The universal temperature and density dependencies of the thermalconductivity of a random network of CNFs are described by a third-orderpolynomial function (SI-units): κCNF = (0.0787 +2.73 × 10–3·T –7.6749 × 10–6·T2 + 8.4637 × 10–9·T3)·(ρsample/ρ0)2, where ρ0 = 1340 kg m–3 and κCF = 1.065·κCNF. Despitea relatively high degree of crystallinity, both CF and CNF samplesshow amorphous-like thermal conductivity, that is, it increases withincreasing temperature. This appears to be due to the nano-sized elementaryfibrils of cellulose, which explains that the thermal conductivityof CNFs and CFs shows identical behavior and differs by only ca. 6%.The nano-sized fibrils effectively limit the phonon mean free pathto a few nanometers for heat conduction across fibers, and it is onlysignificantly longer for highly directed heat conduction along fibers.This feature of cellulose makes it easier to apply in applicationsthat require low thermal conductivity combined with high strength;the weak density dependence of the thermal conductivity is a particularlyuseful property when the material is subjected to high loads. Theresults for thermal conductivity also suggest that the crystallinestructures of cellulose remain stable up to at least 0.7 GPa.Considering the growinguse of cellulose in various applications,knowledge and understanding of its physical properties become increasinglyimportant. Thermal conductivity is a key property, but its variationwith porosity and density is unknown, and it is not known if sucha variation is affected by fiber size and temperature. Here, we determinethe relationships by measurements of the thermal conductivity of cellulosefibers (CFs) and cellulose nanofibers (CNFs) derived from commercialbirch pulp as a function of pressure and temperature. The resultsshow that the thermal conductivity varies relatively weakly with density(ρ The continual improvementsin the processing of nanocellulose during the last decades have alsoincreased the prospects of new cellulose-based products. Because ofits abundance and potential use in a wide range of applications, celluloseand nanocellulose microstructure–property relationships areimportant, and one key property is the thermal conductivity κ.A basic understanding of its variation with parameters such as density,temperature, and microstructure is vital in heat management applications,and it is also important for modeling heat transfer in applicationsof cellulose materials.7 Presently, the variation of κ of cellulose with porosity anddensity is unknown, or not well quantified, and it is also not knownif such a variation is affected by fiber size. Moreover, the temperaturevariation of κ, and the effect of fiber size, is scarcely studieddown to low temperatures; the results will help in understanding theorigin of thermal resistivity in cellulose. Here, we solve the issuesby using pressure as a variable to determine the effect of porosityand density of κ and to reliably measure the thermal conductivityof cellulose and nanocellulose, as a function of both temperatureand pressure. Concurrently, the results show the stability of thecrystalline structures of cellulose up to high pressure and high density.Cellulose is the structural componentof the plant cell wall andtherefore the perfect choice of polymer for real green high-strengthapplications and other environmentally benign applications associatedwith structures based on macromolecules or nanoparticles.1 The latterare made up of bundles of cellulose chains, and a common model isthat these form a repeated pattern of crystalline and amorphous sectionsalong the fibril, but another model with a crystalline core and amorphousshell has also been proposed.9 In the common model,each crystalline section is of the order of 100 nm in length and itis referred to as a cellulose nanocrystal (CNC). The elementary fibrilsconsist of up to about 40 individual cellulose molecules/chains, whichform a strong network through a hydrogen-bonded network with bothintra- and inter-chain bonds.Cellulose resides in the plant cell walls in the form of cellulosicfibers. Each fiber is composed of several microfibrils (5–50nm in diameter), which, in turn, are composed of elementary fibrilsof 3–5 nm in diameter.10 Native cellulose, cellulose I,is typically divided into two substructures: Iα and Iβwith, respectively, triclinic and monoclinic structures. Iαand Iβ have reported densities of about 1.61 and 1.63 g/cm,respectively. It has been suggested that higher plants consist ofvarious mixtures of these forms with the thermodynamically stableform Iβ being dominant.The hydrogen-bonded network andbond orientations can vary significantlyand may give rise to several different structures, or polymorphs,dependent on the source, extraction method, and treatment.11 CNCscan be extracted from cellulose fibers (CFs) through strong acid hydrolysis,which the amorphous parts cannot resist, while the crystalline partsremain stable.12 A different form of nanocelluloseis produced through mechanical treatment, with or without pretreatments.A common method is to use a high-pressure homogenizer, a microfluidizer,or an ultrafine grinder in which the cellulose pulp is subjected tolarge shear forces that disintegrate the CFs. This produces cellulosenanofibers (CNFs), or microfibrillated cellulose, which contain bothamorphous and crystalline regions and could be up to several micrometerslong and 10–100 nm in diameter. CNFs consist of several elementaryfibrils, but the number and microstructure can vary, for example,dependent on the processing time.Nanocellulose is typically producedby two different main methods:(i) chemical treatment and (ii) mechanical treatment with or withoutchemical/biological pretreatment.In this study, we have establishedthe effect of porosity, density,and temperature on κ of nonporous and porous samples of CFsand CNFs derived from a commercial birch pulp by using pressure asa variable.13 The chemical composition was 70wt % cellulose, 21 wt % hemicellulose, and 5 wt % lignin  and thenanofiber separation process has been described in detail in a previousstudy.