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{"title"=>"Data simulation in machine olfaction with the R package chemosensors", "type"=>"journal", "authors"=>[{"first_name"=>"Andrey", "last_name"=>"Ziyatdinov", "scopus_author_id"=>"8867287700"}, {"first_name"=>"Alexandre", "last_name"=>"Perera-Lluna", "scopus_author_id"=>"29068159700"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84896270508", "sgr"=>"84896270508", "pui"=>"372616962", "pmid"=>"24586410", "doi"=>"10.1371/journal.pone.0088839"}, "id"=>"6c96dc0c-cc17-3f21-922b-fd4e2770c42d", "abstract"=>"In machine olfaction, the design of applications based on gas sensor arrays is highly dependent on the robustness of the signal and data processing algorithms. While the practice of testing the algorithms on public benchmarks is not common in the field, we propose software for performing data simulations in the machine olfaction field by generating parameterized sensor array data. The software is implemented as an R language package chemosensors which is open-access, platform-independent and self-contained. We introduce the concept of a virtual sensor array which can be used as a data generation tool. In this work, we describe the data simulation workflow which basically consists of scenario definition, virtual array parameterization and the generation of sensor array data. We also give examples of the processing of the simulated data as proof of concept for the parameterized sensor array data: the benchmarking of classification algorithms, the evaluation of linear- and non-linear regression algorithms, and the biologically inspired processing of sensor array data. All the results presented were obtained under version 0.7.6 of the chemosensors package whose home page is chemosensors.r-forge.r-project.org.", "link"=>"http://www.mendeley.com/research/data-simulation-machine-olfaction-r-package-chemosensors", "reader_count"=>21, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>3, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>2, "Student > Postgraduate"=>1, "Student > Master"=>7, "Other"=>1, "Student > Bachelor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>3, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>2, "Student > Postgraduate"=>1, "Student > Master"=>7, "Other"=>1, "Student > Bachelor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>7, "Unspecified"=>1, "Environmental Science"=>2, "Mathematics"=>1, "Agricultural and Biological Sciences"=>2, "Neuroscience"=>1, "Psychology"=>1, "Chemistry"=>1, "Computer Science"=>5}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>7}, "Neuroscience"=>{"Neuroscience"=>1}, "Chemistry"=>{"Chemistry"=>1}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}, "Computer Science"=>{"Computer Science"=>5}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>2}}, "reader_count_by_country"=>{"United Kingdom"=>1}, "group_count"=>2}

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• http://doi.org/10.1021/ac5046824
• http://doi.org/10.1016/j.snb.2017.02.042
• http://doi.org/10.1016/j.dib.2015.02.011

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• {"id"=>"463763152723644416", "text"=>"PLOS ONE: Data Simulation in Machine Olfaction with the R Package Chemosensors http://t.co/nPtYPD9NcZ", "created_at"=>"2014-05-06T19:32:06Z", "user"=>"InnovArch", "user_name"=>"Mohan K. Bavirisetty", "user_profile_image"=>"http://pbs.twimg.com/profile_images/378800000468854440/91e995b05d412df4ea3500e298c6c1d4_normal.png"}

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/1399557"], "description"=>"<p>The array was exposed to six gas classes: pure analyte A at concentrations 0.01 and 0.05 (labels A 0.01 and A 0.05), pure analyte C at concentrations 0.1 and 1 (C 0.1 and C 1), and two binary mixtures of A and C (A 0.01, C 0.1 and A 0.05, C 1). The concentrations were given at volume fraction units <i>vol.%</i>, and the measurement of each gas class was repeated 10 times. The distribution of the scores shows that the sensors in array have more affinity to analyte C that to analyte A. The plot is produced by the plotPCA method applied to the sensor array.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Sensory systems", "neuroscience", "Olfactory system", "algorithms", "software engineering", "Software tools", "corresponding", "gathered", "consisting", "12", "sensors", "types"], "article_id"=>945408, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Andrey Ziyatdinov", "Alexandre Perera-Lluna"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0088839.g003", "stats"=>{"downloads"=>2, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scoreplot_corresponding_to_the_Principal_Component_Analysis_of_the_sensor_array_data_gathered_from_the_array_consisting_of_12_sensors_of_types_1_2_and_3_/945408", "title"=>"Scoreplot corresponding to the Principal Component Analysis of the sensor array data gathered from the array consisting of 12 sensors of types 1, 2 and 3.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-26 03:47:35"}
