Investigating the breaking energy of different potato cultivars at the harvest stage with santam device

Potato is an important vegetable that grows all over the world and is considered as an important product in developing and developed countries for 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 usithe 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.

First, 5 different varieties of potatoes (Agria, Spirit, Sante, Marfona and Jelly) were prepared from Ardabil Agricultural Research Center immediately after harvest. After preparing the varieties, 21 samples of each potato variety were prepared using a cutting cylinder and then data collection was done with santam machine.To measure the rupture energy of potato samples, santam device (available in the Biosystem Engineering Department of Mohaghegh Ardabili University) was used. For this purpose, each potato variety was subjected to a compressive force at three loading speed levels (10, 40 and 70 mm/min) with 7 repetitions. Then, using the amount of rupture force and deformation (surface area under the force-deformation curve), the amount of rupture energy was calculated. The data obtained from the experiment were analyzed statistically with Minitab 18 software.

Conclusion:

 The amount of rupture energy in 5 different varieties of potato was obtained using santam device and equation 1. The values obtained for 5 potato cultivars were analyzed using Minitab18 software and the results are given in Table (1).The results of the analysis of variance for the firmness of 5 different potato cultivars were significant at the 1% level and the coefficient of variation was 9.6. In Figure 2, you can see the average results.According to Figure 2, it is clear that the lowest amount of rupture energy is related to the Agria variety and the highest is related to the Jali variety. Also, it can be found that with a loading speed of 10 mm/min, the highest amount of rupture energy is obtained in all figures.In this research, the firmness level for 5 different potato cultivars was calculated using the santam machine available at Mohaghegh Ardabili University and the area under the force-deformation curve. The amount of calculated rupture energy has the ability to be used as a method for the proper selection of different potato cultivars. The use of this method in potato cultivars will be very useful for factories such as chips factories and processing units, and it is also expected that similar methods related to mechanical properties such as crispness and hardness and with the help of different statistical methods to optimize production and The processing of agricultural products can be used in the food industry, which leads to more customer-friendliness and can also reduce agricultural waste.