کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
222952 464318 2015 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
E-nose combined with chemometrics to trace tomato-juice quality
ترجمه فارسی عنوان
بینی همراه با شیمی درمانی برای ردیابی کیفیت گوجه فرنگی
کلمات کلیدی
بینی الکترونیکی، آب گوجه فرنگی گیلاس، ذخیره سازی، کیفیت ردیابی، طبقه بندی نیمه نظارت، رگرسیون حداقل مربعات جزئی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی مهندسی شیمی (عمومی)
چکیده انگلیسی


• Quality of raw fruit was traced by detecting the squeezed juices using an e-nose.
• A novel semi-supervised classifier based on spectral clustering was applied.
• The new classifier outperforms four supervised linear and nonlinear classifiers.
• Regression models based on 20% and 70% of the whole dataset were compared.
• Quality indices of cherry tomatoes were successfully predicted.

An e-nose was presented to trace freshness of cherry tomatoes that were squeezed for juice consumption. Four supervised approaches (linear discriminant analysis, quadratic discriminant analysis, support vector machines and back propagation neural network) and one semi-supervised approach (Cluster-then-Label) were applied to classify the juices, and the semi-supervised classifier outperformed the supervised approaches. Meanwhile, quality indices of the tomatoes (storage time, pH, soluble solids content (SSC), Vitamin C (VC) and firmness) were predicted by partial least squares regression (PLSR). Two sizes of training sets (20% and 70% of the whole dataset, respectively) were considered, and R2 > 0.737 for all quality indices in both cases, suggesting it is possible to trace fruit quality through detecting the squeezed juices. However, PLSR models trained by the small dataset were not very good. Thus, our next plan is to explore semi-supervised regression methods for regression cases where only a few experimental data are available.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Food Engineering - Volume 149, March 2015, Pages 38–43
نویسندگان
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