• {"files"=>["https://ndownloader.figshare.com/files/1399570"], "description"=>"<p>Two methods, linear PLS and non-linear SVR, were tested on the regression task of analyte C given at concentration 0.1, 0.4, 1 and 2 vol.%. Three arrays composed of 24 sensors, different in the types of sensor, were compared in terms of the root-mean-square error in prediction (RMSEP). For each array, the non-linear models outperform the linear models. All three arrays show similar performance with the SVR method, and it is hard to pick the best array.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Sensory systems", "neuroscience", "Olfactory system", "algorithms", "software engineering", "Software tools", "drift-free"], "article_id"=>945421, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Andrey Ziyatdinov", "Alexandre Perera-Lluna"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0088839.t006", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_on_prediction_of_concentration_of_gas_C_under_drift_free_conditions_/945421", "title"=>"Performance on prediction of concentration of gas C under drift-free conditions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-26 03:47:35"}
• {"files"=>["https://ndownloader.figshare.com/files/1399555"], "description"=>"<p>The scenario is defined as a regression on analyte A with both training and validation sets consisting of three pulses of concentrations of 0.01, 0.02 and 0.05%. The plot method applied to a scenario object shows only the unique labels given at training and validation sets. One can apply the show method to a scenario object to get more detailed information.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Sensory systems", "neuroscience", "Olfactory system", "algorithms", "software engineering", "Software tools", "validation", "applied", "regression"], "article_id"=>945406, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Andrey Ziyatdinov", "Alexandre Perera-Lluna"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0088839.g002", "stats"=>{"downloads"=>2, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Plot_showing_the_training_and_validation_set_product_of_the_plot_method_applied_to_a_regression_scenario_/945406", "title"=>"Plot showing the training and validation set, product of the plot method applied to a regression scenario.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-26 03:47:35"}
• {"files"=>["https://ndownloader.figshare.com/files/1399569"], "description"=>"<p>Two methods, linear PLS and non-linear SVR, were tested on the regression task of analyte A given at concentration 0.01, 0.02, 0.05 and 0.1 vol.%. Three arrays composed of 24 sensors, different in the types of sensor, were compared in terms of the root-mean-square error in prediction (RMSEP). For each array, the non-linear models outperform the linear models. The first array and the SVR method yield the best performance.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Sensory systems", "neuroscience", "Olfactory system", "algorithms", "software engineering", "Software tools", "drift-free"], "article_id"=>945420, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Andrey Ziyatdinov", "Alexandre Perera-Lluna"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0088839.t005", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_on_prediction_of_concentration_of_gas_A_under_drift_free_conditions_/945420", "title"=>"Performance on prediction of concentration of gas A under drift-free conditions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-26 03:47:35"}
• {"files"=>["https://ndownloader.figshare.com/files/1399566"], "description"=>"<p>Simulation models, their classes and associated data sets of parameters computed for the seventeen UNIMAN sensors.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Sensory systems", "neuroscience", "Olfactory system", "algorithms", "software engineering", "Software tools", "simulation"], "article_id"=>945417, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Andrey Ziyatdinov", "Alexandre Perera-Lluna"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0088839.t002", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Organization_of_simulation_models_in_the_chemosensors_package_/945417", "title"=>"Organization of simulation models in the <i>chemosensors</i> package.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-26 03:47:35"}
