کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6540225 158852 2016 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Multivariate analysis of hyper/multi-spectra for determining volatile compounds and visualizing cooking degree during low-temperature baking of tubers
ترجمه فارسی عنوان
تجزیه و تحلیل چند متغیری از بیشه / طیف چندگانه برای تعیین ترکیبات فرار و تجسم درجه پخت و پز در پخت کم دمبرگ غده
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
This study was conducted to assess the potential feasibility of using hyperspectral imaging (900-1700 nm) for rapid determination of the volatility of tuber compositions (VTC) and prediction of the tuber cooking degree (TCD) in low temperature baking (LTB). Tuber samples of six categories from different origins were imaged and calibrated. The partial least squares regression (PLSR) and three-layer back propagation artificial neural network (TBPANN) models were established to predict VTC and TCD using the entire spectral range and the feature wavelengths. The optimal combination of characteristic wavelengths (991, 1031, 1071, 1138, 1252, 1403, 1460 and 1641 nm) were selected by first derivative and mean centering iteration algorithm (FMCIA) rather than other conventional methods. Based on the qualified eight wavelengths, the FMCIA-TBPANN approach yielded greater overall performance for predicting both VTC and TCD. Furthermore, the distribution maps of VTC and TCD were generated using a resulting function to visualize each pixel of spectral image. This demonstrated the capability of spectral imaging technique for rapid and accurate evaluation of VTC and TCD during LTB.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers and Electronics in Agriculture - Volume 127, September 2016, Pages 561-571
نویسندگان
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