. The approach is demonstrated with simulation and textile examples, in which the physical characteristics of the variation patterns are clearly revealed. The computer codes for implementing the approach are available in the spc4sts R package.

IoT Based Framework: Mathematical Modelling and Analysis of Dust Impact on Solar Panels

The solar photovoltaic performance is governed by manifold parameters viz. temperature, irradiance, dust on solar module, photoactive material, panel orientation. Among these dust is a critical impediment, as its accumulation on panel surface degrades its productivity; while frequent cleaning sessions affect module’s life and result into PV destruction. Accordingly, the need to know dust thickness responsible for deteriorating panel’s capability and adequate cleaning time of solar panels to produce optimum yields is requisite. This paper aims to discern a right cleaning time, owing to a particular dust thickness so as to conserve the panel efficiency using internet of things (IoT). The mathematical correlations of PV efficiency and current with thickness of accumulated dust are derived using linear regression. Further, these equations are associated with an IoT-based platform which remotely monitors and records PV output current; thereafter dust thickness corresponding to a significant current reduction is estimated. For this, experimental data of 46 inverters with total 114,819.30 kWh productions in a month with an average of 4416.13 kWh/day is accessed and the results pertaining to mathematical analysis exhibit a decline in current by 1 A with 5.51 × 10−3 mm thickness of dust.

Nanocomposites of Polyoxometalates and Chitosan‐Based Polymers as Tuneable Anticancer Agents

Representative polyoxometalates with proven bio‐active properties, namely {Sb9W21}, {P2W18} and {Mo7O24}, were encapsulated into chitosan and carboxymethyl chitosan (CMC) to afford nanoscale composites. The pristine POMs were characterized with a wide range of analytical techniques, including powder X‐ray diffraction as well as FT‐IR and 31P NMR spectroscopy. Size and morphology of their nanocomposites were determined within the 100–200 nm size range by dynamic light scattering (DLS) methods in combination with scanning and transmission electron microscopy. Their stability was monitored by UV/Vis and DLS measurements over several days. The cytotoxicity of pristine and encapsulated POMs was investigated on HeLa and MRC‐5 cell lines, representing cancer and normal cell lines, respectively. Our results suggest that CMC encapsulation enhances the specificity towards cancer cells, especially in treatment approaches where a lower drug dose would be required. The anticancer activity of POMs emerged as a dynamic property that depends on manifold parameters, such as the POM structure, the polymeric drug carrier type, and the investigated cell line.

Handling and Stability Analysis of Vehicle Plane Motion

The work analyzes the properties of handling and bicycle vehicle model motion stationary states manifold stability taking into account drift force nonlinear characteristics. Determining single two-axle vehicle nonlinear model stationary states and analyzing their stability were based on a graphical method (Y. M. Pevzner, H. Pacejka). It has its disadvantages: the absence of evident analytical stability criteria for the entire wheeled vehicle circular stationary states manifold. And also the absence of global stability threshold characteristics in the controlled parameter space. The task part suggests developing methods for building bifurcation manifold or critical parameters manifold (longitudinal velocity and wheel turning angle) with which the divergent loss of stability occurs. Known H. Troger, K. Zeman and Fabio Della Rossaa, GiampieroMastinub, Carlo Piccardia results are based on parameter continuation numerical methods which makes the quality analysis of drift force nonlinear characteristics impact on the entire stationary states manifold stability conditions more difficult. A compelling grapho-analytic approach towards bifurcation manifold building and getting circular stationary states analytical stability conditions based on moving from nonlinear drift forces on axles dependencies to their inverse dependence is developed in the suggested work. This methodology allows defining dangerous/safe stability threshold conditions in the control parameters space.

Strategy for assessment of the colloidal and biological stability of H1N1 influenza A viruses

Current influenza vaccines are mostly formulated as liquids which requires a continuous cold chain to maintain the stability of the antigen. For development of vaccines with an increased stability at ambient temperatures, manifold parameters and their influences on the colloidal stability and activity of the antigen have to be understood. This work presents a strategy to examine both, the colloidal stability and the remaining biological activity of H1N1 influenza viruses under various conditions after an incubation of 40 days. H1N1 phase diagrams were generated for several pH values and different initial H1N1 and NaCl concentrations. It was shown that the highest H1N1 recoveries were obtained for pH 6 and that moderate amounts of NaCl are favorable for increased recoveries. In contrast to colloidal stability, the highest remaining HA activity was observed at pH 9. The electrostatic and hydrophobic surface properties of H1N1 were investigated to reveal the mechanisms accounting for the decrease in stability. Secondly, the capability of virus precipitation by polyethylene glycol in combination with determination of surface hydrophobicity was proven to be useful as a predictive tool to rank stability under different conditions. This methodology enables the rapid assessment of aggregation propensity of H1N1 formulations and the influence on the activity of the virus particles and might become a standard tool during the development of vaccine formulations.

Adaptive Laplacian Eigenmap-Based Dimension Reduction for Ocean Target Discrimination

It is well known that polarimetric synthetic aperture radar (PolSAR) backscattering features are highly influenced by the variation of incidence angle (VIA), which usually hampers the classification of most grazing-angle-sensitive targets, such as land and ocean targets. To relieve this issue, various feature extraction approaches have been suggested to enhance the class discriminability while reducing the observed feature dimensionality. The Laplacian eigenmap-based dimension reduction (DR) has been proven to be an effective way to deal with VIA problems, provided that the manifold parameters [e.g., the heat kernel (HK)] have been optimally sought, which is often difficult in practice. In this letter, an adaptive Laplacian eigenmap-based DR method is presented to find a learned subspace where the local geometry with discriminative prior knowledge is preserved as much as possible while near optimal HK and scale factor parameters are automatically identified. The learned feature representation is then employed for the subsequent classification. The improved Laplacian eigenmap algorithm was validated by three uninhabited-aerial-vehicle-synthetic-aperture-radar L-band PolSAR images from the Gulf Deepwater Horizon oil spill, which were clearly impacted by the VIA phenomenon. The experimental results showed that the proposed algorithm works well in ocean target discrimination compared with the current common methods.

Development of flow methods for the determination of N-acetyl-L-cysteine in pharmaceutical formulations

Background: N-acetyl-L-cysteine (NAC) is a synthetic aminothiol antioxidant that has been in clinical use for more than 40 years, primarily as a mucolytic agent and in the management of paracetamol (acetaminophen) poisoning. There is a need for a new, robust, inexpensive, and rapid method of determination of NAC in pharmaceutical formulations, in order to assure proper quality control (QC). Patient safety during therapy, on the other hand, is indirectly linked to proper QC of the medication. Novel flow methods for the determination of NAC, such as the flow-injection method (FIA) or the sequential-injection method (SIA), have been developed and validated. Both methods are kinetic methods, which means that the measurement of the analytical signal is made under dynamic conditions in which the concentrations of reactants and products are changing as a function of time. The proposed flow methods of analysis (FIA and SIA) are interesting alternatives in NAC determinations instead of conventional batch methods and chromatography with different detectors. The advantages afforded by the flow methods of analysis are high sample frequency, low consumption of sample and reagents, low contamination risks, and significant reproducibility that provides high precision and enhanced selectivity as a result of the kinetic nature of the recorded analytical signal. Furthermore, the proposed flow methods of analysis require very limited laboratory bench space and necessary instrumentation. Methods: The proposed methods are based on the reduction of Cu(II)-neocuproine reagent to Cu(I)-neocuproine with the analyte, in a Britton-Robinson buffer solution (pH 3.0). The non-steady-state absorbance of the formed yellow Cu(I)-neocuproine complex is measured at 458 nm. For the flow-injection method the three-line manifold with one reaction coil was used. Optimization of manifold parameters and experimental conditions were carried out by means of univariate method. The sequential-injection manifold consisted of a Cheminert® M50 pump (VICI Valco), a syringe-free stepper motor-driven pump, a 10-port selection valve model (C25-3180D) with a multiposition actuator control module (EMHCA-CE ; VICI Valco). Both flow systems use a spectrophotometric detector. Results: Using a flow-injection method of analysis, a linear calibration curve is established in a concentration range of 6 × 10−7 to 4 × 10−5 mol/l NAC with a detection limit of 9.4 × 10−8. On the other hand, using the sequential-injection method of analysis, linearity was obtained in the concentration range of 4 × 10−6 to 3 × 10−4 mol/l. The detection limit was found to be 1.2 × 10−6 mol/l. The proposed methods are simple, rapid, sensitive and reproducible (FIA: RSD 0.9 %, n = 100 ; SIA: RSD 1.9 %, n = 100). In addition, the proposed methods are sensitive enough to enable determination of near-nanomole amounts of NAC without expensive instruments with an analytical frequency of 120 / h (FIA) and 60 / h(SIA). Discussion: The proposed methods can be applied for the determination of NAC in pharmaceutical preparations. Therefore, they could be useful for QC of NAC-containing medications, indirectly improving patient safety during therapy (e. g. by reducing the risk of under- or over-dosage).

