Digital Evaluation of Tobacco Style and Quality by Using ......Qiaoling Li, Ph.D. China Tobacco Fujian Industrial Co., Ltd 2019_ST26_LiQiaoling.pdf SSPT2019 - Document not peer-reviewed

Digital Evaluation of Tobacco Style and Quality by

Using Support Vector Machine Algorithm with

Thermal Analysis Spectra

Qiaoling Li, Ph.D.

China Tobacco Fujian Industrial Co., Ltd

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Background

1

In the processes of tobacco blend design and maintenance, the evaluation of

tobacco style and quality is very important.

Tobacco B

Chemical

analysis

Artificial

sensory

analysis

Reactants

Tobacco leaves

Products

Cigarette smoke

Tobacco A

Differences in style

and quality

High demands on formulators,Heavy workload

Complicated to detect and analyze

Pyrolysis and

combustion

reactions

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Background

2

In the processes of tobacco blend design and maintenance, the evaluation of

tobacco style and quality is very important.

Tobacco B

Chemical

analysis

Artificial

sensory

analysis

Reactants

Tobacco leaves

Products

Cigarette smoke

Tobacco A

Differences in style

and qualityPyrolysis and

combustion

reactions

Simple, fast and low cost

Process

Analysis

-5

-4

-3

-2

-1

0

375 475 575 675 775

DTG

(%

/min

)

Temperature (K)

Thermal analysis spectra

Tobacco A

Tobacco B

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3

Questions

Tobacco style

and quality

01

02

Is it possible ?

How to achieve the digital evaluation?

03 What can it do in practical application?

-5

-4

-3

-2

-1

0

375 475 575 675 775

DTG

(%

/min

)

Temperature (K)


Tobacco A

Tobacco B

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4

01 Is it possible ?

The pyrolysis and combustion characteristics of tobacco are closely related

to the cigarette burning behavior.

Figure 1 Schematic diagram of the major processes occurring in cigarette combustion

Tobacco Pyrolysis Combustion Ash

Volatile

gasesGases

Air

Smoke

Feedback LossHeat

Pyrolysis Zone Combustion Zone

Char

Self-sustaining burning cycle

Reference[1] Baker R R, Bishop L J. The pyrolysis of tobacco ingredients[J]. Journal of Analytical and Applied Pyrolysis, 2004, 71(1): 223–311.

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-0.025

-0.02

-0.015

-0.01

-0.005

0

300 400 500 600 700 800

DTG

(m

g/K

)Temperature (K)


5

01 Is it possible ?

The pyrolysis and combustion reactions process can be visually expressed

by thermal analysis spectra.

Pyrolysis

reaction

Combustion

reaction

Tobacco leave

Char

Ash

Combustion

characteristic

Pyrolysis

characteristic

Reference[2] YAN Cong, XIE Wei, LI Yuefeng, et al. Numerical simulation of cigarette smoldering[J]. Tobacco Science & Technology, 2014, 6: 15–21.

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-0.025

-0.02

-0.015

-0.01

-0.005

0

300 400 500 600 700 800

DTG

(m

g/K

)Temperature (K)


6

01 Is it possible ?



Full analysis

Pyrolysis

reaction

Combustion

reaction

Tobacco leave

Char

Ash

Combustion

characteristic

Pyrolysis

characteristicTobacco

composition


R1: MoistureR2: VolatileR3:HemicelluloseR4: CelluloseR5: Lignin

R1

R2 R3 R4

R5

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-0.025

-0.02

-0.015

-0.01

-0.005

0

300 400 500 600 700 800

DTG

(m

g/K

)Temperature (K)


7

01 Is it possible ?



Full analysis

Pyrolysis

reaction

Combustion

reaction

Tobacco leave

Char

Ash

Combustion

characteristic

Pyrolysis


composition

Physical

structure



R1

R2 R3 R4

R5

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-0.025

-0.02

-0.015

-0.01

-0.005

0

300 400 500 600 700 800

DTG

(m

g/K

)Temperature (K)


8

01 Is it possible ?



Full analysis

Pyrolysis

reaction

Combustion

reaction

Tobacco leave

Char

Ash

Combustion

characteristic

Pyrolysis


composition

Physical

structure



R1

R2 R3 R4

R5

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-0.025

-0.02

-0.015

-0.01

-0.005

0

300 400 500 600 700 800

DTG

(m

g/K

)Temperature (K)


9

01 Is it possible ?



Full analysis

Pyrolysis

reaction

Combustion

reaction

Tobacco leave

Char

Ash

Combustion

characteristic

Pyrolysis


composition

Physical

structure

The yield of cigarette smoke and the smoke composition Taste



R1

R2 R3 R4

R5

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01 Is it possible ?

