Prediction of Raw Milk Microbial Quality Using Data Mining Techniques

• Ahmad Imran

  School of Management Technology, Sirindhorn International Institute of Technology, Thammasat University

• Komolavanij Somrote

  School of Management Technology, Sirindhorn International Institute of Technology, Thammasat University

• Chanvarasuth Pisit

  School of Management Technology, Sirindhorn International Institute of Technology, Thammasat University

抄録

Data mining techniques were applied to predict raw milk quality in terms of methylene blue reduction time (MBRT) from the independent parameters of raw milk inspection parameters such as travel time, temperature of milk, solid-not-fat, %fat, acidity and specific gravity. Predictive models were developed and the performance of 3 data mining algorithms namely; Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and K-Nearest neighbor (KNN), was measured in terms of average error and Root Mean Square Error (RMSE). MLR showed high and inconsistent RMS error in 3 randomly picked data partitions whereas KNN and ANN were able to predict the MBRT values from the physico-chemical quality parameters, KNN was the preferred algorithm (K=7, RMSE of 1.7). The models were applied to a new set of data (n=78) without showing them the output parameter (MBRT). The predicted values of MBRT were plotted against the actual observed values to classify milk into 4 quality grades.<br>

収録刊行物

• 農業情報研究

  農業情報研究 19 (3), 64-70, 2010

  農業情報学会

キーワード

• Raw milk
• microbial quality
• data mining
• ANN
• KNN
• dairy

詳細情報

• CRID: 1390001204461035776
• NII論文ID: 130000395642
• DOI: 10.3173/air.19.64
• ISSN: 18815219
• ISSN: 09169482
• 本文言語コード: en
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可