–3. Thesamples plates were thereafter stored in a desiccator until characterizationby X-ray diffraction, thermogravimetric analysis (TGA), and measurementsof thermal conductivity.The CFs and CNFs were dried at 25–35°C under dynamic vacuum until the weight remained constant (∼4days). The CNF sample was thereafter ground in liquid nitrogen. Aftergrinding, CNFs were in a mixed form of powder with 1–5 mm sizedflakes, whereas the (unground) CF sample was in the form of irregularlyshaped granules. Subsequently, the samples were inserted in a pressand subjected to ca. 0.1 GPa in a piston-cylinder type die. This producedplates of 39 mm diameter and ca. 4 mm thickness with an atmosphericpressure in-die density of about 1200 kg m17 The degree of crystallinity of polymer samples isoften estimated by X-ray diffraction analysis via the ratio betweenthe crystalline and total  areas of theX-ray peaks measured for the solid polymer state but also via slightlymodified methods, for example, based on the peak area of the melted polymer.19 These methodshave been carefully reviewed by Kavesh and Schultz,19 and because of the limitations of the X-ray methods, theyreferred the results to apparent degrees of crystallinities. For efficientdetermination and comparison of the crystallinity of many cellulosesamples, Segal et al.20 introduced a somewhatdifferent X-ray method and concept—crystallinity index (CI),as “a time-saving empirical measure of relative crystallinity”.Because of its simplicity, it has become a widely used method in studiesof cellulose. The Segal method is based on the height of the strongestBragg reflection (IBragg max) andthe maximum height of the amorphous contribution (Iamorph)Accurate determination of the degree of crystallinity of celluloseby means of X-ray powder diffraction (XRD) is challenging and a highlydiscussed topic to date.16 Although the Segal method is commonly used,several recent studies suggest that results based on full patternfitting of the diffraction data provide a better description of thedegree of crystallinity.21For microcrystalline cellulose,themaximum amorphous intensity corresponds to the local minimum at 2θ≈ 19° between the cellulose Bragg reflections.XRD data for CF and CNF samples before and after the experimentwere collected using Cu Kα radiation  in a 2θ range of 5°–60°using a 1/4° fixed diffraction slit and a 2° fixed anti-scatteringslit. Samples were prepared on a zero-background silicon sample holder.Instrumental background was measured with identical settings and acquisitiontime with an empty sample holder as the reference. Full pattern fittingof the instrumental background-corrected XRD data was performed withthe WinPLOTR program  of Fullprof Suite (version July-2017).a = 6.72, b = 5.96, c = 10.40, α= 118.08, β = 114.80, γ = 80.37) and Iβ 22 was conducted with zero shift (sample displacement)and particle size peak broadening as the only additionally refinedparameters. In the last step, background coefficients were refinedas well.In the fitting procedure used here, a mathematical background basedon a 4th order Chebyshev polynomial function was initially generated.A first approximation of the coefficients was derived from a fit ofthe experimental patterns with exclusion of the main Bragg peak areas;then, profile fitting (Thomson–Cox–Hastings profilefunction) for a two-phase mixture of cellulose Iα (Aamorph) and Bragg reflection contribution (Acryst), gives CIarea  and (ii) CISegal using the mathematical background asreference . CIArea as wellas CISegal-inst and CISegal-math are compiled and compared in Furthermore, we have calculatedtwo different CIs based on theSegal method 1:(i) CIarea derived by full pattern profile fitting givesca. 30% lower crystallinity than CISegal-inst, andit differs even more from CISegal-math, which givescrystallinities above 90%. The high apparent crystallinity of CISegal-math is due to the assumption of no amorphousscattering contribution to the “mathematical background”as opposed to 100% in the calculation of CIarea. The latterassumption appears to be more realistic; therefore, CISegal-math probably provides unreasonably high CI, but it corroborates theother two estimates that suggest insignificant CI changes due to thehigh-pressure treatment. Area of the CNF sample increasedslightly after the high-pressure study, whereas that of CF decreased;CISegal-inst increased for both samples. The small,and non-systematic, changes suggest that the samples’ microstructureswere essentially unaffected by the high-pressure treatment; the bestestimate of CI is between 30 and 65%.CI–1.TGA  was used to determine the water content ofthe CF and CNF samples both before and after the high-pressure experiment2. The saAs shown in nonporous sample is the density of the sample without voids or the nonporous density,which is about 1500 kg m–3 at atmospheric pressureand 1510 kg m–3 at 0.06 GPa. The measurements providedata for sample density as a function of pressure and, thus, porosityas a function of pressure up to 0.1 GPa. The measurements of a CFsample gave a porosity of 0.11 and density of 1340 kg m–3 at 0.06 GPa. We use the same data for CNF as for CF to calculatethe density dependence of the thermal conductivity. A calculationof the compressibility of the samples in the pressure range above0.05 GPa (without lube) gave the same compressibility for CNF andCF samples to within 1.5%, but with a standard error of 7%. 23The sample densityand porosity were determined in a separate high-pressure experiment.A piston cylinder device of 39 mm internal diameter was mounted ina small hydraulic press together with a displacement sensor and aload cell. The sample was first pressure-cycled up to ca. 0.1 GPa,lubed, and thereafter again pressurized 3 to mimi25 The hot-wireprobe was a 0.1 mm-diameter Ni wire, which was inserted in a custom-made,ca. 13 mm deep and 39 mm internal diameter, Teflon sample cell. Awire of ca. 40 mm length was sandwiched between two pre-pressed platesof CFs or CNFs (see above for sample preparation) and sealed witha tightly fitting Teflon lid. The cell was mounted on a bottom pistonand inserted in a pressure cylinder of 45 mm internal diameter. Thewhole assembly was thereafter transferred to a fully automatic hydraulicpress, which supplied the load. Temperature was varied by coolingor warming the whole pressure vessel using liquid nitrogen and anexternal electric heater; it was measured using an internal chromelversus alumel thermocouple. Pressure was determined from the load/areawith an empirical correction for friction which has been establishedusing the pressure dependence of the resistance of a manganin wire.The inaccuracies in temperature and pressure are estimated as 0.5K and 40 MPa (at 1 GPa), respectively.The transient hot-wiremethod was used to measure the thermal conductivity κ with anestimated inaccuracy of ±2%.24 and for two CF samples it