• {"files"=>["https://ndownloader.figshare.com/files/1399564"], "description"=>"<p>×<b>7 showing the response to 12 different gases composed of analytes A and C.</b> The map was constructed for the array of 1 K sensors based on the affinity coefficients computed per three analytes A, B and C for each sensor, as proposed in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088839#pone.0088839-Raman2\" target=\"_blank\">[23]</a>. The response of sensor array for each gas was projected onto the map, and the colour on the heatmaps encode the magnitude of the signals in the SOM cells computed by averaging the signals from sensors assigned to the given cell. The activity of the SOM increases as the concentration of analytes increases (direction from left to right). The distribution of the SOM activity in response to different gases show that the right part of the map contain sensors with more affinity to analyte A, while the left part has sensor with more affinity to analyte C.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Sensory systems", "neuroscience", "Olfactory system", "algorithms", "software engineering", "Software tools", "self-organizing"], "article_id"=>945415, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Andrey Ziyatdinov", "Alexandre Perera-Lluna"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0088839.g008", "stats"=>{"downloads"=>5, "page_views"=>32, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Heatmap_of_a_self_organizing_map_SOM_of_size_7_/945415", "title"=>"Heatmap of a self-organizing map (SOM) of size 7", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-26 03:47:35"}
• {"files"=>["https://ndownloader.figshare.com/files/1399565"], "description"=>"<p>Dynamic range of concentrations for three gases A, B and C, which correspond to three analytes in the reference UNIMAN data set: ammonia, propanoic acid and n-butanol, respectively.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Sensory systems", "neuroscience", "Olfactory system", "algorithms", "software engineering", "Software tools", "concentrations", "gases"], "article_id"=>945416, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Andrey Ziyatdinov", "Alexandre Perera-Lluna"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0088839.t001", "stats"=>{"downloads"=>6, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Dynamic_range_of_concentrations_for_three_gases_used_in_the_chemosensors_package_/945416", "title"=>"Dynamic range of concentrations for three gases used in the <i>chemosensors</i> package.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-26 03:47:35"}
• {"files"=>["https://ndownloader.figshare.com/files/1399559"], "description"=>"<p>The array was exposed to six gas classes: pure analyte A at concentrations 0.01 and 0.05 (labels A 0.01 and A 0.05), pure analyte C at concentrations 0.1 and 1 (C 0.1 and C 1), and two binary mixtures of A and C (A 0.01, C 0.1 and A 0.05, C 1). The concentrations were given at volume fraction units <i>vol.%</i>, and the measurement of each gas class was repeated 10 times. The distribution of the scores shows that the sensors in array are balanced in terms of affinity to analytes A and C. The plot is produced by the plotPCA method applied to the sensor array.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Sensory systems", "neuroscience", "Olfactory system", "algorithms", "software engineering", "Software tools", "corresponding", "gathered", "consisting", "12", "sensors", "types", "14"], "article_id"=>945410, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Andrey Ziyatdinov", "Alexandre Perera-Lluna"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0088839.g005", "stats"=>{"downloads"=>4, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scoreplot_corresponding_to_the_Principal_Component_Analysis_of_the_sensor_array_data_gathered_from_the_array_consisting_of_12_sensors_of_types_1_2_3_13_14_and_17_/945410", "title"=>"Scoreplot corresponding to the Principal Component Analysis of the sensor array data gathered from the array consisting of 12 sensors of types 1, 2, 3, 13, 14 and 17.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-26 03:47:35"}
• {"files"=>["https://ndownloader.figshare.com/files/1399572", "https://ndownloader.figshare.com/files/1399573", "https://ndownloader.figshare.com/files/1399574"], "description"=>"<div><p>In machine olfaction, the design of applications based on gas sensor arrays is highly dependent on the robustness of the signal and data processing algorithms. While the practice of testing the algorithms on public benchmarks is not common in the field, we propose software for performing data simulations in the machine olfaction field by generating parameterized sensor array data. The software is implemented as an R language package chemosensors which is open-access, platform-independent and self-contained. We introduce the concept of a virtual sensor array which can be used as a data generation tool. In this work, we describe the data simulation workflow which basically consists of scenario definition, virtual array parameterization and the generation of sensor array data. We also give examples of the processing of the simulated data as proof of concept for the parameterized sensor array data: the benchmarking of classification algorithms, the evaluation of linear- and non-linear regression algorithms, and the biologically inspired processing of sensor array data. All the results presented were obtained under version 0.7.6 of the chemosensors package whose home page is chemosensors.r-forge.r-project.org.