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CTMJ | traditionalmedicinejournals.com Chinese Traditional Medicine Journal | 2023 | Vol 6 | Issue 6 

 

    ISSN : 2693 6356 

2023 | Vol 6 | Issue 6 

 
 

 
 

 
 

Abstract—The bark of the magnolia tree, which includes both the aromatic and medicinal varieties known as magnolia 

officinalis and magnolia officinalis, is often used in Chinese patent medicines and clinical prescriptions. The chemical 

composition of Hou Po is a key indicator of its quality, which is in turn connected to its therapeutic efficacy; the site of origin 

plays a pivotal role in determining its quality. An innovative approach to quickly, correctly, and fully determining where Hou Po 

came from and what its key chemical components are is the goal of this research.  

Methods: The magnolol and honokiol components were analysed using high performance liquid chromatography, while the 

magnocurarine and magno- fluorine components were analysed using ultra-performance liquid chromatography. We found out 

what was in the water-soluble extracts by using the cold soak technique. The colorimeter and E-nose were used to identify the Hou 

Po samples' smell and colour, respectively. 

In order to determine where Hou-Po came from and what chemicals were in its water-soluble extracts, we used a number of 

statistical techniques to build discriminant models that relied on E-nose and colorimeter data. With a classification accuracy of 

99.53%, the Random Forest classifier in conjunction with the ten-fold cross-validation approach outperformed the other models 

tested. For each of the five chemical components, the correlation coefficient between experimental and predicted values was more 

than 0.96. This research concludes that the electronic nose and colorimeter show promise as quantitative and qualitative tools for 

assessing the quality of Chinese herbal remedies. 

 

 
 

 

 

A novel quality evaluation method for magnolia bark using 

electronic nose and colorimeter data with multiple statistical 

algorithms 

Jiahui, Shao, Yuebao, Yuetong, Guangzhao, Huiqin, Yonghong * 

School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 102488, China 

 

  

 

Introduction 

 

Magnolia officinalis REHD. & WILS. and 

Magnolia officinalis REHD. & WILS. are the two 

species of the Magnoliaceae family from which 

the Chinese word "hou po" (dry bark) is derived. 

Gingko biloba (Rehde & Wilkinson)1 For 

hundreds of years, people have turned to Hou Po, 

a traditional Chinese herbal medicine (CHM), for 

relief from a variety of gastrointestinal issues. A 

large number of clinical prescriptions and patent 

medications in China use it as an active 

ingredient. Traditional macroscopic identification 

based on subjective observations and linguistic 

descriptions is now the most utilized approach for 

assessing Hou Po quality in the market. Two kinds 

of chemical components are officially specified in 

the 2015 edition of the People's Republic of China 

Pharmacopeia.1 Clinical usage of Hou Po is 

hindered by the inadequacy of current criteria for 

assessing and monitoring its quality.1  

The quality of Hou Po is influenced by several 

things, but one of the most essential is where it is 

made. The provinces of Hubei, Sichuan, Zhejiang, 

Guangxi, and Fujian are among those in China 

where you may find Hou Po grown in a mix of 

natural and artificial settings. Distinct origins 

provide unique growth conditions, which in turn 



affect the final product's quality. Provinces like 

Hubei and Sichuan are known for producing high-

quality Hou Po. Specifically, geo-authentic 

medicinal substance is acknowledged for Hou Po 

samples from the Hubei city of Enshi. Research 

has shown that shown that the chemical 

composition, purity, and clinical effectiveness of 

samples originating from various locations vary.2 

Because even highly qualified specialists have a 

hard time telling them apart based on macroscopic 

identification and detecting one or two chemical 

components, Hou Po pieces from different origin 

sites may be readily mistaken in the marketplace 

and for therapeutic purposes.  

