کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8888219 | 1628379 | 2018 | 35 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Automated quantification of defective maize kernels by means of Multivariate Image Analysis
ترجمه فارسی عنوان
اندازه گیری خودکار دانه های ذرت معیوب با استفاده از تجزیه و تحلیل تصویر چند
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کلمات کلیدی
ذرت، تشخیص نقص، میوکوتوکسین ها، تجزیه و تحلیل تصویر چند متغیره، کالیبراسیون چند متغیره،
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
دانش تغذیه
چکیده انگلیسی
This article describes the development of a fast and inexpensive method based on digital image analysis for the automated quantification of the percentage of defective maize (%DM). Defective kernels tend to foster high levels of mycotoxins like Deoxynivalenol (DON), which represents a risk for the health of humans and of farm animals. In this work, 332 RGB images of 83 mixtures containing different amounts of defective maize kernels were acquired using a digital camera. The mixtures were also analysed with a commercial ELISA test kit to determine their concentration of DON, that resulted highly correlated with the amount of defective kernels. Each image was then converted into a signal, named colourgram, which codifies its colour-related information content. The colourgrams were firstly explored using Principal Component Analysis. Then, calibration models of the %DM values were developed using Partial Least Squares (PLS) and interval PLS. The best interval PLS model allowed to predict the %DM values of external test set samples with a root mean square error value equal to 2.6%. Based on the output of this model it was also possible to highlight the defective-maize areas within the images, confirming the significance of the proposed approach.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Food Control - Volume 85, March 2018, Pages 259-268
Journal: Food Control - Volume 85, March 2018, Pages 259-268
نویسندگان
Giorgia Orlandi, Rosalba Calvini, Giorgia Foca, Alessandro Ulrici,