Development of a variety and quality evaluation method for Amomi fructus using GC, electronic tongue, and electronic nose

Hou, Fuguo; Fan, Xue-Hua; Gui, Xinjing; Li, Han; Li, Haiyang; Wang, Yanli; Shi, Junhan; Zhang, Lu; Yao, Jing; Li, Xuelin; Liu, Ruixin

doi:10.3389/fchem.2023.1188219

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…Furthermore, multi-class intelligent sensory data fusion can simulate traditional manual evaluations by combining visual, auditory, taste, and scent-based data, integrating complementary sensory information to improve identification accuracy. Indeed, several studies have clearly demonstrated the advantages of multi-intelligent sensory data fusion ( Zhang et al, 2021b ; Li et al, 2022 ; Hou et al, 2023 ; Wang et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of Bletilla striata and related decoction pieces: a data fusion method combining electronic nose, electronic tongue, electronic eye, and high-performance liquid chromatography data

Li,

Wang,

Lin

et al. 2024

Front. Chem.

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Introduction: We here describe a new method for distinguishing authentic Bletilla striata from similar decoctions (namely, Gastrodia elata, Polygonatum odoratum, and Bletilla ochracea schltr).Methods: Preliminary identification and analysis of four types of decoction pieces were conducted following the Chinese Pharmacopoeia and local standards. Intelligent sensory data were then collected using an electronic nose, an electronic tongue, and an electronic eye, and chromatography data were obtained via high-performance liquid chromatography (HPLC). Partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and back propagation neural network (BP-NN) models were built using each set of single-source data for authenticity identification (binary classification of B. striata vs. other samples) and for species determination (multi-class sample identification). Features were extracted from all datasets using an unsupervised approach [principal component analysis (PCA)] and a supervised approach (PLS-DA). Mid-level data fusion was then used to combine features from the four datasets and the effects of feature extraction methods on model performance were compared.Results and Discussion: Gas chromatography–ion mobility spectrometry (GC-IMS) showed significant differences in the types and abundances of volatile organic compounds between the four sample types. In authenticity determination, the PLS-DA and SVM models based on fused latent variables (LVs) performed the best, with 100% accuracy in both the calibration and validation sets. In species identification, the PLS-DA model built with fused principal components (PCs) or fused LVs had the best performance, with 100% accuracy in the calibration set and just one misclassification in the validation set. In the PLS-DA and SVM authenticity identification models, fused LVs performed better than fused PCs. Model analysis was used to identify PCs that strongly contributed to accurate sample classification, and a PC factor loading matrix was used to assess the correlation between PCs and the original variables. This study serves as a reference for future efforts to accurately evaluate the quality of Chinese medicine decoction pieces, promoting medicinal formulation safety.

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Section: Introductionmentioning

confidence: 99%

Identification of Bletilla striata and related decoction pieces: a data fusion method combining electronic nose, electronic tongue, electronic eye, and high-performance liquid chromatography data

Li,

Wang,

Lin

et al. 2024

Front. Chem.

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A strategy to distinguish similar traditional Chinese medicines by liquid chromatography–mass spectrometry, electronic senses, and gas chromatography–ion mobility spectrometry: Marsdeniae tenacissimae Caulis and Paederiae scandens Caulis as examples

Wang,

Pei,

Chen

et al. 2024

Phytochemical Analysis

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IntroductionMarsdeniae tenacissimae Caulis (MTC), a popular traditional Chinese medicine, has been widely used in the treatment of tumor diseases. Paederiae scandens Caulis (PSC), which is similar in appearance to MTC, is a common counterfeit product. It is difficult for traditional methods to effectively distinguish between MTC and PSC. Therefore, there is an urgent need for a rapid and accurate method to identify MTC and PSC.ObjectivesThe aim is to distinguish between MTC and PSC by analyzing the differences in nonvolatile organic compounds (NVOCs), taste, odor, and volatile organic compounds (VOCs).MethodsLiquid chromatography–mass spectrometry (LC‐MS) was utilized to analyze the NVOCs of MTC and PSC. Electronic tongue (E‐tongue) and electronic nose (E‐nose) were used to analyze their taste and odor respectively. Gas chromatography–ion mobility spectrometry (GC‐IMS) was applied to analyze VOCs. Finally, multivariate statistical analyses were conducted to further investigate the differences between MTC and PSC, including principal component analysis, orthogonal partial least squares discriminant analysis, discriminant factor analysis, and soft independent modeling of class analysis.ResultsThe results of this study indicate that the integrated strategy of LC‐MS, E‐tongue, E‐nose, GC‐IMS, and multivariate statistical analysis can be effectively applied to distinguish between MTC and PSC. Using LC‐MS, 25 NVOCs were identified in MTC, while 18 NVOCs were identified in PSC. The major compounds in MTC are steroids, while the major compounds in PSC are iridoid glycosides. Similarly, the distinct taste difference between MTC and PSC was precisely revealed by the E‐tongue. Specifically, the pronounced bitterness in PSC was proven to stem from iridoid glycosides, whereas the bitterness evident in MTC was intimately tied to steroids. The E‐nose detected eight odor components in MTC and six in PSC, respectively. The subsequent statistical analysis uncovered notable differences in their odor profiles. GC‐IMS provided a visual representation of the differences in VOCs between MTC and PSC. The results indicated a relatively high relative content of 82 VOCs in MTC, contrasted with 32 VOCs exhibiting a similarly high relative content in PSC.ConclusionIn this study, for the first time, the combined use of LC‐MS, E‐tongue, E‐nose, GC‐IMS, and multivariate statistical analysis has proven to be an effective method for distinguishing between MTC and PSC from multiple perspectives. This approach provides a valuable reference for the identification of other visually similar traditional Chinese medicines.

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Research on rapid quality identification method of Panax notoginseng powder based on artificial intelligence sensory technology and multi-source information fusion technology

Li,

Gui,

Wang

et al. 2024

Food Chemistry

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Development of a variety and quality evaluation method for Amomi fructus using GC, electronic tongue, and electronic nose

Cited by 4 publications

References 27 publications

Identification of Bletilla striata and related decoction pieces: a data fusion method combining electronic nose, electronic tongue, electronic eye, and high-performance liquid chromatography data

Identification of Bletilla striata and related decoction pieces: a data fusion method combining electronic nose, electronic tongue, electronic eye, and high-performance liquid chromatography data

A strategy to distinguish similar traditional Chinese medicines by liquid chromatography–mass spectrometry, electronic senses, and gas chromatography–ion mobility spectrometry: Marsdeniae tenacissimae Caulis and Paederiae scandens Caulis as examples

Research on rapid quality identification method of Panax notoginseng powder based on artificial intelligence sensory technology and multi-source information fusion technology

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