An additional data fusion strategy for the discrimination of porcini mushrooms from different species and origins in combination with four mathematical algorithms
文献类型： 外文期刊
作者： Qi, LuMing 1 ; Li, JieQing 3 ; Liu, HongGao 3 ; Li, Tao 4 ; Wang, YuanZhong 2 ;
作者机构： 1.Chengdu Univ Tradit Chinese Med, State Key Lab Breeding Base Systemat Res Dev & Ut, Chengdu 611137, Sichuan, Peoples R China
2.Yunnan Acad Agr Sci, Inst Agroprod Proc Sci & Technol, Kunming 650221, Yunnan, Peoples R China
3.Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming 650201, Yunnan, Peoples R China
4.Yuxi Normal Univ, Coll Resources & Environm, Yuxi 653100, Peoples R China
期刊名称：FOOD & FUNCTION （ 影响因子：5.396； 五年影响因子：5.534 ）
ISSN： 2042-6496
年卷期： 2018 年 9 卷 11 期
页码：
收录情况： SCI
摘要： Porcini are a source of popular food products with many beneficial functions and the internal quality of these mushrooms is largely determined by many factors. An additional data fusion strategy based on low-level data fusion for two portions (cap and stipe) and mid-level data fusion for two spectroscopic techniques (UV and FTIR) was developed to discriminate porcini mushrooms from different species and origins. Based on a finally obtained data array, four mathematical algorithms including PLS-DA, k-NN, SVM and RF were comparatively applied to build classification models. Each calibrated model was developed after selecting the best debug parameters and then a test set was used to validate the established model. The results showed that the SVM algorithm based on a GA procedure searching for parameters had the best performance for discriminating different porcini samples with the highest cross-validation, specificity, sensitivity and accuracy of 100.00%. Our study proved the feasibility of two spectroscopic techniques for the discrimination of porcini mushrooms originated from different species and origins. This proposed method can be used as an alternative strategy for the quality detection of porcini mushrooms.
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