JOINT OFFSHORE WIND FIELD MONITORING WITH SPACEBORNE SAR AND PLATFORM-BASED DOPPLER LIDAR MEASUREMENTS

Abstract. The increasing demand for renewable energy resources has promoted the construction of offshore wind farms e.g. in the North Sea. While the wind farm layout consists of an array of large turbines, the interrelation of wind turbine wakes with the remaining array is of substantial interest. The downstream spatial evolution of turbulent wind turbine wakes is very complex and depends on manifold parameters such as wind speed, wind direction and ambient atmospheric stability conditions. To complement and validate existing numerical models, corresponding observations are needed. While in-situ measurements with e.g. anemometers provide a time-series at the given location, the merits of ground-based and space- or airborne remote sensing techniques are indisputable in terms of spatial coverage. Active microwave devices, such as Scatterometer and Synthetic Aperture Radar (SAR), have proven their capabilities of providing sea surface wind measurements and particularly SAR images reveal wind variations at a high spatial resolution while retaining the large coverage area. Platform-based Doppler LiDAR can resolve wind fields with a high spatial coverage and repetition rates of seconds to minutes. In order to study the capabilities of both methods for the investigation of small scale wind field structures, we present a direct comparison of observations obtained by high resolution TerraSAR-X (TS-X) X-band SAR data and platform-based LiDAR devices at the North Sea wind farm alpha ventus. We furthermore compare the results with meteorological data from the COSMO-DE model run by the German Weather Service DWD. Our study indicates that the overall agreement between SAR and LiDAR wind fields is good and that under appropriate conditions small scale wind field variations compare significantly well.

Spine Segmentation in Medical Images Using Manifold Embeddings and Higher-Order MRFs

We introduce a novel approach for segmenting articulated spine shape models from medical images. A nonlinear low-dimensional manifold is created from a training set of mesh models to establish the patterns of global shape variations. Local appearance is captured from neighborhoods in the manifold once the overall representation converges. Inference with respect to the manifold and shape parameters is performed using a higher-order Markov random field (HOMRF). Singleton and pairwise potentials measure the support from the global data and shape coherence in manifold space respectively, while higher-order cliques encode geometrical modes of variation to segment each localized vertebra models. Generic feature functions learned from ground-truth data assigns costs to the higher-order terms. Optimization of the model parameters is achieved using efficient linear programming and duality. The resulting model is geometrically intuitive, captures the statistical distribution of the underlying manifold and respects image support. Clinical experiments demonstrated promising results in terms of spine segmentation. Quantitative comparison to expert identification yields an accuracy of 1.6 ± 0.6 mm for CT imaging and of 2.0 ± 0.8 mm for MR imaging, based on the localization of anatomical landmarks.

