کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5762656 | 1624961 | 2017 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Sweet and nonsweet taste discrimination of nectarines using visible and near-infrared spectroscopy
ترجمه فارسی عنوان
طعم و مزه شیرین و بدون شیرین از نکتارها با استفاده از طیف سنجی قابل مشاهده و نزدیک مادون قرمز
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موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
علوم زراعت و اصلاح نباتات
چکیده انگلیسی
The feasibility of using visible and near-infrared spectroscopy technology combined with multivariate analysis to discriminate cv. 'Big Top' and cv. 'Diamond Ray' nectarines has been studied. These varieties are very difficult to differentiate visually on the production line but show important differences in taste that affects the acceptance by final consumers. The relationship between the diffuse reflectance spectra and the two nectarine varieties was established. Five hundred nectarine samples (250 of each variety) were used for the study. Tests were performed by using a spectrometer capable of measuring in two different spectral ranges (600-1100Â nm and 900-1700Â nm). These spectral ranges were used to develop two accurate classification models based on linear discriminate analysis (LDA) and partial least squares discriminate analysis (PLS-DA). Later, selection techniques were applied to select the most effective wavelengths. The results showed that the PLS-DA model achieved better accuracy and less latent variables than LDA model, and specifically, good results with 100% classification accuracy were obtained using only the 600-1100Â nm spectral range for the two models and eight selected wavelengths. These results places visible and near-infrared spectrocopy as an accurate classification tool for nectarine varieties with a very similar appearance but different tastes that could be potentially used in an automated inspection system.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Postharvest Biology and Technology - Volume 133, November 2017, Pages 113-120
Journal: Postharvest Biology and Technology - Volume 133, November 2017, Pages 113-120
نویسندگان
V. Cortés, S. Cubero, N. Aleixos, J. Blasco, P. Talens,