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Journal articles on the topic 'Environmental chemistry Statistical methods'

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Published: 10 December 2022
Last updated: 28 January 2023

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1

Ziegel, Eric R., and J. Einax. "Chemometrics in Environmental Chemistry: Statistical Methods." Technometrics 38, no. 4 (November 1996): 412. http://dx.doi.org/10.2307/1271332.

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2

Whitbeck, Michael. "Chemometrics in environmental chemistry, statistical methods." Chemometrics and Intelligent Laboratory Systems 34, no. 1 (August 1996): 131–32. http://dx.doi.org/10.1016/0169-7439(96)00008-1.

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3

Czermiński, J., A. Iwasiewicz, Z. Paszek, A. Sikorski, and Richard G. Brereton. "Statistical methods in applied chemistry." Analytica Chimica Acta 244 (1991): 296. http://dx.doi.org/10.1016/s0003-2670(00)82518-0.

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4

Simeonov, Vasil. "Basic Multivariate Statistical Methods for Environmental Monitoring Data Mining: Introductory Course for Master Students." Chemistry-Didactics-Ecology-Metrology 25, no. 1-2 (December 1, 2020): 35–56. http://dx.doi.org/10.2478/cdem-2020-0002.

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Abstract The present introductory course of lectures summarizes the principles and algorithms of several widely used multivariate statistical methods: cluster analysis, principal components analysis, principal components regression, N-way principal components analysis, partial least squares regression and self-organizing maps with respect to their possible application in intelligent analysis, classification, modelling and interpretation to environmental monitoring data. The target group of possible users is master program students (environmental chemistry, analytical chemistry, environmental modelling and risk assessment etc.).
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5

Bucur, Elena, Andrei Vasile, Luoana Florentina Pascu, Carol Blaziu Lehr, and Gabriela Geanina Vasile. "Environmental Impact Assessment Regarding Indoor Air Quality Using Statistical Methods." Revista de Chimie 69, no. 11 (December 15, 2018): 3225–28. http://dx.doi.org/10.37358/rc.18.11.6718.

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This paper brings into attention of the indoor air specialists new information regarding the assessment of the potential cumulated impact of the air chemical compounds and microclimate factors on materials; for exemplification it was selected a wooden church made by oak and the monitoring values for temperature, humidity and the concentration of four chemical compounds with a destructive potential on organic materials generally: NO2, SO2, O3 and PM2.5.
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6

Hipel, K. W. "Stochastic and statistical methods in hydrology and environmental engineering." Stochastic Hydrology and Hydraulics 9, no. 1 (March 1995): 1–11. http://dx.doi.org/10.1007/bf01581755.

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7

Bruggemann, Rainer, and Lars Carlsen. "Partial Order in Environmental Chemistry." Current Computer-Aided Drug Design 16, no. 3 (June 2, 2020): 257–69. http://dx.doi.org/10.2174/1573409915666190416160350.

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Background: The theory of partial order is a branch of Discrete Mathematics and is often seen as pretty esoteric. However, depending on a suitable definition of an order relation, partial order theory has some statistical flavor. Here we introduce the application of partial order for environmental chemistry. Objective : We showed that partial order is an instrument, which at the same time, has both data exploration - and evaluation potency. Methods: The partial order theory was applied in this study. It depends on four indicators which describe the environmental hazards of chemicals. Results : Nineteen organic chemicals were found within a monitoring study in the German river Main and were taken as an exemplary case. The results indicated that chemicals can have a high risk on the environment, however, the type of risk is different and should not conceptually merge into a single quantity. Conclusions: Partial order theory is of help to define different regulations and environmental management plans.
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8

Hölzel, Helen, Maximilian Muth, Dominik Lungerich, and Norbert Jux. "Cover Picture: Addressing Environmental Challenges of Porphyrin Mixtures Obtained from Statistical Syntheses (Chemistry ‐ Methods 3/2021)." Chemistry–Methods 1, no. 3 (February 23, 2021): 135. http://dx.doi.org/10.1002/cmtd.202100014.

