بررسی تغییرات سفتی و بافت میوه گلابی در بارگذاری نیروهای مختلف و دوره‌های متفاوت انبارداری با شبکه عصبی مصنوعی

نوع مقاله : مقاله پژوهشی لاتین

نویسندگان

• محمد واحدی ترشیزی
• محسن آزادبخت

گروه مکانیک بیوسیستم دانشگاه علوم کشاورزی و منابع طبیعی گرگان

https://doi.org/10.22067/ifstrj.v15i6.75049

چکیده

در این تحقیق به بررسی اثر نوع بارگذاری‌های دینامیکی و استاتیکی و دوره انبارداری بر میزان سفتی گلابی پرداخته شد. برای این کار ابتدا گلابی‌ها به سه گروه 27 تایی برای سه بارگذاری استاتیکی لبه نازک، استاتیکی لبه پهن و دینامیکی دسته‌بندی شده و بارگذاری شدند. هر یک از گروه‌های بارگذاری شده در سه دوره 5، 10 و 15 روزه انبار دار شده و بعد از هر دوره انبارداری با استفاده از آزمون غیرمخرب CT-Scan از تغییر بافت گلابی‌ها عکس‌برداری شد و سپس میزان سفتی بافت گلابی با استفاده از سفتی‌سنج اندازه‌گیری شد. همچنین داده‌ها با استفاده از دو شبکه مصنوعی MLP و RBF شبیه‌سازی و مورد بررسی قرارگرفت. نتایج نشان داد که با افزایش دوره انبارداری و میزان نیروی بارگذاری در هر سه نوع بارگذاری میزان سفتی به‌طور معنی داری (سطح 1%) کاهش یافت. همچنین بافت گلابی در بارگذاری دینامیکی به شدت نسبت به دوبارگذاری دیگر تخریب شده است. بهترین مقادیر شبکه عصبی مصنوعی برای فشار لبه پهن (12 نرون- RBF) (R2 Wide edge= 0.9738– RMSE Wide edge= 0.3419- MAE Wide edge= 0.268) و برای فشار لبه نازک(4 نرون -RBF) (R2Thin edge= 0.9946– RMSE Thin edge=0.170977- MAE Thin edge= 0.133) و در نهایت برای بارگذاری دینامیکی (8 نرون- RBF) (R2 Dynamic loading = 0.9933– RMSE Dynamic loading =0.230- MAE Dynamic loading=0.187) بوده است.

کلیدواژه‌ها

• گلابی
• سفتی
• بارگذاری
• انبار داری
• شبکه عصبی
• CT-Scan

عنوان مقاله [English]

Study on Firmness and texture changes of pear fruit when loading different forces and stored at different periods using artificial neural network

نویسندگان [English]

• Mohammad Vahedi Torshizi
• Mohsen Azadbakht

Department of Bio-System Mechanical Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.

چکیده [English]

This study evaluated the effect of different dynamic and static loadings and different storage periods on the firmness of pear fruit. Pear fruit was first segregated into three groups of 27 pear in order to undergo three loadings: static thin-edge compression loading, static wide-edge compression loading and dynamic loading. All loaded pears were stored in accordance with three storage period designs: 5-day storage, 10-day storage, and 15-day storage. Following each period, the variations of pear texture were scanned by using the CT-Scan technique as a non-destructive test. Then, the firmness of pear texture was measured using a penetrometer. Data were simulated and evaluated using MLP and RBF artificial neural networks. The results showed that with increasing storage time and loading force , the firmness significantly decreased (1% level) in all three types of loading, In addition, pear texture was destructed under dynamic compression loading in order to compare with other two loadings. Best value artificial neural network for wide edge loading (12 neuron-RBF) was (R2 Wide edge= 0.9738– RMSE Wide edge=0.3419- MAE Wide edge =0.268) and for thin edge loading (4 neuron-RBF) was (R2Thin edge = 0.9946– RMSE Thin edge =0.170977- MAE Thin edge =0.133), also for dynamic loading (8 neuron-RBF) was (R2 Dynamic loading = 0.9933– RMSE Dynamic loading =0.230- MAE Dynamic loading= 0.187).

کلیدواژه‌ها [English]

• Pear
• Firmness
• Loading
• storage
• Artificial neural network

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