Geographical traceability of Eucommia ulmoides leaves using attenuated total reflection Fourier transform infrared and ultraviolet-visible spectroscopy combined with chemometrics and data fusion

Wang, Chao-Yong; Tang, Li; Jiang, Tao; Zhou, Qiang; Li, Jing; Wang, Yuanzhong; Kong, Chui‐Hua

doi:10.1016/j.indcrop.2020.113090

Cited by 23 publications

(9 citation statements)

References 40 publications

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“…A weak peak of 2925 cm −1 side is mainly attracted to asymmetric stretching vibration of ‐CH 2 . Absorbance at approximately 1375 cm −1 about ‐CH 3 is scissored 40 . The intense peaks of 1200–900 cm − 1 are related to bending band of C‐O‐H and ‐CH 2 , C‐C and C‐O stretching, indicate the deformation of carbohydrates I 41 .…”

Section: Resultsmentioning

confidence: 97%

“…Absorbance at approximately 1375 cm À1 about -CH 3 is scissored. 40 The intense peaks of 1200-900 cm À1 are related to bending band of C-O-H and -CH 2 , C-C and C-O stretching, indicate the deformation of carbohydrates I. 41 The band of 1690 cm À1 is related to C=O group vibrations with ketones, carboxylic acids, and as same compounds absorb vibrational energy in 1300-1150 cm À1 .The result of C-O group that the existence of carbonyls and carboxyls may be related to appear of the terpenoids constituents.…”

Section: Spectral Characterization Of Analysismentioning

confidence: 99%

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Data fusion of multiple‐information strategy based on Fourier transform near infrared spectroscopy and Fourier‐transform mid infrared for geographical traceability of Wolfiporia cocos combined with chemometrics

Zhang²,

Zuo

et al. 2022

Journal of Chemometrics

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Owing to the widespread concern relating to herb safety and quality, there is a momentum to discriminate the geographical traceability of fungus with multiple‐information technologies. In this study, we attempted to evaluate the fusion strategy of multiple‐information for the geographical traceability of this fungus based on Fourier transform near infrared spectroscopy (FT‐NIR) and Fourier‐transform mid infrared spectroscopy (FT‐MIR) with chemometrics. From all results, (1) comparative visualization of t‐distributed stochastic neighbor embedding (t‐SNE) emerged that the superior effect of FT‐NIR spectroscopy was better than FT‐MIR, which were separated according to the samples of different regions; (2) in view of optimized spectrum, the geographical traceability of extreme learning machine (ELM) model was better than the partial least squares discriminant analysis (PLS‐DA) and support vector machine (SVM) models that the accuracy of training set for all models was 100%; (3) the accuracy of train and test for ELM model were 100% with single spectrum, low‐level, and mid‐level fusion of FT‐NIR and FT‐MIR based on principal components (PCs) and competitive adaptive weighting algorithm (CARS), which was suitable for geographical traceability and evaluation of this fungus. (4) The three models based on successive projection algorithm (SPA) were not dissatisfied for the geographical traceability of this fungus. (5) The result of hierarchical cluster analysis (HCA) was consistent with the above results, which could distinguish samples according to different regions. All results showed that models could be developed as superior model on the foundation of single spectrum based on FT‐NIR, low‐level or mid‐level (except SPA) fusion of ELM, which could be employed in geographical traceability of Wolfiporia cocos, effectively.

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Section: Resultsmentioning

confidence: 97%

Section: Spectral Characterization Of Analysismentioning

confidence: 99%

Data fusion of multiple‐information strategy based on Fourier transform near infrared spectroscopy and Fourier‐transform mid infrared for geographical traceability of Wolfiporia cocos combined with chemometrics

Zhang²,

Zuo

et al. 2022

Journal of Chemometrics

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“…The quality of medicinal plants directly affects their therapeutic effects. Geographical environmental factors are the key factors affecting the accumulation of medicinal phytochemicals [ 3 ]. Both Europe and China have geographical indication certification products, which recognise the quality in a specific geographical area.…”

Section: Introductionmentioning

confidence: 99%

ResNet Model Automatically Extracts and Identifies FT-NIR Features for Geographical Traceability of Polygonatum kingianum

