Species discrimination and total polyphenol prediction of porcini mushrooms by fourier transform mid-infrared (FT-MIR) spectrometry combined with multivariate statistical analysis

Food Science & Nutrition
Xiu-Ping LiYuan-Zhong Wang

Abstract

The plateau specialty agricultural products, wild porcini mushrooms, have great value both as a superb cuisine and as a potential medication. Due to quality different between species added with the fraud behavior in sales process, make poor quality or poisonous sample inflow into the market, which pose a health risk for consumers, but also disrupted the mushroom market. Traditional analysis way is time-consuming and laborious. Therefore, the aim of this study is to develop a way using fourier transform mid-infrared (FT-MIR) spectrometry and data fusion strategies for the fast and accurate species discrimination and predict amount of total polyphenol in four porcini mushrooms. The t-distributed stochastic neighbor embedding based on mid-level data fusion showed two species of Boletus edulis and B. umbriniporus have been identified. The order of correct rate of PLS-DA models was mid-level data fusionq (100%) > mid-level data fusione (97.06%) = mid-level data fusionv (97.06%) = stipes (97.06%) > low-level data fusion (94.12%) > caps (91.18%). The order of correct rate of grid-search support vector machine models was low-level data fusion (100%) > caps (94.12%) > stipes (91.18%), and the order of particle swarm optimization support...Continue Reading

References

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Methods Mentioned

BETA
infrared spectroscopy
PCA

Software Mentioned

SVM
MATLAB
PSO
Omnic
GS
SIMCA

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