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9

Veselík, Petr, Marie Sejkorová, Aleksander Nieoczym, and Jacek Caban. "Outlier Identification of Concentrations of Pollutants in Environmental Data Using Modern Statistical Methods." Polish Journal of Environmental Studies 29, no. 1 (December 9, 2019): 853–60. http://dx.doi.org/10.15244/pjoes/112620.

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10

Sinha, Parikhit, Michael B. Lambert, and V. Lyle Trumbull. "EVALUATION OF STATISTICAL METHODS FOR LEFT-CENSORED ENVIRONMENTAL DATA WITH NONUNIFORM DETECTION LIMITS." Environmental Toxicology and Chemistry 25, no. 9 (2006): 2533. http://dx.doi.org/10.1897/05-548r.1.

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11

Yidana, Sandow Mark. "Groundwater Classification Using Multivariate Statistical Methods: Birimian Basin, Ghana." Journal of Environmental Engineering 136, no. 12 (December 2010): 1379–88. http://dx.doi.org/10.1061/(asce)ee.1943-7870.0000291.

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12

Görlitz, Linus, Zhenglei Gao, and Walter Schmitt. "Statistical Analysis of Chemical Transformation Kinetics Using Markov-Chain Monte Carlo Methods." Environmental Science & Technology 45, no. 10 (May 15, 2011): 4429–37. http://dx.doi.org/10.1021/es104218h.

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13

Lynch, Joanna M., David M. Barbano, Patrick A. Healy, and J. Richard Fleming. "Performance Evaluation of the Babcock and Ether Extraction Methods: 1989 through 1992." Journal of AOAC INTERNATIONAL 77, no. 4 (July 1, 1994): 976–81. http://dx.doi.org/10.1093/jaoac/77.4.976.

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Abstract Results of the collaborative studies of the performance of the Babcock method (989.04) and the modified Mojonnier ether extraction method (989.05) for the determination of the fat content of raw milk were published in 1988. Method performances were characterized by using the harmonized International Organization for Standardization–International Union of Pure and Applied Chemistry–Association of Official Analytical Chemists (ISO/IUPAC/AOAC) guidelines for method validation, and the methods were approved official first action. During 1989 through 1992, the split-sample collaborative study format was used to monitor the performance of these methods as a part of an ongoing quality assurance program for a group of laboratories. Seven blind duplicate samples of raw milk were sent from a central laboratory once every 2 months to each participating laboratory (11 to 17 laboratories). Data were analyzed by using the same statistical procedures used in the 1988 study. Over time, both the within- and between-laboratory performances of both methods were as good as or, in most cases, better than the results from 1988. The data demonstrate that the statistical protocol for collaborative studies can be used effectively as the basis for a multilaboratory quality assurance program and that the method performance achieved in a collaborative study can be maintained and even improved with time.
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14

Gennaro, M. C., C. Abrigo, E. Marengo, C. Baldin, and M. T. Martelletti. "Determination of creatinine in human serum. Statistical intercalibration of methods." Analyst 120, no. 1 (1995): 47. http://dx.doi.org/10.1039/an9952000047.

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15

Costa, Vinícius, Ariane Neiva, and Edenir Pereira-Filho. "Chromium speciation in leather samples: an experiment using digital images, mobile phone and environmental concepts." Eclética Química Journal 44, no. 1 (January 28, 2019): 62. http://dx.doi.org/10.26850/1678-4618eqj.v44.1.2019.p62-74.

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This study proposes a new and simple method for Cr speciation and Cr(VI) determination in leather samples using digital images. The experiments were performed using a mobile phone and a free app called PhotoMetrix that was used to obtain and process data. The results obtained from PhotoMetrix were compared to reference methods using UV−Vis spectroscopy. A statistical evaluation between both proposed and the reference methods using two-sample t-test did not show a significant difference at a 95% confidence level. Bovine leather samples (4 samples) tanned with Cr salts and ovine leather samples (3 samples) tanned with vegetable tannin were analyzed. The proposed method presented limits of detection (LOD) and quantification (LOQ) of 0.6 and 2 mg/kg, respectively. In addition, the proposed method using PhotoMetrix and digital images can provide undergraduate students an opportunity to learn topics such as quantitative analyses, environmental chemistry, speciation chemistry, image processing and treatment of statistical data. The results demonstrated that the proposed method can be applied to routine analyses and in experimental analytical chemistry courses.
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16

VILLA, Sabrina Moura, Thamiris Brandino STELLATO, Joyce Rodrigues MARQUES, Mainara Generoso FAUSTINO, Douglas Batista SILVA, Lucilena Rebelo MONTEIRO, Tatiane B. S. Carvalho DA SILVA, Marycel E. Barbosa COTRIM, and Maria Aparecida F. PIRES. "QUALITY ASSURANCE OF ANIONS ENVIRONMENTAL MONITORING IN IPEN S ENVIRONMENTAL MONITORING PROGRAM." Periódico Tchê Química 14, no. 27 (January 20, 2017): 91–96. http://dx.doi.org/10.52571/ptq.v14.n27.2017.90_periodico27_pgs_91_96.pdf.

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This work aims to assess the internal and external quality control of the anion analysis, accomplished at IPEN, using chromatography technique ions and Statistical Methods for data analysis. So it was possible to conclude that the system is over control, generating reliable results
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17

De Beer, Jacques O., Bart M. J. De Spiegeleer, Jos Hoogmartens, Isabelle Samson, Desir� L. Massart, and Martine Moors. "Relationship between content limits and assay methods: an interlaboratory statistical evaluation." Analyst 117, no. 6 (1992): 933. http://dx.doi.org/10.1039/an9921700933.

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18

Winslow, Stephen D., Barry V. Pepich, John J. Martin, George R. Hallberg, David J. Munch, Christopher P. Frebis, Elizabeth J. Hedrick, and Richard A. Krop. "Statistical Procedures for Determination and Verification of Minimum Reporting Levels for Drinking Water Methods." Environmental Science & Technology 40, no. 1 (January 2006): 281–88. http://dx.doi.org/10.1021/es051069f.

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19

Taghavi, Nasrin, Robert K. Niven, David J. Paull, and Matthias Kramer. "Groundwater vulnerability assessment: A review including new statistical and hybrid methods." Science of The Total Environment 822 (May 2022): 153486. http://dx.doi.org/10.1016/j.scitotenv.2022.153486.

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20

Ratti, S. P., G. Belli, and G. Salvadori. "Risk analysis and assessment in environmental sciences: Use of statistical methods to handle the information." Toxicological & Environmental Chemistry 55, no. 1-4 (July 1996): 51–72. http://dx.doi.org/10.1080/02772249609358323.

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21

Oke, I. A. "Reliability and Statistical Assessment of Methods for Pipe Network Analysis." Environmental Engineering Science 24, no. 10 (December 2007): 1481–90. http://dx.doi.org/10.1089/ees.2007.0001.

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22

Su, George C. C. "A Comparison of Statistical and Empirical Detection Limits." Journal of AOAC INTERNATIONAL 81, no. 1 (January 1, 1998): 105–10. http://dx.doi.org/10.1093/jaoac/81.1.105.

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Abstract Quantitatable detection limits for 43 diverse analytes analyzed by a variety of analytical methods determined by 2 statistical methods and one empirical method were compared. One statistically derived quantitatable detection limit is the U.S. Environmental Protection Agency's minimum level (ML), which is 10 times the standard deviation of replicates of a single concentration or 3.18 times the method detection limit (MDL). The other statistical method is the alternate minimum level (AML), which is based on statistical calculations of replicates of multiple concentrations but places more weight on the lower concentrations. The empirical method is based on the analysts' experience with actual samples. All 3 approaches agree to within a factor of 5 nearly 70% of the time. This agreement suggests an uncertainty factor of 5 for laboratory analysis at the lowest concentrations at which instruments can perform quantitative analysis. The results also show that both statistical methods probably adequately account for normal matrix effects.
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23

Cristóvão, Raquel O., Victor M. S. Pinto, António Gonçalves, Ramiro J. E. Martins, José M. Loureiro, and Rui A. R. Boaventura. "Fish canning industry wastewater variability assessment using multivariate statistical methods." Process Safety and Environmental Protection 102 (July 2016): 263–76. http://dx.doi.org/10.1016/j.psep.2016.03.016.

