Have a personal or library account? Click to login
Statistical Analysis for Comparison of the Results Obtained by Capillary Columns and Packed Columns in the Determination of Water Yield in Smoke Condensates Analyzed in Cigarettes for the 24th Asia Collaborative Study Cover

Statistical Analysis for Comparison of the Results Obtained by Capillary Columns and Packed Columns in the Determination of Water Yield in Smoke Condensates Analyzed in Cigarettes for the 24th Asia Collaborative Study

Open Access
|Sep 2020

Full Article

INTRODUCTION

The international standard 10362-1:1999 “Cigarette-Determination of water in smoke condensate – Part 1: Gas chromatographic method” (1) specifies only packed columns in the methodology for water yield analysis even though recently the use of capillary columns for both water and nicotine (2) has become relatively more popular across global laboratories.

Indeed, capillary columns were used in the recently updated CORESTA Recommended Method No 57, “Determination of water in tobacco and tobacco products by gas chromatographic analysis” (3).

Based on this situation, ISO/TC 126 decided in 2015 to create a Working Group 17 (WG 17) to revise ISO10362-1:1999 to include capillary columns as one of the methodology options, starting with discussion and a systematic review. At the first ISO/TC 126/WG 17 meeting, the necessity for a comparison of water yields using either packed or capillary columns in the methodology, was identified.

Yield data were gathered and analysed from several collaborative studies e.g., of 24th Asia Collaborative Study 2015 (ACS, the meeting was held in Bali in 2016), of the European Collaborative Study 2016 (EUCS, organized by the Committee on Tobacco and Tobacco Smoke of the German Institute for Standardization (DIN)) and of the CORESTA CM8 collaborative study 2017 (4). Water yield comparability was presented and confirmed in all collaborative studies at the WG17 meeting held in November 2017. The results from the monitor cigarette (CM8) from the CORESTA collaborative study were published by Crumpler et al. (4), but only one brand for test samples was used.

On the other hand, ACS 2015 and EUCS 2016 used five different brands for test samples. Although the results of EUCS didn’t contain datasets obtained with the combination of packed columns and linear smoking machines, the results of ACS (5) contained datasets obtained with all combinations of GC columns and smoking machines. Consequently, WG17 decided to use the results of ACS for the revision of the international standard.

This paper has been written to provide a comprehensive statistical comparison in terms of water yield differences between packed and capillary columns obtained from the 24th Asia Collaborative Study (2015) covering cigarettes across a typical range of “tar” yields.

EXPERIMENTAL
Asia Collaborative Study – Participants

64 laboratories participated in the 24th Asia Collaborative Study (ACS) held in 2015. All participating laboratories are listed in Table A of Appendix A. The laboratories marked with an asterisk provided more than one dataset which were obtained by using various combinations of linear and rotary smoking machines.

In this study, 86 datasets were submitted with their water yields analysed. They consisted of 46 datasets measured by capillary column, 39 datasets measured by packed column, and 1 dataset without mention which column was used.

Protocol

Participants were requested to follow the protocol “ACS” to analyse five test samples, including CORESTA Monitor CM8 (6) as listed in Table 1, and to report on parameters as listed in Tables 2 and 3.

Table 1

List of samples.

CodeSample nameSupplierOriginNFDPM level (mg/cig)Butt length (mm)
AMevius One BoxJTJapan135
BMarlboro Clear 3 BoxPMILithuania335
CKent 6 KS BoxRJRUSA635
DMevius BoxJTJapan1035
ECORESTA Monitor (CM8)CORGermany14.1*33

Suppliers: JT: Japan Tobacco Inc., PMI: Philip Morris International, RJR: R J Reynolds, COR: CORESTA

CM8 was provided by Cerulean in this year.

*

NFDPM was quoted from ‘CORESTA Approved Monitor No.8 (CM8) use of condition, June 2015’ (6).

Table 2

List of parameters to be reported.

Outline
Test periodSeptember 1st to November 30 th, 2015
Data set / sampleOne data set consists of 6 test results obtained from 6 runs. One test result was defined as the average yield obtained from 20 cigarettes in a single run.
Test parametersTPM, water, nicotine, NFDPM, CO, puffs
Original data sets86 data sets from 64 laboratories for each sample
Table 3

Dataset per sample.

