A practical method superior to traditional spectral identification: Two-dimensional correlation spectroscopy combined with deep learning to identify Paris species

Yue, JiaQi; Huang, Heng-Yu; Wang, Yuanzhong

doi:10.1016/j.microc.2020.105731

Cited by 42 publications

(25 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The discriminative performance of the model is judged by the accuracy and loss value. The loss function plays a vital role in deep learning . The model reaches the state of convergence by minimizing the loss function to reduce the error of the model-predicted value …”

Section: Resultsmentioning

confidence: 93%

“…38 The number of hidden layer neurons was set to 71. As shown in Table 3 39 The model reaches the state of convergence by minimizing the loss function to reduce the error of the model-predicted value. 40 Figure 3 shows representative synchronous, asynchronous, and integration 2DCOS (i2DCOS) images, from which it can be clearly seen that the synchronous 2DCOS of fresh P. portentosus has a significant difference at the 6944 and 5154 cm −1 bands.…”

Section: Different Models Establishedmentioning

confidence: 99%

See 1 more Smart Citation

Method Superior to Traditional Spectral Identification: FT-NIR Two-Dimensional Correlation Spectroscopy Combined with Deep Learning to Identify the Shelf Life of Fresh Phlebopus portentosus

Wang

et al. 2021

ACS Omega

Self Cite

View full text Add to dashboard Cite

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 supportvector 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.

show abstract

Section: Resultsmentioning

confidence: 93%

Section: Different Models Establishedmentioning

confidence: 99%

Method Superior to Traditional Spectral Identification: FT-NIR Two-Dimensional Correlation Spectroscopy Combined with Deep Learning to Identify the Shelf Life of Fresh Phlebopus portentosus

Wang

et al. 2021

ACS Omega

Self Cite

View full text Add to dashboard Cite

show abstract

“…Studies have shown that synchronous 2DCOS is more suitable for the identification of medicinal plants (Yue et al, 2021 ). Therefore, we combined it with deep learning to identify samples from different suitable areas.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Ecological Suitability and Quality Suitability of Panax notoginseng Under Multi-Regionalization Modeling Theory

Yue

Zuo

et al. 2022

Front. Plant Sci.

View full text Add to dashboard Cite

Panax notoginseng is an important medicinal plant in China, but there are some limitations in the ecological suitability study, such as incomplete investigation of species distribution, single regionalization modeling, and lack of collaborative evaluation of ecological suitability, and quality suitability. In this study, the maximum entropy model was used to analyze the ecological suitability of P. notoginseng under current and future climates. The multi-source chemical information of samples was collected to evaluate the uniformity between quality and ecology. The results showed that the current suitable habitat was mainly in southwest China. In the future climate scenarios, the high suitable habitat will be severely degraded. Modeling based on different regionalization could predict larger suitable habitat areas. The samples in the high suitable habitat had both quality suitability and ecological suitability, and the accumulation of chemical components had different responses to different environmental factors. Two-dimensional correlation spectroscopy combined with deep learning could achieve rapid identification of samples from different suitable habitats. In conclusion, global warming is not conducive to the distribution and spread of P. notoginseng. The high suitable habitat was conducive to the cultivation of high-quality medicinal materials. Actual regionalization modeling had more guiding significance for the selection of suitable habitats in a small area. The multi-regionalization modeling theory proposed in this study could provide a new perspective for the ecological suitability study of similar medicinal plants. The results provided a reference for the introduction and cultivation, and lay the foundation for the scientific and standardized production of high-quality P. notoginseng.

show abstract

“…As a new type of CNN, ResNet introduces the residual module into the deep neural network and abandons the redundant network structure. It addresses the problems of vanishing gradient and network degradation of traditional CNN 23,27 . As shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…22 Yue et al found that a deep learning model based on Fourier-transform infrared (FTIR) two-dimensional correlation spectroscopy (2DCOS) is an effective method to identify Pairis species, with an accuracy of 100%. 23 The generalized 2DCOS improves the resolution of the original spectrum and helps to analyze subtle changes in the spectrum. The application of 2DCOS combined with deep learning in the comprehensive safety evaluation of edible mushrooms is a tentative exploration.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Liu

et al. 2021

J Sci Food Agric

Self Cite

View full text Add to dashboard Cite

BACKGROUND: How to quickly identify poisonous mushrooms is a worldwide problem, because poisonous mushrooms and edible mushrooms have very similar appearances. Even some edible mushrooms must be processed further before they can be eaten. In addition, mushrooms from different geographical origins contain different levels of heavy metals. Eating frequent mushrooms with excessive heavy metal content can also cause food poisoning. This information is very important and needs to be informed to consumers in advance. Through the demand for the safety of porcini mushrooms in the Yunnan area we propose a hierarchical identification system based on Fourier-transform near-infrared (FT-NIR) spectroscopy to evaluate the edible safety of porcini species. RESULTS: We found that deep learning is the most effective means to identify the edible safety of porcini, and the recognition accuracy was 100%, by comparing two pattern recognition tools, deep learning and partial least square discriminant analysis (PLS-DA). Although the accuracy of the PLS-DA test set is 96.10%, the poisonous porcini is not allowed to be wrongly judged. In addition, the cadmium (Cd) content of Leccinum rugosiceps in the Midu area exceeded the standard. Deep learning can trace Le. rugosiceps geographic origin with an accuracy of 100%.CONCLUSION: The overall results show that deep learning methods based on FT-NIR can identify porcini that is at risk of being eaten. This has useful application prospects in food safety.

show abstract

A practical method superior to traditional spectral identification: Two-dimensional correlation spectroscopy combined with deep learning to identify Paris species

Cited by 42 publications

References 30 publications

Method Superior to Traditional Spectral Identification: FT-NIR Two-Dimensional Correlation Spectroscopy Combined with Deep Learning to Identify the Shelf Life of Fresh Phlebopus portentosus

Method Superior to Traditional Spectral Identification: FT-NIR Two-Dimensional Correlation Spectroscopy Combined with Deep Learning to Identify the Shelf Life of Fresh Phlebopus portentosus

Evaluation of Ecological Suitability and Quality Suitability of Panax notoginseng Under Multi-Regionalization Modeling Theory

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

Contact Info

Product

Resources

About