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Abstract
Nowadays, wild edible bolete mushrooms are more and more attractive among consumers due to their natural health, nutrition, and delicious characteristics. Appropriate analytical techniques together with multivariate statistics analysis are required for the quality control and evaluation of these edible mushrooms. Ultraviolet-visible (UV-Vis) and infrared (IR) technologies have the advantages of time-saving, low-cost, and environmentally friendly, are now prominent among major analytical technologies for quality evaluation of bolete mushrooms. Chemometrics methods have been developed to solve classification and regression issues of bolete mushrooms in combination with spectrum. This paper reviewed the most recent applications of UV-Vis and IR technology coupled with chemometrics in wild edible bolete mushrooms, including the identification of species, origin, and storage duration, fraud detection, and antioxidant properties evaluation, and discussed the limitations and prospects of spectroscopy technologies in the researches of bolete mushrooms, excepting to provide a reference for further research and practical application of wild edible bolete mushrooms.
Disclosure statement
The authors declare that there are no conflicts of interest.
| Abbreviations | ||
| UV-Vis | = | ultraviolet-visible |
| NIR | = | near infrared |
| MIR | = | mid infrared |
| PCA | = | principal component analysis |
| HCA | = | hierarchical cluster analysis |
| 2DCOS | = | two-dimensional correlation spectroscopy |
| 2D-IR | = | two-dimensional infrared spectroscopy |
| Resnet | = | residual convolutional neural network |
| SNR | = | signal to noise ratio |
| SG | = | Savitzky-Golay |
| CWT | = | continuous wavelet transform |
| SNV | = | standard normal variate |
| MSC | = | multiplicative scatter correction |
| SPORT | = | sequential preprocessing through orthogonalization |
| t-SNE | = | t-distributed stochastic neighbor embedding |
| SO-PLS | = | sequential and orthogonalized partial-least squares |
| SO-CovSel | = | sequential and orthogonalized covariance selection |
| VIP | = | variable importance in projection |
| SIMCA | = | soft independent modeling of class analogy |
| UNEQ | = | unequal dispersed classes |
| PCA-MD | = | principal component analysis-mahalanobis distance |
| PLS-DM | = | partial least squares-density modeling |
| PLS-DA | = | partial least squares-discriminant analysis |
| LDA | = | linear discriminant analysis |
| k-NN | = | k-nearest neighbors |
| SVM | = | support vector machine |
| RF | = | random forest |
| PCR | = | principal component regression |
| PLSR | = | partial least squares regression |
| SVR | = | support vector regression |
| ANN | = | artificial neural network |
| sPLS-DA | = | sparse partial least squares-discriminant analysis |
| i2DCOS | = | integrative two dimensional correlation spectra |
| ELM | = | extreme learning machine |