Application of polynomial design of multiplexers to the implementation of a manifold microstrip triplexer

This article presents a microstrip implementation of a manifold multiplexer. The design procedure is based on evaluation, through an efficient synthesis algorithm recently proposed, of the characteristic polynomials of the triplexer, including the channel filters. Both the manifold and channel filters parameters are also available at the end of the synthesis. This approach, based on polynomial modeling of the triplexer, has been experimentally validated for the first time through a practical implementation in planar technology. The fabricated prototype consists of three channel filters of third order centered at 1.84, 1.95, and 2.14 GHz, with bandwidth of 75, 60, and 60 MHz respectively. This work illustrates in detail the design and implementation of the practical triplexer, starting from the initial specifications and obtaining the final physical dimensions of the structure. Following the technique here shown, each filter is synthesized taking into account for the loading effects of the whole manifold; the filters dimensioning can be carried out with the filters separated from the multiplexer. In this way the optimization tasks are greatly simplified as compared with previous techniques for multiplexer design. The measured results of a manufactured prototype show return losses under 17 dB and minimum isolation between channels equal to 25 dB. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 23:690–698, 2013.

An automatic Flow System of Rapid on-Line Digestion and Pre-Concentration for In-Field Determination of Trace Total Mercury in Seawater

An automatic flow system of rapid on-line sampling, digestion, and pre-concentration for in-field determination of total mercury in seawater has been developed. In this system, both UV radiator and bromine monochloride (1%, v/v) were used to make the sample digestion rapid and reliable. And, the active gold columns were used for the effective preconcentration of mercury. Double loops connected to an 8-way valve were adopted to quantitate the volume of samples and reductant, respectively. With the hyphenation of the flow system and cold vapor atomic fluorescence spectrometry (CVAFS), the detection limit for total mercury was as low as 0.04 ng/L. The manifold parameters, including the carrier gas flow, digestion time, purging time, and reagent flow rates, were optimized. The calibration curve (p = 0.0566C+0.0174, n = 8, R 2 = 0.9994) was obtained within the range of 0.18-200 ng/L. The relative standard deviation (RSD) was 1.9% (n = 12), evaluated from 12 successive measurements of a real seawater sample at 5.2 ng/L mercury level. The recovery ranged from 99.4% to 109%. The analysis speed of the system was 6.5 min per sample. Furthermore, the proposed method was applied to in-field analysis of sixteen real seawater samples for total mercury. The samples were also analyzed with US EPA method (EPA, 1631) in the laboratory. The correlation coefficient between the results obtained with the two methods was 0.9762.

Numerical study of straight-parallel PEM fuel cells at automotive operation

A lab-scale proton exchange membrane fuel cell (PEMFC) is investigated at automotive operating condition. The comparison of straight-parallel PEMFC and serpentine PEMFC is carried out with detailed description of these flow-field configurations. A three-dimensional model is developed taking into account electrochemical reaction and evaporation/condensation of water which can affect on the overall flow field. The straight-parallel PEMFC has considerably low internal pressure drop which is beneficial to automotive application. Non-uniform temperature and current density distributions due to flow maldistribution are identified as a challenge to the straight-parallel PEMFC. To improve uniformity of these variables, we conducted an investigation on the manifold parameters. The result indicates that the wider manifold configuration has better cell performance as well as more uniform temperature and current density distributions than the narrower manifold configuration. This is primarily caused by improved uniformity on the flow velocity profile among parallel channels.

