Rapid discrimination and quantification of kudzu root with its adulterant part using FT-NIR and a machine learning algorithm

Qiu, Ting; Yang, Yuanzhen; Sun, Haojie; Hu, Tingting; Wang, Xuecheng; Wang, Yaqi; Wu, Zhenfeng; Zhong, Lei; Zhu, Weifeng; Yang, Ming

doi:10.1016/j.vibspec.2021.103289

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…Additionally, the absorption peak around 2278 nm mainly results from the first overtone stretching of C-H in aliphatic compounds such as fatty acids (He et al, 2017). Furthermore, the absorption peaks around 1942 nm and 2112 nm mainly attributed to the characteristic absorption of N-H and -NH 2 groups, which are associated with the presence of proteins and amino acids in PTR (Wang et al, 2013;Qiu et al, 2021). Therefore, the obtained spectral profile provides a solid foundation for qualitative analysis of various PTR attributes using chemometrics and hyperspectral techniques.…”

Section: Spectral Characteristics Of Ptrmentioning

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: Spectral Characteristics Of Ptrmentioning

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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“…As a robust machine learning algorithm, SVM has been widely used in the fields of image processing and pattern recognition (Qiu et al, 2021). However, traditional SVMs may exhibit certain limitations when dealing with large-scale and high-dimensional multimedia data, especially in modeling nonlinear relationships (Urmanova & Khamraeva, 2021).…”

Section: Related Workmentioning

confidence: 99%

Visual Design of Digital Display Based on Virtual Reality Technology with Improved SVM Algorithm

Zuo

2024

ICST Transactions on Scalable Information Systems

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NTRODUCTION: With the rapid development of virtual reality (VR) technology, digital displays have become increasingly important in various fields. This study aims to improve the application of virtual reality technology in the visual design of digital displays by improving the support vector machine (SVM) algorithm. The visual design of digital displays is crucial for attracting users, enhancing experience and conveying information, so an accurate and reliable algorithm is needed to support relevant decisions. OBJECTIVES: The purpose of this study is to improve the SVM algorithm to more accurately identify features related to the visual design of digital displays. By exploiting the nonlinear mapping and parameter optimization of the SVM algorithm, it aims to improve the performance of the model so that it can better adapt to complex visual design scenarios. METHODS: In the process of achieving the objective, multimedia data related to digital displays, including images and videos, were first collected. Through feature engineering, features closely related to visual design were selected, and deep learning techniques were applied to extract higher-level feature representations. Subsequently, the SVM algorithm was improved to use the kernel function for nonlinear mapping, and the penalty parameters and the parameters of the kernel function were adjusted. Cross-validation was used in the training and testing phases of the model to ensure its generalization performance. RESULTS: The improved SVM algorithm demonstrated higher accuracy, recall and precision compared to the traditional method by evaluating it on the test set. This suggests that the model is able to capture visual design features in digital displays more accurately and provide more reliable support for relevant decisions. CONCLUSION: This study demonstrates that by improving the SVM algorithm, more accurate visual design can be achieved in digital displays of virtual reality technology. This improvement provides reliable algorithmic support for the design of digital displays and provides a more prosperous, immersive experience for users. Future research can further optimize the algorithm and iterate with user feedback to continuously improve the visual design of digital displays in virtual reality environments.

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“…Thus, the quality of drugs and therapeutic effect were significantly improved. Qiu et al (2021) reported that enzymatic browning was the main factor of metabolic changes during the processing of Salvia miltiorrhiza Bunge [Lamiaceae; Salviae miltiorrhizae radix et rhizoma] using metabolomic data, which was helpful to select the best processing technology of Salviae miltiorrhizae radix et rhizoma according to the actual needs. Therefore, a combination of NIR spectroscopy, metabolomics, Gas Chromatography-ion Migration Spectrometry (GC-IMS) ( Xu et al, 2022 ), online stepwise background subtraction-based Ultra high performance Liquid chromatography-Quadrupole-time-of-Flight Tandem Mass Spectrometry (UHPLC-Q-TOF-MS/MS) dynamic detection ( Guo et al, 2022 ) and other digital intelligence techniques should be considered to monitor the odor, metabolic components and chemical components during the PCMM processing, and to provide real-time and data-based test results to the cloud, with reduced falsification of testing and optimized drug quality.…”

Section: Applications Of Digital and Intelligent Technologies In Proc...mentioning

confidence: 99%

Application of digital-intelligence technology in the processing of Chinese materia medica

Zhang,

Cao

et al. 2023

Front. Pharmacol.

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Processing of Chinese Materia Medica (PCMM) is the concentrated embodiment, which is the core of Chinese unique traditional pharmaceutical technology. The processing includes the preparation steps such as cleansing, cutting and stir-frying, to make certain impacts on the quality and efficacy of Chinese botanical drugs. The rapid development of new computer digital technologies, such as big data analysis, Internet of Things (IoT), blockchain and cloud computing artificial intelligence, has promoted the rapid development of traditional pharmaceutical manufacturing industry with digitalization and intellectualization. In this review, the application of digital intelligence technology in the PCMM was analyzed and discussed, which hopefully promoted the standardization of the process and secured the quality of botanical drugs decoction pieces. Through the intellectualization and the digitization of production, safety and effectiveness of clinical use of traditional Chinese medicine (TCM) decoction pieces were ensured. This review also provided a theoretical basis for further technical upgrading and high-quality development of TCM industry.

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Rapid discrimination and quantification of kudzu root with its adulterant part using FT-NIR and a machine learning algorithm

Cited by 8 publications

References 19 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

Visual Design of Digital Display Based on Virtual Reality Technology with Improved SVM Algorithm

Application of digital-intelligence technology in the processing of Chinese materia medica

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