کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6664397 | 1427063 | 2018 | 39 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Near-infrared hyperspectral imaging for non-destructive classification of commercial tea products
ترجمه فارسی عنوان
تصویربرداری هیپرکتراپی نزدیک به مادون قرمز برای طبقه بندی غیر مخرب محصولات چای تجاری
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کلمات کلیدی
طیف سنجی تصویربرداری، هیککوبه، چند متغیره، تجسم داده، روش های همسایگی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
مهندسی شیمی (عمومی)
چکیده انگلیسی
Tea is the most consumed manufactured drink in the world. In recent years, various high end analytical techniques such as high-performance liquid chromatography have been used to analyse tea products. However, these techniques require complex sample preparation, are time consuming, expensive and require a skilled analyst to carry out the experiments. Therefore, to support rapid and non-destructive assessment of tea products, the use of near infrared (NIR) (950-1760â¯nm) hyperspectral imaging (HSI) for classification of six different commercial tea products (oolong, green, yellow, white, black and Pu-erh) is presented. To visualise the HSI data, linear (principal component analysis (PCA) and multidimensional scaling (MDS)) and non-linear (t-distributed stochastic neighbour embedding (t-SNE) and isometric mapping (ISOMAP)) data visualisation methods were compared. t-SNE provided separation of the six commercial tea products into three groups based on the extent of processing: minimally processed, oxidised and fermented. To perform the classification of different tea products, a multi-class error-correcting output code (ECOC) model containing support vector machine (SVM) binary learners was developed. The classification model was further used to predict classes for pixels in the HSI hypercube to obtain the classification maps. The SVM-ECOC model provided a classification accuracy of 97.41â¯Â±â¯0.16% for the six commercial tea products. The methodology developed provides a means for rapid, non-destructive, in situ testing of tea products, which would be of considerable benefit for process monitoring, quality control, authenticity and adulteration detection.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Food Engineering - Volume 238, December 2018, Pages 70-77
Journal: Journal of Food Engineering - Volume 238, December 2018, Pages 70-77
نویسندگان
Puneet Mishra, Alison Nordon, Julius Tschannerl, Guoping Lian, Sally Redfern, Stephen Marshall,