Physical Simulation at Hot Deformation

Today the numerical simulation of hot deformation processes is very advanced. But it requires mathematical models for metalphysical processes as for microstructure development, which take place during the deformation. Until now such models were developed for many steel grades and non-ferrous materials. For new steels as multi-phase steels laboratory investigations are required, in order to determine the optimal processing technologies of these materials. This applies also to the modelling. So far it is impossible, to calculate sole by mathematical solutions the manifold parameters of metalphysical processes and microstructure, for this reason laboratory trials and simulations are needed implicitly. Even for well known materials such procedures can be essential and useful. Using the multi-functional simulation system Gleeble HDS-V40 it is shown, which possibilities a physical simulation offers today. Starting with the annealing conditions, followed by microstructure development up to cooling, selected examples reflect the results of property development during hot deformation processes. The differences between conventional deformation after re-heating and deformation after direct-charging will be presented. The last-mentioned concept offers in its combination of near-netshape casting and direct charging special benefits, especially saving of energy.

Spectrophotometric simultaneous determination of nitrite, nitrate and ammonium in soils by flow injection analysis

A new rapid flow injection procedure for the simultaneous determination of nitrate, nitrite and ammonium in single flow injection analysis system is proposed. The procedure combines on-line reduction of nitrate to nitrite and oxidation of ammonium to nitrite with spectrophotometric detection of nitrite by using the Griess-llosvay reaction. The formed azo dye was measured at 543 nm. The influence of reagent concentration and manifold parameters were studied. Nitrite, nitrate and ammonium can be determined within the range of 0.02–1.60 μg mL −1, 0.02–1.60 μg mL −1 and 0.05–1.40 μg mL −1, respectively. R.S.D. values ( n = 10) were 2.66; 1.41 and 3.58 for nitrate, nitrite and ammonium, respectively. This procedure allows the determination and speciation of inorganic nitrogen species in soils with a single injection in a simple way, and high sampling rate (18 h −1). Detection limits of 0.013, 0.046 and 0.047 μg mL −1were achieved for nitrate, nitrite and ammonium, respectively. In comparison with others methods, the proposed one is more simple, it uses as single chromogenic reagent less injection volume (250 mL in stead of 350 mL) and it has a higher sampling rate.

A Double-Line Sequential Injection System for the Spectrophotometric Determination of Copper, Iron, Manganese, and Zinc in Waters

A double-line sequential injection system was developed for the spectrophotometric determination of several metal ions in waters. The proposed double-line configuration was used to enable adding sample and chromogenic reagents as merging zones. The methodology was applied to the spectrophotometric determination of copper, iron, manganese, and zinc in samples of diverse origins at the range of 0.15-5.00, 0.10-10.0, 0.48-4.00, and 0.11-5.00 mg/L, respectively. Different chromogenic reagents and detection wavelengths were used. The chromogenic reagents for iron and manganese were 1,10-phenanthroline and formaldoxime, respectively. Copper and zinc were both determined using the analytical reagent zincon. Analytical characteristics of the methodology, such as manifold parameters, buffer pH, and reagent concentrations were optimized, and interference of some of the metal ions commonly present in water sample was assessed. Results of the analysis were in agreement with those obtained by atomic absorption spectrometry. Repeatability, expressed as the relative standard deviation for 10 consecutive injections of water samples, was lower than 6%. The determination rate was approximately 36/h.

Spectrophotometric Determination of Nitrite and Nitrate in Cured Meat by Sequential Injection Analysis

ABSTRACT: A robust, automated, labor‐saving, accurate, and economical sequential injection system was developed for simultaneous determination of nitrite and nitrate in cured meat samples, based on the Shinn reaction. Nitrite is coupled and diazotized with sulfanilamide and N‐(1‐naphtyl)‐ethylenediamine dihydrochloride, to form a colored compound that absorbs at 538 nm. Nitrate is previously in‐line reduced to nitrite in a copperized cadmium column and measured as nitrite. The solutions' aspiration sequence, the influence of reagent and buffer concentrations, the manifold parameters, and the characteristics of the reducing column were studied. Nitrite and nitrate can be determined within the 0.030 to 1.22 of N‐NO2− and 0.034 to 3.95 mg/L of N‐NO3− ranges, respectively, at a sampling rate of 9/h. Detection limits of 9 μg/L of N for nitrite and 9 μg/L of N for nitrate were obtained, and the conversion rate of nitrate to nitrite was 100.6%± 1.8%. The results were in good agreement with those obtained by the reference methods, with relative standard deviations (r.s.d.) better than 3.70% for nitrites and 2.42% for nitrates.

