To identify wild and cultivated Gastrodia elata quickly and accurately, this study is the first to apply three‐dimensional correlation spectroscopy (3DCOS) images combined with deep learning models ...to the identification of G. elata. The spectral data used for model building do not require any preprocessing, and the spectral data are converted into three‐dimensional spectral images for model building. For large sample studies, the time cost is minimized. In addition, a partial least squares discriminant analysis (PLS‐DA) model and a support vector machine (SVM) model are built for comparison with the deep learning model. The overall effect of the deep learning model is significantly better than that of the traditional chemometric models. The results show that the model achieves 100% accuracy in the training set, test set, and external validation set of the model built after 46 iterations without preprocessing the original spectral data. The sensitivity, specificity, and the effectiveness of the model are all 1. The results concluded that the deep learning model is more effective than the traditional chemometric model and has greater potential for application in the identification of wild and cultivated G. elata.
The taste of fresh mushrooms is always appealing. Phlebopus portentosus is the only porcini that can be cultivated artificially in the world, with a daily output of up to 2 tons and a large sales ...market. Fresh mushrooms are very susceptible to microbial attacks when stored at 0–2 °C for more than 5 days. Therefore, the freshness of P. portentosus must be evaluated during its refrigeration to ensure food safety. According to their freshness, the samples were divided into three categories, namely, category I (1–2 days, 0–48 h, recommended for consumption), category II (3–4 days, 48–96 h, recommended for consumption), and category III (5–6 days, 96–144 h, not recommended). In our study, a fast and reliable shelf life identification method was established through Fourier transform near-infrared (FT-NIR) spectroscopy combined with a machine learning method. Deep learning (DL) is a new focus in the field of food research, so we established a deep learning classification model, traditional support-vector machine (SVM), partial least-squares discriminant analysis (PLS-DA), and an extreme learning machine (ELM) model to identify the shelf life of P. portentosus. The results showed that FT-NIR two-dimensional correlation spectroscopy (2DCOS) combined with the deep learning model was more suitable for the identification of fresh mushroom shelf life and the model had the best robustness. In conclusion, FT-NIR combined with machine learning had the advantages of being nondestructive, fast, and highly accurate in identifying the shelf life of P. portentosus. This method may become a promising rapid analysis tool, which can quickly identify the shelf life of fresh edible mushrooms.
Poria originated from the dried sclerotium of Macrohyporia cocos is an edible traditional Chinese medicine with high economic value. Due to the significant difference in quality between wild and ...cultivated M. cocos, this study aimed to trace the origin of the fungus from the perspectives of wild and cultivation. In addition, there were quite limited studies about data fusion, a potential strategy, employed and discussed in the geographical traceability of M. cocos. Therefore, we traced the origin of M. cocos from the perspectives of wild and cultivation using multiple data fusion approaches. Supervised pattern recognition techniques, like partial least squares discriminant analysis (PLS-DA) and random forest, were employed in this study using. Five types of data fusion involving low-, mid-, and high-level data fusion strategies were performed. Two feature extraction approaches including the selecting variables by a random forest-based method—Boruta algorithm and producing principal components by the dimension reduction technique of principal component analysis—were considered in data fusion. The results indicate the following: (1) The difference between wild and cultivated samples did exist in terms of the content analysis of vital chemical components and fingerprint analysis. (2) Wild samples need data fusion to realize the origin traceability, and the accuracy of the validation set was 95.24%. (3) Boruta outperformed principal component analysis (PCA) in feature extraction. (4) The mid-level Boruta PLS-DA model took full advantage of information synergy and showed the best performance. This study proved that both geographical traceability and optimal identification methods of cultivated and wild samples were different, and data fusion was a potential technique in the geographical identification.
