بررسی تغییرات چقرمگی سیب زمینی در دوره انبارمانی

سیب زمینی، یکی از مواد غذایی اصلی در رژیم غذایی مردم جهان می باشد. از این رو مطالعه روی جنبه ‌های مختلف آن، از اهمیت زیاد و ویژه ای برخوردار است. به دلیل تعدد زیاد واریته های این محصول و برخی مواقع عدم آشنایی واحدهای فرآوری با ارقام آن و نیز وقت گیر بودن و عدم دقت زیاد در شناسایی ارقام مختلف سیب زمینی توسط کارشناسان و زارعین، و اهمیت شناسایی ارقام سیب زمینی و نیز سایر محصولات کشاورزی در هر مرحله از پروسه‌ی صنایع غذایی، مطالعه خواص مکانیکی این محصول ضروری به نظر می رسد. این مطالعه با هدف بررسی خواص مکانیکی ارقام مختلف سیب زمینی انجام شد. در پژوهش حاضر، از دستگاه سنتام موجود در گروه مهندسی بیوسیستم دانشگاه محقق اردبیلی جهت تعیین خواص مکانیکی استفاده شد. بر اساس نتایج به دست آمده دو رقم جلی و مارفونا در طول دوره انبارمانی به لحاظ چقرمگی تغییرات زیادی نداشتند

investigating changes in potato firmness during storage

potato is an important vegetable that grows all over the world and is considered as an important product in developing and developed countries for the human diet as a source of carbohydrates, proteins, and vitamins. this product is native to south america and its origin is from peru, and after wheat, rice and corn, it is the fourth product in the food basket of human societies. according to the statistics of the food and agriculture organization of the united nations, the area under cultivation of this crop in iran in 2017 was 161 thousand hectares and the crop harvested from this area is about 5.1 million tons. traditional methods of determining potato varieties were based more on morphological features, but with the production of new products, there was a need for methods that were faster and more recognizable. meanwhile, the high-performance artificial neural network can be used to classify cultivars. an artificial neural network can classify and detect cultivars, is flexible and is used in mostagricultural products. azizi conducted a study on 120 potatoes in 10 different cultivars using a visual and image processing machine with a matlab r2012 software toolbox to detect texture, shape parameters and potato cultivars. first, potato cultivars were classified use the ng lda method, which obtained 66.7% accuracy. this method also erred in distinguishing the two cultivars agria and savalan and also classified the two cultivars fontane and satina in other classes. they also used artificial neural networks to classify potato cultivars, in which the network was 82.41% accurate with one hidden layer and 100% accurate with two hidden layers. in this study, it was found that different types of potatoes can be identified and identified with a very high level of accuracy using the three color characteristics, textural and morphological features extracted by the visual machine and the use of a non-linear classifier artificial neural network. categorized. in another study that was conducted using neural networks and image processing on 5 sweet potato cultivars, the researchers showed that this method was successful and could classify sweet potato cultivars with 100% accuracy. by determining and examining the existing relations between the force and the deformation of agricultural products up to the point of surrender, the range of forces harmful to fruit can be determined so that harvesting and transportation machines are designed in such a way that the forces from them do not exceed this range. on the other hand, one of the ways to determine the degree of ripeness of the fruit is to touch and press it with the thumb, which is an experimental way and depends on the skill of the person touching it. the mechanical penetration test of the fruit can be an indicator to check the ripeness of the fruit by quantifying this diagnosis and using this diagnosis to determine the optimal harvest time. several types of research have been conducted on the physical and mechanical properties of agricultural products in iran and other countries. in a research conducted by ali mohammadi and rasakh to determine some mechanical properties of lime fruit under quasi-static loading, the results showed that the effect of loading speed, loading direction and size of a lime on the breaking force of lime is significant. as the size of the lemon decreases, the breaking force and deformation decrease, and also with increasing loading speed, the braking force increases. in another research conducted by mohd nejad and khosdada, the effect of size, speed and direction of loading on the mechanical properties of lime was investigated and the results showed that the interaction of loading speed and size on fracture energy and toughness and the main effects of size, loading speed and the loading direction is significant on the modulus of elasticity, but none of the effects on the rupture force is significant.