Two dimensional correlation spectroscopy combined with ResNet: Efficient method to identify bolete species compared to traditional machine learning

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

doi:10.1016/j.lwt.2022.113490

Cited by 40 publications

(24 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In species identification, the researchers used partial least squaresdiscrimination analysis (PLS-DA), support vector machines (SVM) and ResNet models to identify five species of boletes, of which the classifiable ResNet model showed an advantage. 23 Dong et al identified seven species of boletes as similar results. 20 Therefore, ResNet model had become a practical method beyond traditional spectral analysis.…”

Section: Residual Neural Network Models Analysismentioning

confidence: 67%

“…In different color of image, although the synchronous 2D‐COS images of different colors have great visual differences, the modeling results are almost the same 22 . In different preprocessed methods of spectra, the preprocessed spectra did not necessarily improve the accuracy of the model, even some preprocessed spectra reduce the accuracy of the model 23 . The results show that 2D‐COS has higher accuracy than one‐dimensional spectra, and excellent accuracy was also achieved without preprocessing of the spectra.…”

Section: Resultsmentioning

confidence: 94%

“…In previous studies, ResNet models had compared the accuracy of species identification and geographic origins identification with traditional machine learning models. In species identification, the researchers used partial least squares‐discrimination analysis (PLS‐DA), support vector machines (SVM) and ResNet models to identify five species of boletes, of which the classifiable ResNet model showed an advantage 23 . Dong et al identified seven species of boletes as similar results 20 .…”

Section: Resultsmentioning

confidence: 99%

“…22 In different preprocessed methods of spectra, the preprocessed spectra did not necessarily improve the accuracy of the model, even some preprocessed spectra reduce the accuracy of the model. 23 The results show that 2D-COS has higher accuracy than one-dimensional spectra, and excellent accuracy was also achieved without preprocessing of the spectra. Therefore, these repetitive works were not present in this study.…”

Section: Synchronous Two-dimensional Correlation Spectroscopy Images ...mentioning

confidence: 89%

See 3 more Smart Citations

ResNet models for rapid identification of species and geographical origin of wild boletes from Yunnan, and MaxEnt model for delineation of potential distribution

Chen

Liu

et al. 2022

Journal of Chemometrics

View full text Add to dashboard Cite

Yunnan is known for its rich biodiversity and is known as the Wild Mushroom Kingdom. Boletes are a world-renowned wild edible mushroom, with unique sensory characteristics, nutritional value and medicinal value extraordinary.However, the species and geographical origin of boletes influence their price and quality. In this study, a method was developed to identify species and geographical origin simultaneously. Therefore, Fourier transform near-infrared (FT-NIR) data sets of boletes were collected and converted to two-dimensional correlation spectroscopy (2D-COS). On this basis, the species and geographic origins of boletes were identified using Residual neural network (ResNet) image analysis model. The results showed that FT-NIR could identify boletes species and geographical origins, 7000-4000 cm À1 band was more suitable for species identification, 7000-5300 cm À1 band was more suitable for geographical origins identification. In addition, the environmental factors with high contribution to the distribution of boletes were screened based on the maximum entropy (MaxEnt) model. This allows characterization of the potential geographic distribution of boletes. The results showed that precipitation factors played a vital role in its distribution and might even be responsible for the difference in chemical composition.

show abstract

Section: Residual Neural Network Models Analysismentioning

confidence: 67%

Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Section: Synchronous Two-dimensional Correlation Spectroscopy Images ...mentioning

confidence: 89%

See 2 more Smart Citations

ResNet models for rapid identification of species and geographical origin of wild boletes from Yunnan, and MaxEnt model for delineation of potential distribution

Chen

Liu

et al. 2022

Journal of Chemometrics

View full text Add to dashboard Cite

show abstract

“…The multiple correlation coefficient (R 2 ) is another important parameter that provides an estimate of the model fit. Testing was performed with 200 random permutations to avoid over-fitting the model [ 19 ]. Jack-knifing was used to estimate standard errors and confidence intervals for the data [ 20 ].…”

Section: Methodsmentioning

confidence: 99%

Anti-Nutritional Factors and Protein Dispersibility Index as Principal Quality Indicators for Soybean Meal in Diet of Nile Tilapia (Oreochromis niloticus GIFT), a Meta-Analysis

Wang

Dou

et al. 2022

Animals

View full text Add to dashboard Cite

Soybean meal (SBM) is the most important plant protein source in animal feed. This study investigated the characteristics of different SBMs, produced by soybeans from America and Brazil (SBM-A and SBM-B) in 2017–2021 under the same controlled conditions. The effects of different SBMs on the growth performance of Nile tilapia (Oreochromis niloticus, GIFT) and apparent digestibility coefficients (ADCs) of nutrients and energy were studied. The results showed that protein dispersibility index (PDI), urease activity (UA), glycinin and fiber were the four primary key indicators for distinguishing the characteristics of the tested SBMs. The meta-analysis results suggested that UA, glycinin, and fiber showed a negative effect on the survival rate (SR) and weight gain rate (WGR) of the Nile tilapia, whereas β-conglycinin, PDI, and nitrogen solubility index (NSI) had a positive effect on the SR and WGR of the fish. The ADCs of dry matter, the gross energy, phosphorus, crude protein, valine (Val), lysine (Lys), histidine (His), serine (Ser), and glutamate (Glu) of the Diet-A group (SBM-A inclusion) were significantly higher than those in the Diet-B group (SBM-B inclusion) (p < 0.05). However, no significant difference was found in ADCs of macro-nutrients between the two SBMs (p > 0.05). Overall, PDI, UA, glycinin, and fiber were the main indicators reflecting the characteristics of the tested SBMs, and UA, glycinin, β-conglycinin, and PDI had the greatest impact on the growth performance of Nile tilapia in this study. PDI was a more sensitive indicator than NSI for representing the protein quality of SBM.

show abstract

Building deep learning and traditional chemometric models based on Fourier transform mid‐infrared spectroscopy: Identification of wild and cultivated Gastrodia elata

Liu

et al. 2023

Food Science & Nutrition

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Two dimensional correlation spectroscopy combined with ResNet: Efficient method to identify bolete species compared to traditional machine learning

Cited by 40 publications

References 37 publications

ResNet models for rapid identification of species and geographical origin of wild boletes from Yunnan, and MaxEnt model for delineation of potential distribution

ResNet models for rapid identification of species and geographical origin of wild boletes from Yunnan, and MaxEnt model for delineation of potential distribution

Anti-Nutritional Factors and Protein Dispersibility Index as Principal Quality Indicators for Soybean Meal in Diet of Nile Tilapia (Oreochromis niloticus GIFT), a Meta-Analysis

Building deep learning and traditional chemometric models based on Fourier transform mid‐infrared spectroscopy: Identification of wild and cultivated Gastrodia elata

Contact Info

Product

Resources

About