Application of Generalized Regression Neural Network With Ultraviolet-
Visible Spectrometry Methods for Detection of Extra Virgin Olive Oil
YUAN Yuan1*, ZHANG Jin2
1. Department of Engineering Management, Sichuan College of Architectural Technology, Deyang 618000, China
2. College of Information Science and Engineering, Shanxi Agricultural University, Taigu 030801, China
Abstract：With the continuous prosperity of China's economy, people have put forward higher requirements for material living standards. Food that prevents diseases and improves physical function has become a “hot spot” in the consumer market. Oil can provide energy for the human body. Edible oil is one of the major ways for human beings to get oil, and high-quality vegetable oil contains substances that are more beneficial to human health, such as monounsaturated fatty acids, polyphenols, squalene, vitamin E, and other nutrients. Because of the physical cold pressing process, extra virgin olive oil keeps almost all the nutrients in its olive fruit, and the oleic acid content is as high as 70%. Therefore, although an “imported product”, extra virgin olive oil has been a “favorite” in the vegetable oil market since it entered the Chinese market, and its price is also significantly higher than ordinary vegetable oil on the market. Driven by interests, the phenomenon of making and selling fake super virgin olive oil has been repeatedly banned, and the means of making and selling fake olive oil have been constantly updated and iterated, resulting in the repeated prohibition of fake and inferior products in the domestic olive oil market. Adulterated oil products will not only harm the lives and property of consumers but also affect the production and sales of legitimate operators, disrupt the sales market, destroy the market order, and affect the public's recognition of super virgin olive oil. The adulteration of vegetable oil is one of the urgent problems facing food safety at present. To realize the rapid, accurate, and low-cost identification and detection of the adulteration of vegetable oil and extra virgin olive oil, a method for qualitative and quantitative analysis of vegetable oil based on a generalized regression neural network and UV-Vis spectrum is proposed. Because the generalized regression neural network performs well in learning speed and nonlinear mapping ability, and the diffusion factor is the only optimization parameter of the network, it does not need backpropagation and repeated iteration. Compared with other detection technologies, UV-Vis technology has overwhelming advantages in terms of the detection cycle, stability, and low maintenance cost. This method has achieved 100% discrimination in the qualitative identification of vegetable oil and achieved the results that the determination coefficient (R2) is better than 0.988 75 and the root mean square error (RMSE) is better than 0.038 33 in the quantitative detection of extra virgin olive oil adulteration. The results showed that the model showed excellent predictive ability in identifying vegetable oils and in the quantitative detection of adulteration in extra virgin olive oil. Therefore, the method based on a generalized regression neural network algorithm and UV-Vis spectrum has important potential for application in vegetable oil's qualitative and quantitative detection.
Key words：Qualitative and quantitative; Vegetable oil; Extra virgin olive oil; Ultraviolet-Visible Spectroscopy; Generalized regression neural network
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