Yang

et al. 2022

Foods

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Medicinal plants have incredibly high economic value, and a practical evaluation of their quality is the key to promoting industry development. The deep learning model based on residual convolutional neural network (ResNet) has the advantage of automatic extraction and the recognition of Fourier transform near-infrared spectroscopy (FT-NIR) features. Models are difficult to understand and interpret because of unknown working mechanisms and decision-making processes. Therefore, in this study, artificial feature extraction methods combine traditional partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) models to understand and compare deep learning models. The results show that the ResNet model has significant advantages over traditional models in feature extraction and recognition. Secondly, preprocessing has a great impact on the feature extraction and feature extraction, and is beneficial for improving model performance. Competitive adaptive reweighted sampling (CARS) and variable importance in projection (VIP) methods screen out more feature variables after preprocessing, but the number of potential variables (LVs) and successive projections algorithm (SPA) methods obtained is fewer. The SPA method only extracts two variables after preprocessing, causing vital information to be lost. The VIP feature of traditional modelling yields the best results among the four methods. After spectral preprocessing, the recognition rates of the PLS-DA and SVM models are up to 90.16% and 88.52%. For the ResNet model, preprocessing is beneficial for extracting and identifying spectral image features. The ResNet model based on synchronous two-dimensional correlation spectra has a recognition accuracy of 100%. This research is beneficial to the application development of the ResNet model in foods, spices, and medicinal plants.

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“…It can optimize gradient vanishing and exploding in ResNet, which has been successfully applied to TCM and fungus based on two dimensional correlation spectroscopy (2DCOS) images, including the species, storage period, geographical traceability, etc. [ 12 , 18 , 19 ]. 2DCOS can effectively solve the spectral overlap and improve the apparent spectral resolution.…”

Section: Introductionmentioning

confidence: 99%

A method of two-dimensional correlation spectroscopy combined with residual neural network for comparison and differentiation of medicinal plants raw materials superior to traditional machine learning: a case study on Eucommia ulmoides leaves

Wang

2022

Plant Methods

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Background Eucommia ulmoides leaf (EUL), as a medicine and food homology plant, is a high-quality industrial raw material with great development potential for a valuable economic crop. There are many factors affecting the quality of EULs, such as different drying methods and regions. Therefore, quality and safety have received worldwide attention, and there is a trend to identify medicinal plants with artificial intelligence technology. In this study, we attempted to evaluate the comparison and differentiation for different drying methods and geographical traceability of EULs. As a superior strategy, the two-dimensional correlation spectroscopy (2DCOS) was used to directly combined with residual neural network (ResNet) based on Fourier transform near-infrared spectroscopy. Results (1) Each category samples from different regions could be clustered together better than different drying methods through exploratory analysis and hierarchical clustering analysis; (2) A total of 3204 2DCOS images were obtained, synchronous 2DCOS was more suitable for the identification and analysis of EULs compared with asynchronous 2DCOS and integrated 2DCOS; (3) The superior ResNet model about synchronous 2DCOS used to identify different drying method and regions of EULs than the partial least squares discriminant model that the accuracy of train set, test set, and external verification was 100%; (4) The Xinjiang samples was significant differences than others with correlation analysis of 19 climate data and different regions. Conclusions This study verifies the superiority of the ResNet model to identify through this example, which provides a practical reference for related research on other medicinal plants or fungus.

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Geographical traceability of Eucommia ulmoides leaves using attenuated total reflection Fourier transform infrared and ultraviolet-visible spectroscopy combined with chemometrics and data fusion

Cited by 23 publications

References 40 publications

Data fusion of multiple‐information strategy based on Fourier transform near infrared spectroscopy and Fourier‐transform mid infrared for geographical traceability of Wolfiporia cocos combined with chemometrics

Data fusion of multiple‐information strategy based on Fourier transform near infrared spectroscopy and Fourier‐transform mid infrared for geographical traceability of Wolfiporia cocos combined with chemometrics

ResNet Model Automatically Extracts and Identifies FT-NIR Features for Geographical Traceability of Polygonatum kingianum

A method of two-dimensional correlation spectroscopy combined with residual neural network for comparison and differentiation of medicinal plants raw materials superior to traditional machine learning: a case study on Eucommia ulmoides leaves

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