Rapid quality evaluation of four kinds of Polygoni Multiflori Radix Praeparata by electronic eye combined with chemometrics

Electronic eye and electronic tongue data fusion combined with a GETNet model for the traceability and detection of Astragalus

BACKGROUND

Astragalus is a widely used traditional Chinese medicine material that is easily confused due to its quality, price and other factors derived from different origins. This article describes a novel method for the rapid tracing and detection of Astragalus via the joint application of an electronic tongue (ET) and an electronic eye (EE) combined with a lightweight convoluted neural network (CNN)-transformer model. First, ET and EE systems were employed to measure the taste fingerprints and appearance images, respectively, of different Astragalus samples. Three spectral transform methods - the Markov transition field, short-time Fourier transform and recurrence plot - were utilized to convert the ET signals into 2D spectrograms. Then, the obtained ET spectrograms were fused with the EE image to obtain multimodal information. A lightweight hybrid model, termed GETNet, was designed to achieve pattern recognition for the Astragalus fusion information. The proposed model employed an improved transformer module and an improved Ghost bottleneck as its backbone network, complementarily utilizing the benefits of CNN and transformer architectures for local and global feature representation. Furthermore, the Ghost bottleneck was further optimized using a channel attention technique, which boosted the model's feature extraction effectiveness.

RESULTS

The experiments indicate that the proposed data fusion strategy based on ET and EE devices has better recognition accuracy than that attained with independent sensing devices.

CONCLUSION

The proposed method achieved high precision (99.1%) and recall (99.1%) values, providing a novel approach for rapidly identifying the origin of Astragalus, and it holds great promise for applications involving other types of Chinese herbal medicines. © 2024 Society of Chemical Industry.

Metabolomic analysis revealed the edible and extended-application potential of specific Polygonum multiflorum tissues

The diverse applications of various tissues of Polygonum Multiflorum (PM) encompass the use of its leaf and bud as tea and vegetables, as well as the utilization of its expanded root tubers and caulis as medicinal substances. However, previous studies in the field of metabolomics have primarily focused on the medicinal properties of PM. In order to investigate the potential for broader applications of other tissues within PM, a metabolomic analysis was conducted for the first time using UPLC-Q-TOF-MS/MS on 15 fresh PM tissues. A total of 231 compounds, including newly discovered compounds such as torosachrysone and dihydro-trihydroxystilbene acid derivatives, were identified within PM. Through clustering analysis, the PM tissues were categorized into edible and medicinal parts, with edible tissues exhibiting higher levels of phenolic acids, organic acids, and flavonoids, while the accumulation of quinones, dianthrones, stilbenes, and xanthones was observed in medicinal tissues. Comparative analysis demonstrated the potential application of discarded tissues, such as unexpanded root tuber (an industrial alternative to expanded root tuber) and young caulis (with edible potential). Moreover, the quantification of representative metabolites indicated that flowers and buds contained significant amounts of flavonoids or phenolic acids, suggesting their potential as functional food. Additionally, the edible portion of PM exhibited a high content of quercitrin, ranging from 0.59 to 10.37 mg/g. These findings serve as a valuable point of reference for the expanded utilization of PM tissues, thereby mitigating resource waste in this plant.

Recent trends of machine learning applied to multi-source data of medicinal plants

In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.

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

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.

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

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.