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24

Duboudin, Cédric, Philippe Ciffroy, and Hélène Magaud. "EFFECTS OF DATA MANIPULATION AND STATISTICAL METHODS ON SPECIES SENSITIVITY DISTRIBUTIONS." Environmental Toxicology and Chemistry 23, no. 2 (2004): 489. http://dx.doi.org/10.1897/03-159.

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25

Bakshi, Bhavik R., and Utomo Utojo. "A common framework for the unification of neural, chemometric and statistical modeling methods." Analytica Chimica Acta 384, no. 3 (April 1999): 227–47. http://dx.doi.org/10.1016/s0003-2670(98)00776-4.

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26

Pohoata, Alin, and Emil Lungu. "A Complex Analysis Employing ARIMA Model and Statistical Methods on Air Pollutants Recorded in Ploiesti, Romania." Revista de Chimie 68, no. 4 (May 15, 2017): 818–23. http://dx.doi.org/10.37358/rc.17.4.5559.

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Air pollution is an everyday issue, very relevant to public authorities, requiring control and monitoring to provide data for decision-making policies. The objective of this study was to evaluate the air quality in Ploiesti city, Romania and to observe the advantages and limitations of the some statistical methods used in forecasting air quality. Data for six air quality parameters collected at monitoring stations in Ploiesti during the 2013 year were statistically analyzed. Principal component analysis (PCA) was used to provide a relevant description in factors that can be explained in terms of different sources of air pollution. The measured pollutants data were statistically analyzed using the auto-regressive integrated moving average (ARIMA) method in order to assess the efficiency of using this method in forecasting the environmental air quality. The results revealed that ARIMA method has some limitations and do not produce satisfactory results for certain air pollutants such as PM10 and CO, even the forecasted period is short. By comparison, the ARIMA model obtained for NOx , NO2 , or O3 time series, provides good results, with relative errors around 5%.
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27

Hammody, Z., S. Argov, R. K. Sahu, E. Cagnano, R. Moreh, and S. Mordechai. "Distinction of malignant melanoma and epidermis using IR micro-spectroscopy and statistical methods." Analyst 133, no. 3 (2008): 372. http://dx.doi.org/10.1039/b712040k.

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28

Hughes, Michael R., A. John Bailer, and Debra L. Denton. "Toxicant- and response-specific comparisons of statistical methods for estimating effective concentrations." Environmental Toxicology and Chemistry 20, no. 6 (June 2001): 1374–80. http://dx.doi.org/10.1002/etc.5620200629.

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29

Hagan, Paul, and Dymphna Fellowes. "Multivariate statistical methods in battery research." Journal of Power Sources 122, no. 1 (July 2003): 77–84. http://dx.doi.org/10.1016/s0378-7753(03)00344-6.

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30

Dührkop, Kai. "Computational methods for small molecule identification." it - Information Technology 61, no. 5-6 (October 25, 2019): 285–92. http://dx.doi.org/10.1515/itit-2019-0033.

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Abstract Identification of small molecules remains a central question in analytical chemistry, in particular for natural product research, metabolomics, environmental research, and biomarker discovery. Mass spectrometry is the predominant technique for high-throughput analysis of small molecules. But it reveals only information about the mass of molecules and, by using tandem mass spectrometry, about the mass of molecular fragments. Automated interpretation of mass spectra is often limited to searching in spectral libraries, such that we can only dereplicate molecules for which we have already recorded reference mass spectra. In my thesis “Computational methods for small molecule identification” we developed SIRIUS, a tool for the structural elucidation of small molecules with tandem mass spectrometry. The method first computes a hypothetical fragmentation tree using combinatorial optimization. By using a Bayesian statistical model, we can learn parameters and hyperparameters of the underlying scoring directly from data. We demonstrate that the statistical model, which was fitted on a small dataset, generalizes well across many different datasets and mass spectrometry instruments. In a second step the fragmentation tree is used to predict a molecular fingerprint using kernel support vector machines. The predicted fingerprint can be searched in a structure database to identify the molecular structure. We demonstrate that our machine learning model outperforms all other methods for this task, including its predecessor FingerID. SIRIUS is available as commandline tool and as user interface. The molecular fingerprint prediction is implemented as web service and receives over one million requests per month.
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31

Bărbulescu, Alina, and Lucica Barbeş. "Statistical methods for assessing water quality after treatment on a sequencing batch reactor." Science of The Total Environment 752 (January 2021): 141991. http://dx.doi.org/10.1016/j.scitotenv.2020.141991.

