کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6539375 | 1421098 | 2018 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Combination of computer vision and backscattering imaging for predicting the moisture content and colour changes of sweet potato (Ipomoea batatas L.) during drying
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
This study seeks to investigate the potential of using combined computer vision (CV) and laser-induced backscattering imaging (LLBI) in monitoring the quality attributes of sweet potato during drying. CV and backscattered images of 4â¯mm thickness sweet potato slices were captured after every one-hour of drying, at drying temperatures of 50-70â¯Â°C. Reference quality properties, such as moisture content, Lâ, aâ and bâ colour coordinates were measured hourly under the same drying conditions. Principal component analysis (PCA) and partial least square regression (PLS) were applied to the extracted combined CV (based on RGB) and backscattering imaging parameters to analyse the quality changes of sweet potato during drying. The results showed that there was significant effect of drying temperature and time on combined CV and backscattering imaging parameters. The combined optical method showed good correlation with moisture content and colour properties i.e Lâ and aâ of sweet potato with R2â¯>â¯0.7. Specifically, the redness (aâ) gave the highest coefficient of determination (R2) of 0.80, while the moisture ratio (MR) showed the lowest root mean square error of validation (RMSEV) with the value of 0.18. Thus, this study has shown that combined CV and backscattering imaging parameters can serve as a non-destructive tool for detecting the changes in quality parameters of sweet potato during drying.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers and Electronics in Agriculture - Volume 150, July 2018, Pages 178-187
Journal: Computers and Electronics in Agriculture - Volume 150, July 2018, Pages 178-187
نویسندگان
Daniel I. Onwude, Norhashila Hashim, Khalina Abdan, Rimfiel Janius, Guangnan Chen,