differed by 30% whenthe thermal conductivity differed only by 3%. Because of the observeddiscrepancy in the heat capacity per unit volume, we used it onlyas a fitting parameter and do not report the data. Measurements ofκ of CFs and CNFs were done on the same batch of pulp. However,we have also compared results of CFs of two different batches andκ of these differed less than 5%. In the measurements of κ,the heat wave penetration depth is 1–2 mm in the radial directionof the 40 mm long hot-wire probe, which is immersed in the sample.Consequently, we here provide κ of a sample with randomly orientedmicrofibers. In each measurement ofκ, the Ni-wire (hot-wire) was subjected to a 1.4 s durationheat-pulse of nominally constant power, and its electrical resistancewas measured as a function of time. Subsequently, the temperaturerise of the wire was calculated by using the relation between itsresistance and temperature; the wire acted as both a heater and asensor for the temperature rise. The analytical solution for the temperaturerise with time was fitted to 29 measured data points for the hot-wiretemperature rise with κ and the heat capacity per unit volumeas fitting parameters. The heat capacity per unit volume is determined(fitted) mainly by the first of the 29 measurement points,–3; this should be a good estimate also forthe CNF sample. All κ results reported here relate to CF andCNF samples with randomly oriented microfibers  on cooling are typical of structurallydisordered materials such as amorphous  and semicrystallinematerials with low degree of crystallinity. κ(T) of amorphous states is roughly described by a function κ∝ Tx–,with x being slightly negative or close to zero.This is much different from the behavior of single crystals and polycrystallinematerials, which typically show κ ∝ T–1 at similar temperatures. As a rough distinction,one may therefore refer to κ(T), which is describedby a function with x close to zero or negative asamorphous-like (or glass-like) and κ(T) describedby positive x as crystal-like. The reason for thedifferent behaviors is that κ of crystals is limited by phonon–phonon(Umklapp) scattering, and the number of phonons increases with temperature,26 whereas phonon propagation in amorphous materialsis limited by (temperature-independent) structural disorder .18 However, a general description of the change from amorphous-liketo crystal-like κ(T) in terms of CI is notpossible. Amorphous-like κ has been found for several semicrystallinepolymers such as nylon-6,28 polyethylene(PE),29 and poly-l-lactide.30 In all these cases, it is possible to significantlyincrease CI by thermal treatments to study the effect of crystallinityon κ. For example, κ(T) of nylon-6 changesfrom amorphous-like to crystal-like behavior when CI increases from30 to 56%. However, in a study of poly-l-lactide with CIsin the range 0–56%, κ(T) was amorphous-likein the entire range.30 Such different behaviorsmay be due to differences in the microstructures. More specifically,in a study of PE, it was shown that the crystal-like behavior of κwas strongly promoted by an increase of the nano-sized lamellar thickness.29 Two different PE samples with similar CI, butone with larger lamellar thickness, displayed significantly differentdegrees of crystal-like behavior of κ(T) . Thus, even if CF and CNFsamples may show high CI, which here is in the range 30–65%(see T) may still be amorphous-like due to the inherent nano-sizedstructure of CFs. That is, their building blocks of thin elementaryfibrils (3–5 nm in diameter) with crystalline regions thatare either separated by amorphous regions ∼100–900 nmapart along the microfibrils1 or have less-ordered(amorphous) surface regions of cellulose chains.9 Indeed,Adachi et al.31 have recently shown thatκ(T) of individual CNFs is amorphous-like andsuggested that it is due to their nano-sized structure. IndividualCNFs show κ = (2.2 ± 1.2) W m–1 K–1 at 300 K and a remarkable (weak) decrease on cooling,31 which is corroborated by molecular dynamicssimulations.32 We can conclude that theamorphous-like κ(T) of the CF and CNF samplesare in qualitative agreement with Adachi et al.’s31 result for κ along individual CNFs; theamorphous-like κ(T) is therefore likely associatedwith the inherent nano-sized structure of CFs and in origin similarto that of other nanomaterials such as carbon nanotubes33 and Si nanowires.34  This also explains the identical temperaturebehavior and small difference in magnitude of κ(T) for CFs and CNFs. Fibrillation should increase the thermal resistancein the sample, but since κ is strongly limited by the inherentnano-sized structure and the already existing microfibril boundaries,it does not affect the temperature dependence of κ and onlyweakly its magnitude (ca. 6%). Thus, boundaries of fibrils appearas a possible source for strong, temperature-independent, phonon scattering;in this case, the phonon mean free path becomes limited to the distancebetween boundaries. Structural studies have also shown increasingdisorder in chain packing and hydrogen bonding outward from the centerof elementary fibrils,35 which furtherdecreases the crystalline size in this direction. dominant ≈ hvk–1T–1 where v is the phonon velocity, T is the temperature, k is Boltzmann’sconstant, and h is Planck’s constant, is ofthe order of the diameter of an elementary fibril, which makes thephonon concept questionable for phonon propagation across a fibril.)Amorphous and semicrystalline polymers with low crystallinity showamorphous-like κ, whereas polymers with a high degree of crystallinityare expected to show crystal-like κ.–65%see , κ(T) ma–3 at a pressure below 0.5 GPa (36 suggest that it achieved the nonporous density after applyinga compaction pressure slightly above 0.1 GPa. As depicted in –3 at 0.5 GPa and porous CF and CNF samples (1340 kg m–3) show identical temperature behavior of κ. To estimate thedensity of nonporous CFs and CNFs, we used the bulk modulus of cellulose B = 11.6 GPa in the 0.2–0.6 GPa range37 and an estimated atmospheric nonporous densityof 1500 kg m–3.In order to significantly reduce voids and form a dense randomnetwork of CNFs and CFs, the samples were thereafter pressurized to0.5 GPa; this produced nonporous, or close to nonporous, samples,which were temperature-cycled at 0.5 GPa 4. Our co 0.5 GPa 3. Moreov–3 at 0.1 GPa. The resultson temperature cycling nonporous CNFs at 0.1 GPa are also depictedin Finally, the samples werepressurized up to 0.9 GPa, depressurizedto near ambient pressure, and repressurized up to 0.1 GPa at roomtemperature. This produces a well-compacted sample with an estimateddensity of about 1510 kg mp) of CFs and CNFs differduring the initial pressurization. The approach to reach the nonporousstate is somewhat more gradual for CNFs than for CFs. The CF samplereached the (near) nonporous state at about 0.35 GPa, whereas theCNF sample approached the state at 0.5 GPa. We attribute this to differencesin particle size/shape and the mixture of flakes and powder in CNF dueto densification typically levels off. The accelerated increase ofκ(p) near 0.75 GPa may therefore be due toa gradual, or second-order, transformation, but the change is toosmall for conclusive evidence of a transformation. Although this typeof behavior has not been noted under similar conditions in other polymermaterials,29 it occurs near a transition inTeflon, making the interpretation of the finding further uncertain.Strong densification of materials can induce structural changes,and such are typically observed as discontinuous changes in κ.A continuous change of κ with no significant abrupt changesin either the temperature dependence 4 or the 28 but suchchanges are less likely to occur in polymers kept at temperaturesnear room temperature and below. XRD patterns measured before andafter the high-pressure studies suggest no change in CI  was reported for a film formed by self-organizationby slowly evaporating a diluted aqueous CNC suspension under ambientconditions. Films formed by casting CNC suspensions under differentshear rates showed increasingly higher values with increasing shearrate, which was attributed to shear-induced ordering.4 Diaz et al.4 also studied κof a single CNC (Iβ) by molecular dynamics simulations and reportedκ = (5.7 ± 0.9) W m–1 K–1 along the fiber direction and κ = (0.72 ± 0.12) W m–1 K–1 in the traverse direction.As mentioned above, Adachi et al.31 reportedκ = (2.2 ± 1.2) W m–1 K–1 along individual CNFs at 300 K, which is consistent with our result(0.54 W m–1 K–1) for a dense networkof randomly oriented CNFs. A model of such network with negligiblethermal resistance between the fibers suggests κ = κ0/π,39 where κ0 is the thermal conductivity along the fiber, that is, κ =(0.7 ± 0.4) W m–1 K–1 fora dense CNF network based on Adachi et al.’s result.31 Conversely, our results combined with the model39 suggest κ = 1.7 W m–1 K–1 along individual CNFs.To compare withliterature results, we use data for nanocellulose,which have been studied recently. We reiterate that the results measuredin this study relate to random networks of CNFs and CFs, which isdifferent from the structure of sheets/papers in which fibers typicallyshow a preferred orientation. The effect of both changing crystalsize and direction of heat flow on κ of (non-woven) nanocellulosesheets/papers made from various sources such as tunicate, bacterialcellulose, cotton, and wood pulp was studied and reviewed by Uetaniand co-workers; for CNF 5 is in bg, which is defined byTo quantify theeffect of density and porosity on κ, we usethe Bridgman parameter 37 In this case, κ was measured after thesamples had been subjected to 0.9 GPa at room temperature and 373K at 0.5 GPa, which should ensure that the samples were nonporous.The results are presented in g = 1.8 and 1.5, respectively, which is in the lower rangeof the density dependences measured for polymers, especially consideringthat these are semicrystalline states. Typically, amorphous, liquid,and glassy polymers show values near g = 3,41 whereas semicrystalline polymers show somewhat higher values, forexample, low-density PE with g ≈ 5 and highdensity PE with g ≈ 10 at 100 °C,41 with few exceptions such as semicrystallineisotactic poly(propylene) with g = 1.85.42 Our direct measurements of g in the porous states (ε < 0.11) support the finding of g-values near 2 for CF and CNF samples.To determine theBridgman parameter of nonporous CF and CNF samples,we used the data on pressurization in the 0.2–0.6 GPa rangecombined with density values measured for cellulose (cotton).g of2.4 for both CNFs and CFs. Moreover, we can calculate an average valueusing data at low pressure (0.06 GPa) and the results for the nonporoussamples at atmospheric pressure (estimated density = 1500 kg m–3), which gives g = 2.0 for CF and g = 2.2 for CNF  = CκCNF(T),where C = 1.065, which describes the best third-orderpolynomial fit of κCF(T) to within0.5% in the 100–300 K range.28 and PE29 have produced highly crystallized states and significantchanges in the microstructures  aswell as new crystalline structures, we have also investigated thepossibility of similar changes for CNFs; the prospects of such transformationsare best at high temperatures and high pressures. The CNF sample wastherefore heated up to 423 K at 0.9 GPa, which is close to the maximumcapacity of the pressure vessel. The temperature was chosen as thehighest possible for long-time annealing without the risk of rapiddecomposition processes. An increase in crystallinity and/or crystal–crystaltransformations, under highly densified conditions, will most likelycause increasing values for κ. However, the measurements showedweakly decreasing κ with time, suggesting a slow decompositionprocess, and the treatment was therefore aborted; the sample was cooleddown to room temperature and recovered under ambient conditions forcharacterization by XRD and TGA. The XRD of the recovered sample showedno significant changes in the pattern compared to that before thehigh-pressure experiment (see Since high-pressure high-temperature(HPHT) treatmentsof polymerssuch as nylon-6ment see . Consequ–1 K–1 for a dense random network of CNFs (ρ= 1500 kg m–3) and ∼0.43 W m–1 K–1 for a porous network ; values for the correspondingnetworks of CFs are about 6% higher. The result for a dense randomnetwork of CNFs is in good agreement with the corresponding resultbased on κ along individual CNFs.31The thermal conductivities of CF and CNF samples show positivetemperature dependence or amorphous-like behavior. We attribute thisto the nano-sized building blocks, elementary fibrils, of the fibersand the amorphous-like κ of individual nanofibers (or microfibrils).The elementary fibrils limit the phonon mean free path to a few nanometersfor heat conduction across fibers, and it can only be significantlylonger for highly directed heat conduction along the fibers. Thisexplains the identical temperature dependence of κ of CF andCNF samples and accounts for the size of κ for a random densenetwork of CNFs and CFs, that is, κ is virtually independentof the fiber size. At 295 K, κ is ∼0.54 W m–3; the universal temperature and density dependencies for CNF arewell described by the relation κCNF = (0.0787 + 2.73× 10–3·T – 7.6749× 10–6·T2 +8.4637 × 10–9·T3)·(ρ/ρ0)2, where ρ isthe sample density and ρ0 = 1340 kg m–3 is a reference density; κ of CF is described by the same functionbut is a factor of 1.065 larger. Both CFs and CNFs show an unusuallyweak density (pressure) dependence of κ. It varies as the densitysquared, that is, κ increases by 2 for a 1% increase in density,whereas it typically varies proportionally to the cube of densityfor amorphous polymers and even stronger for semicrystalline polymers.This property and the high thermal resistivity across fibers, combinedwith its high strength, makes cellulose particularly interesting inapplications involving high loads.The temperature behavior of κ of CF and CNF samplesis independentof density and porosity in the range 1340–1560 kg mThe lack of significant discontinuouschanges in κ and itsderivative during pressure and temperature cycling of CF and CNF samplessuggests that the crystalline structures of cellulose are stable upto at least 0.7 GPa and likely up to 0.9 GPa. Moreover, the essentiallyunchanged X-ray pattern of the recovered CF and CNF samples showsthat no significant irreversible changes occurred during treatmentat 0.9 GPa for temperatures up to 423 K. In particular, the degreeof crystallinity of cellulose remained unchanged by this treatment.


The higher alcohols 2-phenylethanol, tryptophol, and tyrosol are a group of yeast-derived compounds that have been shown to affect the aroma and flavour of fermented beverages. Five variants of the industrial wine strain AWRI796, previously isolated due to their elevated production of the ‘rose-like aroma’ compound 2-phenylethanol, were characterised during pilot-scale fermentation of a Chardonnay juice. We show that these variants not only increase the concentration of 2-phenylethanol but also modulate the formation of the higher alcohols tryptophol, tyrosol, and methionol, as well as other volatile sulfur compounds derived from methionine, highlighting the connections between yeast nitrogen and sulfur metabolism during fermentation. We also investigate the development of these compounds during wine storage, focusing on the sulfonation of tryptophol. Finally, the sensory properties of wines produced using these strains were quantified at two time points, unravelling differences produced by biologically modulating higher alcohols and the dynamic changes in wine flavour over aging. Saccharomyces cerevisiae performs a wide range of industrial fermentations that functionally depend upon its ability to convert sugars to ethanol and carbon dioxide efficiently. While performing this primary function, S. cerevisiae also produces a range of secondary metabolites, such as esters, volatile fatty acids, higher alcohols, and volatile sulfur compounds (VSCs), which contribute substantially to the flavour and aroma of wine [ of wine , beer [2 of wine . Of thes of wine .Higher alcohols, also known as fusel alcohols, are compounds with more than two carbon atoms. These alcohols are derived from yeast amino acid metabolism via the Ehrlich pathway . Amino a2), which is widely used in winemaking as an additive due to its antimicrobial and antioxidant effects, to yield the tryptophol-2-sulfonate (TOL-SO3H) adduct [3H from TOL seems to be increased by bottle aging, particularly for white wines [3H on the taste and mouthfeel properties of fermented beverages remains unclear, it has recently been linked with bitterness [Except for 2-phenylethanol (2-PE), which is derived from phenylalanine and associated with a ‘rose-like’ odour quality , the ind) adduct . This re) adduct , and thete wines . Althougtterness .Higher alcohols are substrates for acetate ester production, a reaction catalysed by yeast alcohol acetyltransferases. Many acetate esters are associated with ’fruity’ and ‘floral’ aromas in wine . For exaS. cerevisiae strains that improve the organoleptic properties of sake, wine, and bread [Due to the ability of 2-PE and 2-PEA to impart floral notes, these compounds present an opportunity to shape wine style. The ability to influence the production of 2-PE and 2-PEA in white wines would be beneficial because, although naturally occurring, they are usually present at relatively low concentrations and are unlikely to impart a definitive character ,21. The nd bread ,24,25.2S). Similarly, a range of compounds derived from methionine by either enzymatic or non-enzymatic reactions are also associated with negative attributes in wine, such as methanethiol (MeSH) (‘sewage and rubber’ aromas), methional (‘cooked potato’ aroma) and methyl thioacetate (MeSAc) (‘sulfurous and cheesy’ aromas) [2S by yeast has been extensively studied, and commercial low H2S producing strains have been generated [Conversely, and with the exception of a small group of polyfunctional thiols associated with ‘fruity’ and ‘tropical’ characters , most VS aromas) ,8,27. Whenerated , little 3H and TOL during wine ageing are also reported. Formal sensory analysis was conducted on the Chardonnay wines produced by each strain at two time points, and the links between the resulting changes to chemical composition and sensory properties are presented and explored.This paper explores the influence of compounds derived from the metabolism of both aromatic and sulfur-containing amino acids in the sensory profile of Chardonnay wine over time. For this purpose, we characterised five variants of a commercially available wine yeast strain (AWRI796), which were previously isolated because of their high 2-PE production phenotype. Here we show that these variants influence not only the production of 2-PE and 2-PEA but also the concentration of the higher alcohols TOL, TyrOH, and methionol, as well as other odoriferous VSCs derived from the amino acid methionine. The progression of these compounds and the equilibrium shift between TOL-SOPreviously, we isolated a range of variants from the commercial wine yeast AWRI796 that were resistant to toxic fluorinated analogues of phenylalanine. These variants were shown to overproduce 2-PE and 2-PEA to different extents in laboratory-scale fermentations . A singlp < 0.0001, The effects of variations in 2-PE, TyrOH and TOL concentration on wine sensory properties were determined using five mutants of AWRI796 