Normalized root mean square error (NRMSE)

( ) ( )

( )

2

1

2

1

/ /

(%) 100

/

N

i ii

N

ii

dm dt dm dt

NRMSE

dm dt

=

=

−

=

A B

A

Difference degree

Crop years Cities Stalk positionsProvinces

Probability (P value) =0.2717 P=0.2310 P=0.0002P=0.0003

Reference [3] LI Qiaoling, CHEN Kunyan, LIU Zechun, et al. TGA-based analysis of pyrolysis differential between different tobacco samples[J]. Tobacco Science & Technology, 2017, 50(8): 75–79, 102.

300 350 400 450 500 550 600 650 700 750 800 850 900

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

DT

G (

%/m

in)

Temperature （K）

300 350 400 450 500 550 600 650 700 750 800 850 900

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

DT

G (

%/m

in)

Temperature （K）

300 350 400 450 500 550 600 650 700 750 800 850 900

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

DT

G (

%/m

in)

Temperature （K）

300 350 400 450 500 550 600 650 700 750 800 850 900

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

DT

G (

%/m

in)

Temperature （K）


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01 Is it possible ?

Normalized root mean square error (NRMSE)

( ) ( )

( )

2

1

2

1

/ /

(%) 100

/

N

i ii

N

ii

dm dt dm dt

NRMSE

dm dt

=

=

−

=

A B

A

Difference degree

Crop years Cities Stalk positionsProvinces

Probability (P value) =0.2717 P=0.2310 P=0.0002P=0.0003

Reference [3] LI Qiaoling, CHEN Kunyan, LIU Zechun, et al. TGA-based analysis of pyrolysis differential between different tobacco samples[J]. Tobacco Science & Technology, 2017, 50(8): 75–79, 102.

300 350 400 450 500 550 600 650 700 750 800 850 900

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

DT

G (

%/m

in)

Temperature （K）

300 350 400 450 500 550 600 650 700 750 800 850 900

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

DT

G (

%/m

in)

Temperature （K）

300 350 400 450 500 550 600 650 700 750 800 850 900

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

DT

G (

%/m

in)

Temperature （K）

300 350 400 450 500 550 600 650 700 750 800 850 900

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

DT

G (

%/m

in)

Temperature （K）

Style Quality


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01 Is it possible ?

The style and quality of 88 tobacco samples were sensory evaluated and

classified by 20 tobacco experts.

88 single-grade tobacco samples

Fujian province Yunnan province

1: Fujian Upper

2: Fujian Lower

3: Fujian Middle-1

4: Fujian Middle-2

5: Yunnan Upper

6: Yunnan Lower

7: Yunnan Middle-1

8: Yunnan Middle-2

Planting areas

Stalk positions

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01 Is it possible ?

Figure 2. Thermal analysis spectra of the tobacco leaves in each category

400 500 600 700 800

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

DT

G (

%/m

in)

Temperature（K）

Category 1, Fujian Upper

400 500 600 700 800

-5

-4

-3

-2

-1

0

8

9

10

Category 2, Fujian Lower

DT

G (

%/m

in)

Temperature（K）

400 500 600 700 800

-5

-4

-3

-2

-1

0 Category 3, Fujian Middle-1

DT

G (

%/m

in)

Temperature（K）

11

12

13

14

15

16

17

18

19

20

400 500 600 700 800

-5

-4

-3

-2

-1

0

28

29

30

31

32

33

34

35

Category 4, Fujian Middle-2

21

22

23

24

25

26

27

DT

G (

%/m

in)

Temperature（K）

400 500 600 700 800

-5

-4

-3

-2

-1

0 Category 5, Yunnan Upper

36

37

38

39

40

41

43

43

44

DT

G (

%/m

in)