Test parameterSmoking machineNumber of runsReported data / sample
TPM (mg/cig)Linear 20 port6 runs 5 cig/port × 4 ports/run6 test results, mean and standard deviation
Water (mg/cig)
Nicotine (mg/cig)
NFDPM (mg/cig)Linear 16 port
CO (mg/cig)Linear 10 port
Puff count (puffs/cig)Rotary6 runs (20 cig/run)

As shown in Table 1, the four sample brands covered the range in NFDPM from 1 to 10 mg/cig, NFDPM of CM8 was reported to be 14.1 mg/cig (6). CORESTA Monitor CM8 was provided by Cerulean or Borgwaldt KC year by year in turn and Cerulean provided it in that particular year.

One dataset consists of six test results obtained from six runs, as shown in Table 2. One test result was defined as the average yield obtained from 20 cigarettes in a single run. For linear smoking machines, four ports per brand were used within each run, and five cigarettes were smoked per port. Rotary smoking machine always used 20 cigarettes per run.

Raw data

Raw datasets are listed in APPENDIX B with the type of smoking machine and GC column, water yields for each run, mean and standard deviation (SD) for five test samples.

Statistical analysis – Exclusion of outliers

Numerical outlier technique: Cochran’s and Grubbs’ tests were applied in accordance with ISO 5725-2:1994 (7) to exclude outlying datasets prior to the determination of repeatability and reproducibility. Cochran’s test was applied to identify an outlier with statistically deviant standard deviation. Grubbs’ test was applied to identify an outlier in a univariate dataset that follows an approximately normal distribution.

After Cochran’s test, Grubbs’ test was applied to the mean values of the remaining datasets according to chapter 7.3.4.3 in ISO5725-2 (7). Grubbs’ test consists of two types of tests. One is to determine whether the largest or smallest observation is the outlier, this is called the single Grubbs’ test (chapter 7.3.4.1 in ISO 5725-2). The second test is to determine whether the two largest observations or two smallest observations are the outliers, it is called the double Grubbs’ test (chapter 7.3.4.2 in ISO 5725-2).

In the first step, the datasets were sorted in descending order for mean value. The Grubbs’ statistics Gp was calculated using the maximum mean value xp, ground mean and standard deviation s. Where the Grubbs’ statistics was higher than 1% of critical value (8), the maximum value was considered as outlier. The same process was applied for the dataset with the minimum mean value. When outliers are determined by single Grubbs’ test, the Grubbs’ test is completed. If there is no outlier in single Grubbs’ test, double Grubbs’ test for the two smallest or two largest observations should be applied.

Repeatability and reproducibility estimation

Water yield repeatability (r) and reproducibility (R) were calculated for all types of gas chromatographic columns and each type of GC column seperately, with all types of smoking machines according to ISO 5725-2:1994 (7), by using the data that remained after the removal of outliers.

RESULTS
Statistical analysis
1
Mean and standard deviation

Mean and standard deviation for each dataset were calculated and are listed in Table 4.

Table 4

Mean and standard deviation (SD) of water yields for each data set (unit: mg/cig).