Macrofungus is defined as the fungus that grows an observable sporocarp. The sporocarps of many species are commonly called mushrooms and consumed by people all around the world as food and/or ...medicine. Most macrofungi belong to the divisions Basidiomycetes and Ascomycetes, which are estimated to contain more than 80,000 species in total. We report the draft genome assemblies of macrofungi (83 Basidiomycetes species and 7 Ascomycetes species) based on Illumina sequencing. The genome sizes of these species ranged from 27.4 Mb (Hygrophorus russula) to 202.2 MB (Chroogomphus rutilus). The numbers of protein-coding genes were predicted in the range of 9,511 (Hygrophorus russula) to 52,289 (Craterellus lutescens). This study provides the largest genomic dataset for macrofungi species. This resource will facilitate the artificial cultivation of edible mushrooms and the discovery of novel drug candidates.
is a traditional medicinal plant, and processing has significantly impacts its quality.
Therefore, untargeted gas chromatography-mass spectrometry (GC-MS) and Fourier transform-near-infrared ...spectroscopy (FT-NIR) were used to analyze the 14 processing methods commonly used in the Chinese market.It is dedicated to analyzing the causes of major volatile metabolite changes and identifying signature volatile components for each processing method.
The untargeted GC-MS technique identified a total of 333 metabolites. The relative content accounted for sugars (43%), acids (20%), amino acids (18%), nucleotides (6%), and esters (3%). The multiple steaming and roasting samples contained more sugars, nucleotides, esters and flavonoids but fewer amino acids. The sugars are predominantly monosaccharides or small molecular sugars, mainly due to polysaccharides depolymerization. The heat treatment reduces the amino acid content significantly, and the multiple steaming and roasting methods are not conducive to accumulating amino acids. The multiple steaming and roasting samples showed significant differences, as seen from principal component analysis (PCA) and hierarchical cluster analysis (HCA) based on GC-MS and FT-NIR. The partial least squares discriminant analysis (PLS-DA) based on FT-NIR can achieve 96.43% identification rate for the processed samples.
This study can provide some references and options for consumers, producers, and researchers.
Edible wild mushrooms are one of the popular ingredients due to their high quality and unique flavor and nutrients. To gain insight into the effect of drying temperature on its composition, 86 ...Boletus bainiugan were divided into 5 groups and dried at different temperatures. Headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) was used for the identification of volatile organic compounds (VOCs) of Boletus bainiugan. The 21 differential VOCs that distinguish different drying temperatures of Boletus bainiugan were identified. 65 °C retained more VOCs. There were differences in their types and content at different temperatures, proteins, polysaccharides, crude fibers, and fats. Fourier transform near-infrared (FT-NIR) spectroscopy, Fourier transform infrared (FTIR) spectroscopy, and two-dimensional correlation spectroscopy (2DCOS) images were successfully characterized for differences in the chemical composition of Boletus bainiugan. Partial least squares discriminant analysis (PLS-DA) verified the variability in the chemical composition of Boletus bainiugan with the coefficient of determination (R2) = 0.95 and predictive performance (Q2) = 0.75 with 92.31% accuracy. Next, infrared spectroscopy provides a fast and efficient assessment of the content of Boletus bainiugan nutrients (proteins, polysaccharides, crude fibers, and fats).
Display omitted
•Effect of drying temperature on VOCs and nutrient content of Boletus bainiugan.•FT-NIR, FTIR, and 2DCOS to characterize chemical compositional variability.•IR spectroscopy combined with PLS-DA to verify chemical compositional variability.•Rapid assessment of the nutritional composition of boletes.
To prevent fraud in Boletus bainiugan commodities, this study provides the market with two fast and stable identification models for accurate identification of Boletus bainiugan origins, storage ...periods and species. Partial least squares discrimination analysis (PLS-DA), Support vector machine (SVM), Residual convolutional neural network (ResNet) and Data-driven soft independent modeling of class analogy (DD-SIMCA) models were built by combining with Fourier transform near-infrared spectroscopy (FT-NIR). The results show that the ResNet model is significant in solving the Boletus bainiugan origin identification problems. The ResNet model had the best performance and highest accuracy compared to the PLS-DA and SVM models. The DD-SIMCA model was the preferred method for the one-class classification problem, achieving an accuracy of over 96% for the Boletus bainiugan storage period and species identification. Non-target class classification accuracy reached 100%. In summary, FT-NIR combined with ResNet and DD-SIMCA models were able to solve the related identification problems of Boletus bainiugan with more satisfactory results.
