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دسته بندی:
داده کاوی - data mining
سال انتشار:
2019
عنوان انگلیسی مقاله:
Data mining approach based on chemical composition of grape skin for quality evaluation and traceability prediction of grapes
ترجمه فارسی عنوان مقاله:
رویکرد داده کاوی مبتنی بر ترکیب شیمیایی پوست انگور برای ارزیابی کیفیت و پیش بینی قابلیت ردیابی انگور
منبع:
Sciencedirect - Elsevier - Computers and Electronics in Agriculture, 162 (2019) 514-522: doi:10:1016/j:compag:2019:04:043
نویسنده:
Brenda V. Canizoa, Leticia B. Escuderoa, Roberto G. Pelleranob, Rodolfo G. Wuillouda,⁎
چکیده انگلیسی:
The knowledge of wine origin is an important aspect in winemaking industries due to the Denomination of
Controlled Origin. In this work, a data mining algorithms comparison study of grape-skin samples from five
regions of Mendoza, Argentina, and builds classification models capable of predicting provenance based on
multi-elemental composition, were developed. Inductively coupled plasma mass spectrometry (ICP-MS) was
used to determine 29 elements (Ag, As, Ce, Co, Cs, Cu, Eu, Fe, Ga, Gd, La, Lu, Mn, Mo, Nb, Nd, Ni, Pr, Rb, Sm, Te,
Ti, Tl, Tm, U, V, Y, Zn and Zr). Four classification techniques, including multinomial logistic regression (MLR), knearest
neighbors (k-NN), support vector machines (SVM), and random forests (RF) were assessed. The best
results were achieved for SVM and RF models, with 84% and 88.9% prediction accuracy, respectively, on the 10-
fold cross validation. The RF variable importance showed that Rb (rubidium) was the most relevant components
for prediction.
Keywords: Machine learning | Grape-skins | Mineral content | Provenance
قیمت: رایگان
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