FOOD SCIENCE

• Component Analysis • Previous Articles     Next Articles

Infrared Spectroscopy Combined with Multivariate Statistical Analysis to Quickly Identify Different Species of Bolete Mushrooms

YANG Tianwei1, 2, ZHANG Ji2, SHI Yundong3, LI Tao3, WANG Yuanzhong2,*, LIU Honggao1,*   

  1. 1. College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China;
    2. Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China;
    3. College of Resources and Environment, Yuxi Normal University, Yuxi 653100, China
  • Online:2015-12-25 Published:2015-12-24
  • Contact: WANG Yuanzhong, LIU Honggao

RichHTML

2

PDF (PC)

314

Abstract:

Fourier transform infrared spectroscopy combined with multivariate statistical analysis was used to establish a
rapid method for the identification of different species of edible bolete mushrooms. The infrared spectral characteristics of 93
bolete samples of 10 different species were analyzed. The original infrared spectra were pretreated by multiplicative signal
correction (MSC), standard normal variate (SNV), second derivative, Norris smooth, orthogonal signal correction (OSC) and
wavelet compression. The optimized spectral data were used to establish a mahalanobis distance classification model and a
partial least squares discriminant analysis (PLS-DA) model. The results showed that the characteristic absorption peaks of
protein, polysaccharide and amino acid appeared at wavenubmers around 3 325, 2 934, 2 927, 1 637, 1 547, 1 402, 1 375,
1 259, 1 453, 1 081, and 1 029 cm−1. The cumulative contribution rates were 95.58% and 95.54% in the PLS-DA model
based on MSC + SD + ND (15:5) and SNV + SD + ND (15:5) pretreatment, respectively. The Mahalanobis distance
classification model was established base on the two pretreatment methods and the prediction accuracies of validation
set were 90% and 95% respectively. Bolete species could not be well distinguished by the PLS-DA model, when the data
were pretreated by the MSC + SD + ND (15:5) and SNV + SD + ND (15:5). PLS-DA analysis of the original spectra after
optimization with orthogonal signal correction wavelet compression (OSCW) could distinguish different species of boletes.
The Mahalanobis distance classification model could reflect the classification of the samples and compute the greatest
similarity with the tested species, which can provide a reliable basis for the classification of edible mushrooms and for the
identification of unknown species. OSCW pretreatment combined with PLS-DA analysis can effectively identify different
species of boletes, providing an auxiliary method for the identification of wild edible mushrooms.

Key words: infrared spectroscopy, boletes, discrimination, Mahalanobis distance, partial least squares discriminant analysis (PLS-DA)

CLC Number: