Alexandra Buergy

Agnès Rolland-Sabaté
email: agnes.rolland-sabate@inrae.fr

Alexandre Leca

Catherine M G C Renard

Apple puree's texture is independent from fruit firmness

Keywords: Borkh., Processing, Cell adhesion, Pectin, Mealiness

How cellular and molecular structure of raw fruits impact puree's texture is still an unresolved question. Texture variations of purees obtained from four apple cultivars of contrasted texture (Braeburn, Gala, Golden Delicious, Granny Smith) and two modalities (mealiness, fruit load) after two contrasted processes were investigated. Although puree's viscosity strongly varied between cultivars (562-1368 mPa.s), it did not correlate with apple firmness, except for Granny Smith. This cultivar had the firmest fruits (3.2 N) and the most viscous purees (1368 mPa.s), in accordance with large particles (around 650 µm), high pulp wet mass and serum viscosity. Mealy Braeburn apples showed lower puree's viscosity (562 mPa.s) than their not-mealy homologues (779 mPa.s). This was due to reduced cell adhesion, maybe because of lower (arabinose+galactose)/rhamnose ratio, leading to smaller particles during processing. Process also impacted puree's viscosity (692-939 mPa.s), with more viscous purees obtained with the high temperature-low shear process.

Four apple (Malus domestica Borkh.) cultivars, namely Braeburn (BR), Gala (GA), Golden Delicious (GD) and Granny Smith (GS) were grown in Mallemort, France and harvested in August and September 2018 corresponding to the commercial harvest dates. BR, GA, GS and half of the GD trees (GD1) were thinned chemically to reduce fruit load (Supplementary Table S1). The other half of the GD trees were not thinned (GD2), resulting in more but smaller fruits.

Apples were stored for one month at 4 °C in normal atmosphere in order to reduce the starch content. Half of the BR apples (BRM) were stored for 11 days at 24 °C and a relative humidity between 90% and 100% (customised phytotron, Froid et Mesures, Beaucouzé, France) prior to processing in order to accelerate development of mealiness [START_REF] Barreiro | Comparison between sensory and instrumental measurements for mealiness assessment in apples. A collaborative test[END_REF].

The day before processing, apples were separated into two equivalent groups, one determined for puree processing, one for raw apple characterization. For analysis of raw fruits, three replicates of ten representative apples were chosen per cultivar. After determination of fruit texture at 23 °C, apples were cored and cut in 12 equal portions. The pieces were separated equally into three groups and cut vertically. Only four pieces per apple, systematically spread over sides and height, were retained in order to obtain a batch of 40 apple pieces that were immediately frozen in liquid nitrogen and then stored at -20 °C to isolate the alcohol insoluble solids (AIS) [START_REF] Renard | Variability in cell wall preparations: quantification and comparison of common methods[END_REF].

Puree preparation

For all cultivars, each process was conducted in triplicate. Apples (3 kg) were cored, sliced into 12 equal pieces and processed under vacuum into puree by a cooker-cutter (RoboQbo Qb8-3, RoboQbo, Bentivoglio, Italy). To inactivate apple pectin methylesterase and only consider chemical pectin degradation, temperatures higher than 59 °C [START_REF] Denes | Purification, properties and heat inactivation of pectin methylesterase from apple (cv Golden Delicious)[END_REF] were applied in both processes (Supplementary Fig. S1). Process I consisted in grinding at 3000 rpm during temperature increase (202 s) and the following 15 min at 70 °C. The purees were then pasteurized (95 °C, 2 min). Process II comprised grinding at 3000 rpm during temperature increase (360 s), followed by 400 rpm for 17 min at 95 °C. Half part of each puree was refined by an automatic sieve (Robot Coupe C80, Robot Coupe SNC, Vincennes, France) of 0.5 mm, removing skin and particles larger than the sieve opening.

Once purees reached room temperature, rheology, particle size and pulp wet mass were analysed. Pulp and serum were separated by centrifugation of the puree (7690 x g, 15 min, 15 °C) and then frozen separately (-20 °C) until AIS extraction.

Physico-chemical characterization

Texture of raw apples

Texture of raw apples was determined by a puncture test via a multipurpose texture analyser (TAPlus, Lloyd Instruments, Farenham, UK), using a punch probe of a diameter of 2 mm. This allowed to penetrate into a peeled apple section without compression of the tissue. The puncture probe penetrated up to a depth of 17 mm in order to assess a large range of the parenchyma and thus to be representative of the whole fruit. Firmness of apple flesh was calculated as the ratio of penetration energy, averaged in the plateau region of the loaddeflection curve, to the height of testing, giving the puncture mean load. Crunchiness was estimated by calculating the linear distance between consecutive points from the forcedistance curve in a range of 10 mm at the load plateau [START_REF] Gregson | Evaluation of numerical algorithms for the instrumental measurement of bowl-life and changes in texture over time for ready-to-eat breakfast cereals[END_REF]. The test was repeated 10 times on three different apples of the same cultivar.

Rheology of the purees and sera

Samples were analysed at 22.5 °C using a stress-controlled rheometer (Physica MCR301), equipped with a Peltier cell (CPTD-200) and a measuring cylinder (CC27/S), all from Anton Paar (Graz, Austria). A vane measuring system (FL100/6W) with a 3.46 mm gap was used for purees. For flow curves, viscosity was followed over a logarithmically distributed range (shear rate values between 10 and 250 s -1 ), recording one point every 15 s. Apparent viscosity at 50 s -1 was chosen to compare the puree's textures as it represents the approximate shear rate in mouth [START_REF] Shama | Identification of stimuli controlling the sensory evaluation of viscosity II. Oral methods[END_REF]. Amplitude sweep tests were measured from 0.01 to 100% at a constant angular frequency (10 rad/s), recording five points per decade. The time required to measure each point was defined by the software. The values of Gʹ and Gʹʹ were averaged in the linear viscoelastic range.

Considering the gap of the measuring system, rheological analysis was theoretically not adapted for purees containing particles larger than 1 mm. However, NR purees, the only samples containing particles (skin fragments) larger than 1 mm, showed high repeatability.

The method was thus considered valid for internal comparison.

Serum viscosity was analysed by a flow curve (10 to 1000 s -1 , 8 min) using a double gap cylinder geometry set (DG27). The value at a shear rate of 100 s -1 was retained, as it was expected that oral perception of serum would require a higher shear rate than purees.

Particle size distribution

The particle size distribution was analysed by laser granulometry (Mastersizer 2000, Malvern Instruments, Malvern, UK) as described previously [START_REF] Buergy | Pectin modifications in raw fruits alter texture of plant cell dispersions[END_REF]. Each sample was analysed twice and the Malvern's software averaged the size distribution over three repeated measurements on the same sample.

Pulp wet mass (PWM) and water retention capacity (WRC)

The PWM was calculated as the ratio of the pulp weight after centrifugation to the initial weight of the puree and expressed in g/100 g [START_REF] Espinosa | Effect of processing on rheological, structural and sensory properties of apple puree[END_REF]. The amount of water retained by the mass of the pulp's cell wall polysaccharides (g/g dry weight) was defined as the WRC [START_REF] Robertson | Hydration properties of dietary fibre and resistant starch: A European collaborative study[END_REF]. It was estimated as the relation between the PWM and the pulp's dry weight. The fibres' mass in the wet mass is generally negligible and was thus not considered.

Analytical

Chemicals

Acetic anhydride, methanol-d3 and o-methylimidazole were from Acros Organics (Geel, Belgium). Acetone (pure) was from Carlo Erba Reagents (Val-de-Reuil, France). Acetic acid (glacial), ammonia solution (35 g/100 mL), methanol (LC-MS grade), sodium sulphate (anhydrous) and sulphuric acid were from Fisher Scientific (Loughborough, UK). Sodium hydroxide and phenol were from Merck KGaA (Darmstadt, Germany). Calcium chloride, galacturonic acid, myo-inositol, m-hydroxydiphenyl, potassium hydroxide, sodium acetate, sodium borohydride, sodium hydrogen phosphate, sodium tetraborate and sugars (arabinose, fucose, galactose, glucose, mannose, rhamnose, xylose) were from Sigma-Aldrich (Steinheim, Germany). Dichloromethane and ethanol (96 g/100 mL) were from VWR Chemicals (Fontenay-sous-Bois, France). All reagents were used without further purification.

Cell wall isolation

Cell wall polysaccharides were isolated as alcohol insoluble solids (AIS).

The frozen apple pieces were freeze-dried and finely ground before AIS were extracted with ethanol (700 mL/L) as described by [START_REF] Bourvellec | Phenolic and polysaccharidic composition of applesauce is close to that of apple flesh[END_REF]. AIS were expressed in mg/g fresh weight (FW).

Pulp was water-washed and AIS were prepared according to [START_REF] Buergy | Pectin modifications in raw fruits alter texture of plant cell dispersions[END_REF]. Once free sugars were absent [START_REF] Dubois | Colorimetric method for determination of sugars and related substances[END_REF], samples were dried by solvent exchange, followed by 48 h in a drying oven at 40 °C. The AIS of the pulp was determined as the relation between the dry pulp weight and the initial pulp weight after waterwashing and expressed in mg/g FW.

Serum precipitation

AIS of the serum was alcohol-precipitated as described by [START_REF] Buergy | Pectin modifications in raw fruits alter texture of plant cell dispersions[END_REF]. The pectin content in the serum was roughly estimated by calculating the ratio between the weight of the sample after freeze-drying and the initial weight of serum (100 mL), expressed in mg/g FW.

Cell wall polysaccharide analysis

Neutral sugars and myo-inositol (internal standard) were analysed after acid hydrolysis [START_REF] Saeman | Techniques for the determination of pulp constituents by quantitative paper chromatography[END_REF]. The free sugars were derivatised to volatile alditol acetates [START_REF] Englyst | Determination of the non-starch polysaccharides in plant-foods by gas-liquid-chromatography of constituent sugars as alditol acetates[END_REF] and analysed using a Clarus 500 gas chromatograph (PerkinElmer, Waltham, USA), equipped with a flame ionization detector (FID) and a OPTIMA® capillary column (30 m × 0.25 mm i.d., 0.25 µm film thickness, Macherey-Nagel, Düren, Germany) at 230 °C, using helium as carrier gas.

The acid hydrolysates were tested spectrophotometrically for galacturonic acid (GalA) using the m-hydroxydiphenyl assay [START_REF] Blumenkrantz | New method for quantitative determination of uronic acids[END_REF].

Methanol content was determined by stable isotope dilution assay after saponification [START_REF] Renard | Comparison of the cell wall composition for flesh and skin from five different plums[END_REF]. It was analysed on a Trace 1300 gas chromatograph (Thermo Scientific, Waltham, USA) with a TG-WaxMS capillary column (30 m × 0.25 mm i.d., 0.5 µm film thickness, Thermo Scientific, Waltham, USA), coupled to a ISQ LT single quadrupole mass spectrometer (Thermo Scientific, Waltham, USA).

The degree of methylation (DM) was calculated as molar ratio of methanol to GalA and expressed in %.

Starch determination

Starch was quantified in the AIS of raw apples using the total starch assay kit K-TSTA (Megazyme, Wicklow, Ireland). As residual polyphenols in the AIS might inactivate the enzymes, the enzyme concentrations were doubled compared to the manufacturer's instructions. Each sample was analysed twice. All values for AIS, neutral sugars, GalA and methanol were corrected by the respective starch content in each sample.

High performance size-exclusion chromatography coupled to multi-angle laser light scattering (HPSEC-MALLS)

Molar mass and size distribution of soluble pectins were determined by HPSEC-MALLS, coupled to a differential refractive index detector (Shimadzu, Tokyo, Japan). The equipment, detectors and sample preparation were the same as reported previously [START_REF] Buergy | Pectin modifications in raw fruits alter texture of plant cell dispersions[END_REF].

Serum pectins (2.5 mg AIS/mL eluent, 100 µL) were eluted at 40 °C with an acetate buffer (0.2 mol/L, pH 3.6) at a flow rate of 0.6 mL/min and separated by three PolySep-GFC columns (P3000, P5000 and P6000, 300 × 7.8 mm) and a guard column, all from Phenomenex (Le Pecq, France). Data were treated by ASTRA ® software (Wyatt Technology Corporation, version 7.3.2.19 for PC) as described before [START_REF] Buergy | Pectin modifications in raw fruits alter texture of plant cell dispersions[END_REF]. 10

Statistical analysis

Fruit texture was determined on thirty apples. All other measurements were conducted once for each of the three replications, except starch determination and particle size distribution that were analysed twice for each of the three replications. At least two of three serum pectins were injected on the HPSEC-MALLS system. As the Shapiro-Wilk test indicated that not all results were normally distributed, they were compared with Kruskal-Wallis non-parametric test [START_REF] Kruskal | Use of Ranks in One-Criterion Variance Analysis[END_REF] at the 95% level of significance. The XLSTAT package [START_REF] Addinsoft | XLSTAT statistical and data analysis solution[END_REF] for Microsoft Excel was used. Pooled standard deviations were calculated for each series of replicated measurement using the sum of individual variances weighted by the individual degrees of freedom [START_REF] Box | Statistics for experimenters: An introduction to design, data analysis, and model building[END_REF]. Principal component analysis (PCA) was performed using the package "FactoMineR" [START_REF] Lê | FactoMineR: An R Package for Multivariate Analysis[END_REF], linear regression using the package "ggplot2" [START_REF] Wickham | ggplot2: Elegant Graphics for Data Analysis[END_REF], both for R statistical software (R Core Team, 2018).

Results

Apple texture

Determination of apple firmness and crunchiness (Table 1) confirmed significant differences in apple texture, depending on the cultivar. GS showed the highest firmness and crunchiness values, whereas GD1 and GD2 showed the lowest values. Here, fruit thinning of GD apples did not lead to different fruit textures. BR was firm and crunchy, while BRM was firm but not crunchy, confirming acquisition of the mealy texture of BRM apples [START_REF] Barreiro | Comparison between sensory and instrumental measurements for mealiness assessment in apples. A collaborative test[END_REF].

GA revealed intermediate firmness and crunchiness values.

Cell wall composition of raw apples

Apple fruit texture might be influenced by the cell wall structure and composition (Table 1).

AIS of raw apples were in a usual range of 15-27 mg/g FW (Le [START_REF] Bourvellec | Phenolic and polysaccharidic composition of applesauce is close to that of apple flesh[END_REF][START_REF] Massiot | Composition, physico-chemical properties and enzymatic degradation of fibres prepared from different tissues of apple[END_REF][START_REF] Renard | Variability in cell wall preparations: quantification and comparison of common methods[END_REF], with GS showing the highest values. Except BR, with noticeably high starch contents, all cultivars were nearly starch-free. Amounts of GalA in the AIS varied between cultivars. DM were also significantly different between cultivars but all pectins were highly methylated (> 50%). The pectic RG I branching was estimated as the ratio of neutral sugars (arabinose+galactose)/rhamnose. Since the AIS of the whole fruit was used to calculate this ratio, some galactose and arabinose residues of apple hemicelluloses xyloglucan and arabinoxylan, respectively, were included in this equation.

However, their amounts are quite less important than arabinose and galactose of RG I side chains [START_REF] Fügel | A novel approach to quality and authenticity control of fruit products using fractionation and characterisation of cell wall polysaccharides[END_REF], leading only to a slight overestimation of the actual ratio. It was highest in GS and lowest in GA pectins. Pectins of BRM apples were slightly less branched than BR apples.

Rheological characterization of apple purees

Although the refining step generated less viscous purees, refined and not refined purees showed the same trends between cultivars and the two processes. Therefore, only not refined purees are detailed in the results section. The data of refined purees are reported in Supplementary Table S3.

The chosen cultivars and processes generated a wide range of textures (Table 2), with "cultivar" being the major parameter. The highest viscosities were obtained with GS and the lowest with GA and BRM. Purees prepared with BR apples were more viscous than purees of their mealy homologue. Purees of GD2 showed slightly higher viscosity values than GD1.

Process II produced systematically slightly more viscous purees and higher Gʹ and Gʹʹ. All samples showed higher Gʹ than Gʹʹ values, corresponding to a structured viscoelastic product with soft solid-like behaviour, in accordance with another study on apple purees [START_REF] Espinosa | Effect of processing on rheological, structural and sensory properties of apple puree[END_REF].

Analysis of texture determinants in purees

Particle size distribution

Particle size was demonstrated to be the most important factor influencing puree's texture in "real" systems [START_REF] Buergy | Pectin modifications in raw fruits alter texture of plant cell dispersions[END_REF], i.e. without dilution or concentration of the purees. Here, particle size showed a monomodal distribution for all cultivars and both process conditions (Fig. 1).

Generally, Process I generated smaller particles. An exception was BRM. While BR showed cell clusters around 400 µm (Process I) and 650 µm (Process II), apple flesh of BRM was ground to individual cells around 200 µm with both processes. Among all cultivars, GS showed the largest particles (500-800 µm). GD2 showed slightly larger particles than GD1.

Pulp wet mass and water retention capacity

In "real" systems, PWM (Table 2) is considered to have a second order impact on puree's texture [START_REF] Buergy | Pectin modifications in raw fruits alter texture of plant cell dispersions[END_REF]. It differed significantly between cultivars but not between the two processes. However, purees prepared by Process II showed slightly higher PWM for all cultivars, except BRM. WRC (Table 2) was also not significantly influenced by the process.

GS had notably high PWM and WRC.

Serum viscosity, soluble pectin content and molar mass

Process II increased serum viscosity and soluble pectin content in the sera of BR, BRM, GD1 and GS but not of GA and GD2 (Table 2). GS purees had remarkably high serum viscosities.

Serum viscosity of BR was similar to BRM for Process I but higher for Process II.

Molar mass and molecular size were determined for soluble pectins of Process II. GS pectins exhibited the highest molecular size (lowest elution volume) and BRM a smaller size than BR, which eluted before (Fig. 2). All other cultivars had medium sizes between BR and BRM (not shown). GS had the highest molar mass at the main peak (459x10 3 g/mol), whereas molar masses of BR (277x10 3 g/mol) and BRM (230x10 3 g/mol) were not significantly different.

Discussion

A PCA was conducted on raw apples' and purees' characteristics. The first two principal components (PC1 and PC2) explained together more than 70% of the total variance. Variables related to apple texture (firmness and crunchiness) were grouped in the correlation circle (Fig. 3A). Neither starch nor GalA contents could be correlated to fruit texture, whereas the DM showed a slightly negative correlation. Calcium crosslinks can be formed between HG pectins in the middle lamella of parenchyma cells if pectin molecules exhibit more than 10 consecutive unmethyl-esterified GalA residues [START_REF] Kohn | Intermolecular calcium ion binding on polyuronatespolygalacturonate and polyguluronate[END_REF]. They are associated with improved cell adhesion [START_REF] Gwanpua | Slow softening of Kanzi apples (Malus x domestica L.) is associated with preservation of pectin integrity in middle lamella[END_REF][START_REF] Jarvis | Intercellular adhesion and cell separation in plants[END_REF] but cannot always be linked to firmer fruits [START_REF] Li | Analyses of microstructure and cell wall polysaccharides of flesh tissues provide insights into cultivar difference in mealy patterns developed in apple fruit[END_REF][START_REF] Ng | Cell wall structures leading to cultivar differences in softening rates develop early during apple (Malus x domestica) fruit growth[END_REF]. Some recent findings indicate that calcium crosslinks between HG molecules limit their association with cellulose, leading to reduced cell wall mechanical properties [START_REF] Lopez-Sanchez | Nanostructure and poroviscoelasticity in cell wall materials from onion, carrot and apple: Roles of pectin[END_REF]. In apple, decreased RG I branching correlates with a loss of fruit texture due to decreased cell adhesion [START_REF] Pena | Loss of highly branched arabinans and debranching of rhamnogalacturonan I accompany loss of firm texture and cell separation during prolonged storage of apple[END_REF] and their possible ability to control turgor pressure in complement to xyloglucan and cellulose [START_REF] Lahaye | Water and cell wall contributions to apple mechanical properties[END_REF]. A decrease in RG I side chains might also loosen the cell wall structure due to decreased interactions with cellulose [START_REF] Zykwinska | Evidence for in vitro binding of pectin side chains to cellulose[END_REF], xyloglucan [START_REF] Popper | Xyloglucan-pectin linkages are formed intraprotoplasmically, contribute to wall-assembly, and remain stable in the cell wall[END_REF] and between RG I galactan side chains [START_REF] Makshakova | Metrics of rhamnogalacturonan I with β-(1→4)-linked galactan side chains and structural basis for its self-aggregation[END_REF]. This might facilitate the access of pectinolytic enzymes to their substrates, resulting in reduced fruit firmness. RG I branching was the highest in pectins from GS (Table 1), which also had the highest apple firmness and crunchiness. Pectins of BRM apples were slightly less branched than BR apples due to a loss of arabinose and galactose side chains in BRM, also associated with mealiness in apples [START_REF] Nara | Involvement of terminal-arabinose andgalactose pectic compounds in mealiness of apple fruit during storage[END_REF]. Nevertheless, no simple relation linking RG I branching to apple texture was found, so that no correlation was detected in the PCA. Cell shape and packing [START_REF] Lapsley | The cellular structure of selected apple varieties[END_REF][START_REF] Mcatee | A rapid method of fruit cell isolation for cell size and shape measurements[END_REF] as well as structural organization of plant cell wall polysaccharides and cellular water partition [START_REF] Lahaye | Water and cell wall contributions to apple mechanical properties[END_REF][START_REF] Lahaye | Cellulose, pectin and water in cell walls determine apple flesh viscoelastic mechanical properties[END_REF] might also be considered to fully explain textural differences but were not analysed here.

Both apple firmness and crunchiness were orthogonal to rheological properties of the puree (apparent viscosity, Gʹ and Gʹʹ) in Fig. 3A and thus varied independently, as confirmed in Fig. 3D. [START_REF] Bourles | Impact of vacuum cooking process on the texture degradation of selected apple cultivars[END_REF] reached the same conclusion for the texture of cooked apple slices. As an exception, GS had the firmest and crunchiest apples (Table 1) and showed the highest puree's texture (Table 2). Fruit thinning did not induce different fruit textures. GD2 purees were, however, slightly more viscous than GD1, linked to particle size, while Buergy et al. ( 2020) described higher viscosity and bigger particles for GD1 purees. The harvest year or, most probably, the late fruit thinning in this study might explain this difference.

Rheological properties of the puree formed a group in the PCA's correlation circle (Fig. 3A) with several factors (particle size, PWM and serum viscosity) that are known to alter puree's texture [START_REF] Espinosa | Effect of processing on rheological, structural and sensory properties of apple puree[END_REF][START_REF] Leverrier | Influence of particle size and concentration on rheological behaviour of reconstituted apple purees[END_REF][START_REF] Rao | The structural approach to rheology of plant food dispersions[END_REF]. Interestingly, RG I branching and AIS of raw apples were also part of this group, whereas WRC of the pulp was not. Correlation of particle size, PWM and serum viscosity to apparent viscosity of the purees could be confirmed with Fig. 3E-G. However, several slopes were visible, indicating that cultivars responded differently to processing. Especially GS showed particularly high particle sizes, PWM and serum viscosities, resulting in highly viscous purees.

Cultivars could be clearly distinguished on the sample map (Fig. 3B) and thus strongly affected puree's viscosity. Process I and II could not be differentiated on the sample map (Fig. 3C), indicating limited impact of process. However, all purees prepared with Process II were slightly shifted to positive values on PC1. This was in accordance with higher viscosity, Gʹ and Gʹʹ, linked to increased particle size, PWM and serum viscosity. Studies on both apple [START_REF] Espinosa-Munoz | The significance of structural properties for the development of innovative apple puree textures[END_REF] and carrot [START_REF] Appelqvist | Morphologies, volume fraction and viscosity of cell wall particle dispersions particle related to sensory perception[END_REF] dispersions revealed that large particles are perceived as grainy and crunchy, whereas high PWM leads to more consistent and dry textures in mouth. In addition, high serum viscosities increase the perception of consistency and reduce the sensation of graininess due to elevated pectin content. Hence, GS purees were expected to show higher consistency than other cultivars, whereas Process II might increase consistency perception for all cultivars.

Process II generally induced larger particles (Fig. 1) because of lower grinding speed, leading to more viscous purees. An exception was BRM. While BR purees showed cell clusters, apple flesh of BRM was ground to individual cells with both processes. Cell adhesion appeared reduced in mealy apples, as even a low grinding speed was sufficient to induce tissue fragmentation during processing. Reduced cell adhesion in BRM apples might be correlated to decreased RG I branching (Table 1) as had been hypothesized before [START_REF] Nara | Involvement of terminal-arabinose andgalactose pectic compounds in mealiness of apple fruit during storage[END_REF][START_REF] Pena | Loss of highly branched arabinans and debranching of rhamnogalacturonan I accompany loss of firm texture and cell separation during prolonged storage of apple[END_REF]. Due to smaller particles, BRM purees were less viscous than BR purees. Among all cultivars, GS showed the largest particles (Fig. 1), in accordance with the highest puree's texture. Cell adhesion in GS apples seemed particularly high, maybe due to higher RG I branching (Table 1). Initially larger cells in GS could be excluded as [START_REF] Buergy | Pectin modifications in raw fruits alter texture of plant cell dispersions[END_REF] showed similar particle sizes for individualised cells in GS and GD apples.

In several cultivars, pectin solubilisation was favoured by high temperatures of Process II (Table 2), as already reported in tomato and carrot purees [START_REF] Lin | Chemical properties of water-soluble pectins in hot-and cold-break tomato pastes[END_REF][START_REF] Moelants | The effects of process-induced pectin changes on the viscosity of carrot and tomato sera[END_REF]. This increased serum viscosity. However, soluble pectin content alone could not explain the remarkably high serum viscosities of GS. The high macromolecular size and molar mass of soluble GS pectins also contributed to viscosity increase. BR showed higher serum viscosities than BRM for Process II, although the AIS content of both modalities was similar (Table 2). Molar mass was also similar but BRM pectins were smaller (Fig. 2) and thus contributed less to serum viscosity. BRM purees showed similar particle sizes and similar PWM for both processes but different puree's textures. Serum viscosity, higher for Process II, coincided with the higher puree's texture for this process. Serum viscosity could thus have a significant impact on puree's texture when particle size and PWM were identical.

Conclusions

Apple texture could not predict rheological properties of purees, although the cultivars strongly affected puree's viscosity. Remarkably high puree's texture obtained for GS was in accordance with the biggest particles, highest PWM and serum viscosity. Mealy apples showed reduced cell adhesion and were thus more susceptible to cell fragmentation during puree processing, resulting in less viscous purees. The process had a limited effect, although the high temperature-low shear process (Process II) produced more viscous purees. The lower grinding speed generated bigger particles and the higher temperature increased both the PWM and the serum viscosity because of facilitated pectin solubilisation. Combined effects of temperature regime and shear intensities could modulate puree's texture and thus mitigate poor performance of some cultivars.

Declaration of competing interest

Declarations of interest: none. The values of AIS and cell wall composition of raw apples were corrected for the starch content. Raw apple's characteristics in the PCA: Firmness, Crunchiness, AISraw apple (alcohol insoluble solids of the raw apples) Starch, GalA (galacturonic acid), DM (degree of methylation), RG I branching (ratio of neutral sugars (arabinose+galactose)/rhamnose).

Figure captions

Puree's characteristics in the PCA: ɳapp (apparent viscosity of the puree at 50 s -1 ), Gʹ, Gʹʹ (puree's storage and loss modulus, respectively, at an angular frequency of 10 rad/s), Particle size (particle size d 0.9), PWM (pulp wet mass), WRC (water retention capacity of the pulp), ɳserum (serum viscosity at 100 s -1 ), AISserum (alcohol insoluble solids of the serum). Braeburn (triangle); Braeburn, mealy (inverted triangle); Gala (circle); Golden Delicious, reduced fruit load (square); Golden Delicious, high fruit load (lozenge); Granny Smith (pentagon). Empty The ratio (Ara+Gal)/Rha estimated RG I branching and was calculated using the yields of neutral sugars arabinose (Ara), galactose (Gal) and rhamnose (Rha), expressed in mg/g AIS Fresh weight; PSD: Pooled standard deviation (degrees of freedom: 174 for firmness and 612 crunchiness (n for each cultivar = 30), 30 for starch (n for each cultivar = 6) and 12 for other 613 analyses (n for each cultivar = 3)). 614 615

Fig. 1 .

 1 Fig. 1. Particle size distribution of purees issued from different apple cultivars and modalities.

Fig. 2 .

 2 Fig. 2. Normalized chain concentration and molar mass versus elution volume of soluble

Fig. 3 .

 3 Fig. 3. Principal component analysis (PCA) (A-C) and scatterplots (D-G) of textural

  symbols represent Process I (70 °C, 3000 rpm) and filled symbols represent Process II (95 °C, 400 rpm).

  Fig. 1 627

Table 1 .

 1 Textural characteristics and cell wall composition of raw apples depending on the cultivar and modalities (mealiness, fruit load), followed byKruskal-Wallis H and P values.

	Cultivar	Firmness Crunchiness (N) (dimensionless) (mg/g FW) (mg/g AIS) (mg/g AIS) (%) AIS Starch GalA DM Ara + Gal Rha
	BR	2.5	14	22	52	377	67	19
	BRM	2.7	11	21	5	383	59	15
	GA	2.4	12	20	4	328	76	12
	GD1	1.9	11	23	3	318	82	18
	GD2	2.1	11	26	3	268	82	19
	GS	3.2	14	27	3	363	67	28
	PSD	0.4	0.9	0.5	2	19	5	2
	H value	102	109	16	10	15	14	15
	P value	< 0.001	< 0.001	0.007	0.066	0.012	0.013 0.011

Table 2 .

 2 Rheological characteristics and texture determinants of not refined apple purees 616 depending on the cultivar, modalities (mealiness, fruit load) and the process (Process I: 70 °C, 617 3000 rpm; Process II: 95 °C, 400 rpm), followed by

	Cultivar Process	ɳapp 50 s -1 Gʹ (mPa.s) (Pa)	Gʹʹ (Pa)	PWM (g/100 g) (g/g DW) (mPa.s) WRC ɳserum 100 s -1 Serum AIS (mg/g FW)
	BR	I	630	1080	216	26	14	8	0.5
		II	1088	1509	323	28	12	17	0.7
	BRM	I	456	965	200	29	13	5	0.3
		II	680	1373	310	28	16	9	0.8
	GA	I	480	720	138	24	14	7	0.2
		II	681	934	194	27	14	10	0.1
	GD1	I	632	984	191	28	15	11	0.5
		II	943	1391	291	32	12	14	2.6
	GD2	I	776	1246	247	29	12	12	0.9
		II	1060	1601	342	30	11	14	1.0
	GS	I	1467	1836	386	37	23	52	1.4
		II	1937	1794	543	44	17	155	3.9
	PSD		55	121	31	1	1	3	0.2
		H value 23	24	24	28	22	24	24
	Cultivar
		P value < 0.001	< 0.001 < 0.001 < 0.001	< 0.001	< 0.001	< 0.001
		H value 10	7	8	2	2	6	4
	Process
		P value 0.002	0.010	0.004	0.137	0.206	0.012	0.049

(Supplementary TableS2). BR: Braeburn; BRM: Braeburn, mealy; GA: Gala; GD1: Golden

Delicious, reduced fruit load; GD2: Golden Delicious, high fruit load; GS: Granny Smith;

Acknowledgements

This work was carried out as part of "Interfaces" flagship project, publicly funded through ANR (the French National Agency) under the "Investissements d´avenir" program with the reference ANR-10-LABX-001-01 Labex Agro and coordinated by Agropolis Fondation under the reference ID 1603-001. Studies conducted with the phytotron and the HPSEC-MALLS system were supported by the various CPER Platform 3A funders: European Union, European Regional Development Fund, the French Government, the Sud Provence-Alpes-Côte d'Azur Region, the Departmental Council of Vaucluse and the Urban Community of Greater Avignon.


Amandine Vallata
email: amandine.vallata@u-bordeaux.fr

Jennifer O'loughlin

Semanur Cengelli

Laetitia Minary

François Alla
email: francois.alla@u-bordeaux.fr

Predictors of cigarette smoking cessation in adolescents: a systematic review

Keywords: smoking cessation, predictors, adolescents, guidelines

Purpose

To update a systematic review published in 2012 that identified predictors of cigarette smoking cessation among adolescents.

Methods

The PubMed and Web of Science databases were searched for relevant articles published between September 2010 and January 2018, using the following keywords: smoking OR tobacco OR cessation; quit OR stop; longitudinal OR prospective OR cohort. Our search identified 3,399 articles. Inclusion criteria included longitudinal studies (intervention and cohort studies) evaluating cigarette smoking cessation in young people (aged 10-24 years).

After screening, in total, 34 articles were included in the review.

Results

In total, 63 predictors of smoking cessation among adolescents were identified, with 36 new predictors that were not identified in the previous review: 9 sociodemographic factors, 13 psychosocial factors, 5 behavioral factors, 19 social influences factors, 8 smoking related variables, 6 environmental factors, 2 health related variables, and 1 genetic factor.

Conclusions

To increase the probability of successful smoking cessation, strategies targeting young smokers should consider both individual and environmental predictors of cessation.

IMPLICATIONS AND CONTRIBUTION

This systematic review identified predictors of smoking cessation in adolescents. These results could enhance the effectiveness of tobacco control strategies for this priority target population.

According to the Global Burden of Disease Study, 6.4 million deaths worldwide were attributed to tobacco in 2015 [START_REF] Reitsma | Smoking prevalence and attributable disease burden in 195 countries and territories, 1990-2015: a systematic analysis from the Global Burden of Disease Study 2015[END_REF]. The average age of cigarette smoking onset is 15-16 years in both Europe and the United States [START_REF] Marcon | Trends in smoking initiation in Europe over 40 years: A retrospective cohort study[END_REF][START_REF]The Health Consequences of Smoking: 50 Years of Progress. A Report of the Surgeon General[END_REF], with nearly 90% of adult smokers starting smoking before the age of 18 years [START_REF]The Health Consequences of Smoking: 50 Years of Progress. A Report of the Surgeon General[END_REF]. Among adolescents, the prevalence of smoking, defined as having smoked in the last 30 days, is 21% in Europe and 13.6% in the United States [START_REF]ESPAD Report 2015: Results from the European School Survey Project on Alcohol and Other Drugs[END_REF][START_REF]The Health Consequences of Smoking: 50 Years of Progress. A Report of the Surgeon General[END_REF].

Therefore, in addition to ongoing smoking prevention efforts, there is a critical need for effective smoking cessation programs targeting adolescents. However, there is limited evidence on the effectiveness of smoking cessation programs for youth, and those that appear to be effective may promote only short-term cessation [START_REF] Fanshawe | Tobacco cessation interventions for young people[END_REF][START_REF] Hutton | A systematic review of randomized controlled trials: Web-based interventions for smoking cessation among adolescents, college students, and adults[END_REF][START_REF] Villanti | Smoking-cessation interventions for U.S. young adults: a systematic review[END_REF][START_REF] Villanti | Smoking-Cessation Interventions for U.S. Young Adults: Updated Systematic Review[END_REF][START_REF] Garrison | Smoking cessation interventions for adolescents: a systematic review[END_REF][START_REF] Donovan | Smoking cessation programs for adolescents[END_REF]. Whereas 71% of adolescent smokers have tried to quit, relapses are common, with 34% relapsing within the first week and 92% of smokers relapsing within the year following the attempt to quit [START_REF] Bancej | Smoking cessation attempts among adolescent smokers: a systematic review of prevalence studies[END_REF].

Given the limited evidence available, many adolescent cessation interventions were developed without a complete understanding of the barriers and facilitators of youth cessation. In a systematic review of longitudinal studies published between 1984 and 2010, Cengelli et al. identified predictors of cessation (i.e., defined as abstaining from smoking for at least 6 months) in adolescents and young adults [START_REF] Cengelli | A systematic review of longitudinal population-based studies on the predictors of smoking cessation in adolescent and young adult smokers[END_REF]. That systematic review identified five factors significantly associated with cessation: having friends who do not smoke, having intentions to not smoke in the future, resisting peer pressure to smoke, older age at smoking onset, and having a negative attitude towards smoking. Since the Cengelli et al. publication, the social and legislative contexts related to tobacco have evolved considerably in many countries. One notable change was the enactment of the WHO Framework Convention on Tobacco Control in 2005 and its MPOWER policies in 2008 [13,14]. The 2018 report informed of appreciable progress in implementation of these policies (e.g., more than 90% of the parties indicated having banned smoking in all public places and having implemented price policies) [15].

Evidence regarding predictors of smoking cessation has also evolved. The objective of this review is to provide an update on the individual and environmental factors associated with cigarette smoking cessation in adolescents.

METHODS

This systematic review adhered to the PRISMA recommendations, which proposes a structured approach with a four-phase flow diagram, and a 27-item checklist [16].

Eligibility criteria

The previous review by Cengelli et al. included articles published between January 1, 1984and August 31, 2010. Herein, articles published between September 1, 2010and January 31, 2018 were reviewed. Eligibility criteria included articles published in English that targeted an adolescent population. The WHO defines adolescents as individuals aged 10-19 years, with the definition of "young people" extending to age 24 years [17]. To enhance sensitivity, this review included longitudinal studies investigating smoking cessation among study populations aged 10-24 years, or an average and/or median age within this range. It focused on cigarette consumption only. No studies were excluded based on sample size, follow-up duration, participant socioeconomic status, or country of origin. In an effort to provide a comprehensive review of possible predictors, unlike Cengelli et al., this review included studies that defined cessation as abstaining from smoking for less than 6 months, and those that evaluated the effectiveness of smoking cessation interventions. Studies of any design were included, provided that the study analyzed the link between one or more variables and smoking cessation. Literature reviews were excluded to avoid duplication.

Information sources

The PubMed and Social Sciences Citation Index in the Web of Science databases were first searched in April 2016 and March 2017, respectively. Both searches were updated in June 2018. Databases were searched using the same keywords as those used by Cengelli et al., with the exception of removing age limits from the search algorithm to broaden the search and to capture all studies that included an adolescent population. Searches were conducted by one author (AV), and the search results were managed in a shared library using "Zotero" reference management software.

Search

PubMed was searched using the following search strategy: (((((smoking[MeSH Major Topic]

Study selection

Article selection involved five steps: (i) identification of the search results and removal of duplicates; (ii) removal of articles published between January 1, 2010 andAugust 31, 2010 and between February 1, 2018 andDecember, 31 2018, as these months could not be excluded from the Web of Science search; (iii) selection of relevant articles based on the title; and (iv) selection of relevant articles based on the abstract. If the age range of the sample evaluated was not clear in the abstract, the full-text article was reviewed. Articles that did not focus exclusively on adolescents but identified individuals aged 10-24 years in the analyses were retained; (v) the articles included after the title and abstract screening were downloaded for full-text review. The entire screening process was conducted independently by two authors (AV, FA), and the results were compared between them. Disagreement was resolved by discussion between the two authors, or by a third reviewer (LM).

Data collection process

Two data extraction tables were developed prior to the full-text article review to 1) summarize the study characteristics and 2) categorize cigarette smoking cessation predictors. Extraction of descriptive data was completed by three authors (AV, LM, FA). AV and FA extracted data on predictors independently and then compared their results to ensure reliability. Data relevant to the review were highlighted in each article and recorded in the extraction tables.

Data were extracted directly from the articles. Cited articles were reviewed to supplement missing methodologic information when necessary. No authors were contacted. follow-up period was used. We retained multivariable analysis, or if not available, bivariate analysis.

Several data relating to cessation predictors were extracted: (i) article identification (first author, year of publication), (ii) potential predictor studied or identified, (iii) level of statistical significance and (iv) direction of the effect (positive, negative). Relapses were considered cessation failures, and factors associated with relapses were considered barriers to cessation. Potential predictors were categorized into one of eight categories, comprising the five broad categories identified by Cengelli et al. and three additional categories. The categories were sociodemographic factors (e.g., ethnicity, sex), psychosocial factors (e.g., self-efficacy, beliefs about smoking), behavioral factors (e.g., alcohol consumption, physical activity), social influences (e.g., friends smoke, parents' quitting), smoking-related factors (e.g., intensity of smoking, nicotine dependence), environmental factors (e.g., tobacco price, anti-smoking laws), health-related factors (e.g., BMI, cough), and genetic factors (e.g., role of genotype). For articles that evaluated an intervention, predictors related to the sample or the study environment but not the intervention (e.g., number of sessions in the program, techniques used, etc.) were included.

Synthesis of results

The potential predictors were enumerated under one of the eight categories. Extracted data were reported in rows corresponding to the source article. As new potential predictors were identified, additional columns were created. If the predictor was reported in multiple articles, the results were added to the existing column for that predictor and in the row corresponding to the source article. Predictors labelled differently but representing the same concept were grouped together (e.g., "drink alcohol", "alcohol consumption"). AV compiled the groupings and labels, and FA validated these categorizations in discussion with AV. The identified predictors were compared with those in Cengelli et al. to assess the similarities and differences.

A table summarizes the number of articles that describe each potential cigarette smoking cessation predictor, the number of articles that report a statistically significantly association between the predictor and cessation, and the direction of the association.

When there was consistency in the direction of the association across articles, the predictor was considered positive or negative for cessation, depending on its direction of influence.

When the direction of the association differed across studies, it was classified as "inconsistent". When a predictor was statistically significant in only one subgroup (e.g., hispanic women, occasional smokers), it was recorded as statistically significant, and the subgroup was reported in the data extraction tables. Lastly, each potential predictor was categorized into one of four groups: (i) probable predictor (i.e., the variable was statistically significant in at least three studies or, if only three or four studies were conducted, then two studies needed to show statistically significant association, as in the Cengelli et al. review), (ii) possible predictor (i.e., the variable was statistically significant in one or two studies), (iii) probably unrelated (i.e., the variable was not significant in at least two studies), or (iv) insufficient evidence (i.e., the variable was not significant in one study).

Synthesis of both reviews

The predictors reported in Cengelli et al. were classified as above. Next, to realize a synthesis of this earlier review and the current review, all the predictors were reclassified as: (i) "probable" if the variable was classified as probable in at least one of the two reviews, (ii) "possible" if the variable was classified as possible in at least one of the two reviews (unless it was classified as probable), (iii) "probably unrelated" if the variable was classified as "probably unrelated" in one of the two reviews (unless it was classified as probable or possible), and (iv) "insufficient evidence" if the variable was classified as "insufficient evidence" in both reviews or was identified in only one review.

RESULTS

Study selection

In total, 3,399 references were identified from the PubMed (n = 1,460) and Web of Science (n = 1,939) searches. Duplicates (n = 617) and studies conducted outside the designated timeframe (n = 316) were excluded (Figure 1). The remaining 2,466 articles underwent screening. Of the articles screened, 1,729 were eliminated during the title review and 635 during the abstract review. After screening, 66 articles were included in the full-text review.

Two full-text articles could not be obtained and were excluded. The exclusion criteria were as follows:

(i) studies that did not identify cessation predictors [i.e., studies that investigated the prevalence of smoking, impacts of smoking or cessation, representations of smoking and attitudes towards smoking (e.g., for youth, what is a low-intensity smoker, a heavy smoker, an addicted smoker; reactions to graphic visual health warnings on cigarette packages), attempts to quit, intentions to quit, effects of smoking cessation treatments (e.g., varenicline vs. nicotine replacement therapy), cessation cost-effectiveness, analytic methods, implementation of cessation program (without analysis of cessation predictors), or predictors related to an intervention (e.g., age of the practitioner, number of sessions, etc.)],

(ii) studies that included target populations other than healthy adolescents [i.e., studies that included adults only, made no distinction between adults and adolescents, investigated individuals with a specific condition (e.g., cancer, cardiovascular diseases, HIV, schizophrenia, depression, primary care group), or those that focused on a subpopulation],

(iii) cross-sectional studies or literature reviews, (iv) studies that investigated products other than cigarettes [electronic cigarettes (e-cigarettes) when unrelated to smoking cessation, chewing tobacco, or snus (i.e., moist tobacco powder placed in the mouth)].

Following the full-text review, 34 articles were retained (Figure 1) .

Study characteristics

The retained studies (n = 34) are described in Supplementary Table 1. The majority of the studies were conducted in North America (n = 18), followed by Europe (n = 10) and Asia (n = 6). The follow-up duration ranged from 1 month to 12 years. Twelve studies included an intervention. The sample size ranged from 60 to 9,022 adolescents. Data collection methods included self-report questionnaires (via the internet, in class, at home), telephone interviews and face-to-face interviews (at home, school). Two studies did not conduct a multivariable analysis (Bachman 2012 and[START_REF] Ham | Differences Between 1-Month Quitters and Relapsers in Biospsychosocial Characteristics Among Male Smokers in Korea[END_REF].

The definition of quitting varied across articles, with some studies not defining quitting in terms of duration. In this analysis, abstinence was characterized as "continuous" (n = 7), "prolonged" (n = 2) or "point prevalence" (n = 25). As defined by Hughes et al., "Continuous abstinence refers to abstinence between quit day and a follow-up time; prolonged abstinence refers to sustained abstinence after an initial grace period or to a period of sustained abstinence between two follow-ups (the two are equivalent); point prevalence abstinence refers to the prevalence of abstinence during a time window immediately preceeding followup" [18].

Synthesis of results

Supplementary Table 2 summarizes the potential cigarette smoking cessation predictors evaluated, how they were grouped, and the corresponding predictors in Cengelli et al. Table 1 describes the number of statistically significant predictors out of the total number of potential predictors investigated in each study according to predictor category. The number of potential predictors evaluated ranged from 1 to 21, and the maximum number of statistically significant predictors in one article was nine. Statistically significant associations were found in 85 of the 211 analyses considered (nota bene: each predictor could be investigated several times). In total, 73 conceptually distinct potential predictors were considered in the 211 separate analyses (Supplementary Table 3). Of these conceptually distinct predictors, 30 were categorized as social influences or psychosocial factors. Nearly half (36/73) of the potential predictors were examined in only one article, 9 were considered in two articles, 10 were considered in three articles, 2 in four articles, 7 in five articles, 3 in six articles, and 1 in seven, nine, eleven, twelve, thirteen and fourteen articles. The predictors considered in more than seven articles were: age, gender, educational level, self-efficacy/confidence in quitting smoking, and nicotine dependence. The most common predictor assessed was age.

Of the 73 potential predictors, 49 achieved a statistically significant association with smoking cessation in at least one article. Among the statistically significant predictors, 16 were positively associated (i.e., facilitators), 26 were negatively associated (i.e., barriers) with cessation, and 6 had inconsistent results, with studies finding both positive and negative associations with cessation (Supplementary Table 3). The one remaining predictor found that the direction of the association between body mass index and cessation varied by subgroup and the attempt to lose weight (e.g., it was negative for white women who had tried to lose weight but positive for hispanic women).

This review identified 11 factors that are "probably" associated with smoking cessation in adolescents, including 1 factor (self-efficacy/confidence in quitting) that promotes cessation, 5 factors that impede cessation (having friends that smoke, household members that smoke, frequency/intensity of smoking, nicotine dependence and daily smoking), and 5 factors with inconsistent findings (race/ethnicity, older age, intention to quit, use of e-cigarettes and presence of smoke-free legislation) (Supplementary Table 3).

Of the 38 factors "possibly" associated with cessation, 15 promote cessation [sociodemographic (n = 3), psychosocial (n = 1), behavioral (n = 1), social influences (n = 4), smoking-related (n = 1), environmental (n = 3), health-related (n = 1) and genetic (n = 1)], 21

impede cessation [sociodemographic (n = 2), psychosocial (n = 4), behavioral (n = 1), social influences (n = 9), smoking-related (n = 3) and environmental (n = 2)], 1 factor had inconsistent findings (social influences), and 1 factor had inconsistent findings in the direction of association across subgroups (health-related) (Supplementary Table 3).

We identified nine factors that were "probably unrelated" to cessation, including high socioeconomic status, living in an urban area, employed, concerns about the health hazards of smoking, concerns about weight gain on quitting, feeling stressed/bad/anxious, alcohol consumption, quitting with a close relation (that is trying to stop with family members or friends who also attempt to quit smoking), and withdrawal symptoms (Supplementary Table 3).

Lastly, 15 factors were classified in the "insufficient evidence" group [sociodemographic (n =

2), psychosocial (n = 3), behavioral (n = 2), smoking-related (n = 3), environmental (n = 2), and health-related (n = 3)] (Supplementary Table 3).

Synthesis of both reviews

Supplementary Table 4 presents the results of synthesis of the two reviews, including the probable and possible factors associated with adolescent smoking cessation with their direction of association, the factors unrelated to cessation, and the factors with insufficient evidence.

A total of 18 factors were "probable" predictors of cessation, including eight new factors identified in this review. Of these probable factors, five were positively associated with cessation (self-efficacy/confidence in quitting, no intention to smoke cigarettes, negative beliefs about smoking, older age at smoking onset, perception that health is good), six were negatively associated with cessation (friends who smoke, household members who smoke, frequency/intensity of smoking, daily smoking, nicotine dependence, received cigarette coupons), and seven had inconsistent findings (race/ethnicity, older age, higher level of parental education, single-parent households, intention to quit, use of e-cigarettes, presence of smoke-free legislation). In addition, 45 factors were "possible" predictors, of which 28 were newly identified in this review. Of the possible factors, 22 were positively associated with cessation [sociodemographic (n = 3), psychosocial (n = 5), behavioral (n = 3), social influences (n = 7), environmental (n = 3), and genetic (n = 1)], 21 were negatively associated with cessation [sociodemographic (n = 2), psychosocial (n = 4), behavioral (n = 2), social influences (n = 9), smoking-related (n = 3), environmental (n = 1)], 1 social influences factor had inconsistent findings, and 1 health-related factor had inconsistent findings in the direction of an association across subgroups (Supplementary Table 4).

Eleven factors were "probably unrelated" to cessation, including six factors newly identified in this review (high socioeconomic status, living in an urban area, employed, feeling stressed/bad/anxious, concerns about the health hazards of smoking, concerns about weight gain on quitting, no perceived prevalence of smoking among peers (that is the awareness by an adolescent of smoking among his peers), having exemplars who modelled cigarette use, quitting with a close relation, withdrawal symptoms, and pregnancy) (Supplementary Table 4).

Lastly, 36 factors were classified in the "insufficient evidence" group, including 15 factors newly identified in this review (Supplementary Table 4).

DISCUSSION

This systematic review corroborates the findings of Cengelli et al. regarding probable and possible predictors of smoking cessation in adolescents. In addition, this review identified 36 new predictors, including 8 probable and 28 possible predictors.

The review highlights several modifiable factors that should be considered when conceptualizing and developing cessation interventions for adolescents, particularly predictors linked to their mental representations about smoking, such as positive/negative beliefs about smoking, advantages of quitting, or confidence in quitting smoking. Beyond interventional factors, these predictors should be considered in a global cessation strategy. Indeed, another study showed that adolescent smoking cessation strategies that combine several facilitators (e.g., nicotine replacement therapy, psychosocial support, environmental intervention) are effective in promoting cessation [39,53].

Like in adult smokers[54-56], we found that the frequency and intensity of smoking as well as nicotine addiction are major barriers to quitting in adolescents. Other studies showed that smoking cessation programs may help adolescents to reduce cigarette consumption by reducing their nicotine dependence[57] or using replacement therapy in regular smokers [39,58]. However, unlike adult smokers, several studies found that adolescents are generally unaware of their level of nicotine dependence and often attempt to quit without counselling or nicotine replacement therapy, despite the fact that many are already highly dependent [59][60][61]. Thus, a first step to helping adolescents to stop smoking could be to make them aware of their dependence, and to encourage them to see a tobacco cessation specialist.

In our study "Presence of smoke-free legislation" was inconsistent but was based on only three studies, and the definitions of the concept varied across these studies. The other environmental factors were significant and consistent in the direction of an association with cessation. According to our study adolescents are influenced by their environment, so it is critical that opportunities to smoke are minimized in environments frequented by adolescents.

While schools and recreational areas could prohibit smoking, few jurisdictions have implemented such policies. For example, only 40% of 67 schools in Wales had an explicit smoking policy that applied to all individuals on their school campuses [62]. This study supports the necessity of public policy to continue efforts to limit the opportunities to smoke for adolescents, and to limit access to cigarettes for young people by prohibiting the sale of tobacco products to minors [13].

This review implies that self-efficacy/confidence in quitting, no intention to smoke cigarettes and negative beliefs about smoking are probable predictors of cessation in adolescents. These predictors also apply to smoking onset among adolescents. Indeed, a longitudinal study in Sweden found that low to moderate self-esteem and less negative attitudes towards smoking predicted smoking onset in adolescents [63]. These findings imply that adolescents' perceptions throughout the smoking process should be considered. Supporting self-efficacy among tobacco users may include helping to identify and build on their success of past attempts to quit, to develop a perceived sense of control with the withdrawal symptoms, to manage cravings in different social situations and to develop a plan for quitting (e.g., identifying a quit-line, changing smoking habits, discussing benefits and strategies) [64,65].

This review also implies that social environment and having peers who smoke are barriers to quitting in adolescents. The influence of peers extends beyond their use of cigarettes. Ragan found that their own beliefs about smoking shape the smoking behavior of their friends [66].

Highlighting the importance of social networks, the onset of tobacco, cannabis, and alcohol use among adolescents is also often influenced by their peers

Strengths and limitations

One strength of this review is the broadened inclusion criteria, compared with those used by

  ) OR tobacco[MeSH Major Topic]) AND (cessation OR quit OR stop) AND (longitudinal OR prospective OR cohort)) AND (('2010/09/01'[PDat]:'2018/01/31'[PDat]) AND Humans[Mesh] AND English[lang]). For the Social Sciences Citation Index in Web of Science, the following search strategy was used: TOPIC: (smoking OR tobacco) AND TOPIC: (cessation OR quit OR stop) AND TOPIC: (longitudinal OR prospective OR cohort) AND LANGUAGE: (English) AND YEAR PUBLISHED: (2010-2018).

Table 1 .

 1 Number of statistically significant predictors identified out of the total number of potential predictors investigated in each study according to predictor categoryNumber of statistically significant predictors identified out of the total number of potential predictors

  Cengelli et al., which enabled the identification of three new predictor categories (environmental, health-related and genetic factors), and of studies evaluating smoking cessation interventions. The limitations of this study include the lack of available data in some of the included articles, and the marked heterogeneity across studies in terms of variables, measurement methods and statistical thresholds, which prevented performing meta-analyses.The variables included in the adjustments differed among the studies analyzed. Thus, some of the inconsistent results may be related to the adjusted-for variables[83]. Although the use of probable, possible, probably unrelated and insufficient evidence categories does not account for the strength of the associations, this methodology offers pragmatic guidance for research and policy. It is worth noting that the direction of the association was consistent across studies for 42 of 49 factors. Since quality indicators vary considerably across different assessment tools, study quality was not ranked. However, similar to previous reviews, each study reviewed was described in-depth so that readers can assess its quality[84]. Finally, it was not the objective of our study to analyze the mechanisms of action of cessation predictors and our methodology did not allow us to do so. Further work is needed to analyze the mechanisms linking these determinants to cessation (e.g., factors favoring the initiation of cessation vs. those favoring cessation maintenance). CONCLUSION This systematic review identified 18 factors as probably associated and 45 factors as possibly associated with smoking cessation in adolescents. Individual and environmental factors should be considered in the development of tobacco cessation interventions for adolescents, particularly the influence of social network and psychosocial factors. Practical application of the recommendations of this study could promote smoking cessation in adolescents.

Table 2 . Number of studies that investigated potential predictors of smoking cessation within each category and the number of potential predictors investigated in each category, among adolescents

 2 Smoking-related factors, social influences, sociodemographic factors and psychosocial factors were the most frequently investigated potential predictors (Table2).

	Mukhopadhyay 2011	3/6	0/1	0	1/1	1/1	0	1/1		6/10
	Nagelhout 2013	0/3	1/1	0	0	2/2	1/3	0		4/9
	Schaefer 2013	0	0	0	1/1	0	0	0		1/1
	Scherphof 2013	0	0/1	0	0	1/1	0	0		1/2
	Schuck 2011	0/2	1/3	0	0/1	2/5	0	0		3/11
	Steinmetz-Wood 2017	1/5	0	0	1/1	0	0/1	0		2/7
	Walker 2014	1/4	1/6	0/1	4/5	2/2	0	0/1		8/19
	Wang 2017	0	0	0	0	0/1	0	0		0/1
	Wang 2018	0	0	0	0	1/1	0	0		1/1
	Wong 2010	0	3/7	0/2	2/6	0/4	0/1	1/1		6/21
	Wong 2011	0/4	4/7	0/1	0/4	0/4	0	0/1		4/21
	Wong 2016	1/1	1/1	0	0/1	0/1	0	0		2/4
	Yang 2015	1/4	0	0	5/5	0	0	0		6/9
		15/56*	17/43	2/11	21/36	20/43	7/12	2/9	1/1	85/211

*For example, this means that 15 analyses achieved statistical significance of the 56 analyses conducted across all articles (n = 34); cut-off for statistical significance = 0.05

  can present barriers to cessation[52]. But at the same time, adolescents are ambivalent about the role of adults in their smoking behavior. Indeed, a qualitative study found that adolescents want the freedom to smoke and object to their parents' and teachers' attempts to prevent them from smoking; however, adolescents recognize the parental duty to prevent them from smoking, and the dereliction of this duty can lead to feelings of unimportance[71]. This explains the difficulty for adults, particularly parents, to engage in the appropriate behavior with adolescent smokers.Several factors had inconsistent results across studies. Factors amenable to intervention (e.g., intention to quit, support network, use of e-cigarettes, etc.) require further investigation, and factors that require specific contexts or implementation methods should be tested under appropriate conditions (e.g., smoke-free legislation)[74]. The inconsistent results on the effect of parental education level may be explained by the findings ofGagné et al.[75]. Those authors found that adolescents with parents in the lowest education category were more likely to seriously consider quitting within the next 6 months and to have previously unsuccessful quit attempts compared with adolescents whose parents had a professional or university degree. However, that study also found that adolescents with parents in the lowest education category were more likely to have a positive opinion of smoking by the young compared with those with parents in higher education categories. These findings highlight the complex relationship between family socioeconomic status and adolescent tobacco consumption and warrant further investigation.

	The influence of parenting practices of education on smoking cessation is unclear within this
	review: psychological control (i.e., "the degree to which parents use negative psychological
	manipulation, verbal abuse, guilt tripping, neglect/disengagement, and withdrawal of love")
	may facilitate smoking cessation, while behavioral control (i.e., "the extent to which parents
	monitor, set clear rules, and conduct consistent discipline on their child's behavior") and
	parental responsiveness (i.e., "the extent to which parents are supportive, warm, and attentive
	to their child") This review has several implications for public policy, including that commercial tobacco Finally, this review illustrates that predictors of smoking cessation in adolescents include both
	advertising will hinder cessation efforts, and increasing tobacco prices will positively effect individual and environmental level factors. Public health programs and policies targeting
	cessation efforts. For example, in the United Kingdom, anti-tobacco legislation successfully cessation in adolescents must consider intervening in local and national contexts. Moreover, it
	reduced the prevalence of smoking among youth, especially in girls, with a 4.3% reduction in is necessary to consider the context to ensure transferability when scaling these interventions.
	[67]. Plus, interventions prevalence of regular smoking[72]. These findings underscore the importance of evidence-Indeed, transferability necessitates adaptation to the implementation context[76-79]. This
	targeting the social environment have successfully prevented smoking onset in based strategies such as MPOWER to promote smoke-free environments (e.g., bans on review identifies predictors and leads to recommendations that may inform the development,
	adolescents[68]. Recognizing the critical importance of the social environment, smoking tobacco advertising)[14]. The SimSmoke model, which simulates the effect of country-wide implementation and scaling of smoking cessation interventions[79]. Moreover, it could help
	cessation interventions have incorporated the environment by using social marketing tobacco control measures on smoking prevalence and premature tobacco-related mortality, to identify the key functions underpinning successful smoking cessation programs, and thus
	strategies (e.g., "Moi(s) sans tabac" in France)[69,70]. implies that the implementation of strong tobacco control policies in 15 European countries contribute to theory-based evaluation[79-81]. Ongoing qualitative research will complement
	could prevent 7.7 million premature tobacco-related deaths between 2011 and 2040[73].

these results to better understand the role of the environmental context in smoking cessation among adolescents

[82]

.

Table 2 . Number of studies that investigated potential predictors of smoking cessation within each category and the number of potential predictors investigated in each category, among adolescents

 2

	Predictor category	No. of studies that	No. of potential
		investigated potential	predictors investigated
		predictors within each	within each category
		category
		(n = 34)
	Sociodemographic factors	16	12
	Psychosocial factors	16	13
	Behavioral factors	9	5
	Social influences	18	17
	Smoking-related factors	21	12
	Environmental factors	6	8
	Health-related factors	6	5
	Genetic factors	1	1

This review was funded by the Lorraine region (a French regional council), the Institute of Public Health Research/National Cancer Institute/ARC Foundation, the Ligue Nationale Contre le Cancer, and the Regional Health Agency of Nouvelle-Aquitaine.

The funded sources were not involved in the conduct of this review.

ACKNOWLEDGEMENTS

JOL holds a Canada Research Chair in the Early Determinants of Adult Chronic Disease. CONTRIBUTORS AV, FA, LM and JOL designed the study. AV conducted the literature searches and screened

Data items

The extracted study characteristics included (i) article identification (first author, year of publication, place and year of study), (ii) description of the sample [age at baseline recorded as the mean and range and/or school grade, sample size, sample type (representative, convenience, purposive) and source of recruitment], (iii) study design [evaluation of the intervention (yes/no), follow-up duration, type of data collection (questionnaire, interview, online, at home, during schools hours)], (iv) analysis (analytic sample size, analytic method), and (v) outcome(s) evaluated (abstinence recorded as continuous, prolonged or pointprevalence [18], duration of abstinence). For articles that assessed abstinence at multiple intervals post-cessation (e.g., 1 week, 1 month, etc.), the interval corresponding to the longest

1

Figure 1: flow diagram depicting the selection process for review inclusion

From: Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009)