可见/近红外光谱的葡萄籽油掺伪检测系统

doi:10.3964/j.issn.1000-0593(2020)01-0202-07

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Abstract Various kinds of adulterated grape seed oil and concealed adulterated means cause a severe problem in food safety detection. In order to regulate the edible oil market, it is especially important to provide a convenient and reliable method for identifying the quality of grape seed oil. However, traditional methods for chromatography and mass spectrometry are time consuming, reagent intensive, highly specialized, etc.; and the near infrared spectrometer that realizes non-destructive analysis is expensive and has high operating environment requirements. Thus, a visible/near infrared spectrometer with low cost and high accuracy was designed to discriminate grape seed oil adulteration. Firstly, a visible/near infrared spectrometer hardware platform based on USB6500-Pro detector was built, and a simple human-computer interaction interface based on Qt was designed to realize the collection and processing of spectral data and the display of grape seed oil adulteration discrimination results. Secondly, for the spectral noise brought by hardware and detection environment, wavelet transform was used to filter out noise and reduce spectral distortion. Finally, considering that the existing quality discrimination models based on machine learning often rely on the known oil training sample set to predict the different adulterated categories; and driven by interest adulteration means will emerge in endlessly which will result in the emerging of new adulteration categories not in the original training set, the existing quality identification methods are difficult to give accurate results. Therefore, a discrimination method for known and new adulterated oil spectra was designed in the detection system. This method was realized by two steps: (1) classification: the extreme learning machine (ELM) classifier model was established by using the training set in the modeling database to realize the preliminary judgment of the preliminary adulteration category; (2) correction: the automatic clustering algorithm was then used to further correct the prediction result. If a clustering center is generated with the correction data set, it is proved that the prediction result is correct and belongs to the known adulteration category in the modeling database; if two cluster centers are generated, the prediction result is incorrect and the sample is a new adulteration category which does not appear in the modeling database. The result of the accurate adulterated category was eventually obtained. In order to test the performance of the system, five classes of oil, including pure grape seed oil, and grape seed oil blended with different proportions of soybean oil, corn oil, sunflower oil and blend oil were analyzed by the visible/near infrared hardware platform and their spectroscopy data were collected. It contains 30 sets of data for each class of oil, totals 150 sets. Before inputting the visible/near infrared spectroscopy data into the detection system, they were firstly de-noised by wavelet threshold method and pre-processed by multiple scattering correction. Assuming that the first four classes were known adulteration class in the modeling database and the fifth class was new adulteration class, samples from each of the four known adulteration classes were divided into 20 training sets and 10 test sets by using K-S algorithm. Then, ELM classification model was established by using 80 training sets, and 40 test sets were input into ELM for preliminary discrimination. The discrimination results were further analyzed and corrected by clustering. There was one clustering center, which meant that the ELM model discriminated accurately and could recognize 100% of the known classes. However, when 30 samples from the new adulterated class were put into the ELM model, all of them were discriminated as pure grape seed oil. The discrimination results were further clustered and corrected. There were two clustering centers, which showed that the model was misjudged and the fifth class was qualitatively determined as a new adulterated class. The experimental results showed that the designed visible/near infrared spectroscopy detection system was simple and fast, and can identify not only the known adulteration categories but also the new adulteration categories.

Key words：Extreme learning machine; Visible/near infrared spectra; Clustering; Grape seed oil adulteration

Received: 2018-11-08 Accepted: 2019-03-09

ZTFLH:

O657.3

Corresponding Authors: CHAI Qin-qin E-mail: qq.chai@fzu.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2020)01-0202-07 OR https://www.gpxygpfx.com/EN/Y2020/V40/I01/202