There are only two ingredients listed in the 2015 

edition of the People's Republic of China 

Pharmacopoeia: magno-lol and honokiol, with a 

combined level of no less than 2%. Because Hou 

Po's chemical makeup is diverse and varied, this 

may directly contribute to ambiguity. Determining 

the whole chemical profile of Hou Po—which 

includes volatile oils, lignans, alkaloids, and 

phenylethanolic glycosides—is insufficient.3 Hou 

Po gets its distinctive scent from volatile oils. Hou 

Po samples with a more robust scent are likewise 

thought to be of higher grade based on the 

trustworthy experience of macroscopic 

identification.4 The main components of lignans, 

magnolol and honokiol, have many biological 

effects, including antibacterial, anti-inflammatory, 

anti-neoplastic, and antioxidant properties.5—8 

According to other research, alkaloids in CHMs 

often have substantial  

 

the actions of pharmaceuticals.9, 10 Two of Hou 

Po's alkaloids, magnocurarine and magnocorine, 

have antifungal and antiplatelet activities, 

respectively.12 Consequently, while assessing the 

quality of Hou Po, it is important to consider this 

sort of component. Clinical trials have shown that 

a water decoction of Hou Po can improve 

gastrointestinal motility disorder and limit the 

development of human mesangial cells.13, 14 It is 

in the water-based extract that the phenylethanolic 

glycosides are most abundant. All things 

considered, these four aspects are very relevant to 

Hou Po's quality and should be considered 

accordingly. It is, therefore, critically necessary to 

create a new approach of assessing Hou Po's 

quality.  

Experts in traditional Chinese medicine say that 

color and smell are two of the most important 

ways to identify things at a macro level.4 But 

there has been a lack of specific scientific 

experimental evidence and just nebulous 

descriptions of these elements up to now. We have 

developed a colorimeter and an electronic nose 

(E-nose) to assess the color and odor of CHMs, 

respectively. As far as experimental data go, these 

two methods are fast, practical, and dependable. 

In contrast to other contemporary methods for 

determining CHM quality, such as gas 

chromatography (GC), mass spectrometry (MS), 

and high performance liquid chromatography 

(HPLC), the E-nose and colorimeter necessitate 

minimal sample volume, do not use organic 

reagents, and necessitate only basic sample 

pretreatments. Much more essential than just 

providing the contents of one or more 

components, they provide a complete picture in 

terms of color and scent. Food, agriculture, 

medicine, the environment, and the military have 

all seen increased usage of the E-nose and 

colorimeter in recent years.24 They have also 

been used to differentiate between CHMs of the 

same family25 and distinct varieties26, as well as 

between CHM samples from various locations27, 

with varying processing specifications28, and 

harvested at different times 29 based on odor or 

color traits.  

The current research is focused on creating a new 

way to quickly, precisely, and thoroughly assess 

the quality of Hou Po. In order to distinguish the 

Hou Po samples originating from various 

locations, we will utilize the E-nose and 

colorimeter to detect their aroma and color. The 

next step is to create discriminant models that use 

various techniques to determine the chemical 

component contents of the Hou Po samples and 

their origin.  

 

Materials and methods 

 
Chemicals and reagents 

 

The following sources were used: honokiol (batch 

number T28O6B5149), magnolol (batch number 

KS0912CB14), and magnocurarine (batch number 

M25J9S66499) as reference standards; 

magno⬂orine (batch number 3536) as a standard 

from Shanghai Standard Technology; analytical 

grade methanol (from Beijing Chemical Works, 

Beijing, China); and acetonitrile and methanol 

(from Fisher Scientific, Fair Lawn, NJ) as HPLC 



CTMJ | traditionalmedicinejournals.com Chinese Traditional Medicine Journal | 2023 | Vol 6 | Issue 6 

 

    ISSN : 2693 6356 

2023 | Vol 6 | Issue 6 

 
 

 
 

grade and analytical grade, respectively. A super 

water purification technology was used to obtain 

ultrapure water.  

 

 

The following provinces contributed to the 246 

batches of Hou Po: Zhe-jiang (51 batches), 

Sichuan (88 batches), and Hubei (107 batches). 

Professor Yaojun Yang of the Beijing University 

of Chinese Medicine's Department of Chinese 

Materia Medica (Beijing, China) verified the 

authenticity of all samples. 
 

E-nose analysis 

 

In this investigation, the scents of the Hou Po 

samples were analyzed using the a-Fox3000 E-

nose system (Alpha M.O.S., Toulouse, France). 

An HS-100 autosampler, a detector unit with a set 

of twelve chemical sensors of the metal oxide 

semiconductor type, and pattern recognition 

software (Alpha Soft V11) were the three primary 

components of the system. The twelve metal 

oxide sensors were assigned the numbers S1 

through S12. The sensors were LY2/LG, LY2/G, 

LY2/AA, LY2/GH, LY2/gCTL, T30/1, P10/1, 

P10/2, P40/1, T70/2, and PA/2, in that order. 

