Verified the rapid evaluation of the edible safety of wild porcini mushrooms, using deep learning and <scp>PLS‐DA</scp>

Wang, Li; Liu, Honggao; Li, Tao; Li, Jieqing; Wang, Yuanzhong

doi:10.1002/jsfa.11488

Cited by 23 publications

(7 citation statements)

References 36 publications

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“…It is a supervised method to artificially classify and label the studied samples before model building. The building of model is based on an independent variable X matrix (spectral data) and a dependent variable Y matrix (classification labels) (Wang et al, 2022). SVM is a nonlinear supervised method that has excellent performance in solving classification problems (Dankowska & Kowalewski, 2019;Teye et al, 2014).…”

Section: Pls-da and Svmmentioning

confidence: 99%

“…The accuracy and stability of the model can be improved by selecting the appropriate preprocessing method to minimize some adverse effects. This is a necessary step for traditional chemometrics combined with spectroscopic data to build a model (Chen et al, 2023;Zhang et al, 2021;Wang et al, 2022). There are many problems in the process of resolving one-dimensional spectra that are difficult to solve, such as overlapping of spectral information for large sample sizes and difficulty in obtaining detailed information.…”

Section: Introductionmentioning

confidence: 99%

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FT-MIR combined with 3DCOS-ResNet model for rapid identification of wild and cultivated Gastrodia elata

Liu

et al. 2023

Preprint

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Wild Gastrodia elata resources are in short supply and the market is dominated by cultivated Gastrodia elata resources and the medicinal value of both is very different. It is significant to find a highly accurate and stable technique to identify wild and cultivated Gastrodia elata. It could prevent market fraud and protect the rights of consumers. In this context, this study is the first to combine three-dimensional correlated spectral (3DCOS) images with deep learning to identify wild and cultivated Gastrodia elata. Also, partial least squares discrimination analysis (PLS-DA) and support vector machine (SVM) models are compared with this model. The PLS-DA and SVM models are built based on Fourier transform mid-infrared (FT-MIR) spectral data after nine different preprocessing. The PLS-DA model with second-order derivatives (2D) gives the best results when comparing the effects of the models with different preprocessing. the SVM model with parameters c, g in a reasonable range also gives satisfactory model results. The advantage of the deep learning model over them is that no processing of the original spectral data is required. With only 46 iterations, the accuracy of the model is stable at 100% for the training set, test set and external validation set. The excellent performance of the model allows it to be used as a technical reference to solve studies on the qualitative aspects of Gastrodia elata.

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Section: Pls-da and Svmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FT-MIR combined with 3DCOS-ResNet model for rapid identification of wild and cultivated Gastrodia elata

Liu

et al. 2023

Preprint

Self Cite

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“…In recent years, the deep learning, as a state-of-the-art technique, has been extensively applied in the field of hyperspectral image processing, in which the features could be learned automatically according to the targeted tasks [14][15][16][17][18] . And large number of labeled samples is desirable to ensure the stability of the deep learning models.…”

Section: Introduction mentioning

confidence: 99%

Hyperspectral detection of walnut protein contents based on improved whale optimized algorithm

Zhang¹,

Tian²,

Ma³

et al. 2022

International Journal of Agricultural and Biological Engineering

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Nondestructive and accurate estimation of walnut kernel protein content is important for food quality grading and profitability improvement of walnut packinghouses. Hyperspectral image technology provides potential solutions for walnuts nutrients detection by obtaining both spectral and textural information. However, the redundancy and large computation of spectral data prevent the widespread application of hyperspectral technology for high throughput evaluation. For walnut kernel protein inversion from hyperspectral image, this study proposed a novel feature selection method, which is named as improved whale optimized algorithm (IWOA). In the IWOA, a comprehensive feature selection criterion was applied in the iterative process, which fully considered the relevance of spectra information with target variables, representative ability of the selected wavebands to entire spectra, and redundancy of the selected wavebands. Especially in the relevance with target variables, the amplitude and shape characteristics of the spectra were both taken into consideration. Eight wavelengths around 996, 1225, 1232, 1377, 1552, 1600, 1691 and 1700 nm were then selected as the sensitive wavelengths to walnut protein. These wavelengths showed good correlation with certain chemical compounds related to protein contents mechanistically. Then three protein prediction models were established. After analysis and comparison, the model based on the selected wavelengths got better results with the one based on the full spectrum. Compared to the models based on solely spectral information, the model that combine spectral and textural information outperformed and got the best prediction results. The R 2 in the calibration group was 0.9047, and the root mean square errors (RMSE) was 11.1382 g/kg. In the validation group, the R 2 was 0.8537, and the RMSE was 18.9288 g/kg. The results demonstrated that the combination of the selected wavelengths through the IWOA with the textural characteristics could effectively estimate walnut protein contents. And the proposed method can be extended to the detection and inversion of other nutritional variables of nuts.

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“…Digital image processing is usually used for the identi cation of poisonous crop diseases and poisonous crop diseases, and the image segmentation process is usually performed. When the eld light changes or the background is complex, the e ect of image segmentation will be a ected, thereby reducing the subsequent recognition rate [1][2][3][4]. In addition, this method requires the extraction of a large number of features, and the high computational complexity will reduce the recognition e ciency.…”

Section: Introductionmentioning

confidence: 99%

A Small Sample Recognition Model for Poisonous and Edible Mushrooms based on Graph Convolutional Neural Network

Zhu

Pan

Wang

et al. 2022

Computational Intelligence and Neuroscience

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The automatic identification of disease types of edible mushroom crops and poisonous crops is of great significance for improving crop yield and quality. Based on the graph convolutional neural network theory, this paper constructs a graph convolutional network model for the identification of poisonous crops and edible fungi. By constructing 6 graph convolutional networks with different depths, the model uses the training mechanism of graph convolutional networks to analyze the results of disease identification and completes the automatic extraction of the disease characteristics of the poisonous crops by overfitting problem. During the simulation, firstly, the relevant PlantVillage dataset is used to obtain the pretrained model, and the parameters are adjusted to fit the dataset. The network framework is trained and parameterized with prior knowledge learned from large datasets and finally synthesized by training multiple neural network models and using direct averaging and weighting to synthesize their predictions. The experimental results show that the graph convolutional neural network model that integrates multi-scale category relationships and dense links can use dense connection technology to improve the representation ability and generalization ability of the model, and the accuracy rate generally increases by 1%–10%. The average recognition rate is about 91%, which greatly promotes the ability to identify the diseases of poisonous crops.

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Verified the rapid evaluation of the edible safety of wild porcini mushrooms, using deep learning and PLS‐DA

Cited by 23 publications

References 36 publications

FT-MIR combined with 3DCOS-ResNet model for rapid identification of wild and cultivated Gastrodia elata

FT-MIR combined with 3DCOS-ResNet model for rapid identification of wild and cultivated Gastrodia elata

Hyperspectral detection of walnut protein contents based on improved whale optimized algorithm

A Small Sample Recognition Model for Poisonous and Edible Mushrooms based on Graph Convolutional Neural Network

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