Temperature（K）

400 500 600 700 800

-5

-4

-3

-2

-1

0

45

46

47

48

49

50

Category 6, Yunnan Lower

DT

G (

%/m

in)

Temperature（K）

400 500 600 700 800

-5

-4

-3

-2

-1

0Category 7, Yunnan Middle-1

58

59

60

61

62

63

64

DT

G (

%/m

in)

Temperature（K）

51

52

53

54

55

56

57

400 500 600 700 800

-5

-4

-3

-2

-1

0 Category 8, Yunnan Middle-2

77

78

79

80

81

82

83

84

85

86

87

88

65

66

67

68

69

70

71

72

73

74

75

76

DT

G (

%/m

in)

Temperature（K）

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01 Is it possible ?

Figure 3. Averaged thermal analysis spectra of the tobacco leaves in each category

400 500 600 700 800

-5

-4

-3

-2

-1

0 Category 1, Fujian Upper

DT

G (

%/m

in)

Temperature（K）

400 500 600 700 800

-5

-4

-3

-2

-1

0 Category 2, Fujian Lower

DT

G (

%/m

in)

Temperature（K）

400 500 600 700 800

-5

-4

-3

-2

-1


DT

G (

%/m

in)

Temperature（K）

400 500 600 700 800-5

-4

-3

-2

-1


DT

G (

%/m

in)

Temperature（K）

400 500 600 700 800

-5

-4

-3

-2

-1

0 Category 5, Yunnan Upper

DT

G (

%/m

in)

Temperature（K）

400 500 600 700 800

-5

-4

-3

-2

-1

0 Category 6, Yunnan Lower

DT

G (

%/m

in)

Temperature（K）

400 500 600 700 800

-5

-4

-3

-2

-1

0Category 7, Yunnan Middle-1

DT

G (

%/m

in)

Temperature（K）

400 500 600 700 800-5

-4

-3

-2

-1

0 Category 8, Yunnan Middle-2

DT

G (

%/m

in)

Temperature（K）

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400 500 600 700 800

-5

-4

-3

-2

-1

0 (b) Yunnan province

DT

G (

%/m

in)

Temperature（K）

Category 5, Upper

Category 6, Lower

Category 7, Middle-1


400 500 600 700 800

-5

-4

-3

-2

-1

0

Category 1, Upper

Category 2, Lower



DT

G (

%/m

in)

Temperature（K）

(a) Fujian province

15

01 Is it possible ?

Figure 4. Averaged thermal analysis spectra of the tobacco leaves from the same province but different stalk positions

Volatile

species

Volatile

species

Hemicellulose

Cellulose

Lignin

Hemicellulose

Cellulose

Lignin

Same province

Similar physical structure

Upper

Lower

Middle-1

Middle-2

Different proportions of volatile species

Lower < Upper < Middle-2 < Middle-1

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400 500 600 700 800

-5

-4

-3

-2

-1

0 (f) Middle-2

DT

G (

%/m

in)

Temperature（K）

Category 4, Fujian

Category 8, Yunnan

400 500 600 700 800

-5

-4

-3

-2

-1

0 (e) Middle-1

DT

G (

%/m

in)

Temperature（K）

Category 3, Fujian

Category 7, Yunnan

400 500 600 700 800

-5

-4

-3

-2

-1

0 (d) Lower

DT

G (

%/m

in)

Temperature（K）

Category 2, Fujian

Category 6, Yunnan

400 500 600 700 800

-5

-4

-3

-2

-1

0 (C) UpperD

TG

(%

/min

)

Temperature（K）

Category 1, Fujian

Category 5, Yunnan

16

01 Is it possible ?

Figure 5. Average thermal analysis spectra of the tobacco leaves from the same stalk position but different provinces

Different provinces

Different physical structures

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01 Is it possible ?

Stalk position and grade Province

StyleQuality

Volatile speciesHemicellulose, cellulose,

and lignin

Perceptual knowledge

400 500 600 700 800

-5

-4

-3

-2

-1

0

Category 1

Category 2

Category 3

Category 4

Category 5

Category 6

Category 7

Category 8

DT

G (

%/m

in)

Temperature（K）


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18

Questions

01

02

Is it possible ?



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19

02 How to achieve the digital evaluation ?

For classification problems, support vector machine (SVM) is one of the

recommended supervised learning algorithm.