Relative change in resistance (DR/ R0) was used 

to represent the sensor responses. 

After grinding the Hou Po samples into a powder 

and passing them through a 50-mesh filter, 0.3 g 

was precisely weighed and sealed in a  

 

ten milliliter vial. A vial containing 1000 mL of 

headspace air was fed into the detector unit for 

examination by the sensor array after being 

incubated at 40 ◦C for 240 s at a rotating speed of 

250 r/s. The injection rate was 1000 mL/s and the 

temperature was set at 50 ◦C.  

A constant flow of 150 mL/min of clean air was 

maintained through the sensor chambers during 

the measurement procedure, serving as the carrier 

gas. The sensor data was captured for 120 

seconds, with 1 second intervals. To make sure 

the sensor response values were back to their 

baseline before smelling the next sample, the 600-

second purge time was enough. We measured 

each sample six times.  

All twelve sensors typically respond in the same 

way for a Hou Po sample, as seen in Fig. 1. The 

signal from a single sensor to a Hou Po sample as 

a function of time is shown by each curve. The 

reaction intensity is shown vertically on the x-

axis, while the horizontal axis represents the time 

up to 120 s. The study's analysis index was 

determined by taking the greatest response from 

each sensor.  

 
Colorimeter analysis 

 

In order to identify the color properties of the Hou 

Po samples, a Hitachi U-3010 ultravioletevisible 

spectrophotometer was used. A custom-built 

button-shaped glass color measurement plate, 

color analysis software, a white calibration plate, 

an integrating sphere, and other components made 

up the equipment. The uniform color space system 

employed in this work is the CIE 1976 L*a*b* 

(Fig. 2). The axes L*, a*, and b* are orthogonal in 

this color space. A greater number for the L* 

value suggests a brighter light.  

 

 

 



 
 

Fig. 1. A typical response of 12 sensors measuring of a Hou Po sample. 

 
 

 
 

Fig. 2.  CIE 1976 L*a*b* uniform color space system. 

 

 

example color that is lighter in hue and deeper in 

shade for values that are lower. With a* indicating 

red, -a* green, b* yellow, and -b* blue, the 

corresponding tone directions may be represented 

by the a* and b* values correspondingly. The 

visible color difference between two color points 

is shown by the term DE*ab, which is defined as 

the distance between two points in the color space 

divided by the product of (DL*)2, (Da*)2, 

(Db*)2}1/2.  

For color determination, the Hou Po samples were 

crushed into a powder and passed through a 50-

mesh filter. They were then deposited onto a glass 

color measurement plate fashioned like a button. 

Here are the measuring conditions: The settings 

for the experiment were as follows: starting 

wavelength was 780 nm, ending wavelength was 

380 nm, slit width was 1 nm, light source was 

D65, field of vision was set at 10◦, and the scan-  

 

ing at a rate of 600 nm/min. We measured each 

sample six times.  

 
Determination of magnolol and honokiol 

 

Magnolol and honokiol concentrations were 

determined by high-performance liquid 

chromatography (HPLC) as follows: After 

soaking at room temperature for 24 hours, 0.2 g of 

50-mesh sample powder was added to 25 mL of 

methanol in a conical flask. Just after filtration, 5 

mL of filtrate was carefully measured and 

transferred to a 25 mL volumetric flask. The flask 



CTMJ | traditionalmedicinejournals.com Chinese Traditional Medicine Journal | 2023 | Vol 6 | Issue 6 

 

    ISSN : 2693 6356 

2023 | Vol 6 | Issue 6 

 
 

 
 

was then filled with methanol until it reached a 

volume of 25 mL, and the mixture was mixed. To 

prepare the sample solution for high-performance 

liquid chromatography (HPLC) analysis, this 

mixture was passed through a 0.22-mm filter 

membrane.  

Using a mobile phase consisting of methanol 

(78%) and water (22%), a flow rate of 1 mL/min, 

a column oven temperature of 30 ◦C, an injection, 

and an Agilent ZORBAX SB-C18 column (4.6 

250 mm, 5 mm) from Agilent Technologies Inc. 

in Santa Clara, CA, the HPLC Agilent system was 

assembled.  