No.Smoking machine aGC column bLab. codeSample ASample BSample CSample DSample E

MeanSDMeanSDMeanSDMeanSDMeanSD
1RC01R0.070.0310.140.0370.960.1031.690.0861.900.109
2RC02R0.090.0610.300.0841.010.1541.930.2072.230.172
3LP03AL0.080.0660.170.0530.450.0461.030.0531.210.052
4RP03BR0.070.0520.150.0310.550.1111.220.1671.340.182
5LP04L0.150.0240.330.0460.690.0651.340.0781.560.121
6LP05L0.100.0290.290.0420.680.0571.540.0761.720.092
7LC06AL0.090.0300.380.1760.610.1211.380.2281.620.393
8LC06BL0.200.1040.350.2370.550.1801.000.2161.500.481
9RC07AR0.120.0350.430.0560.960.0481.900.0972.160.132
10RC07BR0.100.0360.350.0390.920.0361.830.1162.170.033
11LC07CL0.070.0220.190.0420.580.0171.230.1511.430.050
12RC08R0.090.0410.270.0420.870.0561.690.0891.970.115
13RC09R0.080.0800.230.0410.800.1181.550.1071.900.090
14RC10R0.040.0230.260.0210.860.0512.020.0492.140.080
15LC11L0.060.0430.210.0430.490.1311.210.1301.320.074
16RP12R0.080.0230.310.0681.050.0851.720.0332.190.126
17RC13AR0.130.0370.290.0380.930.0121.450.1151.520.115
18RC13BR0.130.0170.340.0790.880.0531.540.0951.680.170
19RP14R0.170.0170.370.0710.860.0881.670.1791.900.096
20RC15AR0.140.0530.320.0420.970.0551.690.0371.920.066
21LC15BL0.130.0850.170.1000.510.1131.130.1381.520.215
22RP16R0.080.0590.270.1050.740.1341.540.1131.700.148
23RP17AR0.090.0150.270.0310.770.1031.520.1451.820.112
24LP17BL0.100.0220.210.0340.530.0721.150.1411.310.076
25RC18AR0.100.0040.350.0340.880.0781.630.0961.780.145
26LC18BL0.090.0190.300.0560.660.1581.450.1841.390.203
27LC19AL0.060.0170.250.0130.620.0441.470.0781.630.089
28RC19BR0.130.0380.280.0390.900.0611.690.0732.030.066
29LC19CL0.080.0140.270.0180.670.0241.470.0851.730.112
30RC19DR0.080.0610.250.0540.820.0781.470.0831.820.033
31LP19EL0.060.0130.220.0100.550.0331.360.0291.440.040
32LP20L0.060.0140.270.0390.630.0731.450.0581.620.054
33LC21L0.130.0040.260.0120.770.0121.450.0101.840.039
34RC22R0.110.0830.290.0610.870.1561.800.1972.060.101
35RC23R0.000.0050.240.0760.800.1251.460.0721.800.052
36RC24AR0.110.0050.230.0170.730.0141.430.1191.790.088
37LC24BL0.120.0080.330.0090.640.0161.380.1281.660.050
38RP25R0.100.0140.370.0431.030.1161.940.1702.200.213
39RC26AR0.140.0510.310.0660.910.0451.920.2272.130.137
40RC26BR0.140.0520.380.0441.070.0632.030.1302.330.077
41LC27L0.090.1050.190.0820.570.0881.210.1771.350.108
42LP28L0.210.0780.330.1030.700.1191.550.1541.870.167
43LP29L--0.270.1040.700.0731.410.2621.600.212
44RP30R0.090.0220.350.0641.030.0772.030.1212.100.105
45LP31L0.080.0530.400.3450.550.2131.330.2021.530.236
46RC32AR0.060.0310.200.0930.810.1021.610.0831.880.075
47RC32BR0.070.0410.290.1060.880.1381.730.1201.950.161
48RC33R0.050.0110.240.0410.700.0851.360.1551.580.110
49RP34R0.160.0720.390.0841.210.1342.260.2442.570.321
50LP35L0.110.0920.620.3710.840.2341.750.2612.210.219
51RC36AR0.170.0150.520.0300.890.0561.370.0532.300.060
52RC36BR0.190.0340.520.0250.940.0361.460.0692.430.051
53RC37AR0.080.0840.200.0980.700.1831.530.2311.780.341
54LC37BL0.110.0870.420.1810.850.1711.310.1781.700.152
55RP38R0.140.0710.280.0240.860.0601.730.1001.880.073
56RP39R0.130.0480.350.0691.030.1501.910.2032.010.518
57RP40R0.140.0840.370.0761.300.3292.440.2872.700.219
58RC41R0.110.0310.250.0100.830.1001.420.1171.780.106
59LC42L0.100.0170.260.0440.570.0521.190.0401.520.104
60LP43AL0.210.0570.440.1120.750.0321.360.0721.630.136
61LP43BL0.200.0140.470.1780.730.0151.350.0641.580.069
62LC44AL0.110.1050.270.1860.550.1501.290.1511.660.129
63LC44BL0.070.1080.220.0350.610.1381.350.1861.580.156
64RP45R0.060.0260.230.0320.700.0741.580.1681.710.073
65LP46L0.030.0540.140.1080.500.0871.030.1261.010.079
66LP47L0.190.0720.420.1250.780.0991.550.1721.700.259
67LC48L0.100.0040.300.0100.770.0181.300.0371.780.028
68RP49R0.210.0710.330.1171.050.1041.850.1081.940.129
69RP50AR0.070.0540.230.0400.770.0341.630.1281.830.113
70RP50BR0.040.0310.190.0390.750.0841.650.0831.850.047
71LP50CL0.090.0450.250.0810.560.0741.380.0981.460.127
72RC51R0.110.0230.400.0530.980.1211.950.1222.190.160
73RC52R0.000.0000.050.0620.640.1231.790.1672.180.192
74LP53L0.060.0150.260.0250.750.0101.600.0701.690.047
75RC54R0.090.0210.250.0260.810.0911.500.0901.850.064
76RUN55R0.440.1500.410.1390.940.1201.250.1921.970.195
77LP56L1.430.3681.840.7132.470.9363.821.0504.331.388
78RP57R0.130.0230.350.0530.970.0451.990.1042.260.088
79LP58L0.070.0280.230.0910.520.0511.220.1121.500.113
80LP59L0.720.6071.000.6601.240.6371.790.4933.270.758
81RP60AR0.070.0150.300.0390.850.0991.580.0852.170.127
82LP60BL0.070.0210.270.0460.660.0791.610.1142.410.243
83RP61R0.130.0080.300.0240.710.0161.010.0161.900.012
84RC62R0.040.0430.310.1341.040.1622.260.2882.350.326
85LC63L0.180.0080.340.0150.730.0351.240.0151.690.041
86RP64R0.040.0150.220.0730.590.1021.410.1621.430.170
a