</p></div>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Sensory systems", "neuroscience", "Olfactory system", "algorithms", "software engineering", "Software tools", "simulation", "olfaction"], "article_id"=>945423, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Andrey Ziyatdinov", "Alexandre Perera-Lluna"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0088839.s001", "https://dx.doi.org/10.1371/journal.pone.0088839.s002", "https://dx.doi.org/10.1371/journal.pone.0088839.s003"], "stats"=>{"downloads"=>25, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Data_Simulation_in_Machine_Olfaction_with_the_R_Package_Chemosensors_/945423", "title"=>"Data Simulation in Machine Olfaction with the R Package <i>Chemosensors</i>", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-02-26 03:47:35"}
• {"files"=>["https://ndownloader.figshare.com/files/1399554"], "description"=>"<p>On the X axis of each panel, the index values correspond to the row index in the two input concentration and output sensor data matrices of the data generator. Consequently, the values in the columns of these matrices are plotted jointly on the Y axis, while the legend on the right annotates the column names. Panel (a) shows three pulses of analyte A at three different concentrations 0.01, 0.02 and 0.05 vol.%, while the concentration of the other two analytes B and C are at zero level. Panel (b) shows transient signals of four sensors labelled as S1, S2, S3 and S4 in response to the pulses from Panel (a) when all three noises in the sensor array are set up at the 0.1 level. Panel (c) shows sensor signals in response to the pulses under drift-free conditions, while the other two concentration and sensor noises are remained at the 0.1 level. The signals allow for a visual discrimination between the three pulses.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Sensory systems", "neuroscience", "Olfactory system", "algorithms", "software engineering", "Software tools", "analyte", "concentrations", "signals", "simulation"], "article_id"=>945405, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Andrey Ziyatdinov", "Alexandre Perera-Lluna"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0088839.g001", "stats"=>{"downloads"=>2, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Matrices_of_analyte_concentrations_and_sensor_signals_in_a_simulation_with_a_virtual_array_of_four_sensors_/945405", "title"=>"Matrices of analyte concentrations and sensor signals in a simulation with a virtual array of four sensors.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-26 03:47:35"}
• {"files"=>["https://ndownloader.figshare.com/files/1399568"], "description"=>"<p>The k-nearest neighbors algorithm was tested on three two-class classification scenarios at three difficulty levels. The scenario difficulty was defined as the similarity between two gas classes. The classification model was trained under 10-fold cross-validation procedure with 10 repetitions, and the best value of the k parameter was estimated along possible values 3, 5, 7 and 9 for each classification model. The accuracy in prediction of class labels was used to score the models. The model complexity, expressed in value of parameters k, is observed to increase with greater scenario difficulty. The first model provides a perfect performance with a 100% rate of classification, while the last model displays poor accuracy with a classification rate of 0.74 on the test set.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Sensory systems", "neuroscience", "Olfactory system", "algorithms", "software engineering", "Software tools", "scenarios"], "article_id"=>945419, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Andrey Ziyatdinov", "Alexandre Perera-Lluna"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0088839.t004", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Classification_performance_on_scenarios_given_at_three_different_difficulty_levels_/945419", "title"=>"Classification performance on scenarios given at three different difficulty levels.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-26 03:47:35"}
