A novel quality evaluation method for magnolia bark using electronic nose and colorimeter data with multiple statistical algorithms

Discrimination between raw and ginger juice processed Magnoliae officinalis cortex based on HPLC and Heracles NEO ultra-fast gas phase electronic nose

INTRODUCTION

Magnoliae officinalis cortex (MOC), a traditional Chinese medicine, has been used in treating gastrointestinal diseases since ancient time. According to the Chinese Pharmacopoeia, it includes two kinds of decoction pieces, raw and ginger juice processed Magnoliae officinalis cortex (RMOC and GMOC).

OBJECTIVE

The aim of this paper was to study the differences between non-volatile and volatile components in RMOC and GMOC.

METHODS

The non-volatile components were detected by HPLC fingerprinting coupled with content determination (syringin, magnoflorine, honokiol and magnolol). Meanwhile, their odor information was obtained using a Heracles NEO ultra-fast gas phase electronic nose to conduct radar fingerprint analysis, principal component analysis and discriminant factor analysis, and the volatile components were analyzed qualitatively by the Kovats retention index and the AroChemBase database.

RESULTS

The HPLC fingerprints were established and 20 common peaks were found in all chromatograms with similarity values of more than 0.900. The content determination results showed that the contents of syringin and magnoflorine decreased, while the contents of honokiol and magnolol increased in GMOC. By the gas phase electronic nose, the two decoction pieces could be distinguished obviously and 16 possible compounds were identified. Among them, the relative contents of (-)-α-pinene and β-pinene increased, while β-phellandrene and (+)-limonene levels decreased.

CONCLUSION

The results suggested that honokiol, magnolol, (-)-α-pinene and β-pinene might be the main substances which could enhance the harmonizing effect on the stomach. Moreover, this paper could lay a foundation for exploring the processing mechanism of MOC and provide a novel method for the research of other traditional Chinese medicine with strong aroma.

Application of Multiple-Source Data Fusion for the Discrimination of Two Botanical Origins of Magnolia Officinalis Cortex Based on E-Nose Measurements, E-Tongue Measurements, and Chemical Analysis

Magnolia officinalis Rehd. et Wils. and Magnolia officinalis Rehd. et Wils. var. biloba Rehd. et Wils, as the legal botanical origins of Magnoliae Officinalis Cortex, are almost impossible to distinguish according to their appearance traits with respect to medicinal bark. The application of AFLP molecular markers for differentiating the two origins has not yet been successful. In this study, a combination of e-nose measurements, e-tongue measurements, and chemical analyses coupled with multiple-source data fusion was used to differentiate the two origins. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were applied to compare the discrimination results. It was shown that the e-nose system presented a good discriminant ability with a low classification error for both LDA and QDA compared with e-tongue measurements and chemical analyses. In addition, the discriminating capacity of LDA for low-level fusion with original data, similar to a combined system, was superior or equal to that acquired individually with the three approaches. For mid-level fusion, the combination of different principals extracted by PCA and variables obtained on the basis of PLS-VIP exhibited an analogous discrimination ability for LDA (classification error 0.0%) and was significantly superior to QDA (classification error 1.67-3.33%). As a result, the combined e-nose, e-tongue, and chemical analysis approach proved to be a powerful tool for differentiating the two origins of Magnoliae Officinalis Cortex.

A Novel Method for Quality Evaluation of Gardeniae fructus Praeparatus during Heat Processing Based on Sensory Characteristics and Chemical Compositions

The intrinsic chemical components and sensory characteristics of Gardeniae fructus Praeparatus (GFP) directly reflect its quality and subsequently, affect its clinical curative effect. However, there is little research on the correlation between the appearance traits and chemical compositions of GFP during heat processing. In this study, the major components of five typical processed decoction pieces of GFP were determined. With the deepening of processing, the contents of geniposidic acid and 5-HMF gradually increased, while the contents of deacetyl-asperulosidic acid methyl ester, gardenoside, and two pigments declined. Moreover, the electronic eye, electronic tongue, and electronic nose were applied to quantify GFP’s sensory properties. It was found that the chroma values showed a downward trend during the processing of GFP. The results of odor showed that ammonia, alkenes, hydrogen, and aromatic compounds were the material base for aroma characteristics. Complex bitterness in GF was more obvious than that in other GFP processed products. Furthermore, one mathematical model was established to evaluate the correlation between the sensory characteristics and chemical composition of GFP during five different stages. A cluster analysis and neural network analysis contributed to recognizing the processing stage of GFP. This study provided an alternative method for the exterior and interior correlation-based quality evaluation of herbs.

Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction

Electronic nose is an electronic olfactory system that simulates the biological olfactory mechanism, which mainly includes gas sensor, data pre-processing, and pattern recognition. In recent years, the proposals of electronic nose have been widely developed, which proves that electronic nose is a considerably important tool. However, the most recent studies concentrate on the applications of electronic nose, which gradually neglects the inherent technique improvement of electronic nose. Although there are some proposals on the technique improvement, they usually pay attention to the modification of gas sensor module and barely consider the improvement of the last two modules. Therefore, this paper optimizes the electronic nose system from the perspective of data pre-processing and pattern recognition. Recurrent neural network (RNN) is used to do pattern recognition and guarantee accuracy rate and stability. Regarding the high-dimensional data pre-processing, the method of locally linear embedding (LLE) is used to do dimensionality reduction. The experiments are made based on the real sensor drift dataset, and the results show that the proposed optimization mechanism not only has higher accuracy rate and stability, but also has lower response time than the three baselines. In addition, regarding the usage of RNN model, the experimental results also show its efficiency in terms of recall ratio, precision ratio, and F1 value.