E-eye and FT-NIR combined with multivariate algorithms to rapidly evaluate the dynamic changes in the quality of Gastrodia elata during steaming process

Rapid evaluation of Curcuma origin and quality based on E-eye, flash GC e-nose, and FT-NIR combined with machine learning technologies

Curcuma, a key ingredient in curry and a popular health supplement, has been subject to adulteration and fraudulent origin labeling. In this study, E-eye, Flash GC e-nose, and FT-NIR, combined with machine learning and multivariate algorithms, were employed for origin identification and quantitative prediction of curcuma constituents. The results indicated that E-eye performed poorly in origin classification, while Flash GC e-nose identified flavor markers distinguishing curcuma from different origins but lacked precise quantification. After processing the FT-NIR spectra with SNV, the accuracy of three machine learning models, including SVM, increased from 83.3 % to 100 %. Additionally, PLSR models for three constituents, including curcumin, achieved mean R2 values exceeding 0.99 in both training and prediction sets, demonstrating excellent linearity and predictive accuracy. Overall, the study demonstrated that FT-NIR combined with multivariate algorithms provides an effective and feasible method for rapid origin identification and quality assessment of curcuma.

Exploration of the impact of different drying methods on the quality of Gastrodia elata: A study based on drying kinetics and multidimensional quality evaluation

In this study, the drying kinetics and multidimensional quality of Gastrodia elata (GE) obtained through five different drying methods were analyzed. The Page model provided the best fit. Though widely used, hot-air drying (HA-D) showed mediocre performance. Due to strong heat penetration, microwave drying (MV-D) and infrared drying (IR-D) effectively reduced drying time and energy consumption. However, they experienced Maillard and caramelization reactions, resulting in notable cell structure shrinkage, severe browning, pronounced caramel taste, and poor retention of active ingredients. Conversely, freeze-drying (F-D) and vacuum drying (V-D), due to the involvement of low-temperature or vacuum environments, demonstrated optimal performance in preserving microstructure, antioxidant activity, active ingredients, and flavor. However, F-D was time-consuming (1320 min) and had an energy consumption of 2.69 times that of HA-D, limiting its large-scale application. V-D struck the best balance between energy consumption and product quality, making it a highly promising drying method for GE.

An integrated and rapid evaluation of Curcumae Radix from different botanical origins based on chemical components, antiplatelet aggregation effect and Fourier transform near-infrared spectroscopy

Curcumae Radix (CR) is a widely used traditional Chinese medicine with significant pharmaceutical importance, including enhancing blood circulation and addressing blood stasis. This study aims to establish an integrated and rapid quality assessment method for CR from various botanical origins, based on chemical components, antiplatelet aggregation effects, and Fourier transform near-infrared (FT-NIR) spectroscopy combined with multivariate algorithms. Firstly, ultra-performance liquid chromatography-photodiode array (UPLC-PDA) combined with chemometric analyses was used to examine variations in the chemical profiles of CR. Secondly, the activation effect on blood circulation of CR was assessed using an in vitro antiplatelet aggregation assay. The studies revealed significant variations in chemical profiles and antiplatelet aggregation effects among CR samples from different botanical origins, with constituents such as germacrone, β-elemene, bisdemethoxycurcumin, demethoxycurcumin, and curcumin showing a positive correlation with antiplatelet aggregation biopotency. Thirdly, FT-NIR spectroscopy was integrated with various machine learning algorithms, including Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Subspace K-Nearest Neighbors (Subspace KNN), to classify CR samples from four distinct sources. The result showed that FT-NIR combined with KNN and SVM classification algorithms after SNV and MSC preprocessing successfully distinguished CR samples from four plant sources with an accuracy of 100%. Finally, Quantitative models for active constituents and antiplatelet aggregation bioactivity were developed by optimizing the partial least squares (PLS) model with interval combination optimization (ICO) and competitive adaptive reweighted sampling (CARS) techniques. The CARS-PLS model achieved the best predictive performance across all five components. The coefficient of determination (R2p) and root mean square error (RMSEP) in the independent test sets were 0.9708 and 0.2098, 0.8744 and 0.2065, 0.9511 and 0.0034, 0.9803 and 0.0066, 0.9567 and 0.0172 for germacrone, β-elemene, bisdemethoxycurcumin, demethoxycurcumin and curcumin, respectively. The ICO-PLS model demonstrated superior predictive capabilities for antiplatelet aggregation biotency, achieving an R2p of 0.9010, and an RMSEP of 0.5370. This study provides a valuable reference for the quality evaluation of CR in a more rapid and comprehensive manner.

