Feature Fusion of ICP-AES, UV-Vis and FT-MIR for Origin Traceability of Boletus edulis Mushrooms in Combination with Chemometrics

Abstract

Origin traceability is an important step to control the nutritional and pharmacological quality of food products. Boletus edulis mushroom is a well-known food resource in the world. Its nutritional and medicinal properties are drastically varied depending on geographical origins. In this study, three sensor systems (inductively coupled plasma atomic emission spectrophotometer (ICP-AES), ultraviolet-visible (UV-Vis) and Fourier transform mid-infrared spectroscopy (FT-MIR)) were applied for the origin traceability of 184 mushroom samples (caps and stipes) in combination with chemometrics. The difference between cap and stipe was clearly illustrated based on a single sensor technique, respectively. Feature variables from three instruments were used for origin traceability. Two supervised classification methods, partial least square discriminant analysis (FLS-DA) and grid search support vector machine (GS-SVM), were applied to develop mathematical models. Two steps (internal cross-validation and external prediction for unknown samples) were used to evaluate the performance of a classification model. The result is satisfactory with high accuracies ranging from 90.625% to 100%. These models also have an excellent generalization ability with the optimal parameters. Based on the combination of three sensory systems, our study provides a multi-sensory and comprehensive origin traceability of B. edulis mushrooms.

Differentiation and comparison of Wolfiporia cocos raw materials based on multi-spectral information fusion and chemometric methods

“…The MCC is similar to the Person correlation coefficient that 1 suggests perfect classification, 0 indicates random classification as well as −1 means the worst possible prediction 65 . For the lowest RMSECV, it could guarantee the LVs collected as much as possible and they are not overfitted 66 . What’s more, HCA was applied to evaluate the degree of similarity among different classes of samples using the average fusion spectral information of each class.…”

Correlation between Enzymatic and Non-Enzymatic Antioxidants in Several Edible Mushrooms Species

“…Chemometrics was applied in order to evaluate the traceability of Boletaceae mushrooms samples in combination with UV-visible and Fourier transform infrared (FTIR) spectroscopy [79], respectively in combination with inductively coupled plasma atomic emission spectrophotometer (ICP-AES), ultraviolet-visible (UV-Vis) and Fourier transform mid-infrared spectroscopy (FT-MIR) [80]. Through a chemometric approach were investigated the isotopic markers of A. bisporus origin [81] and the geotraceability of mushrooms [82].…”

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

“…This strategy of mid‐level data fusion has been used to trace specie and geographical origin of Porcini mushrooms (Yao, Li, Liu, Li, & Wang, ) and also to classify organic and nonorganic orange juices (Cuevas, Pereira‐Caro, Moreno‐Rojas, Muñoz‐Redondo, & Ruiz‐Moreno, ), and the manufacturer of beer with same brand and product could be discriminated (Vera et al, ). According to literatures, the several common ways of feature selection are: as follows (1) latent variables (LVs) which selected according to R 2 Y (cum) and Q 2 (cum) based on partial least squares‐discriminant analysis (PLS‐DA) model (Yao et al, ), (2) principal components (PCs) obtained from principal component analysis (PCA) (Vera et al, ), (3) variable importance in the projections (VIPs) picked by the values of VIP >1 (Qi, Liu, Li, Li, & &. Wang, 2).…”

“…According to literatures, the several common ways of feature selection are: as follows (1) latent variables (LVs) which selected according to R 2 Y (cum) and Q 2 (cum) based on partial least squares‐discriminant analysis (PLS‐DA) model (Yao et al, ), (2) principal components (PCs) obtained from principal component analysis (PCA) (Vera et al, ), (3) variable importance in the projections (VIPs) picked by the values of VIP >1 (Qi, Liu, Li, Li, & &. Wang, 2). R 2 Y (cum) and Q 2 (cum) were used to assess the ability of the model to fit data of training set and to predict new sample (test set).…”