تشخیص غیرمخرب بیاتی نان با استفاده از تصاویر فراطیفی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار دانشگاه علوم کشاورزی و منایع طبیعی خوزستان

2 دانشکده علوم و مهندسی صنایع غذایی دانشگاه کشاورزی و منابع طبیعی رامین خوزستان

3 دانشجوی کارشناسی ارشد، گروه مهندسی ماشین‌های کشاورزی و مکانیزاسیون، دانشکده مهندسی زراعی و عمران روستایی، دانشگاه علوم کشاورزی

چکیده

تصویربرداری فراطیفی، ترکیبی از فناوری تصویربرداری و طیف‌سنجی است که مقادیر زیادی از اطلاعات فضایی و طیفی را به‌طور همزمان ارائه می‌دهد، و امروزه به‌عنوان یک ابزار تشخیص غیرمخرب و سریع برای ارزیابی کیفیت و ایمنی مواد غذایی در حال گسترش است. در این پژوهش با استفاده از تصویربرداری فراطیفی، در محدوده طول موج nm400-950 و با وضوح کیفی nm 795/0، چگونگی فرایند بیات شدن نان و تاثیر آن بر رفتار نان بررسی شد. بعد از استخراج مولفه‌های اصلی، به منظور پیش‌بینی ویژگی‌های بافتی از سه روش مدل‌سازی PCR، PLSR و GRNN طی شش روز نگهداری استفاده شد؛ نتایج نشان دادند روش GRNN نسبت به دو روش دیگر دارای بیشترین مقادیر ضریب تشخیص R^2 برای دو ویژگی، ارتجاعیت و سفتی به ترتیب 96/0 و 94/0 و همچنین کمترین مقدار خطا RMSE برای دو ویژگی، پیوستگی و سفتی به ترتیب 11/0 و 32/0 می‌باشد که نشاندهنده توانایی مدل شبکه عصبی رگرسیون تعمیم‌یافته برای پیش‌بینی ویژگی‌های بافتی نان است.

چکیده تصویری

تشخیص غیرمخرب بیاتی نان با استفاده از تصاویر فراطیفی

تازه های تحقیق

  • تصویربرداری فراطیفی برای مطالعه بیاتی نان استفاده شد.
  • سه روش مدل­سازی (PCR، PLSR وGRNN) برای پیش بینی ویژگی های بافت به کار گرفته شد.
  • روش GRNN توانایی خود را در پیش بینی ویژگی های بافتی نان نشان داد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Non-destructive detection of bread staleness using hyperspectral images

نویسندگان [English]

  • Saman Abdanan Mehdizadeh 1
  • Mohammad Noshad 2
  • Fatemeh Nouri 3
1 Associate professor of Agricultural Sciences and Natural Resources University of Khuzestan
2 Department of Food Science & Technology, Faculty of Animal Science and Food Technology, Khuzestan Ramin University of Agricultural & Natural Resources, Mollasani, Iran
3 2. MSc Student Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan
چکیده [English]

Hyperspectral imaging, a technology that combines imaging and spectroscopy, provides extensive spatial and spectral information, simultaneously. It is currently being developed as a non-destructive and rapid diagnostic tool for assessing food quality and safety. In this study, hyperspectral imaging was utilized to investigate the process of bread staleness and its effect on the behavior of the bread crumb within the wavelength range of 950-400 nm and with a resolution of 0.795 nm. Principal components were extracted and three modeling methods - PCR, PLSR, and GRNN - were employed to predict texture characteristics during six days of storage. Based on the findings of this study, it was observed that the General Regression Neural Network (GRNN) method demonstrated superior performance in terms of R2 values for both springiness and stiffness, with values of 0.96 and 0.94, respectively. Furthermore, the GRNN method also exhibited the lowest Root Mean Square Error (RMSE) values for cohesiveness and stiffness, with values of 0.11 and 0.32, respectively. This demonstrates the capability of the generalized regression neural network model to predict the textural characteristics of bread.

کلیدواژه‌ها [English]

  • Bread staling
  • Hyperspectral imaging
  • Non-destructive evaluation
  • Generalized regression neural network
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