Smoking machine R: rotary smoking machine, L: linear smoking machine

b

GC column C: capillary column, P: packed column, UN: unknown

The labcode was randomly assigned and is in no way related to the participation numbers of the laboratories, which were given in numerical order of data submission.

2
Exclusion of outliers

85 datasets, shown in Table 4, were sorted in descending order of their standard deviation. The test statistic C was calculated and compared with 1% of critical value. Cochran’s critical values are given in ISO 5725-2 (7) only up to 40 numbers of data sets.

As the numbers of datasets exceeded 40 in this study, Cochran’s critical value at corresponding numbers of datasets were calculated by use of the approximation in (9) which extends the Cochran’s test beyond 40 data sets.

When the test statistics was higher than the critical value, the dataset was considered an outlier. After the exclusion of an outlier dataset, the test statistic C for the next dataset with a higher standard deviation was calculated and compared to the critical value. Prior to the analysis the maximum number of outlier tests was restricted to four to avoid an excessive exclusion of dataset. The results of Cochran’s test are listed in Table 5. Applying Cochran’s test, two or three datasets were excluded as outliers. Grubbs’ test was applied for the mean values of the remaining datasets. There was no outlier in Grubbs’ test. The excluded datasets determined by Cochran’s test and Grubbs’ test are listed in Table 5.

Table 5

Results of outlier tests.

SampleCochran’s testGrubbs’ testRemaining data sets
A59 L, 56 L, 55 RN/A82
B56 L, 59 L, 35 LN/A82
C56 L, 59 L, 40 RN/A82
D56 L, 59 LN/A83
E56 L, 59 L, 39 RN/A82

N/A: Not applicable

Two or three datasets from a total of 85 datasets were excluded from further statistical evaluation. The number of datasets classified by GC column type and smoking machine type is listed in Table 6.

Table 6

Number of datasets classified by GC column type and smoking machine type.

Sample codeTotal datasetsColumn typeTotal by GC columnLinear smokingRotary smoking
A82Capillary461729
Packed361719
B82Capillary461729
Packed361719
C82Capillary461729
Packed361818
D83Capillary461729
Packed371819
E82Capillary461729
Packed361818
3
Comparison of capillary column with packed column

Datasets were classified by GC column type into capillary data and packed column data. Box plots of each column type for each test sample are shown in Figures 1 to 5.

Figure 1

Box plots of water yield for sample A. ⋄ indicate mean values

Figure 2

Box plots of water yield for sample B.

Figure 3

Box plots of water yield for sample C.

Figure 4

Box plots of water yield for sample D.

Figure 5

Box plots of water yield for sample E.

The diamond-shaped symbols indicate the mean values. The interquartile range (IQR) of capillary columns for sample A was narrower than the IQR of packed columns. IQR of capillary columns for samples B, C, D and E were almost identical to those of the packed columns. The t-test of Welch was applied to confirm the statistical difference in water yield determination by packed columns and capillary columns. The results of the t-test are listed in Table 7.