together with parent strain (control) to produce Chardonnay wine. The variant strains harbour distinct mutations in two of the enzymes involved in aromatic amino acid metabolism: Tyr1p and Aro4p , while an exponential model was a better fit when all strains were included (R2 = 0.97)  . In cont = 0.97) . While t = 0.97) . Notably3H adduct. At the end of alcoholic fermentation, only wines made with the highest TOL producer (AWRI2940) contained TOL-SO3H (0.26 mg L−1), representing a 0.3% molar conversion of TOL into TOL-SO3H. The post-fermentation concentration of total SO2 in the wines averaged 21 mg L−1, with no detectable free SO2 . Before bottling, free SO2 concentration was adjusted to between 35 and 40 mg L−1 (−1 TOL) the yield of TOL-SO3H was at least 80%, while a 26% yield was observed in wines made using the high TOL producer (AWRI2940)  .3H species was reached, with longer storage times (12 and 15 months) having little or no effect on the conversion of the higher alcohol into the sulfonated adduct   the yield was only 36%  . For theonly 36% .3H formation was a significant contributor to SO2 loss during bottle storage. On the day of bottling, the concentration of free SO2 averaged 39 mg L−1 across all wines  than those made with the parent strain (AWRI796), whereas the low 2-PE producer and the high TyrOH-producing strains  accumulated similar concentrations of methionol to the parent . The con2S was observed at any time point  while showing a slightly elevated concentration of dimethylsulfide (DMS). No statistical evidence supporting differences between the strains in the concentration of Hme point .To further confirm the effects that mutations in either Tyr1p or Aro4p might have on methionine catabolism, two of the variants were used to ferment a synthetic grape medium (SGM) with grape-like concentrations of methionine . The twop < 0.05) supports differences between strains in three attributes rated by the panel: ‘yellow colour intensity’, ‘floral aroma’ and ‘grassy flavour’ (p < 0.15) alluded to possible trends among the strains for the attributes ‘grassy aroma’, ‘cooked vegetable/potato aroma’, ‘sweetness’, ‘bitterness’, ‘stone fruit flavour’ and ‘flint flavour’. In particular, wines made with AWRI796 and AWRI4124 were rated highly in the attributes ‘cooked vegetable/potato aroma’ and ‘sweetness’, while there was a trend for ‘bitterness’ to be rated lowest for the low TOL-/TOL-SO3H-producing strains AWRI2936 and AWRI796 compared to the other strains.Sensory descriptive analysis of the wines was performed twice, after 3 and 15 months in-bottle. The analyses were compared to assess the effect of bottle storage on wine sensory attributes. The mean scores for a subset of sensory attributes are summarised in flavour’ . Wines fp < 0.001) that strains produced wines with different intensities of ‘yellow colour’, ‘cooked vegetable/potato aroma’, ‘sourness’ and ‘sweetness’. There was evidence (p < 0.05) for differences in ‘pungency’, ‘rose aroma’ and ‘stone fruit flavour’. Notably, at this time point, judges chose to rate a specific ‘rose aroma’ quality rather than the more general ‘floral’ aroma attribute used to describe the wines at three months. Wine from strain AWRI2940 was notably higher in ‘rose aroma’, as well as in ‘yellow colour’ and ‘sourness’, while lower in ‘cooked vegetable/potato’ and ‘sweetness’. Strains AWRI2936, AWRI2965, and AWRI4124 were rated with intermediate values for ‘rose aroma’  was used to investigate the relationships between wine composition and sensory attributes for both time points at which the wines were evaluated . Chemica3H, methionol), and the compounds 2-PEA, ethyl butanoate, 2- and 3-methylbutyl acetate, 2-methylpropanol, 3-methylbutanol were significant contributors to the sensory differences and were heavily loaded on Factor 1.Both the 3-month and 15-month PLS-R models indicated three optimum factors, explaining 50% and 54% of the variance of the sensory data, respectively. 2 calibration 0.69 and R2 validation 0.51), ‘floral aroma’ , ‘cooked vegetable/potato aroma’ , ‘sweetness’ , ‘viscosity’ , and ‘stone fruit flavour’  were relatively well modelled but not so well predicted, indicated by the low R2 validation. TOL and TOL-SO3H were significantly associated with ‘yellow colour’. No compounds were significant for ‘floral aroma’ but two monoterpenes, cis-rose oxide and α-terpineol, and several esters had moderately sized positive regression coefficients  for this attribute, while 2-PE and 2-PEA were only weakly positively associated. ‘Cooked vegetable/potato aroma’ was associated with several sulfur compounds, notably H2S and MeSH. ‘Bitterness’ was most strongly related to volatile acidity and 2-phenylacetaldehyde, with TyrOH and TOL-SO3H weakly associated. TOL-SO3H and volatile acidity, in addition to 2-PE and 2-PEA, were significantly negatively associated with ‘sweetness’.Of the sensory attributes at the 3-month time-point, ‘yellow colour’ , ‘stone fruit aroma’ , ‘rose aroma’ , ‘cooked vegetable/potato aroma’ , ‘sweetness’ , ‘stone fruit flavour’ , ‘rose flavour’ , and ‘sourness’  were relatively well modelled, while ‘banana confection aroma’, ‘citrus flavour’, ‘pungent aroma’, and ‘bitterness’ were not modelled well. Similar to the model at three months, TOL and TOL-SO3H were again significantly associated with ‘yellow colour’, together with several other compounds. Of the many compounds associated with ‘rose aroma and flavour’, 2-PE and 2-PEA had the largest positive regression coefficients, while the volatiles 2-phenylacetaldehyde, methionol, hexyl acetate, 2- and 3-methylbutyl acetate, ethyl butanoate, 2-methylpropyl acetate, ethyl 2-methyl propanoate, H2S, and non-volatiles TOL and TOL-SO3H were also associated with these attributes. There was strong evidence for an association between ‘cooked vegetable/potato aroma’ and the compounds methional (regression coefficient 0.09) and MeSH (regression coefficient 0.07); although the association with H2S was weak, it had a relatively high regression coefficient of 0.04. There was strong evidence for a negative association between the compounds TOL and TOL-SO3H and ‘sweetness’, and a positive association with ‘sourness’. Although ‘bitterness’ was not well modelled, TOL and TOL-SO3H were positively associated with this attribute, with relatively high regression coefficients.In the 15-month PLS model, similar links between compounds and sensory attributes emerged, but this model was generally stronger and predicted most