Abstract
ABSTRACTIn this study, the individual and data fusion of Fourier transform infrared (FT-IR) spectroscopy and inductively coupled plasma atomic emission spectrometry (ICP–AES) were used for the discrimination of five species of Boletaceae mushrooms with the aid of support vector machine (SVM). First, the original FT-IR spectra of 230 samples with different species were preprocessed and optimized by second derivative (2D), Savitzky–Golay filter (15:1) and standardized normal variate. Second, the datasets of FT-IR spectra and ICP–AES were integrated, and the low-level data fusion strategy was used to classify different species mushrooms. Third, the latent variables of elements concentration and FT-IR spectra were extracted by partial least square discriminant analysis and two datasets were fused into a new matrix. Finally, the classification models were established by SVM. Compared with single spectroscopic technique, the mid-level data fusion strategy can provide better result. Especially, the accuracy of correct classification of samples in calibration and test sets were 100.00% and 98.68%, respectively. The results demonstrated that the mid-level data fusion of FT-IR and ICP–AES can provide higher synergic effect for the discrimination of different species Boletaceae mushrooms, which could be benefited for the further authentication and quality control of edible mushrooms.
Highlights
In China, mushrooms have been used for hundreds years as edible and medicinal resources, and the rate of mushroom consumption is relatively high.[1,2] Apart from characteristic taste, the fruiting bodies of mushrooms are considered as sources of organic nutrients including digestible proteins, carbohydrates, fibre, and certain vitamins, as well as minerals and antioxidants.[3,4,5,6] Yunnan Province is known as its abundant wild edible mushrooms resources
Fourier transform infrared (FT-IR) and inductively coupled plasma atomic emission spectrometry (ICP–AES) were used for discrimination of various species of Boletaceae mushrooms by individual and fused strategies
The pattern recognition method of Support vector machine (SVM) was applied to construct the discriminant models for identification of different species of edible mushrooms
Summary
In China, mushrooms have been used for hundreds years as edible and medicinal resources, and the rate of mushroom consumption is relatively high.[1,2] Apart from characteristic taste, the fruiting bodies of mushrooms are considered as sources of organic nutrients including digestible proteins, carbohydrates, fibre, and certain vitamins, as well as minerals and antioxidants.[3,4,5,6] Yunnan Province is known as its abundant wild edible mushrooms resources. There are more than 880 species of edible mushrooms that have been identified in Yunnan Province, which occupied the 40% of the total number of edible species in the world.[7] it is difficult to distinguish wild-grown edible mushrooms due to their various species and high similarities. Li et al.[8] established a standard DNA barcode for edible boletes; samples of common boletes in the markets regarded as four “species” by merchants were collected. Dentinger et al.[9] used DNA sequencing to identify mushrooms within a commercial packet of dried Chinese boletes purchased in London. They found three new species of boletes. Some edible mushrooms are easy to confuse with toxic ones;[10] so, an effective method for the discrimination of wild-grown edible mushrooms is quite necessary
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