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32

Götz, Rainer, and Raimund Lauer. "Analysis of Sources of Dioxin Contamination in Sediments and Soils Using Multivariate Statistical Methods and Neural Networks." Environmental Science & Technology 37, no. 24 (December 2003): 5559–65. http://dx.doi.org/10.1021/es030073t.

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33

Kaye, Michael M., and Thomas G. Nevell. "Statistical evaluation of methods using headspace gas chromatography for the determination of ethylene oxide." Analyst 110, no. 9 (1985): 1067. http://dx.doi.org/10.1039/an9851001067.

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34

Babatola, J. O., A. M. Oguntuase, I. A. Oke, and M. O. Ogedengbe. "An Evaluation of Frictional Factors in Pipe Network Analysis Using Statistical Methods." Environmental Engineering Science 25, no. 4 (May 2008): 539–48. http://dx.doi.org/10.1089/ees.2007.0086.

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35

Geissler, A., and H. F. Schöler. "The analysis of chloropesticides and PCB in water a statistical evaluation of four enrichment methods." Chemosphere 23, no. 8-10 (January 1991): 1029–41. http://dx.doi.org/10.1016/0045-6535(91)90129-2.

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36

Bibik, Yuriy V. "FEATURES OF APPLICATION OF THE METHODS STATISTICAL MECHANICS IN RESEARCH OF GENERALIZATIONS OF THE CLASSICAL VOLTERRA CHAIN." UNIVERSITY NEWS. NORTH-CAUCASIAN REGION. NATURAL SCIENCES SERIES, no. 3 (215) (September 30, 2022): 11–22. http://dx.doi.org/10.18522/1026-2237-2022-3-11-22.

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An analytical study of one of the generalizations of the classical Volterra chain (coupled Volterra chain) has been carried out. The use of methods and approaches of statistical mechanics in the study made it possible to identify a number of important features of such models, which are currently widely used in research in chemistry, ecology, biology, economics, hydrodynamics, astrophysics, plasma physics. The advantage of the studied model is that the introduction of additional variables makes it possible to take into account a greater number of factors influencing the dynamics of the system without changing its algebraic structure. However, an attempt at a statistical description of the model reveals a significant drawback - the partition function of the system becomes divergent. In this regard, the possibility of an adequate statistical description of the coupled Volterra chain in the case of divergence of its partition function is investigated. For the selected specific model, the sources of the divergence of the partition function are identified, and a method for their cancel out is proposed. The partition function is calculated in an explicit and finite form, which allows us to state that an adequate statistical description of the phenomena described by this model is possible. Given the wide range of applications of the coupled Volterra chain, the results obtained are of interest to theorists and practitioners when analyzing and predicting the results of a statistical study of various generalizations of the model.
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37

McLean, M. I., L. Evers, A. W. Bowman, M. Bonte, and W. R. Jones. "Statistical modelling of groundwater contamination monitoring data: A comparison of spatial and spatiotemporal methods." Science of The Total Environment 652 (February 2019): 1339–46. http://dx.doi.org/10.1016/j.scitotenv.2018.10.231.

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38

Masetti, Marco, Simone Sterlacchini, Cristiano Ballabio, Alessandro Sorichetta, and Simone Poli. "Influence of threshold value in the use of statistical methods for groundwater vulnerability assessment." Science of The Total Environment 407, no. 12 (June 2009): 3836–46. http://dx.doi.org/10.1016/j.scitotenv.2009.01.055.

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39

Garizi, A. Zare, V. Sheikh, and A. Sadoddin. "Assessment of seasonal variations of chemical characteristics in surface water using multivariate statistical methods." International Journal of Environmental Science & Technology 8, no. 3 (June 2011): 581–92. http://dx.doi.org/10.1007/bf03326244.

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40

Salman, A., A. Pomerantz, L. Tsror, I. Lapidot, A. Zwielly, R. Moreh, S. Mordechai, and M. Huleihel. "Distinction of Fusarium oxysporum fungal isolates (strains) using FTIR-ATR spectroscopy and advanced statistical methods." Analyst 136, no. 5 (2011): 988. http://dx.doi.org/10.1039/c0an00801j.

