ResNet and MaxEnt modeling for quality assessment of Wolfiporia cocos based on FT-NIR fingerprints

Zhang, YanYing; Shen, Tao; Zuo, Zhiyan; Wang, Yuanzhong

doi:10.3389/fpls.2022.996069

Cited by 7 publications

(4 citation statements)

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“…Their findings showed that future climate change will increase the risk posed to the survival of this species, whose habitat will be reduced further and degraded (Chen et al, 2022). Using the MaxEnt model, Zhang et al (2022) were able to identify and locate suitable habitats of Wolfiporia cocos distributed mainly in China's Yunnan Province. Accordingly, MaxEnt has become one of the most widely relied-upon tools for studying the distribution of species.…”

Section: Introductionmentioning

confidence: 99%

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The ecological suitability area of Cirsium lineare (Thunb.) Sch.‐Bip. under future climate change in China based on MaxEnt modeling

Fang,

Zhang,

et al. 2024

Ecology and Evolution

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Many kinds of medicinal ingredients occur in Cirsium lineare that have good clinical efficacy, conferring on this species its high medicinal development value. However, with a rapidly changing global climate, it is increasingly imperative to study the factors affecting the habitat distribution and survival of species. We predicted the current and future distribution areas of suitable habitats for C. lineare, analyzed the importance of environmental variables in influencing habitat shifts, and described the alterations to suitable habitats of C. lineare in different periods (modern, 2050s, and 2070s) and scenarios (RCP2.6, RCP4.5, and RCP8.5). The results show that, under the current climate, the total suitable area of C. lineare is about 2,220,900 km2, of which the highly suitable portion amounts to ca. 292,600 km2. The minimum temperature of the coldest month, annual precipitation, and mean daily temperature range are the chief environmental variables affecting the distribution of habitat for C. lineare. In the same period, with rising greenhouse gas emission concentrations, the total suitable area will increase. In general, under future climate change, the suitable habitat for C. lineare will gradually migrate to the west and north, and its total suitable area will also expand. The results of this experiment can be used for the conservation and management of the wild resources of C. lineare. We can choose suitable growth areas to protect the medicinal resources of C. lineare through in situ conservation and artificial breeding.

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Section: Introductionmentioning

confidence: 99%

“…Using the MaxEnt model, Zhang et al. ( 2022 ) were able to identify and locate suitable habitats of Wolfiporia cocos distributed mainly in China's Yunnan Province. Accordingly, MaxEnt has become one of the most widely relied‐upon tools for studying the distribution of species.…”

Section: Introductionmentioning

confidence: 99%

The ecological suitability area of Cirsium lineare (Thunb.) Sch.‐Bip. under future climate change in China based on MaxEnt modeling

Fang,

Zhang,

et al. 2024

Ecology and Evolution

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“…By studying diferent methods for converting onedimensional (1D) signals into 2D graphics, the best method for structural health monitoring was found. Song et al [37] reduced the computational complexity of the model by segmenting the input image and replacing the convolution module in Inception-ResNet with depthwise separable convolution. Das et al [38] introduced a multimodel-integrated network based on Inception-ResNet-v2, which achieved high accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Bearing Fault Diagnosis Method Based on Multibranch SKNet and Enhanced Inception-ResNet-v2

Hu,

Liu,

et al. 2024

Shock and Vibration

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Deep learning has recently received extensive attention in the field of rolling-bearing fault diagnosis owing to its powerful feature expression capability. With the help of deep learning, we can fully extract the deep features hidden in the data, significantly improving the accuracy and efficiency of fault diagnosis. Despite this progress, deep learning still faces two outstanding problems. (1) Each layer uses the same convolution kernel to extract features, making it difficult to adaptively select convolution kernels based on the features of the input image, which limits the network’s adaptability to different input features and leads to weak feature extraction. (2) Large number of parameters and long training time. To solve the above problems, this paper proposes an integrated deep neural network that combines an improved selective kernel network (SKNet) with an enhanced Inception-ResNet-v2, named SIR-CNN. First, based on the SKNet, a new three-branch SKNet was designed. Second, the new SKNet is embedded into a depthwise separable convolution network such that the model can adaptively select convolution kernels of different sizes during training. Furthermore, the convolution structure in the Inception-ResNet-v2 network was replaced by the improved depthwise separable convolution network to achieve effective feature extraction. Finally, the time-frequency maps of the raw vibration signals are obtained through short-time Fourier transform (STFT) and then sent to the proposed SIR-CNN network for experiments. The experimental results show that the proposed SIR-CNN achieves superior performance compared to other methods.