 

has a detection wavelength of 294 nm and a 

volume of 5 mL.  

To get concentrations of 0.162 and 0.3 mg/mL, 

respectively, of honokiol and magnolol, the 

standards were weighed and dissolved in 

methanol. Analyzing the standard solutions 

throughout a range of concentrations for honokiol 

and magnolol, respectively, from 0.162 to 

0.000405 and 0.3 to 0.0015 mg/mL, was done to 

test the linearity of this approach.  

 

Magnocurrine and magnesium determination  

 

We used ultra-performance liquid 

chromatography (UPLC) to identify the primary 

alkaloid components of Hou Po, magnocurarine 

and magnofactorine. After grinding, the Hou Po 

samples were passed through a 50-mesh filter for 

analysis. After that, exactly 0.2 g of powder was 

measured and added to a 50 mL conical flask 

containing 25 mL of methanol. The flask was then 

allowed to soak at room temperature for 24 hours. 

Once the filtration process was complete, the 

sample solution for UPLC analysis was isolated 

by passing it through a 0.22-mm filter membrane.  

This investigation made use of a Waters Acquity 

UPLC BEH-C18 column (2.1 50 mm, 1.7 mm), an 

Empower 3 workstation, and a PDA detector from 

Waters Corp. in Milford, MA. The mobile phase 

consisted of (A) phosphoric acid/water (0.2:100, 

v/v) and (B) acetonitrile. The elution was 

programmed as follows: 0e2 min (9%e11% B), 

2e5 min (11%e12% B), and 5e7 min (12%e15% 

B). The flow rate was 0.4 mL/min. The column 

oven temperature was 35 ◦C. The injection 

volume was 0.5 mL. The detection wavelength for 

magnocurarine was 282 nm, and for magno⬂orine 

it was 268 nm.  

With careful weighing, the magnocurarine and 

magno* fluorine standards were dissolved in 

methanol to concentrations of 0.512 and  

 

0.508 mg/mL, correspondingly. Analyzing the 

reference solutions across a range of 0.0512 to 

0.000512 mg/mL for magnocurarine and from 

0.0508 to 0.0001016 mg/mL for magnoЬorine 

allowed us to test the linearity of this approach.  

 
Determination of water-soluble extracts 

 

After combining 4 grams of 50-mesh sample 

powder with 100 milliliters of water in a 250 

milliliter conical flask, the mixture was allowed to 

soak for 24 hours at room temperature. Following 

filtering, precisely 20 mL of filtrate was added to 

an evaporation pan that had been dried to a 

consistent weight. The pan was then steamed in a 

water bath until completely dry. Afterwards, the 

evaporation pan was dried in a dryer at 105 ◦C for 

three hours, and subsequently, it was chilled in a 

desiccator for thirty minutes. At last, the 

evaporation pan was weighed with great precision. 

One way to measure the concentration of water-

soluble extracts was to compare the pre- and post-

weight of the evaporation pan. 

Statistical analysis 

 

First, the E-nose and colorimeter results were 

processed using ten classifiers from the Weka 

software (https://www.cs.waikato.ac. 

nz/ml/weka/). These classifiers include Bayes Net, 

Naive Bayes Net, Naive Bayes Updateable, 

LibSVM, Logistic Analysis, Multiple Layer 

Perception, RBF Network, NB Tree, Random 

Forest, and Random Tree. The goal was to create 

discriminant models that could distinguish the 

origin places of the Hou Po samples. We used the 

classification accuracy as our metric to assess 

these models. To get the classification accuracy, 



we used both the external test set verification 

technique and the ten-fold cross-validation 

approach. The external test set verification 

approach used a test set that included 30% of the 

whole data set. If the classification accuracy was 

below 80%, the findings would not be taken into 

consideration.  

To create discriminant models for predicting the 

quantities of associated chemical components: 

hon-okiol, magnolol, magnocurarine, magnoorine, 

and water-soluble extracts, the data set was 

analyzed using the Random Forest classifier from 

the Weka program. Use the following metrics: 

root mean squared error (RMSE), mean absolute 

error (MAE), and correlation coefficient (CC) to 

assess the quality of the existing models  

 

 

accuracy in classification. Classifiers LibSVM, 

Logistic Analysis, Multiple Layer Perception, NB 

Tree, Random Forest, and Random Tree 

demonstrated the required degree of feasibility 

and veracity in identifying the origin places of 

Hou Po using E-nose and colorimeter data, as 

shown in the classification accuracies (Table 1). 