• {"files"=>["https://ndownloader.figshare.com/files/1399567"], "description"=>"<p>Description of basic slots of SensorArray class necessary to parameterize a virtual sensor array.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Sensory systems", "neuroscience", "Olfactory system", "algorithms", "software engineering", "Software tools", "slots", "sensorarray"], "article_id"=>945418, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Andrey Ziyatdinov", "Alexandre Perera-Lluna"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0088839.t003", "stats"=>{"downloads"=>2, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Basic_slots_of_SensorArray_class_in_chemosensors_package_/945418", "title"=>"Basic slots of SensorArray class in <i>chemosensors</i> package.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-26 03:47:35"}
• {"files"=>["https://ndownloader.figshare.com/files/1399562"], "description"=>"<p>The concentration values were selected to cover the dynamic range of analyte C and to include the value in the saturation region. All the sensors show a non-linear response to analyte C at the selected concentration range. The plot is produced by the plotBoxplot method applied to the sensor array under drift-free conditions.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Sensory systems", "neuroscience", "Olfactory system", "algorithms", "software engineering", "Software tools", "sensors", "types", "14", "17", "signals", "analyte", "concentrations"], "article_id"=>945413, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Andrey Ziyatdinov", "Alexandre Perera-Lluna"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0088839.g007", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Boxplots_for_array_of_six_sensors_of_types_1_2_3_13_14_and_17_show_the_distribution_of_sensor_signals_in_response_to_analyte_C_at_concentrations_0_1_0_4_1_and_2_/945413", "title"=>"Boxplots for array of six sensors of types 1, 2, 3, 13, 14 and 17 show the distribution of sensor signals in response to analyte C at concentrations 0.1, 0.4, 1 and 2%.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-26 03:47:35"}
• {"files"=>["https://ndownloader.figshare.com/files/1399561"], "description"=>"<p>The concentration values were selected to cover the dynamic range of analyte A and to include the value in the saturation region. All the sensors show a non-linear response to analyte A at the selected concentration range. The three sensors of types 13, 14 and 17 show rather noisy responses. The plot is produced by the plotBoxplot method applied to the sensor array under drift-free conditions.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Sensory systems", "neuroscience", "Olfactory system", "algorithms", "software engineering", "Software tools", "sensors", "types", "14", "17", "signals", "analyte", "concentrations"], "article_id"=>945412, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Andrey Ziyatdinov", "Alexandre Perera-Lluna"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0088839.g006", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Boxplots_for_array_of_six_sensors_of_types_1_2_3_13_14_and_17_show_the_distribution_of_sensor_signals_in_response_to_analyte_A_at_concentrations_0_01_0_02_0_05_and_0_1_/945412", "title"=>"Boxplots for array of six sensors of types 1, 2, 3, 13, 14 and 17 show the distribution of sensor signals in response to analyte A at concentrations 0.01, 0.02, 0.05 and 0.1%.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-26 03:47:35"}
• {"files"=>["https://ndownloader.figshare.com/files/1399558"], "description"=>"<p>The array was exposed to six gas classes: pure analyte A at concentrations 0.01 and 0.05 (labels A 0.01 and A 0.05), pure analyte C at concentrations 0.1 and 1 (C 0.1 and C 1), and two binary mixtures of A and C (A 0.01, C 0.1 and A 0.05, C 1). The concentrations were given at volume fraction units <i>vol.%</i>, and the measurement of each gas class was repeated 10 times. The distribution of the scores shows that the sensors in the array have more affinity to analyte A than to analyte C. The plot is produced by the plotPCA method applied to the sensor array.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Sensory systems", "neuroscience", "Olfactory system", "algorithms", "software engineering", "Software tools", "corresponding", "gathered", "consisting", "12", "sensors", "types", "14"], "article_id"=>945409, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Andrey Ziyatdinov", "Alexandre Perera-Lluna"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0088839.g004", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scoreplot_corresponding_to_the_Principal_Component_Analysis_of_the_sensor_array_data_gathered_from_the_array_consisting_of_12_sensors_of_types_13_14_and_17_/945409", "title"=>"Scoreplot corresponding to the Principal Component Analysis of the sensor array data gathered from the array consisting of 12 sensors of types 13, 14 and 17.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-26 03:47:35"}

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Relative Metric

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F1000Prime