Application of hyperspectral imaging assisted with integrated deep learning approaches in identifying geographical origins and predicting nutrient contents of Coix seeds

Wang, Youyou; Feng, Xinliang; Zhang, Yue; Wang, Siman; Yuan, Yuwei; Lu, Cuncun; Nie, Jing; Nan, Tie-Gui; Yang, Bin; Huang, Luqi; Yang, Jian

doi:10.1016/j.foodchem.2022.134503

Cited by 29 publications

(12 citation statements)

References 28 publications

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“…Recent advancements in deep learning have introduced innovative techniques, particularly attention mechanisms, which enable models to differentially process various features (Sun et al, 2019). By assigning greater weights to key features, attention mechanisms enhance the influence of crucial features, thereby facilitating more accurate judgments and predictions in spectral classification and prediction tasks (Zhang et al, 2022a;Wang Y. et al, 2023). Therefore, the utilization of more complex deep networks and mechanisms, such as attention, holds potential for further improving prediction performance.…”

Section: Discussionmentioning

confidence: 99%

Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix

Hu,

Wang,

Wei

et al. 2023

Front. Plant Sci.

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Accurate assessment of isoflavone and starch content in Puerariae Thomsonii Radix (PTR) is crucial for ensuring its quality. However, conventional measurement methods often suffer from time-consuming and labor-intensive procedures. In this study, we propose an innovative and efficient approach that harnesses hyperspectral imaging (HSI) technology and deep learning (DL) to predict the content of isoflavones (puerarin, puerarin apioside, daidzin, daidzein) and starch in PTR. Specifically, we develop a one-dimensional convolutional neural network (1DCNN) model and compare its predictive performance with traditional methods, including partial least squares regression (PLSR), support vector regression (SVR), and CatBoost. To optimize the prediction process, we employ various spectral preprocessing techniques and wavelength selection algorithms. Experimental results unequivocally demonstrate the superior performance of the DL model, achieving exceptional performance with mean coefficient of determination (R2) values surpassing 0.9 for all components. This research underscores the potential of integrating HSI technology with DL methods, thereby establishing the feasibility of HSI as an efficient and non-destructive tool for predicting the content of isoflavones and starch in PTR. Moreover, this methodology holds great promise for enhancing efficiency in quality control within the food industry.

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Section: Discussionmentioning

confidence: 99%

Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix

Hu,

Wang,

Wei

et al. 2023

Front. Plant Sci.

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“…ma-yuen (Roman.) Stapf) [22]. The proposed model in this study, which has the advantages of variable weight evaluation and continuous spectral information fusion, outperforms the conventional learning models and the individual deep learning module in predicting geographical origins.…”

Section: Quality Control Of Chinese Patent Medicinesmentioning

confidence: 94%

The Application of Artificial Intelligence in the Research and Development of Traditional Chinese Medicine

Ke,

Liu,

Liu

et al. 2024

IJDDP

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Review The Application of Artificial Intelligence in the Research and Development of Traditional Chinese Medicine Zhipeng Ke 1,2, Minxuan Liu 1,2,3, Jing Liu 1,2, Zhenzhen Su 1,2, Lu Li 1,2, Mengyu Qian 1,2, Xinzhuang Zhang 1,2, Tuanjie Wang 1,2, Liang Cao 1,2, Zhenzhong Wang 1,2, and Wei Xiao 1,2, * 1 National Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture, Lianyungang 222106, China 2 Jiangsu Kanion Pharmaceutical Co., Ltd, Lianyungang 222104, China 3 ‍School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210009, China * Correspondence: xw_kanion@163.com Received: 4 September 2023 Accepted: 4 November 2023 Published: 6 March 2024 Abstract: With the accumulation of data in the pharmaceutical industry and the development of artificial intelligence technology, various artificial intelligence methods have been successfully employed in the drug discovery process. The integration of artificial intelligence in Traditional Chinese medicine has also gained momentum, encompassing quality control of Chinese patent medicines, prescriptions optimization, discovery of effective substances, and prediction of side effects. However, artificial intelligence also faces challenges and limitations in Traditional Chinese medicine development, such as data scarcity and complexity, lack of interdisciplinary professionals, black-box models, etc. Therefore, more research and collaboration are needed to address these issues and explore the best ways to integrate artificial intelligence and Traditional Chinese medicine to improve human health.

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“…All the determination coefficients of the four-component models are greater than 0.95, and all the root mean square errors are less than 0.20. Wang et al [9] proposed an attention mechanism (AM), CNN, and LSTM)integrated deep learning model to predict the main nutritional content of Coix seed using hyperspectral imaging. The average accuracy of the ALSTM integrated model shown in Figure 1 is 86.3 ± 3.1%, and the average prediction accuracy is 95.6 ± 2.8%.…”

Section: Detection Of Nutrientsmentioning

confidence: 99%

Study of progress on application of hyperspectral imaging combined with deep learning approaches in detecting foods content

Huang,

Huang

2024

Advanced Fiber Laser Conference (AFL2023)

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Predicting food component content is one of the important research directions in the field of food science and engineering, and it is also an important indicator for evaluating food quality. With the increasing attention to food quality and safety issues, the prediction of food component content is of great significance for quality control in the food industry and food safety monitoring. Traditional prediction of food component content is usually carried out through chemical analysis and physical measurement techniques, which have disadvantages such as long detection times and high costs, and may be influenced by environmental factors and sample variability. They cannot meet the requirements for non-destructive, rapid prediction of multidimensional food component content. Therefore, the development of non-destructive, rapid, and multidimensional detection methods is crucial. Hyperspectral imaging technology is a non-destructive, rapid, and highprecision technology that can obtain a large amount of spectral information from food. Deep learning, as a powerful machine learning method, has the ability to handle large-scale data and extract complex features. With the development of hyperspectral imaging technology and deep learning, an increasing number of studies are combining the two for application in non-destructive food detection. This paper reviews the application of hyperspectral imaging technology combined with deep learning in food quality analysis, including nutrient analysis, traceability identification and maturity assessment.Secondly, the research progress of hyperspectral imaging technology combined with deep learning algorithms in data preprocessing, model building and evaluation is summarised to provide reference for improving the accuracy and efficiency of food analysis.

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Application of hyperspectral imaging assisted with integrated deep learning approaches in identifying geographical origins and predicting nutrient contents of Coix seeds

Cited by 29 publications

References 28 publications

Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix

Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix

The Application of Artificial Intelligence in the Research and Development of Traditional Chinese Medicine

Study of progress on application of hyperspectral imaging combined with deep learning approaches in detecting foods content

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