attributes well. From the three-factor model, ‘yellow colour’  is consistent with that reported in a range of commercial white wines of different ages [2 and TOL was between 13 and 132 in wines with low to moderate TOL concentrations favouring the formation of TOL-SO3H. The high conversion yields are therefore not unexpected. In the wines made with the highest TOL producer, AWRI2940, this molar ratio was as low as 1.8, explaining the lower conversion yield. The TOL concentration found in Chardonnay wines fermented with the low and moderate TOL-producing strains was in the range of those reported in the literature for commercial white wines, typically below 5 mg L−1 [2 due to this reaction would be less than 2 mg L−1. A survey of a large number of commercial bottled Australian white wines [−1 of free SO2 in the most recent vintages assessed, a concentration similar to that found in our Chardonnay wines at bottling (39 mg L−1). These data suggest that the conversion of TOL into TOL-SO3H makes only a limited contribution to the losses of SO2 observed during wine development, which are primarily driven by reaction with dissolved O2 in wines after bottling and by sulfonation of other species [3H concentration and SO2 loss in wine observed here.Our results demonstrate that the time required to reach equilibrium between TOL and its sulfonated adduct, typically between 3 and 6 months, is independent of the initial amount of TOL. Longer storage times have little or no effect on the yield of TOL-SOent ages . The mol5 mg L−1 ,34,35. Ate wines  found an species ,36. This3H. This high accumulation allowed us to assess the possible effects of TOL and TOL-SO3H on white wine sensory properties. After 15 months in-bottle, wines made with AWRI2940 were rated higher in ‘sourness’ and lower in ‘sweetness’, and tended to be more bitter for some assessors even though these wines had slightly elevated fructose and glycerol concentrations  relative to the control. The PLS-R results suggest that both TOL and TOL-SO3H may impart a degree of ‘bitterness’ to these wines while decreasing ‘sweetness’ and increasing ‘sourness’ ratings, probably through well-established taste–taste interactions [Wines made with AWRI2940 accumulated exceptionally high concentrations of both TOL and TOL-SOractions , especia3H in a wine matrix have been published, but Van Gemert [−1) eliciting bitter taste was reported in water [−1) [−1) have been reported to impart bitterness in wine [−1 of TyrOH to white wine and observed no influence on sensory properties, and only when unrealistically high concentrations were added (500 mg L−1) did TyrOH seem to depress the overall ‘flavour quality’ of wine. The fact that our wines made with AWRI2965, with a concentration of TyrOH exceeding 120 mg L−1, were not rated differently to the control wine in palate attributes such as ‘astringency’, ‘bitterness’ or ‘sourness’, suggest a limited effect of this higher alcohol on white wine in-mouth sensory properties.To our knowledge, no sensory recognition or detection thresholds for TOL-SOn Gemert  lists a n Gemert ,14,30, cn Gemert . Consequn Gemert . Differein water , lower ater [−1) . Even th in wine , to the  in wine  added 50Over time in-bottle, the sensory differences among the wines produced by each yeast became larger. Although the magnitude of the strain effect on ‘floral/rose aroma’ was similar across the two time points , an aromAnother aspect related to the effect of aging is the formation of aldehydes from oxidation of the analogous higher alcohols. These aldehydes can have a much lower sensory threshold than their corresponding higher alcohols; for example, the sensory threshold for methional is about 1000-times lower than that of methionol ,42. TherARO8 in a wine strain, encoding for the aromatic transaminase Aro8p, decreased methionol formation after fermentation of a synthetic grape must [ARO8 mutants indicates that by blocking the aromatic Ehrlich pathway, the catabolism of methionine might also be impaired.The production of methionol and other negative VSCs has been shown to be affected by the branch of the Ehrlich pathway responsible for the production of aromatic higher alcohols . Recentlape must . The decARO9 and ARO10 genes are highly induced by aromatic amino acids [ARO9-promoter-BFP reporter gene  it can be described as contributing ‘blackcurrant’ and ‘red fruit’ aromas, and it is considered to enhance the bouquet of some wine styles [The idea that there is competition for methionine between the Erhlich and demethiolation pathways is supported by the VSC profile observed in the pilot-scale wines made with the three homozygous Tyr1p mutants, particularly with the most active strain AWRI2940. Again, AWRI2965, which harbours a mutation (Aro4p) earlier in the amino acid biosynthetic pathway, behaved somewhat differently than the Tyr1p variants. Higher concentrations of methionol were not observed in the wines produced using AWRI2965 but decreased concentrations of other undesirable VSCs such as MeSH, MeSAc or methional were observed. Interestingly, AWRI2965 also produced slightly higher concentrations of DMS in these conditions. Even though DMS is also associated with reductive off-odours and ‘vegetal’ aromas, at low concentrations  culture collection . These v−1 of methionine. SGM was filtered through 0.22 µm Stericup filters (Millipore). Yeast starter cultures were prepared by growing cells aerobically in YPD medium for 24 h to stationary phase at 22 °C. Then, 1 × 106 cells mL−1 were inoculated into 50% diluted SGM medium and grown for another 48 h at 22 °C. The acclimatised cells were inoculated into 100 mL of SGM at a density of 1 × 106 cells mL−1. Fermentations were conducted at 17 °C in 100 mL glass bottles (Schott Duran), fitted with stir bars and stirred at 200 rpm using a magnetic stirrer. The lids of the bottles were fitted with selective H2S detector tubes  to measure the release of H2S during fermentation. Fermentation progress was followed by CO2 weight loss, measured every 24 h. After fermentation, the wines were cold settled at 4 °C for 5 days and sampled for different volatile and non-volatile compound analyses.Laboratory-scale fermentations were performed in triplicate in a synthetic grape medium (SGM) , with a −1, and pH 3.29. A concentration of 25 mg L−1 of SO2 was added to the grape must at the crusher. Yeast strains were grown for 48 h in filter-sterilised neutral grape concentrate , which had been previously diluted to ~6 °Baume and pH