With a maximum classification accuracy of 

99.53%, the Random Forest classifier in 

conjunction with the ten-fold cross-validation 

approach provided the best classification. In order 

to proceed with the studies, the Random Forest 

classifier was paired with the ten-fold cross-

validation approach.  

 

Prediction results of chemical components discriminant 

models based on the Random Forest classifier combined 

with the ten-fold cross-validation method 

 

The water-soluble extracts, honokiol, magnolol, 

magnocurarine, and magnoflorine were analyzed 

using the Random Forest classifier. In Table 2 you 

can see the accuracy of the predictions. With CC, 

MAE, RMSE, RAE, and RRSE values of 0.9781, 

0.1690, 0.3311, 14.6798, and 21.7669, 

respectively, for honokiol, it was determined that 

the selected method—the Random Forest 

classifier coupled with ten-fold cross-validation—

could forecast the concentrations of the five 

component chemicals of Hou Po. 

For a more natural comparison between expected 

and experimental results, see Fig. 3. As the data 

points go closer to the line indicating complete 

agreement in the scatter plots, the anticipated and 

experimental values become much more 

comparable. The most highly correlated of these 

five component prediction models was the one for 

water-soluble extracts. Magnocurarine and 

magnoflowerine models were quite similar.  

 

When it came to determining the Hou Po samples' 

origins and chemical makeup, the existing models 

based on their smell and color offered a quick and 

dependable way to accomplish so.  

 

Table 2 

Prediction accuracies of discriminant models for the five chemical 
components of 

Hou Po based on the Random Forest classifier combined with the ten-

fold cross- validation. 

(RMSE), relative absolute error (RAE), and root relative 
squared  
error (RRSE) were used as the index. The ten-fold cross-
validation method was applied to obtain the prediction 

accuracy. 

 

Results 

 
Classification results of discriminant models from different origin places using ten classifiers 

 
Ten different types  of  classifier  were used  to obtain precise 
Chemical components Values 

 
 

CC MAE RMSE RAE (%) RRSE (%) 
 

 

Honokiol 0.9781 0.1690 0.3311 14.6798 21.7669 

Magnolol 0.9737 0.2017 0.3666 18.8258 24.4372 

Magnocurarine 0.9633 0.0109 0.0193 21.0594 28.3004 

Magnoflorine 0.9663 0.0119 0.0209 19.4218 26.9892 

Water-soluble extracts 0.9818 0.3770 0.5914 15.4062 20.2298 
 

 

Abbreviations: CC: correlation coefficient; MAE: mean absolute error; RMSE: root mean squared error; RAE: relative absolute error; RRSE: root relative squared error. 

 
Table 1 

Classification accuracies of origin place discriminant models from ten classifiers. 
 

Classifiers Classification accuracy (%)   



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    ISSN : 2693 6356 

2023 | Vol 6 | Issue 6 

 
 

 
 

 Ten-fold cross-validation External test set verification 

Bayes Net 77.24 77.88  

Naive Bayes Net 72.63 72.69  

Naive Bayes Updateable 72.63 72.69  

LibSVM 91.53 90.07  

Logistic Analysis 92.68 92.55  

Multiple Layer Perception 99.05 97.29  

RBF Network 79.61 76.52  

NB Tree 97.36 95.94  

Random Forest 99.53 99.32  

Random Tree 97.36 94.81  



 

 
 

Fig. 3. Comparison between the predicted and experimental values of five chemical components of 
Hou Po. 

 

Discussion 

 

The classic technique of CHM authentication, 

macroscopic identification, has the benefits of 

being quick, easy, and effective.4,30 This 

approach is a crucial part of assessing the quality 

of CHM in the People's Republic of China 

Pharmacopoeia. The method's shortcomings, 

however, cannot be disregarded: it is very 

subjective and relies heavily on the assessors' own 

experiences. We have explored the potential of the 

E-nose and the colorimeter to measure the odor 

and color properties of CHMs, in an effort to 

circumvent these limitations. By combining these 

two methodologies with suitable statistical tools, 

the experimental findings demonstrate that a 

system based on instrumental sensory analysis can 

be set up to assess the quality of CHMs in a 

thorough manner.31, 32 Both the scientific 

rationale for macroscopic identification and a 

novel approach to creating an easy, quick, and 

systematic approach of assessing CHM quality. 

