Researching

Journals
  • Advanced Photonics
  • Photonics Insights
  • Advanced Photonics Nexus
  • Photonics Research
  • Advanced Imaging
  • View All Journals
  • Chinese Optics Letters
  • High Power Laser Science and Engineering
    • Articles
  • Optics
  • Physics
  • Geography
  • View All Subjects
    • Conferences
  • CIOP
  • HPLSE
  • AP
  • View All Events
    • News
    About CLP
    Search by keywords or author
    Journals >Chinese Journal of Lasers >Volume 48 >Issue 3 >Page 0311002 > Article
    • Chinese Journal of Lasers
    • Vol. 48, Issue 3, 0311002 (2021)
    Recognition of Food-Borne Pathogenic Bacteria by Raman Spectroscopy Based on Random Forest Algorithm
    Qi Wang1, Wandan Zeng1,*, Zhiping Xia2,*, Zhiping Li2, and Han Qu2
    Author Affiliations
  • 1College of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China
  • 2Military Veterinary Institute, Changchun, Jilin 130062, China
    • show less
    DOI: 10.3788/CJL202148.0311002 Cite this Article Set citation alerts
    Qi Wang, Wandan Zeng, Zhiping Xia, Zhiping Li, Han Qu. Recognition of Food-Borne Pathogenic Bacteria by Raman Spectroscopy Based on Random Forest Algorithm[J]. Chinese Journal of Lasers, 2021, 48(3): 0311002 Copy Citation Text show less
    Original Raman spectra
    Fig. 1. Original Raman spectra
    Download full size
    Raman spectra after normalization
    Fig. 2. Raman spectra after normalization
    Download full size
    Raman spectrum after Savitzky-Golay processing
    Fig. 3. Raman spectrum after Savitzky-Golay processing
    Download full size
    Pareto chart of principle components
    Fig. 4. Pareto chart of principle components
    Download full size
    Frame of random forest algorithm
    Fig. 5. Frame of random forest algorithm
    Download full size
    Model accuracy change with n_estimators
    Fig. 6. Model accuracy change with n_estimators
    Download full size
    Model accuracy change with max_depth
    Fig. 7. Model accuracy change with max_depth
    Download full size
    10-fold cross-validation diagram
    Fig. 8. 10-fold cross-validation diagram
    Download full size
    NumberLatin name
    10869Yersinia enterocolitica
    10870Klebsiella pneumoniae
    21482Salmonella enterica subsp. enterica serovarInfantis
    21530Escherichia coli EHEC O157:H7
    21534Shigella flexneri
    21560Cronobacter sakazakii
    21600Staphylococcus aureus
    21617Vibrio parahaemolyticus
    22933Acinetobacter baumannii
    22956Salmonella enterica subsp. enterica serovarTyphimurium
    23794Vibrio cholerae
    Table 1. CICC numbers and names of eleven food-borne pathogenic bacteria
    Decision tree algorithm
    Input: sample X, sample numbers N, feature counts M
    Output:Decision Tree model
    X→for bagging∥processing X with bagging cycles
    end for
    while extracting ntry(ntry=N)→Xtrain do
    M→mtry(mtry≪M)∥ random selection of mtry attributes
    mtry→the best node
    X→Xsamples// build samples using Bootstrap
    end while
    for (itree=0; 1<itree≤Ntree; itree++)
    ∥ node splitting by optimal attributes to generate decision trees
    end for
    end procedure
    Table 2. Work process of decision tree algorithm
    Food-borne pathogenic bacteria prediction algorithm
    Input: sample X, training set Xtrain, test set Xtest
    Output: K trees, prediction result r
    for all i = 1 to K do
    while j≤N do
    rowsample=rowsample+selectXtrain
    j++
    end while
    while stop condition not true do
    colsample-selectrowsample
    split_Attribute-min{Gini(colsample)}
    ∥ classification attributes are determined by the minimum Gini value
    tree-AddNodesplit_Attribute
    end while
    leaf_node←node
    end for
    for all i-1 to K do
    Ri=Ti_PredictDtest
    R=MostCommon(Ri)
    end for
    end procedure
    Table 3. Process of algorithm used for prediction of food-borne pathogenic bacteria
    ModelAccuracy /%
    PCA + KNN(K-nearest neighbors)88.19
    PCA + logistic regression88.25
    PCA + SVM(support vector machines)83.86
    PCA + decision tree82.63
    PCA + RF(our)91.36
    Table 4. Accuracy of each model
    Abstract
    Get PDF(in Chinese)
    Figures&Tables (12)
    Equations (0)
    References (23)
    Cited By (12)
    Get Citation
    Copy Citation Text
    Qi Wang, Wandan Zeng, Zhiping Xia, Zhiping Li, Han Qu. Recognition of Food-Borne Pathogenic Bacteria by Raman Spectroscopy Based on Random Forest Algorithm[J]. Chinese Journal of Lasers, 2021, 48(3): 0311002
    Download Citation
    Set citation alerts for the article
    Tools
    Share
    Set citation alerts for the article
    Save the article for my favorites
    Paper Information
    Recommended Topics
    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology