HPHC Testing of Tobacco and Smoke to Examine Cigarette Temporal Variability

Contributions to Tobacco & Nicotine Research
formerly: Beiträge zur Tabakforschung International
Volume 31 @ No. 2 @ July 2022
DOI: 10.2478/cttr-2022-0012
HPHC Testing of Tobacco and Smoke
to Examine Cigarette Temporal Variability *
by
Rana Tayyarah 1
, Michael J. Morton 2
, and Jason W. Flora 2
1
ITG Brands, LLC, Greensboro, NC, USA
2
Altria Client Services Inc., Richmond, VA, USA
SUMMARY
Commercial cigarettes were analyzed for harmful and
potentially harmful constituents (HPHCs) in tobacco and
smoke to investigate temporal product variability inde-
pendent of analytical variability over one week, one year,
andthree years. Cigarettes fromthe worldwide market with
various design features were collected over a 3-year period,
stored, and tested concurrently for HPHCs to minimize
analytical variability; repeat testing of reference cigarette
3R4F was included as an analytical control for the study
design. Physical parameters were found to be relatively
consistent. No trends in variability were noted based on
blend type, smoke analyte matrix, or magnitude of an
HPHC’s yield. Combustion-related HPHCs generally
showedlowvariation.Long-termbatch-to-batchvariability
was found to be higher than short-term variability for
tobacco-related compounds that have the potential to vary
over time due to weather and agronomic practices. ?Tar”,
nicotine, and carbon monoxide were tested in multiple labs
and showed greater lab-to-lab variability than batch-to-
batch variability across all phases. Based on the results of
this study, commercial cigarette products appear to have
relatively low product variability. The low analyte variabil-
ity noted in this study with products tested under uncon-
ventionally controlled analytical conditions serves to
indicate that analytical variability may be a significant con-
tributor to overall variability for general product testing
over time and in interlaboratory studies. Laboratory controls
and using a matched reference product across studies and
between laboratories are important to assess testing dif-
ferences and variability. [Contrib. Tob. Nicotine Res. 31
(2022) 112–126]
ZUSAMMENFASSUNG
Handelsübliche Zigaretten wurden in Hinblick auf schädli-
che und potenziell schädliche Bestandteile (HPHCs, harm-
ful and potentially harmful constituents) des Tabaks und
Rauchs analysiert, um die zeitliche Produktvariabilität
innerhalb eines Zeitraums von einer Woche, einem Jahr
und drei Jahren unabhängig von der analytischen Variabili-
tät zu untersuchen. Über einen Zeitraum von drei Jahren
wurden auf dem internationalen Markt Zigaretten mit ver-
schiedenen Designmerkmalen ausgewählt, gelagert und
zeitgleich auf HPHCs untersucht, um die analytische
Variabilität auf ein Minimum zu reduzieren. Als Teil des
Studiendesigns wurden Wiederholungsuntersuchungen der
Referenzzigarette 3R4F als analytische Kontrolle durch-
geführt. Die physikalischen Parameter stellten sich als
relativ konsistent heraus. Es wurden keine Variabilitäts-
trends basierend auf dem Mischungstyp, der Matrix der
Rauchanalyten oder dem Umfang der Ausbeute eines
HPHC festgestellt. Beidenverbrennungsbedingten HPHCs
zeigten sich generell geringe Schwankungen. Bei den
tabakspezifischen Verbindungen,dieaufgrund von Wetter-
bedingungenund AnbaumethodenpotenziellimZeitverlauf
Schwankungen unterliegen können, war die langfristige
Variabilität zwischen verschiedenen Chargen größer, als
die kurzfristige Variabilität. Der Gehalt an ?Teer”, Nikotin
und Kohlenstoffmonoxid wurde in mehreren Labors unter-
*Received: 19th
December 2021 – accepted: 24th
May 2022
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
© 2022 Authors who publish with this journal retain all copyrights and agree to the terms of the above-mentioned CC BY-NC-SA 4.0 license.

sucht. Hier war in allen Studienphasen die Variabilität von
Labor zu Labor größer als die Variabilität zwischen
verschiedenen Chargen. Basierend auf den Ergebnissen
dieser Studie scheinen handelsübliche Zigarettenerzeug-
nisse eine relativ geringe Produktvariabilität aufzuweisen.
Bei den unter ungewöhnlich kontrollierten Analysebedin-
gungen untersuchten Produkten konnte in dieser Studie
eine geringe Variabilität der Analyten festgestellt werden.
Dies könnte ein Hinweis darauf sein, dass bei allgemeinen
Produktuntersuchungen die analytische Variabilität im
Zeitverlauf und bei Laborvergleichsuntersuchungen ein
wichtiger Faktor für die Gesamtvariabilität sein könnte.
Laborkontrollen sowie die studien- und laborübergreifende
Verwendung eines abgestimmten Referenzprodukts sind
daher wichtige Instrumente zur Bewertung von Prüfunter-
schieden und -variabilität. [Contrib. Tob. Nicotine Res. 31
(2022) 112–126]
RESUME
Des cigarettes du commerce furent analysées à la recherche
des composés nocifs et potentiellement nocifs (HPHC,
harmful and potentially harmful constituents) présents dans
le tabac et la fumée afin d’étudier, sur une semaine, une
année et trois ans, la variabilité temporelle des produits in-
dépendammentdelavariabilitéanalytique.Descigarettes en
vente sur le marché mondial et présentant diverses caracté-
ristiques de conception furent sélectionnées sur une période
de trois ans, entreposées et testées simultanément à la
recherche descomposés nocifs et potentiellement nocifs afin
de minimiser la variabilité analytique; des tests répétés sur la
cigarette de référence 3R4F furent également pratiqués en
guise de contrôle analytique pour la conception de l’étude.
Lesparamètresphysiquess’avérèrentrelativementconstants.
Aucune tendance de variabilité ne fut observée selon le type
de mélange, la matrice des analytes de fumée ou la magni-
tude de rendement des HPHC. Les composés nocifs et
potentiellement nocifs liés à la combustion affichèrent, en
règle générale, une faible variation. La variabilité à long
terme entre lots s’avéra plus élevée que la variabilité à court
terme des composés liés au tabac qui potentiellement varient
au fil du temps en raison des conditions météorologiques et
des pratiques agronomiques. Le goudron, la nicotine et le
monoxyde de carbone furent analysés par de multiples
laboratoires et affichèrent une variabilité entre laboratoires
supérieure à la variabilité entre lots dans toutes les phases.
Sur la base des résultats de la présente étude, il semble que
les cigarettes du commerce présentent une variabilité
relativement faible entre produits. La faible variabilité des
analytes observée lors de cette étude portant sur des produits
testés dans des conditions d’analyse contrôlées non conven-
tionnelles concourt à indiquer que la variabilité analytique
pourrait être un facteur contributif significatif de la variabili-
té globale lors des essais généraux des produits au fil du
temps et des études inter-laboratoires. Les contrôles en
laboratoires et l’utilisation d’un produit de référence concor-
dant pour les différentes études et pour les différents labora-
toires sont des conditions importantes pour l’évaluation des
différences et de la variabilité des essais. [Contrib. Tob.
Nicotine Res. 31 (2022) 112–126]
INTRODUCTION
Smoking cigarettes is a cause of serious diseases in smok-
ers; harmful and potentially harmful chemicals (HPHCs)
have been identified by scientists and international regula-
tory bodies for product monitoring and regulatory testing
(1–9). To make robust science-based decisions regarding
tobacco and smoke HPHCs, a full understanding of the
variability caused by either the analytical measurements or
the product variability in these measurements is needed.
Analytical variability can occur due to complexity of
matrices, instability or low HPHC concentrations, and
changes in laboratory, operator, reference chemicals, or
instrumentation over time. In addition, product variability
may result from raw material variability. Tobacco, for
example, is grown in different regions of the world under
a variety of soil conditions, farming practices, and environ-
mental conditions. Like testing, production variables in-
clude location, operator, and equipment. These factors can
lead to variability in cigarette parameters such as ventila-
tion and weight and potentially the levels of HPHCs found
in tobacco and cigarette smoke.
There has been a great deal of collaborative research to
understand and minimize analytical variability. For example,
standardized smoke-collection techniques have been used in
laboratories for many decades (10–12). While those standard-
ized smoke-collection techniques do not represent individual
human exposure nor consumer use of a product, they provide
commonality of methodology for analytical purposes.
The Cooperation Centre for Scientific Research Relative to
Tobacco (CORESTA) and International Organization for
Standardization (ISO) working groups have focused on
developing consensus standardized methods for the mea-
surement of HPHCs in tobacco and smoke through collabo-
rative studies among experienced laboratories (13–14).
In those collaborative studies, product variability was
minimized by using reference products andsingle manufac-
turing batches. While these studies provide invaluable in-
sights into the repeatability and reproducibility of the
smoke collection and short-term analytical variability
within and between laboratories, they do not aid the under-
standing of product variability.
Most research related to product variability has been
conducted by mining of historical data or by real-time
HPHC testing of reference or commercial products in one
or more laboratories (15–23).
For example, HYODO explored the medium-termvariability
of HPHCs from 19 commercial cigarettes on the Japanese
market (23). More recently, OLDHAM et al. reported the
analytical variability of 96 HPHCs of select cigarettes in
the U.S. market at two different timepoints (21). ELDRIDGE
et al. also explored how measured toxicant levels in
commercial products varied over time (20). TAYYARAH
compared the variability of multiple batches of commercial
products to repeat testing of a single batch of a reference
product (24). In many of these studies, greater variability
was found for low-level analytes which is consistent with
the “Horwitz Trumpet” (25–26). These studies also demon-
strated that many constituent measurements have greater
variability than the well-established measurements of ?tar”,
nicotine, and carbon monoxide (TNCO) (27). The results
also showed increased variability between laboratories
113
CTNR @ 31 (2) @ 2022

compared to within a single laboratory. As noted by
MORTONand LAFFOON, the analytical variability over time,
even within a single laboratory, is often greater than the
manufacturing variability (22).
While these studies investigated how measured HPHC
values change in commercial and reference cigarettes over
time, they did not conduct all measurements concurrently
in the same laboratories; product and analytical variability
(e.g., different laboratories, operators, time of measure-
ment) cannot be distinguished.
Thus, the purpose of this study was to develop a systematic
understanding of product variability, as much as possible,
without confounding with analytical variability (13). The
study as reported here, included evaluation of 8 world-wide
commercial cigarettes across a range of conventional
designs. Variability was assessed over short-term(Phase 1:
three batches manufactured within 1 week), medium-term
(Phase 2: four batches manufactured quarterly for 1 year),
and long-term (Phase 3: three batches manufactured yearly
for 3 years) through testing of a range of conventional
parameters and smoke and tobacco analytes. Specifically,
HPHCs recommended or required by international regula-
tory bodies including World Health Organization (WHO)
(9), the U.S. Food and Drug Administration (FDA) (2),
Health Canada (5), and the State Tobacco Monopoly
Administration (STMA) of China (4) were tested. Besides
their specific relevance to tobacco product testing, these
analytes cover a range of yield (ng to mg), some are
present in tobacco and/or smoke. In smoke, these constitu-
ents represent volatile, semi-volatile, and particulate phase
analytes. Thus, it was expected that these may inform
against various qualities of a tobacco product to aid our
understanding of product variability. Several study design
measures were included to minimize or account for
analytical variabililty. For example, for each phase of the
study, batches were collected over time but stored in cold
storage and then tested together, and all testing for a given
constituent was conducted concurrently in the same lab-
oratory. To account for unknown or uncontrolled variables,
a single batch of standard reference cigarette 3R4F was
included and treated in the same manner as a study control
for a comparison of the variability from repeat testing of a
single batch (control) to variability from batch-to-batch
testing of a product. Results, analysis, and implications of
the study are reported herein.
METHODS
Study phases and batch designations
The study was conducted in three phases to evaluate short-
term, medium-term, and long-term product variability (see
Table 1). Results were not compared across products; the
design was effectively eight parallel independent studies.
For Phase 1 of the study, Batches A, B, and C were
collected within 1 week of production. A larger sample size
for Batch B was collected to include this batch in each
phase of the study (B1, B2, B3). For Phase 2, Batches D,
E, and F were collected approximately quarterly with
Batch B, labeled B2, treated as the first quarter batch. For
Phase 3, Batches G and H were collected annually with
Batch B, labeled B3, treated as the first-year batch. Quanti-
ties of a single batch of 3R4F were collected, labelled,
stored, and shipped similarly to the product batches. The
choice of collection timing was based on trying to gradually
incorporate expected and unknown production variables
into the study. It was anticipated that ‘one-week’ would
allow for varied shift, equipment, operator, and raw
material changes such as cigarette paper bobbins or filter
tow bales. Quarterly collections for one year (Phase 2), was
expected to include these typical production variables and
possibly grade changes or blend turn-over depending on a
given manufacturer’s undisclosed production practices.
Collecting product across three years of production, was
expected to magnify each of these and other likely produc-
tion variables.
Choice of samples
The scope of the study was limited to commercial factory-
made cigarettes in order to create ‘real-world’ information.
Referencecigarettes (3R4F)wereincorporatedforcompari-
son purposes. Considerations included: region of the world
(United States, Europe, China); blend (American, Virginia,
Dark Air Cured); non-intense ?tar”-level (<1–20 mg/ciga-
rette); and filter construction. Mentholated products were
excluded due to complexity with cigarette conditioning.
The first certified reference product for cigarettes, 1R6F,
came available during the course of the study, and was
added to the study for informational purposes.
Table 1. Study design and batch designations.
Batch designation
Study phase
A B1, B2, B3 a
C D E F G H
Phase 1
Short-term
Week 1
shift 1
Week 1
shift 2
Week 1
shift 3
Phase 2
Medium-term
Year 1
quarter 1
Year 1
quarter 2
Year 1
quarter 3
Year 1
quarter 4
Phase 3
Long-term
Year 1 Year 2 Year 3
a
B1, B2, and B3 are cigarettes from the same batch that were stored and shipped with each phase of the study. Batch designations were
used for 3R4F data, but all cigarettes were from the same batch.
114 CTNR @ 31 (2) @ 2022

While no attempt was made to control or influence raw
material lots or manufacturing schedules, product volun-
teers agreed to providearelativelyhigh-production-volume
product to avoid limited production runs and low raw
materialturn-over. Specific manufacturinginformationwas
not collected due to confidentiality considerations between
different manufacturers.
Cigarette products
Two Virginia and six American blend commercial products
were chosen to represent a range of design parameters (e.g.,
blend type and ?tar” level, see Table 2). Virginia blends are
typically all flue-cured tobaccos, often with no or limited
added flavors. American blends typically contain flue-
cured, Burley, and Oriental tobaccos with or without added
flavors.
Product collections were from typical commercial produc-
tion runs with no additional controls imposed. Samples
were collected according to the time schedule (Table 1) and
stored at !20 °C prior to shipment to the testing labs. No
significant design changes were noted for the study pro-
ducts during the course of the study.
One batch of 3R4F cigarettes was included as a study
control to compare testing of multiple batches ofproduction
samples with repeat testing of a single batch of 3R4F
cigarettes treated to the same study design parameters.
For data collation, the test samples were distinguished by
Sample-Batch designations such as 1-B1 (corresponding to
Batch B1 from product Sample 1).
Choice of measures
HPHCs tested were based on recommendations or require-
ments of regulatory bodies (World Health Organization,
U.S. Food and Drug Administration, Health Canada,
Chinese State Tobacco Monopoly Administration).
Additional measures as indicators of analytical quality,
such as total particulate matter (TPM), puff count, and
conditioned cigarette weight, were reported as well.
Physical measurements were included as indicators of ciga-
rette production consistency.
This set of measures afforded the potential for trends analysis
based on attributes such as matrix (leaf and smoke); consti-
tuent phase (particulate, semi-volatile, volatile); and relative
level of constituent yield (ng, μg, mg).
Testing design
Testing volunteers were solicited from among industry
laboratories with capabilities for TNCO and at least one
constituent class in the test list. Laboratories were typically
ISO 17025 accredited and it was recommended but not
required for the laboratories to use published or standardi-
zed analytical methods.
All sample batches were shipped and tested concurrently
within each phase of the study. For each constituent and
each phase, a single lab tested all of the samples (n = 5).
Five replicates provided approximately 80% power to de-
tect a ratio of the sample-to-sample standard deviation to
the replicate-to-replicate standard deviation of 1.32 or more
which was deemed adequate power (28). Due to the large
number of replicates for a given phase, testing was expec-
ted to take from several days to several weeks to complete.
Therefore, replicates were interleaved to minimize the
confounding of laboratory drift over the testing time with
the manufacturing variation under study. Due to interlea-
ving, repeat analyses were not required except in cases of
fewer than 3 reportable results. Due to shipping complexi-
ties across multiple countries, not all product samples were
received by all testing laboratories in Phase 3. In these
cases, results are noted in the data tables as NCS (not
calculated due to shipping).
The TNCO testing design varied from the other analytes of
measure. Rather than one laboratory, all laboratories tested
TNCO concurrent to testing of their assigned analyte class.
Study control measures
Certain study design features were put in place to minimize
analytical variability. Batches were collected, held at
!20 °C, and shipped together for testing to avoid temporal
variability during testing. Except for TNCO, all samples
were tested for a given method in a single laboratory at the
same time to avoid lab-to-lab variability or within lab
temporal variability. Replicates were interleaved to prevent
bias in analysis due to analytical drift.
The laboratories reported key information such as TPM,
puff count, and conditioned cigarette weight to assess the
data for obvious anomalies that may be harder to discern in
analyte results. TNCO was carried out in all the laborato-
ries and provided additional insight into the testing variabi-
lity of the products.
Unidentified or unknown contributors to analytical variabi-
lity were naturally accounted for in the analysis through
inclusion of repeat testing of a single production batch of
3R4F as a study control and through the repeat testing of
the “B” batches in each study phase.
Table 2. Sample descriptions.
Sample code Blend
~Non-intense
“tar”
Comment
1 American 10 mg –
2 American 3 mg Charcoal filter
3 Virginia 10 mg –
5 a
NA NA Withdrawn
7 American 1 mg –
8 Virginia 8 mg –
9 American 7 mg
10 (3R4F) American 8 mg
Single batch study
reference
11 (1R6F) American 8 mg Informational
a
Withdrawn after Sample Code assignment but prior to testing.
115
CTNR @ 31 (2) @ 2022

Physical parameters
Moisture, cigarette weight, tobacco weight, filter tip
ventilation, circumference, length, paper permeability, and
resistance to draw were measured using conventional
equipment in common use in industry laboratories (29–31).
Tobacco testing methods
Tobacco was removed from the other cigarette components
in preparation for analysis. As warranted, tobacco samples
were oven-dried for consistent grinding; thus, analytes may
be reported ‘as-is’ or on a dry-weight basis. Replicate
analysis was conducted on separate-grind aliquots.
- Nicotine: Ground tobacco was extracted using liquid/
liquid extraction into organic solvent containing
n-heptadecane as an internal standard followed by
GC-FID analysis according to the “hexane method” for
CRM 62 (32).
- Ammonia: Ground tobacco was extracted using dilute
acid. Filtered extracts were analyzed by IC (33).
- Metals: Arsenic (LOQ 200 ng/g), cadmium (LOQ
300ng/g);digestedtobaccowasanalyzedbyinductively
coupled plasma mass spectroscopy (34).
- Tobacco-specific nitrosamines (TSNAs): N-Nitrosonor-
nicotine(NNN),4-(methylnitrosamino)-1-(3-pyridyl)-1-
butanone (NNK); ground tobacco was extracted using
dilute ammonium acetate, extracts were analyzed by
liquid chromatography tandem mass spectroscopy
(LC-MS/MS) (35).
Smoking collection regimes
Mainstream smoke was generated under both ISO 3308
(non-intense) (10) and ISO 20778 (intense) (11) machine
smoking regimes. Cigarettes were conditioned before
smoking in accordance with ISO 3402 (36).
Cigarette smoke testing methods
- ?Tar”, nicotine, carbon monoxide (TNCO): Cigarettes
were smoked onto glass filter pads which were subse-
quently extracted with alcohol, extracts were analyzed
by GC-FID and GC-TCD. Carbon monoxide was
analyzed by in-line gas trapping and NDIR (10, 11,
37–40).
- Ammonia: Cigarettes were smoked through glass filter
pads with in-line impingers containing dilute acid. Pads
were extracted with the impinger solvent. Extracts are
analyzed by IC (41–42).
- Carbonyls: Acetaldehyde, acrolein, crotonaldehyde,
formaldehyde; mainstreamcigarette smoke was trapped
inanimpingerwithanacidified 2,4-dinitrophenylhydra-
zine (DNPH) solution. After smoking, trizma base was
added to the combined solutions which were subsequen-
tly analyzed by UPLC-PDA (43–44).
- Hydrogencyanide (HCN): Cigarettes were smoked onto
glass fiber filter pads with in-line impingers containing
1 M NaOH (aq). The pads were extracted with 1 M
NaOH by shaking for 2 min by hand. The pad extract
and the impinger solution were analyzed separately by
colorimetric detection using CFA (45).
- Polyaromaticamines(PAAs):4-Aminobiphenyl,1-amino-
naphthalene,2-aminonaphthalene;cigarettesweresmoked
on to glass fiber filter pads. Pads were extracted with
100 mL of 5% HCl (aq) with 30 min of shaking on a wrist-
actionshaker.Afteraliquid/liquidextractionwithdichloro-
methane and cyclohexane the extracts were brought to
pH11with50%NaOH(aq).Subsequently,thePAAswere
extractedwithhexaneandderivatizedwithpentafluoropro-
pionic acid anhydride with an overnight reaction at 5 °C. A
clean-up with Florisil was followed by analysis by GC-MS
(SIM) using deuterated internal standards D-7 2-amino-
naphthalin and D-9 4-aminobiphenyl. The analytical
column was a VF-5ms (30m × 0.25 mm × 0.25 µm). The
oven temperature was programmed at 90 °C for 2 min,
12 °C/min to 220 °C, 20 °C/min to 280 °C for 15 min with
helium 0.7 mL constant flow for carrier gas.
- Polyaromatic hydrocarbons (PAHs): Benzo[a]pyrene
(BaP); cigarettes were smoked onto glass fiber filter pads
whichweresubsequentlyextractedwithmethanol.Extracts
were processed in a series of steps involving dilution,
solvent exchange, and SPE. Processed extracts were
analyzed by GC-MS (SIM) (46–47).
- Tobacco-specific nitrosamines (TSNAs): NNN, NNK;
cigarettes were smoked onto glass filter pads subsequently
extracted using dilute ammonium acetate. Extracts were
analyzed by LC-MS/MS (48).
- Volatile organic compounds (VOC): Acrylonitrile, ben-
zene, 1,3-butadiene, isoprene, toluene; cigarettes were
smoked onto a glass filter pad with in-line chilled,
methanol-containing impingers. Pads were extracted with
theimpingers’solution.ExtractswereanalyzedbyGC-MS
EI, full scan mode (49–50).
Statistical analysis
Data were compared using one-way analysis of variance,
except TNCO which was two-way including interactive
effects. The different handling of TNCO was because multiple
labs analyzed the samples for TNCO and only a single lab
analyzedthesamplesforotherHPHCs.Obviousoutliersnoted
in the raw data supplement were excluded by visual inspec-
tion.
The range of the observed batch means for a set of timepoint
batches was used to aid in the interpretation of whether the
differences in batch values were of practical importance.
Range = (maximum batch mean – minimum batch mean) /
average of means of all batches
RESULTS
The percent differences (also called relative range) for time-
points for a product (testing of multiple batches) or 3R4F
(repeat testing of the same batch) were calculated. Example
relative range data from each constituent class are shown in
Table 3. Relative range results for all constituents and measu-
res are provided in the Supplemental Material (Tables S1–S5).
All raw analytical data may be obtained by contacting the
CORESTAGeneralSecretariat(https://www.coresta.org/contact).
116 CTNR @ 31 (2) @ 2022

Table 3. Relative range (%) among time point for each product sample for select parameters and constituents.
Sample
Timepoint 1 2 3 4 6 7 8 9 3R4F
Physical parameters
Cigarette weight as-is
(mg/cig)
Short 0.8% 0.6% 0.2% 0.9% 0.2% 0.3% 0.3% 0.6% NC a
Medium 1.3%* 1.7%* 1.2%* 1.4%* NC 1.6%* 0.9%* 2.6%* 0.2%
Long 3.4%* 0.3% 0.4% 1.9%* 0.7% 3.2%* 3.8% 1.1% 0.4%
Filter tip ventilation (%)
Short 3.4%* 6.0%* 0.1% 8.4%* 0.9% 19.7%* 1.3% 0.9% NC
Medium 1.8%* 8%* 0.4%* 8.2%* NC 27.1%* 5.5%* 5.0%* 2.4%
Long 10.4%* 8.6% 0.1% 9.0%* 2.8% 14.0%* 0.7% 1.2% 6.6%
Tobacco constituents
Short 1.4% 7.4% 11.9% 3.1% 3.0% 6.5% 14.7%* 27.1%* 3.8%
NNN (ng/g) Medium 41.9%* 31.4%* 51%* 14.6%* 29.4%* 34.5%* 30.5%* 81.5%* 2.8%
Long 34.0%* 31.5%* NCS b
2.3% 13.8%* 28.1%* NCS 57.2%* 3.2%
Short 8.1% 24.7%* 8.1% 12.6%* 10.3% 2.0% 15.4%* 32.9%* 3.3%
NNK (ng/g) Medium 62.1%* 53.5%* 47.4%* 7.5% 37.8%* 33.9%* 28.2%* 58.9%* 9.2%
Long 34.7%* 20.9% NCS 6.4% 36.0% 21.4% NCS 54.4% 8.9%*
Short 2.2%* 1.0% 4.8%* 3.1%* 1.0% 4.8%* 2.1%* 3.9%* 0.4%
Nicotine (µg/g) Medium 9.8%* 13.8%* 4.1%* 3.7%* 5.2%* 5.2%* 3.9%* 8.6%* 1.7%*
Long 8.3%* 5.6%* 14.0%* 2.5%* 2.8%* 4.4%* 0.3% 7.6%* 0.1%
Short 6.7%* 7.5%* 9.4%* 6.7%* 1.5% 0.9% 34.4%* 12.8%* 3.1%
Ammonia (µg/g) Medium 11.6%* 15.0%* 63.7%* 8.6%* 21.1%* 11.0%* 66.5%* 10.3%* 4.8%*
Long 12.5%* 2.1% NCS 24.6%* 10.6%* 10.4%* NCS 17.2%* 1.0%
Smoke constituent – Non-intense regime
Ammonia (µg/cig)
Short 0.9% < LOQ 2.3% 8.3% 2.6% < LOQ 8.7% 2.1% 5.3%
Medium 18.8%* < LOQ 5.5% 5.0% 28.3%* < LOQ 17.7% 19.2% 6.0%
Long 14.7% 8.8% NCS 7.4% 21.5% < LOQ NCS 7.5% 9.2%
Acetaldehyde (µg/cig)
Short 5.0% 9.5% 2.4% 6.5% 6.9%* 51.1%* 5.1% 7.4% 6.8%
Medium 11.3% 11.4% 6.5% 9.3%* 3.2% 67.3%* 3.9% 13.2%* 11.6%*
Long 12.4%* 7.0% 0.5% 5.7% 3.6% 21.6% 0.7% 2.5% 3.3%
Total HCN (µg/cig)
Short 5.8% 8.5% 2.9% 15.9%* 17.9%* < LOQ 15.7%* 16.5% 4.5%
Medium 15.9%* 22.9% 8.0% 8.1% 9.4%* < LOQ 30.1%* 16.4%* 7.7%
Long 20.5%* 31.0% NCS 13.3% 15.3% < LOQ NCS 6.0% 12.1%
4-Aminobiphenyl (ng/cig)
Short 3.2% 5.5%* 3.6% 2.8% 2.0% 17.6%* 4.9% 1.1% 4.5%
Medium 4.5% 2.4% 4.5% 6.5% 6.4%* 51.3%* 20.1%* 5.3% 5.4%
Long 7.4% 6.4% NCS 11.4%* 2.6% 16.9%* NCS 5.2% 3.9%
BaP (ng/cig)
Short 2.2% 3.0% 1.7% 0.3% 1.9% 4.2% 2.2% 2.6% 1.1%
Medium 4.1% 3.6% 2.3% 4.0% 6.1%* 10.4% 2.2% 4.3% 1.6%
Long 8.1% 6.9% 3.9% NC 6.3% 29.9%* 7.5% 3.5% 1.7%
NNN (ng/cig)
Short 13.4% 14.3%* 9.7% 4.0% 11.3% 27.1%* 15.6% 13.1% 2.9%
Medium 36.9%* 11.9% 21.9% 5.7% 8.4% 54.9%* 51.3%* 44.7%* 16.5%
Long 33.1%* 51.7%* NCS 0.7% 12.4% 25%* NCS 45.9%* 10.8%
NNK (ng/cig)
Short 18.0% 14.1%* 8.2% 8.0% 9.9% 40.2%* 12.0% 26.8% 4.0%
Medium 44.6%* 31.7% 25.1% 11.6%* 17.6% 62.9%* 34.4% 36%* 12.6%
Long 28.1%* 47.7%* NCS 3.4% 12.2% 34.0% NCS 32.9%* 12.7%*
Benzene (µg/cig)
Short 6.4% 16.2%* 2.8% 3.9% 2.9% 23%* 3.7% 4.4% 1.3%
Medium 13%* 19%* 6.2% 6.2% 7.7% 81.8%* 11.5%* 9.0% 10.6%*
Long 14.5%* 8.7%* NCS 0.3% 1.7% 28.1%* NCS 6.5% 4.1%
117
CTNR @ 31 (2) @ 2022

Instances of statistically significant differences (ANOVA,
p < 0.05) are noted. For cases for which relative range was not
calculated, the result is noted as “NC” for cases of not reported
or < LOQ analytical results or “NCS” for cases of not tested
due to shipping/receiving issues.
Physical parameters
The percent differences among the batch timepoints across the
entire study, as displayed in Supplemental Table 1, ranged
from0.1%to27.1%butweretypicallybelow10%.Resultsfor
3R4F ranged from 0.1% to 14.0% and were typically below
5%.Filtertipventilation,resistancetodraw (open/closed),and
paper air permeability are shown to have the highest percent
difference among the batch timepoints for the measurements.
Several of the products had statistically significant differences
for the timepoints.
Tobacco
Nicotine,ammonia,TSNAs,andtracemetalsweredetermined
for tobacco samples. TNSAs were determined by the same
testing laboratory that measured smoke TSNAs for matched
analytical methodology.
Arsenic and cadmium levels were near or below the lower
limits of quantitation for many of the samples which resulted
in non-reportable values or artificially elevated percent
differences. Thus, these analytes are not useful for the study
objective and the variability calculations are not reported but
rather are noted as not calculated (NC).
For NNN and NNK, many of the products appear to have
batch-to-batch variability above the level shown by 3R4F
(repeat testing of a single batch) with apparent trending based
onstudyphase.NNNproductvariability averaged approxima-
tely 10%, 40%, and 28% for the products’ short-term,
medium-term, and long-term comparisons, respectively.
Table 3. Contd.
Sample
Timepoint 1 2 3 4 6 7 8 9 3R4F
Smoke constituent - Intense regime
Ammonia (µg/cig)
Short 10.6% 9.9% 0.8% 1.1% 7.2% 3.7% 13.4%* 5.9% 7.2%
Medium 19.0% 47.6% 22.8% 12.8% 30.5%* 7.3% 45.2%* 15.2% 14.1%
Long 1.6% 9%* NCS 1.9% 22.9%* 11.0% NCS 3.0% 7.7%
Acetaldehyde (µg/cig)
Short 1.7% 2.6% 3.5% 3.3% 1.8% 2.7% 4.9% 3.0% 1.4%
Medium 2.7% 5.5% 6.0% 4.9% 8.9%* 6.7% 6.9% 4.7% 3.5%
Long 2.4% 3.8% 1.0% 2.9% 9.4%* 3.9% 10.8%* 0.5% 0.4%
Total HCN (µg/cig)
Short 5.8% 5.2% 7.4% 1.4% 9.6% 4.2% 1.2% 13.6% 4.7%
Medium 6.1% 15.6%* 9.2% 2.9% 9.5% 8.9% 19.2%* 15.8%* 7.5%
Long 13.4%* 10.5%* NCS 10.8% 9.4% 4.7% NCS 13.0% 2.8%
4-Aminobiphenyl (ng/cig)
Short 3.9% 3.2% 2.0% 4.8% 0.7% 0.4% 1.7% 4.7% 2.4%
Medium 10.9% 14.3%* 4.7% 4.1% 8.8%* 5.2% 17.3%* 5.1% 4.6%
Long 10.5% 9.2% NCS 6.8%* 13.7% 9.6%* NCS 4.9% 7.2%
BaP (ng/cig)
Short 2.2% 2.9% 2.8% 2.1% 1.8% 0.4% 3.8% 3.1% 2.3%
Medium 3.7% 3.6% 7.2%* 2.1% 5.6% 2.5% 4.4% 7.3% 6.1%*
Long 5.4% 5.4% 2.5% NC 7.9% 2.5% 8.5% 5.5% 1.4%
NNN (ng/cig)
Short 1.2% 17.3%* 19.2%* 18.9%* 5.3% 3.5% 10.5% 10.1% 4.3%
Medium 37.2%* 26.6%* 62.6%* 4.6% 20.9% 23.7%* 13.9% 52.9%* 10.3%
Long 22.9% 52.9%* NCS 5.5% 10.6% 30.5%* NCS 40.6%* 12.5%
NNK (ng/cig)
Short 6.6% 2.3% 14.8% 9.2% 11.1% 15.8% 21.3%* 21.6%* 5.7%
Medium 34.5%* 41.8%* 18.7%* 8.5% 16.3% 25.8%* 33.6% 26.2%* 12.5%
Long 26.9% 49.5%* NCS 6.6% 25.4%* 11.8% NCS 26.6%* 12.0%
Benzene (µg/cig)
Short 5.0% 2.3% 8.5% 3.4% 6.2% 4.7% 10.5%* 5.0% 2.3%
Medium 4.2% 5.0% 5.1% 3.9% 4.3% 4.7% 9.5%* 7.0% 2.4%
Long 3.7% 5.1% NCS 2.3% 1.5% 1.3% NCS 3.2% 4.9%
* p < 0.05 (Statistically significantly different using ANOVA)
a
NC: not calculated due to not reported or < LOQ values; b
NCS: not calculated due to shipping/receiving issues.
118 CTNR @ 31 (2) @ 2022

Repeat testing variability for 3R4F for the same study phases
was 3.8%, 2.8%, and 3.2%. Results for NNK followed a
similar trend: products’ variability averaged approximately
14%, 41%, and 29% for the three study phases. 3R4F showed
slightlygreaterrepeattestingvariabilitythanforNNNat3.3%,
9.2%, and 8.9%, respectively.
Tobacco nicotine differences were slightly greater for product
batches (~ 3%) collected within one week compared to repeat
testing of 3R4F (0.4%). Differences were slightly greater and
were statistically significant for 1-year and 3-year studies at
approximately 6.8% and 5.7% on average for products
compared to 1.7% and 0.1% for 3R4F. The highest difference
for tobacco nicotine noted among the timepoints was 14.0%
for the Product 3 long-term difference.
Like TSNAs, the tobacco ammonia results were often quite
different from one another in the medium and long-term
batches. The Virginia blend cigarettes (Products 3 and 8)
sometimes showed quite large percent differences (> 60%)
because their levels were low; small changes could result in
large percent differences. For the American blend cigarettes,
short-term ammonia differences among batches were greater
for most products than for 3R4F (3%). Most of the American
blend products showed somewhat greater differences than
3R4F in both the medium and long-term (13% on average
compared to < 5% for 3R4F).
Smoke
Mainstream smoke was collected using non-intense and
intense smoking regimes for the determination of ammonia,
BaP, carbonyls, PAAs, TSNAs, HCN, and VOCs. For each of
these analyte classes, all testing was conducted in one labora-
tory and all samples for a given study phase were tested
concurrently.
On average, across all products and all analytes, the product
differences for non-intense smoking (15%) were greater than
3R4F repeat testing differences (7.2%). Intense results are
similar; on average, sample differences over time are slightly
greater than repeat testing of 3R4F. The differences between
samplesundertheintensesmokingregimewerelowexceptfor
TSNAs.Thissupportspreviousconclusionsregardingventila-
tion’s impact on variability for non-intense smoking (26).
Results of particular interest are noted below.
Product 7 with high filter tip ventilation showed relatively
larger differences for the non-intense regime (as high as
106.4%). This is a very low yielding product; several analytes
were at or below method limits of quantitation (LOQ) and
small differences resulted in large proportional differences.
Ammonia results for Product 3 and 8 (Virginia blend cigaret-
tes) appear to show trending for study phases. For Product 8
intense regime, Phase 1 difference is 13.4% compared to 7.2%
for 3R4F and Phase 2 is 45.2% compared to 14.1%. As with
trace level tobacco metals, variability among the replicates is
relatively high with some values at or below the method LOQ
for non-intense regime results. Phase 3 testing for Product 8
was not completed due to product distribution issues. Pro-
duct 6 ammonia differences also appear to trend up for
medium- and long-term sampling compared to collecting all
batches within a week. Ammonia is a naturally occurring
constituent yet may also be an additive (e.g., processing aid)
in tobacco and/or cigarette paper.
This is an unknown for the study samples since these types of
manufacturing details were not disclosed. Thus, smoke
ammonia variability may be influenced by natural and/or
added content.
As with tobacco analysis, NNN and NNK yields for non-
intense smoking showed statistically significant differences of
modest size in many products for the short-term study (~ 15%
compared to ~ 4% for repeated 3R4F) and relatively larger
magnitudes of difference for the later phases (as much as
62.9% compared to ~ 13% for 3R4F). These compounds are
known to be related to the tobacco levels of those HPHCs, and
they are also knowntovarywidelyintobaccodueto agricultu-
ral variation over time. This trend held for intense smoking as
well.
Smoke – ?Tar”, nicotine, and carbon monoxide
Unlike other smoke analytes, TNCO testing was conducted in
all laboratories.
Batch-to-batch variability for each of the samples was low for
each of the phases and all samples (~ 5%). Conversely, lab-to-
lab variation was statistically significant in all but two compa-
risons across all TNCO samples, analytes, and regimes.
There are also potential interactive effects (e.g., Sample × lab)
noted in the data. Table 4 shows the example of Product 2,
non-intense CO yields for the Phase 3 batches. All batch-to-
batch differences compared to lab-to-lab differences of these
HPHCs are provided in the supplemental materials.
Table 4. Product 2 CO yields from non-intense smoking for
long-term variability testing as an illustration of interactive
effects among laboratories and of the magnitude of differences
that may be expected from testing multiple batches of the
same product in different laboratories using standardized
methodology.
Product 2 Non-intense CO (mg/cig)
Phase 3 samples
Product
range
Laboratory
range
Batch % %
Lab B3 G H – –
1 4.41 4.11 4.64 – –
4 3.85 3.70 3.87 – –
5 3.56 3.64 3.48 – –
6 4.70 4.59 4.87 – –
7 3.92 3.75 4.44 – –
8 4.00 3.75 4.43 – –
9 3.62 3.16 3.92 – –
10 3.53 3.70 3.50 – –
11 4.14 4.02 4.66 – –
Average 3.97 3.82 4.20 9.4 29.0
119
CTNR @ 31 (2) @ 2022

DISCUSSION
Influence of samples
There were no obvious variability trends in the data due to
blend type (American or Virginia) except that Virgina blends
tend to be low in ammonia (51) causing low or < LOQ values.
High filter-tip ventilation primarily influenced analytical
variability and the carbon filter additive affected VOC yields,
but neither of these product design variables was observed as
a major factor influencing batch-to-batch product variability.
Choice of measures
HPHCs tested were based on recommendations or require-
ments of regulatory bodies (World Health Organization, U.S.
Food and Drug Administration, Health Canada, Chinese State
Tobacco Monopoly Administration). Additional measures as
indicators of analytical quality, such as total particulate matter
(TPM), puff count and conditioned cigarette weight, were
reported as well. Physical measurements were included as
indicators of cigarette production consistency.
Thissetof measures afforded the potential for trends analysis
based on attributes such as matrix (leaf and smoke); consti-
tuent phase (particulate, semi-volatile, volatile); and relative
level of constituent yield (ng, μg, mg).
As previously observed by AGNEW-HEARD et al., cigarettes
with relatively high variability of physical parameters tended
to show relatively greater variability for non-intense smoke
yields (52). Constituents that transferred from leaf to smoke
showed similartrendsforbatch-to-batch variability trending.
Both leaf TSNAs and smoke TSNAs showed a greater batch-
to-batch variability than repeat testing for 3R4F and showed
greater differences among batches for the longer-term
studies. This trend was also observed for ammonia and, to a
lesser extent, nicotine. There were no obvious trends noted
based on constituent phase or relative yield.
Statistical and practical differences
Our objective was to expand our understanding of commercial
cigarette product variability in amoreholistic manner. The use
of statistical comparisons was employed to aid objectivity in
our data analysis and in highlighting more complex trends
such as the interactive effects noted for TNCO results. A
review of Table 3 and Supplemental Table S1 will reveal that
some batch differences that were statistically significantly
different were extremely small and of little practical concern.
For that reason, we included percent differences in addition to
statisticalsignificancetobeabletojudgewhethertheobserved
differences were meaningful from a practical perspective.
As an additional safeguard, we included the 3R4F reference
product in the study. The testing design for the 3R4F was the
same as for the commercial products so that we could ask the
question for each set of analyses: “Is the difference observed
for testing multiple batches of Product X greater than that
observed from repeat testing of one batch of the reference?”.
If not, even if relatively high, the difference observed is likely
not due to product (batch) differences but more likely due to
analytical variability.
Like3R4F,BBatcheswerealsosingleproductionbatchesthat
were repeat-tested. In this case, they were each made in the
first week of production but were stored, shipped, and tested
with each phase of the study, and hence are also a type of
design-matched control for associated samples. Though not
evaluated in this data analysis, one may be able to estimate
temporal analytical variability for the study using the Batch B
data and/or the 3R4F data from all of the phases.
Physical parameters
Basic physical parameters were included in the study to
understand manufacturing consistency for the study samples.
If cigarette weight were highly variable, for example, high
smoking yield variability could well follow. Most of the
parameters tested were similar in variability for the products to
the variability observed for 3R4F repeat testing. Some exam-
ples of greater variability for filter tip ventilation were noted.
In particular, Sample 7 with a carbon-containing filter of high
ventilation (~ 80%), showed a percent difference in batches of
14–20% across the three phases compared to approximately
5% for each of the phases for 3R4F.
None of the measures appeared to show differences in magni-
tude based on study phases; short-term, medium-term and
long-term variability were similar and low for all the parame-
ters. This is an indicator of a high degree of manufacturing
process control and likely relatively low product manufactu-
ringvariability.Cigaretteweight,probablythemostfundamen-
tal indicator of production variability, ranged from 0–4%
difference between batches of products collected across 3
years of production.
Tobacco
One of the main reasons to divide the study into phases of < 1
week to as long as 3 years was an attempt to incorporate
tobacco crop-year changes. Metals data did not support a trend
analysis as they typically were near or below the method’s
lower LOQ. This highlights a real-world issue with reporting
requirements for trace or < LOQ analytes. NNN and NNK
yields, conversely, showed clear temporal batch-to-batch
variability. Samples collected within 1 week, in some instan-
ces,hadsignificantvariation(27%),and even greatervariation
over longer periods (82% collected across 1 year’s produc-
tion). Repeat testing of 3R4F (3%, 4%) supports that the
differences are product-related rather than analytically driven.
Repeat testing of the product’s Batch B further supports this
and confirms consistency within a batch as illustrated in
Figure 1 showing tobacco NNN for Product 9 and 3R4F
across all batches. Interestingly, the data seem to indicate that
medium-term variability is higher for NNN and NNK (~ 40%
on average) than for long-termvariability (~ 30% on average).
It is possible that there is an in-common but unknown raw
material or manufacturing variable that is influencing these
results. Or, perhaps ~ 40% and ~ 30% are generally equiva-
lent. Repeat testing of Sample B and of 3R4F confirmed that
this is not an analytical artifact. A study conducted with
additional timepoints may be warranted to better understand
this trend.
120 CTNR @ 31 (2) @ 2022

Smoke
Evaluation of puff count or TPM were included as indicators
of consistency of smoking, regardless of constituent. An
average of all puff counts across the study showed < 5%
difference.
For constituent evaluation, data for combustion-generated
HPHCs showed low relative percent differences for batch
comparisons. See Figure 2 for Product 1 non-intense BaP
results as an example. Sample variation for the study phases
were of a similar magnitude as replicate variability and
approximately 5% or less compared to < 2% for repeat testing
of 3R4F with each phase. The results support that combustion
byproducts are primarily influenced by amount of tobacco
burned and cigarette design variables such as level and
variability of filter tip ventilation.
Conversely, tobacco-related compounds, such as NNN and
NNK, tended to show larger differences in yields between
batches collected over a longer period of time (1 year or 3
years) as compared to samples collected within 1 week. It
follows that tobacco HPHCs vary naturally over time and thus
vary in smoke over time. Tobacco HPHCs, whether tested in
smoke or tobacco, show large temporal variability.
Smoke ammonia may result from a mix of sourcing from
combustion and from transfer from tobacco (22), whereby
products’ smoke ammonia yields had larger batch-to-batch
differencesforbatchesoverlongerperiodsbutdidnotnecessa-
rilycorrelate as strongly betweensmokeandtobaccoresultsas
TSNAs depending on the product.
Smoke – ?Tar”, nicotine, and carbon monoxide
TNCO was originally intended to provide more information
about the samples and not to serve as a laboratory comparison.
Our rationale was that all of the participating laboratories have
full capability and years of experience with these standardized
methodologiesleadingonetoexpectlab-to-labvariabilitytobe
low and therefore allow for additional data points for TNCO.
However, the TNCO lab-to-lab variability was found to be
much higher than batch-to-batch product variability and was
shown to not trend with study phases.
A few potential implications show themselves in the take-
aways from the TNCO analysis. First, this part of the study
was conducted more like a typical interlaboratory study and
showsatypicallevelofvariabilityamongthedatasets.Second,
different laboratories testing the same samples in the same
timeframe showed a range in nicotine from, for example, 0.58
to 0.84 mg/cigarette for the same sample. This large of a
spread in reporting is a caution against over interpretation of
single-point results. Also, differences could be even larger for
morevariableorlesswell-establishedanalyteclasses.Additio-
nally, more formal evaluation of TNCO on this scale would
likely warrant sampling in compliance with ISO 8423 (53),
whichwasconsideredtobeoverburdensomeforthisstudydue
to the study size, and data analysis for tolerances in com-
pliance with ISO 22305 (54).
Figure 1. Tobacco NNN for Product 9 across all batches compared to repeat testing results for 3R4F across the same timeframes.
Shaded regions represent repeat testing of the same batch. A, B1, C are samples collected within 1 week of production. Samples B2, D, E,
and F are samples collected quarterly for 1 year. Samples B3, G, and H are samples collected annually for 3 years. 3R4F was one batch
collected, shipped, and tested like the production samples. Percent values displayed are the spread in timepoint replicate averaged values
to show relative variability of batches within a study phase.
121
CTNR @ 31 (2) @ 2022

Implications of the results
In this study, we purposely stored production samples to test
them concurrently to minimize analytical variability. We also
isolated testing to single laboratories except for TNCO. We
found that the variability in the data was relatively low
compared to studies for which products are controlled in order
to study analytical variability. The one aspect of the study,
TNCO testing, that was designed in a more typical manner,
had more typical (i.e., higher) variability among the data sets.
Thus, the primary implication of the results of the study is that
analytical variability is likely the major contributor to the
overall reporting variability in a typical study.
On the other hand, to facilitate on-going product testing for
whatever the reason, production, product, and analytical
variability often cannot be isolated in this way. Thus, in real-
world testing product variability and analytical variability will
remain confounded.
In evaluation of data sets from different laboratories, one must
consider that all are subject to these same analytical variables
and may use different analytical procedures.
While one is not able to eliminate analytical variability,
reporting of the same control/reference samples across studies
and between laboratories can aid evaluation of the data.
Study limitations
The number of samples of each product is limited and are not
sufficient to provide good quantitative estimates of variability.
For example, in order to ensure that the testing volumes were
manageable, the formal sampling procedures outlined in
ISO 8243 were not followed. Thus, the estimates of product
differences should be regarded as illustrative rather than
quantitative.
Due to the sheer size of the study, additional relevant study
variables such as lab-to-lab variability for a given method and
the impact of sample storage conditions were not included and
cannotbeassessedfromthedata.Additionally,anesteddesign
would be more ideal from a statistical analysis perspective but
too large to have been achievable.
The study design also does not allow for product-to-product
comparisons. Effectively, the design created 8 independent
studies conducted in parallel. Our initial discussions of the
study design included production of a set of cigarettes desi-
gned specifically for the study to be produced by each manu-
facturer; to hold designs/recipes as constants. We decided
against this, as a study of this design would just confirm how
wellamanufacturercouldmakecigarettesforwhichtheyhave
noexperienceandun-optimizedequipment.Thatdesignwould
not have measured actual commercial cigarette variability.
Figure 2. Non-intense smoke BaP for product 1 across all batches compared to repeat testing results for 3R4F across the same
timeframes. Shaded regions represent repeat testing of the same batch. A, B1, C are samples collected within 1 week of production. Samples
B2, D, E, and F are samples collected quarterly for 1 year. Samples B3, G, and H are samples collected annually for 3 years. 3R4F was one
batch collected, shipped, and tested like the production samples. Percent values displayed are the spread in timepoint replicate averaged
values to show relative variability of batches within a study phase.
122 CTNR @ 31 (2) @ 2022

Samples were not controlled for raw material and/or cigarette
making equipment used. Product information such as blend
composition or inventory practices were not disclosed. Thus,
specific factors related to product variability, other than time
of production, cannot be isolated.
Data analysis was limited to the direct scope of the study
objectives. There may be other viable analysis questions and
analysis tools applicable to the data set.
CONCLUSIONS
The design and results of this study provide better understand-
ing of product variability independent of analytical variability.
Physicalparametersgenerallyshowedsmalldifferencesacross
all batches for the 3 years of product collection. This indicates
goodmanufacturingcontrolsand good viability of thesamples
for the study.
Analytes, such as trace metals in tobacco, that are at or near
method LOQ and/or with naturally high replicate variability
provide little information to characterize a product or to
understand product variability. Combustion-related analytes
generally showed small batch-to-batch differences regardless
of the timeperiod,similarorslightly greater than repeat testing
of3R4F.However,tobacco-relatedanalytesincigarettesmoke
such as ammonia and TSNAs showed greater medium-term
and long-term batch-to-batch differences than short-term
(1 week) samples as might be expected for an agriculturally
related product that may be impacted by the seasonal effects
on a crop.
TNCO was the only test conducted by all participating
laboratories. The original intention was to use this well-
established method to gain further information on the batches.
Instead, this additional testing served as a combined batch-to-
batch and lab-to-lab comparison. It was determined that batch-
to-batch TNCO differences (and repeated testing batch
differences) were lower than lab-to-lab differences. This may
indicate that for well-controlled product manufacturing,
analytical factors (such as laboratory differences) are likely a
much greater contributor for some constituent measures than
the actual product-batch differences.
In a controlled study such as this, it is possible to isolate the
product batch differences in a way that is not possible in
routine product monitoring; samples are not typically stored
for extended periods prior to testing, so that multiple batches
can be tested simultaneously. Inclusion of physical parameter
testing, laboratory quality control measures, and standardized
study references across studies and laboratories are aids to
minimizeandunderstandtheinfluenceofanalyticalvariability
for increased product understanding.
IMPLICATIONS FOR TOBACCO REGULATION
Previous studies have provided invaluable insights into the
repeatability and reproducibility of smoke collection and
analytical variability within and between laboratories. Those
studieshavealsoinvestigatedhowmeasuredconstituentvalues
change in commercial and reference cigarettes over time;
(15, 17, 18, 22, 24) however, they did not conduct all measure-
ments concurrently in the same laboratories. Thus, both
commercial cigarette product and analytical variability (e.g.,
different laboratories, operators, time of measurement) are
incorporated in the results. We described the first systematic
evaluation of the inherent commercial cigarette product
variability over short-term (1 week), medium-term (1 year),
and long-term (3 years) periods of time while minimizing
analytical variability. Regulatory implications include: a
caution against data analysis for HPHCs at method limits, and
against comparison of data sets between laboratories or across
time without proper controls to allow for an understanding of
inherent analytical variability.
ACKNOWLEDGMENTS
The authors acknowledge the many task force participants,
testing volunteers, and product volunteers from Altria Client
Services; Beijing Cigarette Factory, CNTC; British American
Tobacco (Germany) GmbH; China Tobacco Anhui Industrial
Co., Ltd.; China Tobacco Hunan Industrial Co., Ltd., CNTC;
Imperial Brands; ITG Brands, LLC; Japan Tobacco Inc.; JT
International; JTI Research & Development, ÖKOLAB;
LiggettTobaccoGroup,LLC;PMIResearch&Development;
RAI Services; Technology Center of China Tobacco Henan
Industrial Co., Ltd.
SUPPLEMENTAL INFORMATION
This publication contains Supplemental Material available at:
www.dx.doi.org/10.2478/cttr-2022-0012
ABBREVIATIONS
CFA Continuous flow analysis
CRM CORESTA Recommended Method
GC-FID Gas chromatography - flame ionization detector
GC-MS EI
Gas chromatography mass spectrometry with
cold electron ionization
GC-TCD
Gas chromatography thermal conductivity
detector
HPHC Harmful and potentially harmful constituents
IC Ion chromatography
LC-MS/MS Liquid chromatography - tandem mass spectrometry
LOQ Limit of quantitation
NC Not calculated
NCS Not calculated due to shipping/receiving issues
NDIR Non-dispersive infrared
NNN N-Nitrosonornicotine
NNK 4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanone
PAA Polyaromatic amine
PAH Polyaromatic hydrocarbon
SIM Gas chromatography with selected ion monitoring
SPE Solid phase extraction
TNCO “Tar”, nicotine, carbon monoxide
TPM Total particulate matter
TSNA Tobacco specific nitrosamine
UPLC-PDA
Ultra-performance liquid chromatography
photodiode array
VOC Volatile organic compound
123
CTNR @ 31 (2) @ 2022

REFERENCES
1. U.S. Department of Health and Human Services (HHS):
The Health Consequences of Smoking: 50 Years of
Progress. A Report of the Surgeon General; HHS, Centers
for Disease Control and Prevention, National Center for
Chronic Disease Prevention and Health Promotion, Office
on Smoking and Health, Atlanta, GA, 2014.
2. U.S. Food and Drug Administration (FDA): Harmful and
Potentially Harmful Constituents in Tobacco Products and
Tobacco Smoke: Established List; FDA, Center for
Tobacco Products, Silver Spring, MD, USA, April 2012.
Availableat: https://www.fda.gov/tobacco-products/rules-
regulations-and-guidance/harmful-and-potentially-harmful-
constituents-tobacco-products-and-tobacco-smoke-
established-list (accessed June 2022)
3. Rodgman,A.andT.A.Perfetti:TheChemicalComponents
of Tobacco and Tobacco Smoke; 2nd
ed., CRC Press, Boca
Raton, FL, USA, 2013.
4. The State Council, The Peoples Republic of China: State
Tobacco Monopoly Administration; Available at:
https://english.www.gov.cn/state_council/2014/10/01/con
tent_281474991089748.htm (accessed April 2022)
5. Health Canada: Tobacco Reporting Regulations; Health
Canada, Minister of Health, 2000. Available at:
https://laws-lois.justice.gc.ca/PDF/SOR-2000-273.pdf
(acessed June, 2022)
6. Brazil National Health Surveillance Agency (ANVISA):
Brazil Resolution RDC No. 90 of the Federal Sanitation
Agency. Effective 27 December 2007.
Ministério da Saúde, Agência Nacional de Vigilância
Sanitária, Brasil (ANVISA) [Brazil National Health
Surveillance Agency]: RESOLUÇÃO DA DIRETORIA
COLEGIADA - RDC Nº 90, DE 27 DE DEZEMBRO DE
2007. Dispõe sobre o registro de dados cadastrais dos
produtos fumígenos derivados do tabaco [RESOLUTION
OF THE COLLEGE COMMITTEE - RDC Nº 90, 27
December 2007. Provides for the registration of cadastral
data of tobacco products.]
7. U.S. Food and Drug Administration (FDA): Harmful and
Potentially Harmful Constituents in Tobacco Products and
Tobacco Smoke; Established List; Federal Register
Volume 77, Issue 64 (April 3, 2012) 20034–20037.
8. U.S. Food and Drug Administration (FDA): Guidance for
Industry. Reporting Harmful and Potentially Harmful
Constituents in Tobacco Products and Tobacco Smoke
Under Section 904(a)(3) of the Federal Food, Drug, and
Cosmetic Act; HHS, FDA, CTP, 2012. Available at:
https://www.fda.gov/media/83375/download (accessed
January, 2021)
9. World Health Organization (WHO): The Scientific Basis
of Tobacco Product Regulation: Second Report of a WHO
Study Group (TobReg); Technical Report Series 951,
WHO, Geneva, Switzerland, 2008.
10.International Organization for Standardization (ISO): ISO
3308:2012RoutineAnalyticalCigarette-SmokingMachine
– Definitions and Standard Conditions; ISO, Geneva,
Switzerland, 2012. Available at: https://www.iso.
org/standard/60404.html (accessed January, 2021)
20778:2018 Cigarettes – Routine Analytical Cigarette
Smoking Machine – Definitions and Standard Conditions
With an Intense Smoking Regime; ISO, Geneva,
Switzerland, 2018. Available at: https://www.iso.org/
standard/69065.html (accessed January, 2021)
12.Health Canada: Health Canada Test Method T-115.
Determination of ?Tar”, Nicotine and Carbon Monoxide in
Mainstream Tobacco Smoke; Health Canada, 1999.
Available at: https://healthycanadians.gc.ca/en/open-
information/tobacco/t100/nicotine (accessed January,
2021)
13.Cooperation Centre for Scientific Research Relative to
Tobacco (CORESTA): https://www.coresta.org (accessed
January, 2021)
14.Purkis, S.W., M. Meger, and R.A. Wuttke: A Review of
Current Smoke Constituent Measurement Activities and
Aspects of Yield Variability; Regul. Toxicol. Pharmacol.
62 (2012) 202–213. DOI: 10.1016/j.yrtph.2011.10.006
15.Rickert, W.S. and W. Wright: Stability of Yields of
Canadian Mandated Analytes From the Kentucky
Reference Cigarette 1R4F: A Time Series Analysis; Paper
ST 26, presented at CORESTA Congress, New Orleans,
USA, 22–27 September 2002. Available at:
https://www.coresta.org/abstracts/stability-yields-canadian-
mandated-analytes-kentucky-reference-cigarette-1r4f-time-
series (accessed January, 2021)
16.Hyodo, T., O. Inoue, H. Katagirim, and A. Mikita: Long-
TermInterlaboratory Comparisons of Selected Analytes in
2R4F Mainstream Smoke; Paper SS 07, presented at
CORESTA Congress, Paris, France, 15–20 October 2006.
Available at: https://www.coresta.org/abstracts/long-term-
inter-laboratory-comparison-selected-smoke-analytes-2r4f-
mainstream-smoke-2066 (accessed January, 2021)
17.Intorp, M., S. Purkis, M. Whittaker, and W. Wright:
Determination of ?Hoffmann Analytes” in Cigarette
Mainstream Smoke. The Coresta 2006 Joint Experiment;
Beitr. Tabakforsch. Int. 23 (2009) 161–202.
DOI: 10.2478/cttr-2013-0859
18.Teillet, B., X. Cahours, T. Verron, S. Colard, and S.W.
Purkis: Comparison of Smoke Yield Data Collected From
Different Laboratories; Beitr. Tabakforsch. Int. 25 (2014)
662–670. DOI: 10.2478/cttr-2013-0943
19.Purkis, S. and M. Intorp: Analysis of Reference Cigarette
Smoke Yield Data From 21 Laboratories for 28 Selected
Analytes as a Guide to Selection of New CORESTA
Recommended Methods; Beitr. Tabakforsch. Int. 26
(2014) 57–73. DOI: 10.2478/cttr-2014-0010
20.Eldridge, A., T.R. Betson, M. Vinicius Gama, and K.
McAdam: Variation in Tobacco and Mainstream Smoke
Toxicant Yields From Selected Commercial Cigarette
Products; Regul. Toxicol. Pharmacol. 71 (2015) 409–427.
DOI: 10.1016/j.yrtph.2015.01.006
21.Oldham, M.J., D.J. DeSoi, L.T. Rimmer, K.A. Wagner,
and M.J. Morton: Insights From Analysis for Harmful and
Potentially Harmful Constituents (HPHCs) in Tobacco
Products; Regul. Toxicol. Pharmacol. 70 (2014) 138–148.
DOI: 10.1016/j.yrtph.2014.06.017
22.Morton, M.J. and S.W. Laffoon: Cigarette Smoke
ChemistryMarketMapsUnderMassachusettsDepartment
of Public Health Smoking Conditions; Regul Toxicol.
Pharmacol. 51 (2008) 1–30.
DOI: 10.1016/j.yrtph.2008.03.001
23.Hyodo, T.: Selected Constituent Yield Variation in the
Smoke of Commercial Cigarette Brands on the Japanese
124 CTNR @ 31 (2) @ 2022

Market; Beitr. Tabakforsch. Int. 27 (2017) 208–223.
DOI: 10.1515/cttr-2017-0022
24.Tayyarah, R.: Multiple Point in Time Evaluation of
Commercial and Reference Cigarette Products for
Abbreviated HPHC Yield for Mainstream Smoke and
Filler; Paper ST59, presented at CORESTA Smoke
Science and Product Technology Joint Study Groups
Meeting, Seville, Spain, 29 September – 3 October 2013.
Availableat: https://www.coresta.org/abstracts/multiple-
point-time-evaluation-commercial-and-reference-
cigarette-products-abbreviated (accessed January, 2021)
25.Horwitz, W.: Evaluation of Analytical Methods Used for
Regulation of Foods and Drugs; Anal. Chem. 54 (1982)
67A–76A. DOI: 10.1021/ac00238a002
26.Wright, C.: Standardized Methods for the Regulation of
Cigarette-Smoke Constituents; Trend Anal. Chem. 66
(2015) 118–127. DOI: 10.1016/j.trac.2014.11.011
27.Purkis, S.W., L. Drake, M. Meger, and D.C. Mariner: A
Review of the UK Methodology Used for Monitoring
Cigarette Smoke Yields, Aspects of Analytical Data
Variability and Their Impact on Current and Future
Regulatory Compliance; Regul. Toxicol. Pharmacol. 56
(2010) 365–373. DOI: 10.1016/j.yrtph.2009.11.008
28.Murphy,K.R.,B.Myors,andA.Wolach:StatisticalPower
Analysis: A Simple and General Model for Traditional and
Modern Hypothesis Tests; 4th
ed., Routledge, Oxford, UK,
2014. ISBN: 9781848725881
9512:2019 Cigarettes – Determination of Ventilation –
Definitions and Measurement Principles; ISO, Geneva,
Switzerland, 2019. Available at: https://www.iso.
org/standard/73027.html (accessed April, 2022)
2971:2013 Cigarettes and Filter Rods – Determination of
Nominal Diameter – Method Using a Non-Contact Optical
Measuring Apparatus; ISO, Geneva, Switzerland, 2013.
Available at: https://www.iso.org/standard/54738.html
(accessed April, 2022)
6565:2015 Tobacco and Tobacco Products – Draw
Resistance of Cigarettes and Pressure Drop of Filter Rods
– Standard Conditions and Measurement; ISO, Geneva,
standard/64265.html (accessed April, 2022)
Tobacco(CORESTA):CORESTARecommendedMethod
No. 62. Determination of Nicotine in Tobacco and
Tobacco Products by Gas Chromatographic Analysis;
(2021). Available at: https://www.coresta.org/
sites/default/files/technical_documents/main/CRM_62-
December2021.pdf (accessed May, 2022)
21045:2018 Cigarettes – Tobacco and Tobacco Products –
Determination of Ammonia – Method Using Ion
Chromatographic Analysis; ISO, Geneva, Switzerland,
2018. Available at: https://www.iso.org/standard/
69733.html (accessed April, 2022)
No. 93. Determination of Selected Metals in Tobacco and
Tobacco Products by ICP-MS; (2021). Available at:
https://www.coresta.org/sites/default/files/technical_docu
ments/main/CRM_62-December2021.pdf (accessed May,
2022)
No. 72. Determination of Tobacco Specific Nitrosamines
in Tobacco and Tobacco Products by Liquid
Chromatography - Tandem Mass Spectrometry; (2017).
Avalailable at: https://www.coresta.org/sites/default/files/
technical_documents/main/CRM_72-July2017.pdf
(accessed January, 2021)
3402:1999 Tobacco and Tobacco Products – Atmosphere
for Conditioning and Testing; ISO, Geneva, Switzerland,
28324.html (accessed January, 2021)
8454:2007 Cigarettes – Determination of Carbon
Monoxide in the Vapor Phase of Cigarette Smoke – NDIR
Method; 2007. Available at: https://www.iso.org/
4387:2019 Cigarettes – Determination of Total and
Nicotine-Free Dry Particulate Matter Using a Routine
Analytical Smoking Machine; ISO, Geneva, Switzerland,
76549.html (accessed January, 2021)
10362-1:2019Cigarettes–DeterminationofWaterinTotal
Particulate Matter From the Mainstream Smoke – Part 1:
Gas-ChromatographicMethod;ISO,Geneva,Switzerland,
2019. Avalailable at: https://www.iso.org/
10315.2013 Cigarettes – Determination of Nicotine in
SmokeCondensates–Gas-ChromatographicMethod;ISO,
Geneva, Switzerland, 2013. Available at:
https://www.iso.org/standard/56744.html (accessed
January, 2021)
23919:2020 Cigarettes – Determination of Ammonia in
Cigarette Mainstream Smoke Using Ion Chromatography;
ISO, Geneva, Switzerland, 2020. Available at:
https://www.iso.org/standard/77345.html (accessed April,
2022.)
23920:2020 Cigarettes – Determination of Ammonia in
Cigarette Mainstream Smoke With an Intense Smoking
Regime Using Ion Chromatography. ISO, Geneva,
standard/77346.html (accessed April, 2022.)
21160:2018 Cigarettes – Determination of Selected
Carbonyls in the Mainstream Smoke of Cigarettes –
MethodUsingHighPerformanceLiquidChromatography;
ISO, Geneva, Switzerland, 2018. Available at:
2022)
Carbonyls in the Mainstream Smoke of Cigarettes With an
Intense Smoking Regime – Method Using High
Performance Liquid Chromatography; ISO, Geneva,
125
CTNR @ 31 (2) @ 2022

45.Health Canada: Health Canada Test Method T-107.
Determination of Hydrogen Cyanide in Mainstream
Tobacco Smoke; Health Canada, 1999. Available at:
https://health.canada.ca/apps/open-information/
tobacco/100PDF/T-107E.PDF (accessed January, 2021)
22634-1:2019 Cigarettes – Determination of
Benzo[a]pyrene in Cigarette Mainstream Smoke Using
GC/MS – Part 1: Method Using Methanol as Extraction
Solvent; ISO, Geneva, Switzerland, 1019. Available at:
2022)
23906-1:2020 Cigarettes – Determination of
Benzo[a]pyrene in Cigarette Mainstream Smoke With an
Intense Smoking Regime Using GC/MS – Part 1: Method
Using Methanol as Extraction Solvent; ISO, Geneva,
48.Wagner, K.A., N.H. Finkel, J.E. Fossett, and I.G. Gillman:
Development of a Quantitative Method for the Analysis of
Tobacco-Specific Nitrosamines in Mainstream Cigarette
Smoke Using Isotope Dilution Liquid
Chromatography/Electrospray Ionization Tandem Mass
Spectrometry; Anal. Chem. 77 (2005) 1001–1006.
DOI: 10.1021/ac048887v
Volatile Organic Compounds in the Mainstream Smoke of
Cigarettes – Method Using GC/MS; ISO, Geneva,
50.International Organization for Standardization ISO): ISO
Volatile Organic Compounds in the Mainstream Smoke of
Cigarettes With an Intense Smoking Regime — Method
UsingGC/MS;ISO,Geneva,Switzerland,2020.Availableat:
2022)
51.Seeman, J.I. and R.A. Carchman: The Possible Role of
Ammonia Toxicity on the Exposure, Deposition,
Retention, and the Bioavailability of Nicotine During
Smoking; Food Chem. Tox. 46 (2008) 1863–1881.
DOI: 10.1016/j.fct.2008.02.021
52.Agnew-Heard,K.A.,V.A.Lancaster;R.Bravo,C.Watson,
M.J. Walters, and M.R. Holman: Multivariate Statistical
Analysis of Cigarette Design Feature Influence on ISO
TNCO Yields; Chem.Res.Toxicol.29 (2016) 1051–1063.
DOI: 10.1021/acs.chemrestox.6b00096
8243:2013 Cigarettes – Sampling; ISO, Geneva,
22305:2006 Cigarettes – Measurement of Nicotine-Free
Dry Particulate matter, nicotine, water and carbon mon-
oxide in cigarette smoke — Analysis of data from collabo-
rativestudiesreportingrelationshipsbetweenrepeatability,
reproducibility and tolerances; ISO, Geneva, Switzerland,
40863.html (accessed June 2022).
Corresponding author:
Rana Tayyarah
ITG Brands, LLC
420 N. English Street
Greensboro, NC 27405, USA
E-mail: rana.tayyarah@itgbrands.com
126 CTNR @ 31 (2) @ 2022

HPHC Testing of Tobacco and Smoke to Examine Cigarette Temporal Variability

Recommended

Recommended

More Related Content

Similar to HPHC Testing of Tobacco and Smoke to Examine Cigarette Temporal Variability

Similar to HPHC Testing of Tobacco and Smoke to Examine Cigarette Temporal Variability (20)

More from Rana Tayyarah

More from Rana Tayyarah (15)

Recently uploaded

Recently uploaded (20)

HPHC Testing of Tobacco and Smoke to Examine Cigarette Temporal Variability