بررسی تغییرات چقرمگی سیب زمینی در دوره انبارمانی

Introduction
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 most agricultural 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.

Methodology
First, potatoes in 5 different varieties (Agria, Esprit, Sante, Marfona and Jelli) were prepared at Ardabil Agricultural Research Center and stored at 4-10 degrees Celsius. One day after preparing the varieties, 21 samples of each potato variety were prepared using a cutting cylinder and then data collection was done. The data collection included mechanical properties.
To determine the toughness of the samples, the santam machine available in the mechanical properties laboratory of the biosystem engineering department of Mohaghegh Ardabili University was used. Each potato variety was subjected to a compressive force at three loading speed levels of 10, 40 and 70 mm/min and with 7 repetitions. Then, using the amount of braking force, deformation and sample volume, the toughness was calculated according to equation (1).
These experiments were carried out in 5 storage periods (at 2-week intervals).

Conclusion
The toughness of different cultivars showed different behavior during the storage period so no changes were observed in the Marfona cultivar for toughness during the storage period, and in the Sante cultivar, the toughness level was almost the same at the beginning and end of the period and only in the middle of the storage period the value There was a slight increase. But in the case of Agria, Sprit and Jali cultivars, it should be said that the changes in toughness do not follow a specific trend and are unpredictable. Also, according to Figure 3, it is quite clear that in all figures, the lower the loading speed, the greater the toughness obtained, and the reason for this is that at a lower loading speed, the breaking force occurs in high values. Falls, and as a result, according to relation 1, the toughness value also increases.
According to Figure 3, during the storage period, the two varieties of Jelli and Marfona (especially the Marfona variety) did not change much in terms of toughness and considering this issue, it is recommended to use these two varieties for some purposes, including frying.
In this research, firmness was calculated for 5 different varieties of potatoes in 5 storage periods using the santam machine available at Mohaghegh Ardabili University and with the help of equation 1. The results showed that Jali and Marfona cultivars maintained their firmness during the storage period, and hence they are recommended for uses such as chips.