The heavy metal contents (Co, Cu, Fe, Mn, Ni, and Zn) of eight species of wild edible mushrooms from China were determined. The analyses were performed using inductively coupled plasma atomic ...emission spectrophotometry after microwave digestion. The contents of Co, Cu, Fe, Mn, Ni, and Zn in caps of mushroom samples were 0.7-7.2, 16.2-70.4, 371-1315, 12.5-29.8, 7.1-58.5, and 77.8-187.4 mg kg
−1
dry matter (dm), respectively, while considerable differences were found to be 1.8-25.9, 9.8-36.3, 288-6762, 13.3-103.9, 5.9-78.7, and 38.7-118 mg kg
−1
dm for stipes. The results indicated that higher levels of Co, Fe, and Ni were found in the mushrooms samples analyzed. Zinc and manganese levels were similar to previous reports, whereas Cu was lower than literature values. Correlation analysis suggested that significant correlations were found between the minerals determined and the greatest amount of contamination is associated with Co, Mn, Ni, and Fe. The results of this study indicate that heavy metal contents in mushroom species are mainly related to the mineral resources of sampling sites.
Secoiridoids could be used as a potential new drug for the treatment of hepatic disease. The content of secoiridoids of
varied in different geographical origins and parts. In this study, a total of ...783 samples collected from different parts of
in Yunnan, Sichuan, and Guizhou Provinces. The content of secoiridoids including gentiopicroside, swertiamarin, and sweroside were determined by using HPLC and analyzed by one-way analysis of variance. Two selected variables including direct selected and variable importance in projection combined with partial least squares regression have been used to establish a method for the determination of secoiridoids using FT-IR spectroscopy. In addition, different pretreatments including multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative and second derivative (SD), and orthogonal signal correction (OSC) were compared. The results indicated that the sample (root, stem, and leaf) with total secoiridoids, gentiopicroside, swertiamarin, and sweroside from west Yunnan had higher content than samples from the other regions. The sample from Baoshan had more total secoiridoids than other samples for the whole medicinal plant. The best performance using FT-IR for the total secoiridoid was with the direct selected variable method involving pretreatment of MSC+OSC+SD in the root and stem, while in leaf, of the best method involved using original data with MSC+OSC+SD. This method could be used to determine the bioactive compounds quickly for herbal medicines.
Paris Polyphylla
Smith var.
yunnanensis
(Franch.) Hand.-Mazz (“Dian Chonglou” in Chinese) is a famous herbal medicine in China, which is usually well known for activities of anti-cancer, hemolysis, ...and cytotoxicity. In this study, Fourier transform infrared (FT-IR) spectroscopy coupled with principal component analysis (PCA) and partial least-squares regression (PLSR) was applied to discriminate samples of
P. polyphylla
var.
yunnanensis
harvested in different years and determine the content of polyphyllin I, II, VI, and VII in
P. polyphylla
var.
yunnanensis
. Meanwhile, ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to study the dynamic changes of
P. polyphylla
var.
yunnanensis
harvested in different years (4, 5, 7, 8, 9, 12, and 13 years old). According to the UPLC-MS/MS result, the optimum harvest time of
P. polyphylla
var.
yunnanensis
is 8 years, due to the highest yield of four active components. By the PCA model,
P. polyphylla
var.
yunnanensis
could be exactly discriminated, except that two 8-year-old samples were misclassified as 9-year-old samples. For the prediction of polyphyllin I, II, VI, and VII, the quantitative results are satisfactory, with a high value for the determination coefficient (
R
2
) and low values for the root-mean-square error of estimation (RMSEE), root-mean-square error of cross-validation (RMSECV), and root-mean-square error of prediction (RMSEP). In conclusion, FT-IR combined with chemometrics is a promising method to accurately discriminate samples of
P. polyphylla
var.
yunnanensis
harvested in different years and determine the content of polyphyllin I, II, VI, and VII in
P. polyphylla
var.
yunnanensis
.
PDF
