نوع مقاله : مقاله پژوهشی
1 مهندسی بیوسیستم، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران
2 دانشگاه محقق اردبیلی، اردبیل، ایران
3 مهندسی بیوسیستم، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، ایران
عنوان مقاله [English]
Potato is an important vegetable that grows all over the world and is considered 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. As expectations for food products with high quality and safety standards increase, it is necessary to determine their characteristics accurately, quickly, and purposefully. In the potato crop, quality evaluation, after harvest and isolation, is very important to provide a reliable and uniform product to the market, because the potato, like many other crops, has the non-uniform quality and care during the harvest stage. While the quality of raw potatoes is primarily determined by the size, shape, color, and attractiveness of the tuber, the quality of potatoes is generally determined by examining the quality of the final product. The quality of processed products is examined in terms of color, flavor, and texture. The quality of most processed products stems from the quality of raw potatoes. Uniformity in size, shape, and composition is essential for optimal quality. During storage, processing, or cooking, potatoes are exposed to a variety of phenomena that affect the final quality of the product. For consumers, the main quality characteristics of potatoes are color, size, and texture. However, quality assessment for industrial potato processing includes various parameters such as dry matter content, starch content and characteristics, shelf life after storage (storage), and after processing. The type of cultivar, physical and chemical composition, and post-harvest storage are important factors that can affect the cooking characteristics of potatoes and potato crops. Quality assessment of agricultural products includes two main methods, quality rating systems based on the apparent properties of agricultural products and quality rating systems based on internal quality assessment, which has gained prominence in recent years. In the meantime, several methods have been developed so far for non-destructive quality classification of agricultural products based on the evaluation of their internal properties, only some of which have been able to meet the above conditions and are technically and industrially justified. To be. In the meantime, spectroscopy can have high efficiency in determining the quality of figures. Spectroscopy is a system that has a different structure and approach from other methods (image processing, neural network, etc.) and can classify and determine the quality of the digit.
First, 3 different potato cultivars were prepared from Ardabil Agricultural Research Center. After preparing the data, data were collected to determine the amount of sugar (SSC) and at the same time, the samples were tested with a spectrometer to determine the wavelengths of the samples. The glucose level of each sample was measured in 18 replications using an SBR-62T ocular refractometer. To do this, the water of the samples was placed on a refractometer at ambient temperature and its sugar level was read in terms of Brix. A PS-100 spectroradiometer (Apogee Instruments, INC., Logan, UT, USA) made in the USA was used to obtain the spectrum of the samples. This ultra-small, lightweight, portable spectrophotometer has a 1nm sprayer-type single-diffuser and a linear silicon CCD array detector with 2048 pixels, which has a range of 250-150 nm (Vis / NIR) cover. There is also the ability to connect fiber optics to the PS-100 spectroradiometer and transfer data to a computer for the purpose of displaying and storing the acquired spectra in the Spectra Wiz software via the USB port. The data obtained from spectral imaging may be affected by the scattering of light by the detector by changing the sample, changing the sample size, surface roughness in the sample, noise caused by the temperature of the device and many other factors, and unwanted information Affect the accuracy of calibration models. Therefore, data processing is required to achieve stable, accurate, and reliable calibration models. The application of non-destructive methods based on spectroscopy in the full range of wavelengths requires a lot of time and money, which makes the practical application of this method almost impossible; therefore, one should look for a way to find the optimal wavelengths and limit the wavelengths to the minimum possible. The partial least squares (PLS) regression method seems ideal in this regard. In this study, in order to build the models, the data were randomly divided into two parts: 80% of the samples were used for cross-training and cross-validation and the rest of the data were used for independent validation.
Mean absorption spectra Vis / NIR absorption spectra for different treatments in the range of 1000-500 nm are shown in Figure 1. Environmental factors (light and heat) as well as the quality of spectrometer expression cause perturbations in the initial and final wavelengths of the spectra, so part of these wavelengths are removed from the data set and as shown in Figure 1, the samples had a roughly similar pattern; This may be due to the color of the samples. According to Figure 1, there are two well-defined peaks for the spectra, and it appears that for the Colombo and Sante cultivars the peaks appeared at around 480 and 1000 nm and for the Milwa cultivar at around 540 and 950 nm. Figure 1 also shows that the absorption rate of the Milwa cultivar is higher than the other two cultivars, which can be due to differences in the number of different substances such as sugar or SSC. Based on the analysis (PCA) results presented in Figure 2, the first principal component (PC-1) describes 67% and the second principal component (PC-3) describes 27% of the variance of the samples tested. As a result, the first two principal components together represent 94% of the data. Due to the fact that the relationship between the properties of different samples during the tests, for various reasons such as technical problems of equipment, data collection, incorrect sampling, etc. in some samples is inappropriate or to correct. To be out. The values of R2 and RMSE for the calibration and validation sets of different regression models (PLS) with raw and processed data are presented in Figure 3, which is equal to 1.