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41

Liang, Zhu, Chang-Ming Wang, Zhi-Min Zhang, and Kaleem-Ullah-Jan Khan. "A comparison of statistical and machine learning methods for debris flow susceptibility mapping." Stochastic Environmental Research and Risk Assessment 34, no. 11 (August 12, 2020): 1887–907. http://dx.doi.org/10.1007/s00477-020-01851-8.

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42

Sekkeravani, Mohsen Abbasi, Ommolbanin Bazrafshan, Hamid Reza Pourghasemi, and Arashk Holisaz. "Spatial modeling of land subsidence using machine learning models and statistical methods." Environmental Science and Pollution Research 29, no. 19 (January 6, 2022): 28866–83. http://dx.doi.org/10.1007/s11356-021-18037-6.

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43

LaBudde, Robert A., and James Harnly. "Probability of Identification: A Statistical Model for the Validation of Qualitative Botanical Identification Methods." Journal of AOAC INTERNATIONAL 95, no. 1 (January 1, 2012): 273–85. http://dx.doi.org/10.5740/jaoacint.11-266.

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Abstract A qualitative botanical identification method (BIM) is an analytical procedure that returns a binary result (1 = Identified, 0 = Not Identified). A BIM may be used by a buyer, manufacturer, or regulator to determine whether a botanical material being tested is the same as the target (desired) material, or whether it contains excessive nontarget (undesirable) material. The report describes the development and validation of studies for a BIM based on the proportion of replicates identified, or probability of identification (POI), as the basic observed statistic. The statistical procedures proposed for data analysis follow closely those of the probability of detection, and harmonize the statistical concepts and parameters between quantitative and qualitative method validation. Use of POI statistics also harmonizes statistical concepts for botanical, microbiological, toxin, and other analyte identification methods that produce binary results. The POI statistical model provides a tool for graphical representation of response curves for qualitative methods, reporting of descriptive statistics, and application of performance requirements. Single collaborator and multicollaborative study examples are given.
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44

Bhat, Mehraj ud din, and Anish C. Pandey. "Statistical Analysis of SO2 and NO2 with Respect to Meteorological Parameters (Gwalior, India)." Pakistan Journal of Analytical & Environmental Chemistry 22, no. 2 (December 23, 2021): 280–87. http://dx.doi.org/10.21743/pjaec/2021.12.07.

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The present study is carried out in Gwalior to know the level of pollutants viz sulphur dioxide (SO2) and nitrogen dioxide (NO2). In this study, both NO2 and SO2 were collected during different seasons, and estimation was done using chemical methods. The methods used for the determination of SO2 and NO2 in the ambient air of Gwalior was (Modified West and Geake method) and (Modified Jacob and Hochheisier). The SO2 from the air stream was absorbed in a sodium tetramer curate solution. NO2 was collected by bubbling air through a sodium hydroxide solution to form a stable solution of sodium nitrate. Meteorological parameters like temperature, relative humidity were recorded by thermometers and hygrometry during the sampling. Rainfall data was taken from Indian Meteorological Department, New Delhi, for four sampling years. The statistical analysis was carried out between the level of pollutants SO2 and NO2 measured and meteorological parameters recorded during the sampling. This study observed that pollutants were very high in winter and summer compared to monsoon and post-monsoon periods due to the heavy transport movement and favourable meteorological conditions like temperature, humidity, rainfall, and wind speed and directions.
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45

Ritter, Vicki, Susan Kircher, Krista Sturm, Patty Warns, and Nancy Dick. "Evaluation of BD BBL CHROMagar Staph aureus Medium Using AOAC and ISO Culture Methods." Journal of AOAC INTERNATIONAL 92, no. 5 (September 1, 2009): 1432–53. http://dx.doi.org/10.1093/jaoac/92.5.1432.