The exterior manifestations of the internal 

chemical components of CHM, including its smell 

and color, are extensive. Further study is needed 

to understand the link between the instrumental 

sensory features and interior chemical components 

of CHMs, since there is less research on the 

material basis of their odor and color 

characteristics.  

Not only does a CHM's chemical makeup 

determine its therapeutic efficacy, but it also 



CTMJ | traditionalmedicinejournals.com Chinese Traditional Medicine Journal | 2023 | Vol 6 | Issue 6 

 

    ISSN : 2693 6356 

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serves as a key quality indicator. Plants' chemical 

make-up varies depending on a variety of 

environmental and geographical variables at their 

point of origin, including but not limited to: 

height, terrain, soil, water quality, weather, 

sunlight, and rainfall. Consequently, CHM quality 

is strongly correlated with its area of origin and 

chemical components. Here, we use the E-nose 

and colorimeter to reliably identify where Hou Po 

come from and to forecast what kinds of 

chemicals are in them. rapidly. Further 

investigation into the relationship between quality, 

provenance, and chemical components is required, 

however E-nose and colorimeter technology do 

provide new ways to assess Hou Po's quality. 

Aside from instrumental sensory analysis, a 

discriminant model should be developed to predict 

the concentrations of additional chemical 

components, as well as to identify variations, 

specifications, and grades of Hou Po. This will 

help to augment and enhance the current method 

of quality assessment.Volatile oils, lignans, 

alkaloids, and phenylethanolic glycosides are the 

four primary types of compounds found in Hou 

Po. Only the contents of the last three chemical 

components have been predicted in this 

investigation using Hou Po's odor- and color 

fingerprints. The fragrant scent of Hou Po comes 

from its volatile components, which the E-nose 

may interact with to measure. In this way, they 

link the E-nose reaction to the scent of Hou Po. 

This further highlights the need of doing more 

investigations to identify the specific volatile 

components responsible for the E-nose reactions 

and how they relate to the Hou Po odor. Further 

investigation into the link between these factors 

and Hou Po quality is required. When utilizing the 

E-nose to assess the quality of Hou Po, volatile 

components are crucial. Detailed descriptions of 

future study findings pertaining to volatile 

components will be provided in subsequent 

studies.  

Conclusions 

 

Three Chinese provinces—Zhejiang, Sichuan, and 

Hubei—provided 246 batches of Hou-Po samples 

for this investigation. Specific procedures were 

used to determine the amounts of various 

chemical components, such as honokiol, 

magnolol, magnocurarine, magnolorine, and 

water-soluble extracts. Using the E-nose and the 

colorimeter, we were able to identify the smell 

and color features of the samples, which helped us 

design a quick and dependable method to 

thoroughly assess the quality of the Hou Po. Using 

a variety of statistical methods on this data, we 

were able to construct multiple discriminant 

models that could identify the locations of Hou 

Po's origin and provide predictions about the 

chemical components it contains. Based on the 

findings, it is feasible to accurately determine the 

source locations and forecast the associated 

chemical components by analyzing the smell and 

color of samples in conjunction with suitable 

statistical algorithms. The most accurate model 

was the one that used Random Forest classifier in 

conjunction with ten-fold cross-validation.  

Both the E-nose and the colorimeter have showed 

promise in this research as tools for quantitative 

and qualitative evaluation of CHM quality. 

Researchers are now working on a more sensitive 

and selective sensor for the volatile components of 

CHM, as well as more efficient feature extraction 

approaches, with the goal of improving the 

discriminant models.  

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2018-09-17 by Liu HX, Li Q, Yan B, Zhang L, 

and Gu Y. Using a network of MOS sensors and 

machine learning algorithms, this bionic 

electronic nose can identify different wine 

qualities. Paper published in Sensors in 2018 with 

the DOI number 19(1):E45.  

Louis A, Coradeschi S, Mani GK, Shankar P, and 

Rayappan JBB were the authors of passage 18. A 

review of electronic noses for food quality. 

“Journal of Food Engineering” 2015;144:103–

111.  