Table 1. The classification of tobacco leaves in SVM

The traditional SVM only supports 2 classifications, that is not enough for 8 categories, so the one-against-all method is adopted. When one category is distinguished, the other categories are regarded as one category.

Reference[4] Hsu C W and Lin C J. A comparison of methods for multiclass support vector machines[J]. IEEE Trans. Neural Networks, 2002. 13(2): 415–425

Province Fujian Province Yunnan Province Total

Stalk position Upper Lower Middle-1 Middle-2 Upper Lower Middle-1 Middle-2

Category 1 2 3 4 5 6 7 8

Number

Training set5 2 8 13 7 5 12 22 74

Testing set2 1 2 2 2 1 2 2 14

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Kernel function often plays an important role when classifying with SVM.

Different kernel functions have different application scopes.

Gaussian kernel function

Binomial kernel function

The number of samples is much larger than the number of feature points

6000 feature points 88 tobacco samples＜

Linear kernel function

References[5] Yuan G X, Ho C H, and Lin C J. Recent advances of large-scale linear classification[J]. Proc. IEEE, 2012, 100(9): 2584–2603[6] Hsu C W, Chang C C, and Lin C J. A practical guide to support vector classification. Technical report, University of National Taiwan, Department of Computer Science and Information Engineering, (2003), pp. 1–12

-5

-4

-3

-2

-1

0

375 475 575 675 775

DTG

(%

/min

)

Temperature (K)


Tobacco A

Tobacco B

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21


The penalty parameter C in SVM also plays an significant role in the

training and prediction.

-4 -2 0 2 4

0.0

0.2

0.4

0.6

0.8

1.0

Training set

Testing set

Ac

cu

rac

y

lg(C)

Figure 6. The influence of the penalty function C on the accuracy of the training set and the testing set

C=1，lg(C)=0

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5 0 0 0 0 0 0 0

0 2 0 0 0 0 0 0

0 0 7 1 0 0 0 0

0 0 0 13 0 0 0 0

0 0 0 0 7 0 0 0

0 0 0 0 0 5 0 0

0 0 0 0 0 0 12 0

0 0 0 0 0 0 0 22

1 2 3 4 5 6 7 8

1

2

3

4

5

6

7

8

True

Predict

2 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0

0 0 2 0 0 0 0 0

0 0 0 2 0 0 0 0

0 0 0 0 2 0 0 0

0 0 0 0 0 1 0 0

0 0 0 0 0 0 1 1

0 0 0 0 0 0 0 2

1 2 3 4 5 6 7 8

1

2

3

4

5

6

7

8

True

Predict

Figure 7. The confusion matrix of the training set Figure 8. The confusion matrix of the testing set

Accuracy=98.6% Accuracy=92.9%

Category 3Fujian Middle-1

Category 4Fujian Middle-2

Category 7Yunnan Middle-1

Category 8Yunnan Middle-2

Misjudgment Misjudgment

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23




Similar style

Light flavor of tobacco

in south China still have differences

1: Fujian Upper

2: Fujian Lower

3: Fujian Middle-1

4: Fujian Middle-2

5: Yunnan Upper

6: Yunnan Lower

7: Yunnan Middle-1

8: Yunnan Middle-2

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24




Similar style


in south China still have differences

400 500 600 700 800

-4

-3

-2

-1

0Different provinces

Yunnan

Guizhou

Sichuan

Henan

Hunan

Fujian

Shandong

Liaoning

DT

G (

%/m

in)

Temperature（K）

In order to verify the effectiveness and practicality of this evaluation method, the most difficult case was chosen.

1: Fujian Upper

2: Fujian Lower

3: Fujian Middle-1

4: Fujian Middle-2

5: Yunnan Upper

6: Yunnan Lower

7: Yunnan Middle-1

8: Yunnan Middle-2

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25




Similar style


in south China still have differences√

1: Fujian Upper

2: Fujian Lower

3: Fujian Middle-1

4: Fujian Middle-2

5: Yunnan Upper

6: Yunnan Lower

7: Yunnan Middle-1

8: Yunnan Middle-2

Stalk positions√This evaluation method not only accurately distinguishes the style, but also gives very accurate results for the classification of tobacco quality.

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26

Questions

01

02

Is it possible ?