A flow-injection spectrophotometric method for nitrate and nitrite determination through nitric oxide generation

A flow injection method with novel spectrophotometric detection for the determination of nitrite and nitrate in foodstuffs is presented. The method is based on the reduction of nitrite and nitrate to nitric oxide, with subsequent reaction with iron (II) and thiocyanate in an acid medium, forming FeSCNNO +. The absorbance of the complex, with a maximum at 460 nm, is proportional to the nitrite and nitrate concentrations. The NO is generated in two stages: (1) reduction of nitrate to nitrite in a cadmium copper reductor column and (2) reduction of the nitrite to NO in a sulfuric acid medium. The influence of reagent concentrations and manifold parameters were evaluated. Nitrite and nitrate can be determined in the range of 0.30–3.00 and 1.00–10.00 mg l −1, respectively. The sampling rate of analyses was 30–40 h −1 and, considering a sample of 5.0 g, the determination limit of the method was 20 and 13 mg kg −1 of nitrate and nitrite, respectively. Nitrite and nitrate were determined in vegetables and meat products by the proposed method. The precision and accuracy of the proposed method were comparable to those of the reference spectrophotometric method (official AOAC reference method for the determination of nitrate in foodstuffs).

A simple simultaneous flow injection method based on phosphomolybdenum chemistry for nitrate and nitrite determinations in water and fish samples

A direct spectrophotometric flow injection method for the simultaneous determination of nitrite and nitrate has been developed. The method is based on the oxidation of a phosphomolybdenum blue complex by the addition of nitrite and the decrease in absorbance of the blue complex is monitored at 820 nm. The injected sample is split into two segments. One of the streams was directly reacted with the above reagent and detected as nitrite. The other stream was passed through a copperised cadmium reductor column where reduction of nitrate to nitrite occurs, and the sample was then mixed with the reagent and passed through the cell of the spectrophotometer to be detected as nitrite plus nitrate. The conditions for the flow injection manifold parameters were optimised by experimental design and the concentration of nitrite and nitrate was determined in the linear range from 0.05 to 1.15 μg ml −1 nitrite and 0.06 to 1.6 μg ml −1 nitrate with a detection limit of 0.01 μg ml −1 for nitrite and 0.025 μg ml −1 for nitrate. The method is suitable for the simultaneous determination of nitrite and nitrate in fish and water samples with a sampling rate of 25±2 sample per hour.

Sequential flow injection spectrophotometric determination of nitrite and nitrate in various samples

A direct spectrophotometric method has been developed for the sequential determination of nitrite and nitrate by flow injection analysis (FIA). The method is based on the reaction of nitrite with safranine O to form a diazonium salt which caused the reddish-orange dye colour of the solution to be changed to blue in acidic media, and which absorbs at 520 nm. The injected sample in the flow injection system is split in two streams. One of the streams is transported through a reductor microcolumn containing copperised cadmium, where nitrate is reduced to nitrite. The two streams are then mixed and treated with the appropriate reagents. The influence of reagent concentrations and manifold parameters was studied. The effect of potentially interfering ion was examined and procedures for elimination of interfering cations are proposed. Nitrite and nitrate can be determined in the range 0.0001–3.00 and 0.005–3.40 μg ml −1, respectively, with a sampling rate of 20±3 h −1. Detection limits (3 σ) of 0.5 and 3 ng ml −1 were obtained for nitrite and nitrate, respectively. Nitrite and nitrate were determined in food samples by the proposed method with satisfactory results.