19. The authors are Kim YH, Yang YJ, Kim JS, 

and those others. A colorimetric sensor that is 

sensitive to aldehydes allows for non-destructive 

monitoring of apple ripening. Paper published in 

2018 in the journal Food Chemistry, volume 267, 

pages 149–156.  

This is the twenty-first paper by Lin H, Man ZX, 

Kang WC, Guan BB, Chen QS, and Xue ZL. A 

new colorimetric sensor array for tracking rice 

storage time has been developed using boron-

dipyrromethene dyes. "Food Chemistry" (2018, 

268:300–300).  

(Maniscalco and Motta, 2021). Electronic nose 

and NMR-based metabolomics: are they practical 

for clinical and inflammatory phenotyping? The 



CTMJ | traditionalmedicinejournals.com Chinese Traditional Medicine Journal | 2023 | Vol 6 | Issue 6 

 

    ISSN : 2693 6356 

2023 | Vol 6 | Issue 6 

 
 

 
 

article is published in the Archiv of Medical 

Research and has the DOI: 49/1:76.  

The authors of the article are Montuschi P, Mores 

N, Trove' A, Mondino C, and Barnes PJ. A 

technological nose for the field of respiratory 

medicine. Asthma. 2013;85(1):72–84.  

Blanco-Rodríguez A, Camara VF, Campo F, and 

colleagues [23]. Creating an electronic nose to 

identify and classify the many odors released by a 

wastewater treatment plant's various processes. 

Scientific Reports, 2018;134:92–100. 24.Using 

the differential electronic nose to detect 

explosives at concentrations as low as parts per 

million (Brudzewski K, Osowski S, Pawlowski 

W.). Actuator Sensor B Citation: Chem. 

2012;161(1):528–533. (25).Authors: Lin H, Yan 

YH, Zhao T, Jr. Using an electronic nose in 

conjunction with multivariate statistical studies, 

we can quickly distinguish between Apiaceae 

plants. Journal of Pharmacology and Biomedical 

Research. 2013;84:1e4. 26.Authors Li C, Xu F, 

Cao C, and more.... Two species of Asari were 

compared by Radix et  

 

Electronic nose, headspace GC-MS, and 

chemometrics for rhizoma.  

“J Pharmaceut Biomed Anal” (2013), 85: 231–

238. 27.Researchers Liu, Wang, Yang, et al. 

Radix Angelicae Sinensis growth zones may be 

quickly and accurately identified using an 

electronic nose in conjunction with multivariate 

statistical studies. “Sensors” (2014, 

14(11):20134e20148). 28.By Xie DS, Peng W, 

Chen JC, and colleagues. A new approach to 

Hawthorn and its derivatives product 

discrimination based on artificial neural networks 

and sophisticated sensory systems. Nutritional 

Science and Biotechnology. 2016;25(6):1545–

1550. 29.The authors of the article are Chen SG, 

Mao J, Han BX, and Chen NF. Electronic nose 

with a metal oxide sensor array for smelling Rosa 

laevigata. Journal of Brazilian Pharmaceutical 

Research, Volume 21, Issue 6, Pages 1150–1154, 

2011. 30.Chen ZZ, Yuen JPS, Jiang ZH, Leung 

KSY, Liang ZT, Hu YN, and Zhao ZZ. A process 

of verification  

 

important to the process of standardizing Chinese 

medicine. (Planta Med. 2006;72(10):865e874.) 

31.We are Zou HQ, Lu G, Liu Y, and others. Is it 

feasible to use an electronic nose in conjunction 

with several mathematical algorithms to 

noninvasively and quickly detect various 

Asteraceae plants? The citation for this article is J 

Food Drug Anaal. 2015;23(4):788–794. 32.The 

authors of the study include Gong JT, Zhao LY, 

Anders B, and others. Acidity of Armeniacae 

semen Amarum involving Bianzhuang Lunzhi. 

Reference: Zhongguo Zhongyao Zazhi. 

2016;41(23): 4375e4381. 33.Xiang Xi, Mi WJ, 

Yan YH, Li Y, Chen HR, and Zou HQ. The active 

components in licorice and their correlation with 

the color of its cross-section. Mater Med, World 

Scientific and Technological Monthly 

Transactions, 2017;19(11):1829–1835.  

 