Table 7

Results of t-test in water yields between capillary columns and packed columns.

Sample code“Tar” (mg/cig)Capillary (mg/cig)Packed (mg/cig)t-test a

MeanSDMeanSD
A10.0970.0430.1040.052ns
B30.2870.0890.2900.074ns
C60.7850.1550.7530.188ns
D101.5340.2721.5580.321ns
E14.11.8360.2871.7910.376ns
a

t-test ns: not significant

No significant difference between packed columns and capillary columns in water yield determination was observed in all test samples.

4
Comparison of water yields by smoking machine type and GC column type

Datasets were classified into four groups by the combination of GC column type and smoking machine type.

Box plots for each test sample are shown in Figures 6 to 10.

Figure 6

Box plots of water yield for sample A.

Figure 7

Box plots of water yield for sample B.

Figure 8

Box plots of water yield for sample C.

Figure 9

Box plots of water yield for sample D.

Figure 10

Box plots of water yield for sample E.

L/Cap means a combination of linear smoking machine and capillary column. L/Packed means a com bination of linear smoking machine and packed column. R/Cap means a combination of rotary smoking machine and capillary column. R/Packed means a combination of rotary smoking machine and capillary column.

IQR and median of the four groups for samples A and B were almost identical. But IQR and median of rotary and linear smoking machines seemed to be different for samples C, D and E. The results of the t-test are listed in Tables 8 and 9. A statistically significant difference in water yields was not observed between capillary columns and packed columns within the same type of smoking machine. On the other hand, statistically significant differences in water yields were observed for samples C, D and E between rotary smoking machines and linear smoking machines within the same type of GC column.

Table 8

Results of t-test in water yields between capillary and packed column within same type of smoking machine (unit: mg/cig).

Sample codeLinearRotary

CapillaryPackedt-test aCapillaryPackedt-test a


MeanSDMeanSDMeanSDMeanSD
A0.1060.0410.1020.058ns0.0920.0440.1010.047ns
B0.2730.0650.2860.082ns0.290.1010.2960.068ns
C0.6320.1000.6450.113ns0.8740.1030.8610.188ns
D1.2870.1371.3910.193ns1.6760.2291.7180.338ns
E1.5760.1591.6140.324ns1.9880.231.9690.346ns
a

t-test ns: not significant

Table 9

Results of t-test in water yields between linear and rotary smoking machine within same type of GC column (unit: mg/cig).

Sample codeCapillaryPacked

LinearRotaryt-test aLinearRotaryt-test a


MeanSDMeanSDMeanSDMeanSD
A0.1060.0410.0920.044ns0.1020.0580.1010.047ns
B0.2730.0650.2900.101ns0.2860.0820.2960.068ns
C0.6320.1000.8740.103**0.6450.1130.8610.188**
D1.2870.1371.6760.229**1.3910.1931.7180.338**
E1.5760.1591.9880.230**1.6140.3241.9690.346**
a

t-test ns: not significant,

**

1% significant

5
Repeatability and reproducibility estimation

The estimated values, which were calculated for all types of GC columns, capillary columns and packed columns are listed in Table 10. In comparison with repeatability and reproducibility defined in ISO 10362-1:1999, the estimated repeatability and reproducibility tends to be smaller than in ISO 10362-1:1999.

Table 10

Estimated r and R for all water data, capillary columns and packed columns (unit: mg/cig).

Sample codeAll dataCapillary columnsPacked columns

MeanrRMeanrRMeanrR
A0.1010.1290.1760.0970.1310.1690.1040.130.187
B0.2860.2250.3070.2840.2090.3120.290.2430.302
C0.7710.2670.5340.7850.2810.5050.7530.2460.572
D1.5250.3650.8181.5180.3490.7731.5340.3790.883
E1.8160.4240.9991.8360.4320.8951.7910.4141.121
DISCUSSION
Comparison of capillary column with packed column

Crumpler et al. (4) also reported that there was no significant difference in water yield of CM8 between packed columns and capillary columns.

In our study, we could show that no significant difference was observed for water yields in cigarette smoke condensate between capillary and packed columns, not only from CM8 but also from other test samples, covering the majority of “tar” value products that are sold in the market.

