A review on the identification of transgenic oilseeds and oils

Pan, Po-Yuan; Xing, Ya Ge; Zhang, Dingwen; Wang, Ji; Wu, Dan; Wang, Xiyan

doi:10.1111/1750-3841.16705

Cited by 5 publications

(1 citation statement)

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“…Lévy飞行策略模拟自然界中动物的随机觅食行走, 假设种群中的蜉蝣均存在一定的概率不直接沿着最优路径移动, 而是根据Lévy飞行策略在最优路径附近进行随机游走, 从而达到跳出当前局部最优位置, 扩大全局搜索能力的目的. 同时为了避免在迭代后期, 蜉蝣一直在全局最优位置周围游走, 而不收敛于全局最优位置, 为Lévy飞行增加步长调整参数 [21] :…”

Section: 蜉蝣优化算法unclassified

Application of terahertz spectroscopy in identification of transgenic rapeseed oils: A support vector machine model based on modified mayfly optimization algorithm

Chen,

2024

Acta Phys. Sin.

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To achieve rapid and accurate identification of genetically modified (GM) and non-GM rapeseed oils, a support vector machine (SVM) model based on an improved mayfly optimization algorithm, coupled with the terahertz time-domain spectroscopy, is proposed. Two types of GM rapeseed oils and two types of non-GM rapeseed oils were selected as research subjects. Their spectral information was acquired using the terahertz time-domain spectroscopy. Observations show that GM rapeseed oils exhibit stronger terahertz absorption characteristics than non-GM rapeseed oils. However, their absorption spectra are highly similar, making direct differentiation challenging through visual inspection alone. Therefore, SVM was used for spectral recognition. Considering that the classification performance of SVM was significantly affected by its parameters, the mayfly optimization algorithm was combined to optimize these parameters. Furthermore, adaptive inertia weight and Lévy flight strategies were introduced to enhance the global search capability and robustness of the mayfly optimization algorithm, addressing the issue of easily becoming trapped in local optima during the optimization process. Moreover, principal component analysis was applied to reduce the dimensionality of the absorbance data in the 0.3-1.8 THz range, aiming to extract critical features, thus enhancing modeling efficiency and reducing redundancy in spectral data. Experimental results demonstrate that the improved mayfly optimization algorithm effectively identifies the optimal parameter combination for SVM, thereby enhancing the overall performance of the identification model. The proposed SVM model, utilizing the improved mayfly optimization algorithm, achieves a recognition accuracy of 100% for the four types of rapeseed oils, surpassing the 98.15% accuracy achieved by the SVM model with the original mayfly optimization algorithm. Thus, this study presents a rapid and effective new approach for discriminating GM rapeseed oils and offers a valuable reference for identifying other genetically modified substances.

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Section: 蜉蝣优化算法unclassified