بررسی خواص کیفی چغندرقند با استفاده از تصویربرداری فراطیفی

In recent decades, various non-destructive technologies have been used to evaluate the quality of fruits and vegetables. Among these methods, hyperspectral imaging has been noticed by researchers as a non-destructive, fast and minimal environmental pollution method to evaluate the properties of agricultural products. The purpose of the research was to investigate the effect of variety, duration and storage conditions on the quality properties of sugar beet and also the feasibility of using hyperspectral images as a new and non-destructive technique in measuring quality properties. Three sugar beet cultivars including Agrit, Muriel and Shokofa cultivars in three different storage conditions (1- in storage conditions with protection 2- without protection i.e., in atmospheric conditions 3- subsoil and the same field conditions) for 45 days of storage and then ultraspectral imaging in the mode Reflectance and in the wavelength range of 400 to 1100 nm was measured once every 15 days. In the next step, the properties of the samples (including soluble solids (SSC), mechanical properties, sugar content, moisture percentage and pH) were determined based on destructive methods. Because the acquired spectra have noise, the effects of disturbing factors were minimized through various pre-processing to provide clearer data for modeling the qualitative characteristics of the samples. Finally, modeling was done using the resulting data from hyperspectral imaging and reference tests based on the least square regression (PLS) method, and in the last step, after validating the resulting models, the most appropriate model was selected. The results show that the hyperspectral imaging method can be used as a valuable tool to predict the quality properties of sugar beet during the storage period.



Introduction

The ever-increasing population growth and increasing demand for food products have made the importance of modern agriculture more visible. Considering that the quality of food is directly related to human health, measuring the quality of agricultural products as one of the important activities in post-harvest technology has received more attention. Sugar beet with the scientific name Beta vulgaris is a biennial plant from the spinach family that is cultivated as an annual plant. During the vegetative growth period, sugar beet has no stem and is seen as a set of large horizontal to vertical leaves. The growth period for sugar production is 6 to 9 months. Iran is the 13th largest producer of this product with the production of 13.5 million tons and the cultivated area of 18.9 thousand hectares (FAO, 2023). Currently, the capacity of the country's sugar factories is less than the amount of beet that is harvested daily, so it is necessary to store the produced beet until the conditions for processing are available (Hazireh, 2019). In the process of sugar production, the quality of sugar beet is one of the most effective and important factors, and in sugar factories, the production product is largely dependent on the quality of sugar beet. Research has shown that the increase in sugar waste and sugar beet weight during the storage period causes a decrease in sugar extraction. The aim of this research is to investigate the variety, time and storage conditions on the quality properties of sugar beet and also the feasibility of using hyperspectral images as a new and non-destructive technique in measuring the quality properties of different varieties of sugar beet under different conditions of time and storage method.



Methodology

Three sugar beet cultivars including Agrit, Muriel and Shokofa cultivars in three different storage conditions (1- in storage conditions with protection 2- without protection i.e. in atmospheric conditions 3- subsoil and the same field conditions) for 45 days of storage and then ultraspectral imaging in the mode Reflectivity and in the wavelength range of 400 to 1100 nm was measured for each sample once every 15 days. In the next step, the properties of the samples (including soluble solids (SSC), mechanical properties, sugar content, moisture percentage and pH) were determined based on destructive methods. Because the acquired spectra have noise, the effects of disturbing factors were minimized through various pre-processings to provide clearer data for modeling the qualitative characteristics of the samples. Finally, modeling was done using the resulting data from hyperspectral imaging and reference tests based on the least square regression (PLS) method, and in the last step, after validating the resulting models, the most appropriate model was selected.



Conclusion

According to the diagram in Figure 1 for SSC, it is clear that the samples stored under the soil with the increase of the storage period in all three cultivars have better preserved their original properties and the least changes in their SSC have occurred and the most changes are related to the free samples. Also, in all storage periods and in all storage conditions, morel variety had the least changes in SSC, and the samples stored underground were less affected by the increase in the storage period than other samples. The results of the comparison of the average data in Figure 4 showed that with the increase of the period, more mass reduction occurred in the samples and also Muriel cultivar had more mass reduction than other items in all conditions. For free samples, the decrease in mass was more intense and occurred with a steeper slope, while the samples stored under the soil had a partial decrease in mass during the storage period. According to the results presented in Figure 5 and 6, it is clear that the sugar content of all cultivars decreased with the increase of the storage period; Muriel variety had the highest and Agrit variety had the lowest sugar level in all periods. However, Muriel variety is more sensitive to time change and the sugar level of this variety has decreased more strongly than the other two varieties. And it is also known that the storage of sugar beet underground moderates the process of sugar reduction to a great extent and the quality of sugar beet is maintained during the storage period. Protected samples also have less reduction in sugar than free samples. The information presented in Table 5 shows that except for the model based on the second derivative preprocessor, all the created models are highly accurate in predicting stiffness and the best performance is related to the Baseline preprocessor.