Comparison of water yields by smoking machine type and GC column type

There was no significant difference in water yields between linear type smoking machines and rotary type smoking machines for samples A and B, which had the lowest water yields. On the other hand, the water yields for samples C, D and E by rotary type smoking machines were higher than those by linear type smoking machine.

The differences in water yields between rotary and linear smoking machines were already observed in the CORESTA Harmonization Study in 1991 (10) and in the CORESTA Collaborative Study conducted by CORESTA CO Sub-Committee (11). Although the differences in CO yields and NFDPM were improved and the differences in water yields became smaller through the improvement of air flow around cigarettes, higher water yields were still observed in the results obtained with rotary smoking machines under the ISO smoking regime (4, 12). Nevertheless, such a difference is no real influence in case of evaluation of the data from each smoking machine type. Comparisons between the two types of columns were carried out with a wider range of “tar” yields for each smoking machine type. This evaluation did not show significant difference between the data from packed columns and capillary columns.

Furthermore it was confirmed that there was no significant difference in the lower range of water yields between packed columns and capillary columns, with or without distinction of smoking machine type.

Comparison of estimated repeatability and reproducibility with the past results

The comparison of repeatability and reproducibility estimated by using all datasets as well as datasets classified by GC column type were made via an F-ratio test with a Bonferroni adjustment for multiple comparisons. No significant differences between capillary and packed columns were observed when comparing repeatability and reproducibility among the samples.

Estimated r and R listed in Table 10 were compared to those of ISO 10362-1:1999 (1) listed in Table 11 by use of 95% of upper limit of prediction bands (ULP) and lower limit of prediction bands (LLP). The ULP and LLP were determined by a linear regression analysis of mean, repeatability and reproducibility defined in ISO 10362-1:1999 (1).

Table 11

Repeatability and reproducibility in ISO 10362-1:1999.

Mean value mw (mg/cig)Repeatability limit r (mg/cig)Reproducibility limit R (mg/cig)
0.0830.1540.241
0.1530.2280.353
0.3380.2720.381
0.9620.4070.734
1.5950.5610.935
3.1870.9081.680

The repeatability estimated by using all datasets as well as datasets classified by GC column type were plotted with those of ISO 10362-1:1999 and 95% of upper and lower limits for prediction bands (Figure 11).

Figure 11

Comparison of estimated repeatability (r) for all, capillary and packed columns with those of ISO 10362-1 and upper and lower limits for prediction bands.

95% LLP: 95% of lower limit for prediction bands 95% ULP: 95% of upper limit for prediction bands

The estimated repeatability for samples A and B were within the upper and lower limits for prediction bands. All of the estimated repeatability for samples C, D and E were smaller than 95% of lower limit for prediction band. This means that the estimated repeatability might be similar or smaller than the values in ISO 10362-1:1999.

The reproducibility estimated by using all datasets as well as datasets classified by GC column types were plotted with those of ISO 10362-1:1999 and 95% of upper and lower limits for prediction bands (Figure 12).

Figure 12

Comparison of estimated reproducibility (R) for all, capillary and packed columns with those of ISO 10362-1 and upper and lower limits for prediction bands.

95% LLP: 95% of lower limit for prediction bands 95% ULP: 95% of upper limit for prediction bands

The estimated reproducibility of packed columns was almost within the 95% upper and lower limit of prediction bands. On the other hand, the estimated reproducibility of capillary columns was smaller than 95% of lower limit of prediction band. This means that the estimated reproducibility of capillary columns is better than the reproducibility in ISO10362-1: 1999.

The estimated r and R by capillary column, packed column and all types of GC column are within 95% of upper limit for prediction band.

The estimated r and R for all data, capillary column and packed column are confirmed to be comparable to those of ISO 10362-1:1999, and these values can cover a wider range of water yields by ISO Smoking Regime.

The comparative results of water yields and estimated r and R strongly suggests that capillary columns are an appropriate alternative column for the gas chromatographic procedure of ISO 10362-1.

Language: English
Page range: 97 - 118
Submitted on: Jun 11, 2020
Accepted on: Aug 30, 2020
Published on: Sep 25, 2020
Published by: Institut für Tabakforschung GmbH
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2020 Hisayuki Takahashi, Masayasu Tanaka, published by Institut für Tabakforschung GmbH
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License.