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Review Articles

Application of UV-Vis and Infrared Spectroscopy on Wild Edible Bolete Mushrooms Discrimination and Evaluation: A Review

, , , &
Pages 852-868 | Published online: 10 Oct 2021
 

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

Additional information

Funding

This work was supposed by the National Natural Science Foundation of China (Grant number: 32160735, 32060570), the Joint Special Project of Agricultural Fundamental Research of Yunnan Province (Grant number: 2018FG001-033), and Special Program for the Major Science and Technology Projects of Yunnan Province (202002AA100007).

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