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Abstract BBL CHROMagar Staph aureus (CSA) medium was evaluated internally and externally for the isolation and enumeration of Staphylococcus aureus in cooked roast beef, smoked salmon, and shell eggs. All food matrixes were processed according to the AOAC Official Method 975.55 and ISO 6888-1:1999. Bacterial counts of S. aureus were compared on CSA to the reference media, Baird-Parker, at low, medium, and high contamination levels. Colony counts were converted to log10 for statistical analysis. Based on the paired t-test and one-way analysis of variance, no statistical difference was noted with the CSA method compared to the AOAC Official Method for the recovery of S. aureus for all food types and contamination levels. Compared to the ISO reference method, no statistical difference was found with the CSA method for any food type or contamination level, with the exception of low-level smoked salmon. A statistical difference was seen in the internal testing with the low-level contaminated smoked salmon where CSA recovered more colonies. The external testing showed no statistical difference with smoked salmon at the low level. The correlation coefficients ranged from 92.6 to 99.4, demonstrating good correlation for overall levels in all food types and methods. The sensitivity and specificity of the CSA method using known isolates was 100. The results of this study demonstrate that CSA is an effective medium for the isolation, enumeration, and presumptive identification of S. aureus in cooked roast beef, smoked salmon, and shell eggs in 24 h using ISO and AOAC official methods.
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46

Wehling, Paul, Robert A. LaBudde, Sharon L. Brunelle, and Maria T. Nelson. "Probability of Detection (POD) as a Statistical Model for the Validation of Qualitative Methods." Journal of AOAC INTERNATIONAL 94, no. 1 (January 1, 2011): 335–47. http://dx.doi.org/10.1093/jaoac/94.1.335.

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Abstract A statistical model is presented for use in validation of qualitative methods. This model, termed Probability of Detection (POD), harmonizes the statistical concepts and parameters between quantitative and qualitative method validation. POD characterizes method response with respect to concentration as a continuous variable. The POD model provides a tool for graphical representation of response curves for qualitative methods. In addition, the model allows comparisons between candidate and reference methods, and provides calculations of repeatability, reproducibility, and laboratory effects from collaborative study data. Single laboratory study and collaborative study examples are given.
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47

Papazova, Petia, and Pavlina Simeonova. "Long-Term Statistical Assessment of the Water Quality of Tundja River." Ecological Chemistry and Engineering S 19, no. 2 (January 1, 2012): 213–26. http://dx.doi.org/10.2478/v10216-011-0016-9.

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Long-Term Statistical Assessment of the Water Quality of Tundja RiverTwo major environmetric methods (Cluster analysis(CA) andPrincipal components analysis(PCA)) were applied for statistical assessment of the water quality of trans-border river Tundja. The study used long-term monitoring data from 26 sampling sites characterized by 12 physicochemical parameters. Clustering of chemical indicators results in 3 major clusters: the first one shows the impact of anthropogenic sources, the second - the impact of agriculture and farming activities and the last one describes the role of the physical parameters on the water quality and also the impact of urban wastes. For better assessment of the monitoring data, PCA was implemented, which identified four latent factors. Two of them - "urban wastes" factor and "agriculture" factor correspond almost entirely to clusters 3 and 2 from the previous statistical analysis. The third one, named "industrial wastes" factor, reveals a specific seasonal behavior of the river system. The last latent factor describes the active reaction of the water body and is determined as "acidity" factor. The linkage of the sampling sites along the river flow by CA formed two clusters with the spatial "upstream-downstream" separation. The apportionment model of the pollution determined the contribution of each one of identified pollution factors to the total concentration of each one of the water quality parameters.
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48

Bird, Patrick, Jonathan Flannery, Erin Crowley, James Agin, David Goins, Lisa Monteroso, C. Barnes, et al. "Evaluation of 3M Molecular Detection Assay (MDA) 2–Listeria for the Detection of Listeria Species in Select Foods and Environmental Surfaces: Collaborative Study, First Action 2016.07." Journal of AOAC INTERNATIONAL 100, no. 1 (January 1, 2017): 82–98. http://dx.doi.org/10.5740/jaoacint.16-0236.

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Abstract 3M Molecular Detection Assay (MDA) 2–Listeria uses loop-mediated isothermal amplification and bioluminescence detection to rapidly detect Listeria species in a broad range of food types and environmental surfaces. Using an unpaired study design, MDA 2–Listeria was compared with the U.S. Department of Agriculture, Food Safety and Inspection Service's Microbiology Laboratory Guidebook Chapter 8.09 “Isolation and identification of Listeria monocytogenes from red meat, poultry and egg products, and environmental samples” reference method for the detection of Listeria in deli turkey and raw chicken breast fillet. Technicians from 13 laboratories located within the continental United States and Canada participated in the collaborative study. Each matrix was evaluated at three levels of contamination: uninoculated control (0 CFU/test portion), low inoculum (0.2–2 CFU/test portion), and high inoculum (2–5 CFU/test portion). Statistical analysis was conducted according to the probability of detection (POD) statistical model. Results obtained for the low-inoculum-level test portions produced a difference between two laboratory POD values (dLPOD) with 95% confidence intervals of 0.04 (–0.08, 0.17) for deli turkey, indicating the difference between the methods was not statistically significant at the P = 0.05. For raw chicken breast fillet, a dLPOD value with 95% confidence interval of 0.16 (0.04, 0.28) indicated a statistically significant difference between the two methods, with an observed higher proportion of positive results by the candidate method than the reference method.
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49

Parker, Albert E., Martin A. Hamilton, and Stephen F. Tomasino. "A Statistical Model for Assessing Performance Standards for Quantitative and Semiquantitative Disinfectant Test Methods." Journal of AOAC INTERNATIONAL 97, no. 1 (January 1, 2014): 58–67. http://dx.doi.org/10.5740/jaoacint.13-105.

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Abstract A performance standard for a disinfectant test method can be evaluated by quantifying the (Type I) pass-error rate for ineffective products and the (TypeII) fail-error rate for highly effective products. This paper shows how to calculate these error rates for test methods where the log reduction in a microbial population is used as a measure of antimicrobial efficacy. The calculations can be used to assess performance standards that may require multiple tests of multiple microbes at multiple laboratories. Notably, the error rates account for among-laboratory variance of the log reductions estimated from a multilaboratory data set and the correlation among tests of different microbes conducted in the same laboratory.Performance standards that require that a disinfectant product pass all tests or multiple tests on average, are considered. The proposed statistical methodology is flexible and allows for a different acceptable outcome for each microbe tested, since, for example, variability may be different for different microbes. The approach can also be applied to semiquantitative methods for which product efficacy is reportedas the number of positive carriers out of a treated set and the density of the microbes on control carriers is quantified, thereby allowing a log reduction to be calculated. Therefore, using the approach described in this paper, the error rates can also be calculated for semiquantitative method performance standards specified solely in terms of the maximum allowable number of positive carriers per test. The calculations are demonstrated in a case study of the current performance standard for the semiquantitative AOAC Use-Dilution Methods for Pseudomonas aeruginosa (964.02) and Staphylococcus aureus (955.15), which allow up to one positive carrier out of a set of 60 inoculated and treated carriers in each test. A simulation study was also conducted to verify the validity of the model's assumptions and accuracy. Our approach, easily implemented using the computer code provided, offers a quantitative decision-making tool for assessing a performance standard for any disinfectant test method for which log reductions can be calculated.
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50

Lunguleac, Tiberiu, Marius Valeriu Hinganu, Cristina Grigorescu, Liviu Ciprian Gavril, Paul Salahoru, Alexandru Patrascu, and Delia Hinganu. "Influence of Environmental Factors on the Primary Spontaneous Pneumotorax." Revista de Chimie 69, no. 10 (November 15, 2018): 2937–47. http://dx.doi.org/10.37358/rc.18.10.6657.

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Pneumothorax is the presence of air between the two pleural effusions, making the pleural space from a virtual cavity to a real one. At present, we are facing a particular phenomenon: trying to establish with certainty the mechanisms underlying the occurrence of primary spontaneous pneumothorax and how to find and implement the best strategies for the application of treatment methods. Considering the classical conception that the incidence of spontaneous pneumothorax has a seasonal variability, we followed its distribution after the season, calendar month, by checking the statistical significance of the data obtained. Regardless of the dynamics factor involved and the likely mechanism of action, it seems to have an influence on the average duration of hospitalization and the length of time elapsed from intervention to discharge.
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