What can it do in practical application?03


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Thermal analysis spectra Machine learning algorithm

Modeling and Classification

Tobacco style and quality

1. Evaluation of tobacco

quality in each year2. Tobacco substitution 3. Cigarette formula desigh

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Machine learning scheme Artificial scheme

8 tobacco leaves in the blend of a cigarette need to be substituted

Thermal analysis spectra Formulator's experience

Application case in tobacco substitution

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10 20 30 40 50 60

-4

-3

-2

-1

0

DT

G (

%/m

in)

Time (min)

Original

Artifical

Machine learning

(1)

10 20 30 40 50 60

-4

-3

-2

-1

0

(2)

DT

G (

%/m

in)

Time (min)

Original

Artifical

Machine learning

10 20 30 40 50 60

-4

-3

-2

-1

0

(3)

DT

G (

%/m

in)

Time (min)

Original

Artifical

Machine learning

10 20 30 40 50 60

-4

-3

-2

-1

0

Original

Artifical

Machine learning

(4)

DT

G (

%/m

in)

Time (min)

10 20 30 40 50 60

-4

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-1

0

(5)

DT

G (

%/m

in)

Time (min)

Original

Artifical

Machine learning

10 20 30 40 50 60

-4

-3

-2

-1

0

(6)

DT

G (

%/m

in)

Time (min)

Original

Artifical

Machine learning

10 20 30 40 50 60

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-1

0

(7)

DT

G (

%/m

in)

Time (min)

Original

Artifical

Machine learning

10 20 30 40 50 60

-4

-3

-2

-1

0

(8)

DT

G (

%/m

in)

Time (min)

Original

Artifical

Machine learning

Figure 9 Thermal analysis spectra of 8 tobacco leaves in machine learning scheme and artificial scheme

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Table 2. Routine chemical components in cut fillers of cigarette before and after substitution

SamplesPuffing

number

Tar

mg·cig–1

Nicotine

mg·cig–1

CO

mg·cig–1

Original cigarette 6.5 11.6 0.94 12.8

Artificial scheme 6.3 11.5 0.97 13.1

Machine learning scheme 6.5 11.4 0.95 12.7

Table 3. Deliveries of tar, nicotine and CO in mainstream smoke of cigarette before and after substitution

SamplesNitrogen

%

Alkaloid

%

Sugar

%

K

%

Cl

%

Original cigarette 2.21 2.30 23.7 2.16 0.45

Artificial scheme 2.14 2.13 23.8 2.19 0.42

Machine learning scheme 2.21 2.21 23.9 2.15 0.43

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Machine learning scheme Artificial scheme

9 tobacco leaves in the blend of a cigarette need to be substituted

Thermal analysis spectra Formulator's experience

Application case in tobacco substitution

≧

Sensory evaluation

The results of sensory evaluation and cigarette smoke data indicated that the machine learning scheme could satisfy the application requirements.

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1. Thermal analysis spectra are the important data source for digital evaluation of tobacco style

and quality.

2. The thermal analysis spectra of 88 single-grade tobacco leaves have combined with SVM

algorithm for modelling and classification.

3. The results verify the effectiveness and practicality of thermal analysis spectra for the digital

evaluation of tobacco style and quality, and the machine learning scheme based on the thermal

analysis spectra is an efficient method for tobacco substitution. 32

Conclusions

-5

-4

-3

-2

-1

0

375 475 575 675 775

DTG

(%

/min

)

Temperature (K)

Tobacco A

Tobacco BTobacco composition

Physical structure

Pyrolysis and combustion characteristics

Accuracy of the training set =98.6% Accuracy of the testing set =92.9%

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Acknowledgements

Anan WuProfessor

Cooperators Group members

Chao Yin

Quanxing Zheng

Yumei Yu

Xiucai Liu

Xiaohua Deng

Tinggui Zhang

Jiawei Zhong

Hanchun Xu

Kai Lin

Pengfei Ma

Chaozhang Huang

Bin LiProfessor

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Documents

Digital Evaluation of Tobacco Style and Quality by Using ......Qiaoling Li, Ph.D. China Tobacco Fujian Industrial Co., Ltd 2019_ST26_LiQiaoling.pdf SSPT2019 - Document not peer-reviewed