Convolutional Neural Networks for Olive Oil Classification

被引:4
|
作者
Vega-Marquez, Belen [1 ]
Carminati, Andrea [1 ]
Jurado-Campos, Natividad [2 ]
Martin-Gomez, Andres [2 ]
Arce-Jimenez, Lourdes [2 ]
Rubio-Escudero, Cristina [1 ]
Nepomuceno-Chamorro, Isabel A. [1 ]
机构
[1] Univ Seville, Dept Comp Languages & Syst, Seville, Spain
[2] Univ Cordoba, Inst Fine Chem & Nanochem, Dept Analyt Chem, Cordoba, Spain
关键词
Convolutional Neural Network; Olive oil classification; GC-IMS method;
D O I
10.1007/978-3-030-19651-6_14
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The analysis of the quality of olive oil is a task that is having a lot of impact nowadays due to the large frauds that have been observed in the olive oil market. To solve this problem we have trained a Convolutional Neural Network (CNN) to classify 701 images obtained using GC-IMS methodology (gas chromatography coupled to ion mobility spectrometry). The aim of this study is to show that Deep Learning techniques can be a great alternative to traditional oil classification methods based on the subjectivity of the standardized sensory analysis according to the panel test method, and also to novel techniques provided by the chemical field, such as chemometric markers. This technique is quite expensive since the markers are manually extracted by an expert. The analyzed data includes instances belonging to two different crops, the first covers the years 2014-2015 and the second 2015-2016. Both harvests have instances classified in the three categories of existing oil, extra virgin olive oil (EVOO), virgin olive oil (VOO) and lampante olive oil (LOO). The aim of this study is to demonstrate that Deep Learning techniques in combination with chemical techniques are a good alternative to the panel test method, implying even better accuracy than results obtained in previous work.
引用
收藏
页码:137 / 145
页数:9
相关论文
共 50 条
  • [1] Classification of olive leaf diseases using deep convolutional neural networks
    Uguz, Sinan
    Uysal, Nese
    [J]. NEURAL COMPUTING & APPLICATIONS, 2021, 33 (09): : 4133 - 4149
  • [2] Classification of olive leaf diseases using deep convolutional neural networks
    Sinan Uğuz
    Nese Uysal
    [J]. Neural Computing and Applications, 2021, 33 : 4133 - 4149
  • [3] NEURAL NETWORKS AND OLIVE OIL
    GOODACRE, R
    KELL, DB
    BIANCHI, G
    [J]. NATURE, 1992, 359 (6396) : 594 - 594
  • [4] Olive-Fruit Variety Classification by Means of Image Processing and Convolutional Neural Networks
    Ponce, Juan M.
    Aquino, Arturo
    Andujar, Jose M.
    [J]. IEEE ACCESS, 2019, 7 : 147629 - 147641
  • [5] Convolutional Neural Networks for event classification
    Rubio Jimenez, Adrian
    Garcia Navarro, Jose Enrique
    Moreno Llacer, Maria
    [J]. NINTH ANNUAL CONFERENCE ON LARGE HADRON COLLIDER PHYSICS, LHCP2021, 2021,
  • [6] Convolutional Neural Networks for Electrocardiogram Classification
    Mohamad M. Al Rahhal
    Yakoub Bazi
    Mansour Al Zuair
    Esam Othman
    Bilel BenJdira
    [J]. Journal of Medical and Biological Engineering, 2018, 38 : 1014 - 1025
  • [7] Flower Classification with Convolutional Neural Networks
    Mitrovic, Katarina
    Milosevic, Danijela
    [J]. 2019 23RD INTERNATIONAL CONFERENCE ON SYSTEM THEORY, CONTROL AND COMPUTING (ICSTCC), 2019, : 845 - 850
  • [8] Convolutional Neural Networks for image classification
    Jmour, Nadia
    Zayen, Sehla
    Abdelkrim, Afef
    [J]. 2018 INTERNATIONAL CONFERENCE ON ADVANCED SYSTEMS AND ELECTRICAL TECHNOLOGIES (IC_ASET), 2017, : 397 - 402
  • [9] Convolutional Neural Networks for ATC Classification
    Lumini, Alessandra
    Nanni, Loris
    [J]. CURRENT PHARMACEUTICAL DESIGN, 2018, 24 (34) : 4007 - 4012
  • [10] Glomerulus Classification with Convolutional Neural Networks
    Pedraza, Anibal
    Gallego, Jaime
    Lopez, Samuel
    Gonzalez, Lucia
    Laurinavicius, Arvydas
    Bueno, Gloria
    [J]. MEDICAL IMAGE UNDERSTANDING AND ANALYSIS (MIUA 2017), 2017, 723 : 839 - 849