{
    "data_id": "45537",
    "name": "Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.5GHz(Urbinati)",
    "exact_name": "Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.5GHz(Urbinati)",
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    "description": "Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.5GHz(Urbinati)\n----------------\n\nThis dataset is part of a series of five different datasets\neach one measured with a different microwave frequency: 9.0, 9.5, 10.0, 10.5, 11.0 GHz.\nPAY ATTENTION: THE DATASET PRESENTED IN THIS PAGE HAS BEEN ACQUIRED AT 9.5 GHz!\nIF YOU ARE LOOKING FOR THE OTHER FOUR DATASETS, VISIT THE OPENML PROFILE\nOF THE AUTHOR OF THIS DATASET.\n\nThe following description is valid for all the five datasets.\n\nDataset description.\nTo detect contaminants accidentally included in industrial food,\nMicrowave Sensing (MWS) can be used as a contactless detection method,\nin particular when the food is already packaged.\n\nMWS uses microwaves to illuminate the target object through a set of antennas,\nrecords the scattered waves, and uses Machine Learning to predict\nthe presence of contaminants inside the target object.\n\nIn this application the target object is a cocoa-hazelnut spread jar\nand each instance (sample) of this dataset consists in 30 scattering parameters\nof the network composed by: antennas, target object (a jar\nw\/ or w\/o a contaminant inside) and medium (i.e. the air) in between.\nThe types of contaminants vary from metal to glass and plastic.\nEach sample has been measured at five different microwave frequencies\nthat are: 9.0, 9.5, 10.0, 10.5, 11.0 GHz.\nPAY ATTENTION: THE DATASET PRESENTED IN THIS PAGE HAS BEEN ACQUIRED AT 9.5 GHz!\nIF YOU ARE LOOKING FOR THE OTHER FOUR DATASETS, VISIT THE OPENML PROFILE\nOF THE AUTHOR OF THIS DATASET.\n\n**Data Set Characteristics:**\n\n  :Microwave frequency used for acquisition: 9.5 GHz\n\n  :Total Number of Instances: 2400\n\n  :Total Number of Uncontaminated Instances: 1200\n\n  :Total Number of Contaminated Instances: 1200\n\n  :Total Number of Classes: 11\n\n  :Target: The last column, column 31, contains the class label as integer value\n\n  :Number of Contaminated Instances Divided Per Class (full explanation and\n  pictures in [2]):\n\n      - \"air_surface\" (i.e. cap-shape plastic with the same dielectric constant of the air): 200\n\n      - \"big_pink_plastic_shere_middle\": 100\n\n      - \"big_pink_plastic_shere_surface\": 100\n\n      - \"glass_fragment_middle\": 100\n\n      - \"glass_fragment_surface\": 100\n\n      - \"small_metal_sphere_middle\": 100\n\n      - \"small_metal_sphere_surface\": 100\n\n      - \"small_plastic_sphere_middle\": 100\n\n      - \"small_plastic_sphere_surface\": 100\n\n      - \"small_triangular_plastic_fragment_surface\": 200\n\n  \"surface\" means that the instance was placed on top of the cocoa-hazelnut spread,\n   at the spread-air interface\n\n  \"middle\" means that the instance was placed in the middle of the jar filled with\n   cocoa-hazelnut spread\n\n\n  :Number of Attributes in a generic Instance: 30\n\n\n  :Attribute Information: This is the 6x6 Scattering Matrix (S):\n\n  S = [        s12, s13, s14, s15, s16,  \n        s21,        s23, s24, s25, s26,  \n        s31, s32,        s34, s35, s36,  \n        s41, s42, s43,        s45, s46,  \n        s51, s52, s53, s54,        s56,  \n        s61, s62, s63, s64, s65,        ]\n\n  The first 30 attributes (columns) of an instance are the 15 elements of the triangular\n  upper part of S. Since each of these elements is a complex number with real and\n  imaginary parts, each instance is a vector of 15x2=30 attributes. The real (real) and\n  imaginary (img) parts of each element are placed one after the other.\n  The scattering parameters are ordered row by row from left to right, e.g.:\n  s12_real, s12_img, s13_real, s13_img, ..., s16_real, s16_img, s21_real, s21_img, ... .\n  The self-scattering parameters, i.e. those placed on the main diagional of S,\n  are not part of the dataset, as exaplined in [1].\n\n\nNote: This dataset is realeased without pre-processing.\n\nFor more information read the reference papers:\n\n[1] L. Urbinati, M. Ricci, G. Turvani, J. A. T. Vasquez, F. Vipiana and M. R. Casu, \"A Machine-Learning Based Microwave Sensing Approach to Food Contaminant Detection,\" 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Seville, Spain, 2020, pp. 1-5, doi: 10.1109\/ISCAS45731.2020.9181293\nLink to the paper: https:\/\/ieeexplore.ieee.org\/abstract\/document\/9181293\n\n[2] M. Ricci, B. Stitic, L. Urbinati, G. D. Guglielmo, J. A. T. Vasquez, L. P. Carloni, F. Vipiana, and M. R. Casu, \"Machine-learning-based microwave sensing: A case study for the food industry,\" IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 11, no. 3, pp. 503-514, 2021\nLink to the paper: https:\/\/ieeexplore.ieee.org\/abstract\/document\/9489295",
    "format": "arff",
    "uploader": "Luca Urbinati",
    "uploader_id": 33136,
    "visibility": "public",
    "creator": "\"Luca Urbinati\"",
    "contributor": null,
    "date": "2023-05-31 12:11:25",
    "update_comment": null,
    "last_update": "2023-05-31 12:11:25",
    "licence": "CC BY-SA",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/api.openml.org\/data\/download\/22116505\/dataset",
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