Polygonati Rhizoma varieties and origins traceability based on multivariate data fusion combined with an artificial intelligence classification algorithm

This study collected multidimensional feature data such as spectra, texture, and component contents of Polygonati Rhizoma from different origins and varieties (Polygonatum kingianum Coll. et Hemsl from Yunnan and Guizhou; Polygonatum cyrtonema Hua from Anhui and Jiangxi; Polygonatum sibiricum Red from Hunan). Multivariate statistical analysis was used to select 39 characteristic factors for distinguishing PR origins and 14 characteristic factors for discriminating PR varieties (VIP > 1 and P < 0.05). In addition, by combining multivariate statistical analysis with a deep belief network (DBN) classification algorithm, a novel artificial intelligence algorithm was developed and optimized. Compared to traditional discriminant analysis methods, the accuracy of this new approach was significantly improved, achieving a 100% discrimination rate for PR varieties and a 100% accuracy rate for tracing the origin of PR. This research provides a reference and data support for constructing intelligent algorithms based on multidimensional data fusion, to achieve food variety discrimination and origin tracing.

Multivariate HPLC system assessment and optimization for traditional Chinese medicine: a case study of Gastrodia elata

The development of HPLC analytical methods for traditional Chinese medicine is intricate and time-consuming, influenced by factors such as column wear, solvent purity, and instrumental settings. A comprehensive evaluation of the HPLC system is crucial to mitigate potential variability and ensure the reliability of data. This is especially important given the complex and synergistic nature of the chemical components in traditional Chinese medicine, necessitating a multivariate measurement system analysis (MSA) to assess multiple correlated quality characteristics effectively. This study introduced a multivariate MSA method based on weighted principal components (WPC) to evaluate the HPLC system for the determination of metabolites in Gastrodia elata. By integrating multiple principal components and assigning weights according to their eigenvalues, the WPC method significantly enhanced both accuracy and robustness. It demonstrated a repeatability and reproducibility (% R&R) of 26.43% and a number of distinct categories (ndc) index of 5, confirming the system's acceptability. A full factorial experimental design was employed to identify key performance factors, leading to the recommendation to use five reference solutions for the standard curve and to triple sample preparations for improved precision and accuracy. Monte Carlo simulations confirmed the reliability of the system, showing % R&R and ndc values that follow a normal distribution, ranging from 19% to 22% and 6.07 to 7.38, respectively. Chromatographic conditions were optimized using a Box-Behnken experimental design. Subsequent validation experiments verified the method's high accuracy and reliability, with all relative standard deviation values for analytical precision, repeatability, and stability below 5%. The method also exhibited high recovery rates, exceeding 91% across three concentration levels, with RSD values under 4%. In conclusion, the application of a WPC-based multivariate MSA enabled a detailed evaluation of the HPLC system, ensuring accurate and reliable measurement of quality attributes. This method exemplified a scientifically rigorous approach for developing analytical methods in traditional Chinese medicine, enhancing both precision and reliability.

A Review of Gastrodia Elata Bl.: Extraction, Analysis and Application of Functional Food

Gastrodia elata Bl. still widely known as a medicinal plant due to its anti-inflammatory, neuroprotection, cardiovascular protection etc. Additionally, these medical applications cannot be separated from its antioxidant, anti-aging, regulating cell apoptosis ability, which make it have potential as a functional food as well as it has been eaten for more than 2,000 years in China. At present, although Gastrodia elata Bl. has appeared in a large number of studies, much of the research is based on drugs rather than foods. The review of Gastrodia elata Bl. from the perspective of food is one of the necessary steps to promote related development, by reviewing the literature on analytical methods of Gastrodia elata Bl. in recent years, critical components change in the extraction, analytical methods and improvement of food applications, all of aspects of it was summarized. Based on the report about physical and chemical changes in Gastrodia elata Bl. to discover the pathway of Gastrodia elata Bl. functional food development from current to the future.

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.