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“…High performance liquid chromatography (HPLC), ultraviolet spectroscopy (UV) and nuclear magnetic resonance spectroscopy (NMR) are widely used [ 5 , 6 , 7 ], but they are tedious and destructive. However, infrared spectroscopy (IR) has gained popularity in recent years due to its non-destructive, convenient, and fast analysis, with high sensitivity [ 8 ]. When combined with chemometrics, it becomes an effective method for the rapid identification of herbal species, their origin and other characteristics.…”

Section: Introductionmentioning

confidence: 99%

Identification of Gentian-Related Species Based on Two-Dimensional Correlation Spectroscopy (2D-COS) Combined with Residual Neural Network (ResNet)

Yang

Cheng

et al. 2023

Molecules

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Gentian is a traditional Chinese herb with heat-clearing, damp-drying, inflammation-alleviating and digestion-promoting effects, which is widely used in clinical practice. However, there are many species of gentian. According to the pharmacopoeia, Gentiana manshurica Kitag, Gentiana scabra Bge, Gentiana triflora Pall and Gentianarigescens Franch are included. Therefore, accurately identifying the species of gentian is important in clinical use. In recent years, with the advantages of low cost, convenience, fast analysis and high sensitivity, infrared spectroscopy (IR) has been extensively used in herbal identification. Unlike one-dimensional spectroscopy, a two-dimensional correlation spectrum (2D-COS) can improve the resolution of the spectrum and better highlight the details that are difficult to detect. In addition, the residual neural network (ResNet) is an important breakthrough in convolutional neural networks (CNNs) for significant advantages related to image recognition. Herein, we propose a new method for identifying gentian-related species using 2D-COS combined with ResNet. A total of 173 gentian samples from seven different species are collected in this study. In order to eliminate a large amount of redundant information and improve the efficiency of machine learning, the extracted feature band method was used to optimize the model. Four feature bands were selected from the infrared spectrum, namely 3500–3000 cm−1, 3000–2750 cm−1, 1750–1100 cm−1 and 1100–400 cm−1, respectively. The one-dimensional spectral data were converted into synchronous 2D-COS images, asynchronous 2D-COS images, and integrative 2D-COS images using Matlab (R2022a). The identification strategy for these three 2D-COS images was based on ResNet, which analyzes 2D-COS images based on single feature bands and full bands as well as fused feature bands. According to the results, (1) compared with the other two 2D-COS images, synchronous 2D-COS images are more suitable for the ResNet model, and (2) after extracting a single feature band 1750–1100 cm−1 to optimize ResNet, the model has the best convergence performance, the accuracy of training, test and external validation is 1 and the loss value is only 0.155. In summary, 2D-COS combined with ResNet is an effective and accurate method to identify gentian-related species.

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ResNet and MaxEnt modeling for quality assessment of Wolfiporia cocos based on FT-NIR fingerprints

Cited by 7 publications

References 50 publications

The ecological suitability area of Cirsium lineare (Thunb.) Sch.‐Bip. under future climate change in China based on MaxEnt modeling

The ecological suitability area of Cirsium lineare (Thunb.) Sch.‐Bip. under future climate change in China based on MaxEnt modeling

An Integrated Bearing Fault Diagnosis Method Based on Multibranch SKNet and Enhanced Inception-ResNet-v2

Identification of Gentian-Related Species Based on Two-Dimensional Correlation Spectroscopy (2D-COS) Combined with Residual Neural Network (ResNet)

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