امکان سنجی قابلیت طیف سنجی مرئی/ فروسرخ نزدیک (Vis/NIR) در طبقه بندی نمونه های لیموترش طی دوره انبارمانی با روش های شناسایی PCA، LDA و SVM

نویسندگان
1 دانشجوی دکتری، گروه علوم و صنایع غذایی، دانشکده کشاورزی، واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران.
2 دانشیار، گروه علوم و صنایع غذایی، دانشکده کشاورزی، واحد ورامین– پیشوا، دانشگاه آزاد اسلامی، ورامین، ایران.
3 استادیار، گروه علوم و صنایع غذایی، دانشکده کشاورزی، واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران.
4 استادیار، گروه زراعت و اصلاح نباتات، دانشکده کشاورزی، واحد ورامین– پیشوا، دانشگاه آزاد اسلامی، ورامین، ایران.
چکیده
امروزه روند افزایشی ضایعات مواد غذایی و محصولات کشاورزی یکی از چالش‌های جدی اکثر کشورها، به ویژه کشورهای در حال توسعه محسوب می‌شود لذا یکی از سیاست‌های جدی دولت‌ها در امر امنیت مواد غذایی، کاهش ضایعات و حفظ کیفیت محصولات کشاورزی است. تاکنون از روش‌های متعددی برای سنجش کیفیت محصولات کشاورزی استفاده شده است که تنها برخی از آنها از لحاظ فنی و صنعتی توجیه پذیرند. روش طیف سنجی مرئی/ مادون قرمز نزدیک (Vis/NIR) از جمله روش‌هایی است که به دلیل سرعت و دقت بالا در ارزیابی خصوصیات کیفی محصولات کشاورزی مورد توجه و استفاده قرار گرفته است. در این راستا، در پژوهش حاضر از طیف سنجی مرئی/ فروسرخ نزدیک به منظور سنجش تغییرات کیفی و طبقه‌بندی نمونه‌های لیموترش واریته کی لایم، طی دوره انبارمانی (10، 20 و 30 روز) استفاده گردید. در ادامه به منظور تحلیل ویژگی‌های کیفی و طبقه بندی داده‌های مستخرج از NIR، از روش های شناسایی الگو شامل تحلیل مؤلفه های اصلی (PCA)، تحلیل تفکیک خطی (LDA) و ماشین بردار پشتیبان (SVM) استفاده شد. نتایج بدست آمده نشان داد که طیف­سنجی مرئی/ فروسرخ نزدیک (Vis/NIR) قادر به تفکیک نمونه­های لیموترش بر اساس مدت زمان نگهداری در انبار است. هرچند روش­های PCA، LDA و SVM توانستند با دقت خوبی نمونه­های لیموترش را با توجه به ویژگی‌های کیفی دسته­بندی کنند، اما روش‌های LDA و SVM با دقت 100% از دقت و برازش مطلوب‌تری برخوردار بودند. همچنین، طبق نتایج، تابع درجه 2، به عنوان بهترین تابع برای ساخت مدلهای دسته­بندی به روش‌های LDA و SVM تعیین و معرفی گردیده‌است.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Feasibility of Visible/Near Infrared (Vis/NIR) Spectrophotometry capability in classification of lemon samples during storage period by PCA, LDA and SVM identification methods

نویسندگان English

niloofar goodarzi 1
Sara Movahhed 2
Mohammad Javad Shakouri 3
Hossein Ahmadi Chenarbon 4
1 Ph.D student, Department of Food Science and Technology, College of Agriculture, North Tehran Branch, Islamic Azad University, Tehran, Iran.
2 Associated Professor, Department of Food Science and Technology, College of Agriculture, Varamin - Pishva Branch, Islamic Azad University, Varamin, Iran.
3 Assistant Professor, Department of Food Science and Technology, College of Agriculture, North Tehran Branch, Islamic Azad University, Tehran, Iran.
4 Assistant Professor, Department of Agronomy, College of Agriculture, Varamin - Pishva Branch, Islamic Azad University, Varamin, Iran.
چکیده English

Today, the increasing process of food waste and agricultural products is one of the serious challenges in the most countries, especially in developing countries, so one of the serious policies of governments in the food security is to reduce the waste and maintain the quality of agricultural products. So far, several methods have been used to measure the quality of agricultural products, only some of which are technically and industrially justified. Vis / NIR Spectrophotometry method is one of the methods that has been considered and used in evaluating the qualitative characteristics of agricultural products due to its high speed and accuracy. In this regard, in the present study, visible/near infrared Spectrophotometry was used to measure the qualitative changes and classification of K-Lime samples of lemon during the storage period (10, 20 and 30 days). In order to analyze the qualitative characteristics and classify the data extracted from NIR, the pattern recognition methods including principal component analysis (PCA), linear Discriminant analysis (LDA) and support vector machine (SVM) were used. The results showed that Visible/Near Infrared (Vis/NIR) Spectrophotometry was able to differentiate its lemon samples based on storage time. Although PCA, LDA and SVM methods were able to classify lemon samples with good accuracy according to qualitative characteristics, but LDA and SVM methods with 100% accuracy had better accuracy and fit. Also, according to the results, the quadratic function has been determined and introduced as the best function for constructing classification models by LDA and SVM methods.

کلیدواژه‌ها English

Near infrared
Visible Spectrophotometry
Lemon
Storage period
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