adjusted to 3.5. Cells were inoculated at a density of approximately 2 × 106 cells mL−1 in 19 L of the Chardonnay juice, and fermentation was conducted at 15 °C in 20 L stainless steel kegs in triplicate. When °Baumé was below 3, wines were moved to 20 °C. Irrespective of the starter culture used, wines got stuck around 1 °Baumé (day 16–18 of fermentation). Ferments were then rescued at day 26 by the addition of the commercial yeast Lalvin EC 1118 . Once alcoholic fermentation had finished (day 31), wines were sulfured with 80 mg L−1 of SO2, and cold-stabilised for approximately 2 months at 0 °C. Before bottling, SO2 concentration was adjusted to between 35–40 mg L−1 of free SO2. Screw-cap sealed bottled wines (375 mL) were stored in the dark at a constant temperature of 15 °C.The pilot-scale winemaking trial with Chardonnay juice was performed by the Wine Innovation Cluster (WIC) winemaking services, according to a standardised white winemaking protocol. Hand-harvested Chardonnay grapes from the McLaren Vale region  were used. The basic chemical parameters of the Chardonnay juice were: 12.7 °Baumé, yeast assimilable nitrogen 217 mg LTargeted analyses of fermentation-derived compounds  were performed by Metabolomics Australia (Adelaide) by GC-MS using a stable isotope dilution assay  at the ecis-rose oxide, α-terpineol, nerol, geraniol) and C13-norisoprenoids (β-damascenone and β-ionone) was performed at 3 months post-bottling by GC-MS on an Agilent 6890 gas chromatograph equipped with a Gerstel MPS2 autosampler and coupled to an Agilent 5973N mass selective detector. Sample preparation was as follows: 10 mL of wine was transferred into a 20 mL crimp-cap, headspace-SPME vial (Grace Davison) with 3 g of NaCl followed by 50 µL of a combined d4-β-damascenone, d3-α-ionone and d3-β-ionone internal standard solution. Instrument control was performed with Agilent G1701EA Revision E.02.02 ChemStation software. The gas chromatograph was fitted with an Agilent DB-5ms 30 m × 0.25 mm × 0.5 um. Helium (Ultra High Purity) was used as the carrier gas with linear velocity 46 cm/s, flow rate 1.6 mL/min in constant flow mode. The oven temperature was started at 40 °C, held at this temperature for 2 min, then increased to 190 °C at 8 °C/min and held at this temperature for 5.25 min. The vial and its contents were heated to 60 °C for 10 min in the heater/agitator with the agitator on for 5 s and off for 2 s at 500 r.p.m. A Supelco grey 2 cm SPME fibre was exposed to the sample during this heating time through the septum. The fibre was then injected into a split/splitless inlet in splitless mode. The analytes were desorbed into a Supelco 0.75 mm ID sleeveless SPME liner for 10 min, which was held at 200 °C. The purge flow to the split vent was 50 mL/min at 2.1 min with the septum purge flow turned off. The mass spectrometer quadrupole temperature was set at 150 °C, the source was set at 230 °C and the transfer line was held at 250 °C. EMV Mode was set to Gain Factor = 1.00 and spectra were recorded in SIM mode.Analysis of monoterpenoids , as described previously [2S and MeSH, respectively. Ethylmethyl sulfide and propyl thioacetate were used as internal standards. Analytes were identified by comparison of their retention times with those of the corresponding pure reference compounds.The VSCs Heviously . ReferenO-hydroxylamine hydrochloride (Sigma-Aldrich). Reference standards for these compounds of the highest purity were purchased from Sigma-Aldrich. Isotopically labelled analogues for furfural, methionol, methional, benzaldehyde, and 2-phenylacetaldehyde were used as internal standards for accurate quantification of these compounds. For the quantitation of 2-methylpropanal and 3-methylbutanal, d5-benzaldehyde was used as an internal standard. Similarly, d4-furfural was used for the determination of 5-methylfurfural. With the exception of d4-furfural , the synthesis of the other isotopically labelled standards was carried out in-house as described in [Analysis of the sulfur-containing compounds methionol and methional, as well as 2-methylpropanal, 3-methylbutanal, furfural, 5-methylfurfural, benzaldehyde, and 2-phenylacetadehyde was performed by GC-MS/MS, as described in . Aldehydribed in .VSCs and aldehydes were analysed 3 and 15 months post-bottling.The concentrations of sugars, ethanol, glycerol, and organic acids  were measured by HPLC using a Bio-Rad HPX-87H column, as described previously . Referen3H were analysed on an Agilent 1200SL HPLC using a Phenomenex Kinetex PFP column  at different time points . The injection volume was 5 µL. The column was eluted at 45 °C with a gradient of 0.1% formic acid in Milli-Q water (A) and 0.1% formic acid in acetonitrile (B) at a flow rate of 0.4 mL min−1. The gradient was as follows: an initial isocratic hold (0% B) for 8 min, then gradient to 5% B over 32 min, gradient to 25% B over 9 min, then gradient to 80% B over 3 min, held isocratically at 80% B for 3 min, and dropped to 0% B and held for another 15 min. Absorbance at 280 nm was monitored with an Agilent 1260 Series G7117C DAD, while fluorescence was monitored at excitation and emission wavelengths of 280 and 350 nm, respectively, with an Agilent 1260 Series G7121B FLD. Quantification of TOL and TyrOH was performed using the absorbance detector, while TOL-SO3H was quantified using the fluorescence detector. Reference standards for TyrOH and TOL were obtained from Sigma-Aldrich. TOL-SO3H was synthesised as previously described by Arapitsas, Guella and Mattivi [2O) with stirring and reacted at room temperature for 48 h. The reaction product was dried under a vacuum (30 °C) and dissolved in H2O. The product was purified using preparative HPLC with a Dionex UltiMate 3000 system, a C18 Synergi Hydro RP column , and solvent system of 100% H2O (A) and 100% acetonitrile (B). Gradient: 0–10 min 0% B, 10–25 min 50% B, 25–35 min 100% B, 8 mL/min flow rate. The structure of TOL-SO3H was confirmed using HRMS and NMR  with samples in D2O at 300 K. Results were processed using Topspin software. M-H mass (m/z): 240.0325; Chemical shifts for 1H-NMR  and 13C-NMR (D2O) concur with those previously reported [TyrOH, TOL, and TOL-SO Mattivi  with modreported .Quantitative descriptive analysis (QDA) sensory studies  were conp values were determined by a two-tailed Student’s t test. For the sensory data, ANOVA was carried out using Minitab 19. The effects of the yeast strain treatment, judge, judge by strain, ferment replicate nested into strain, judge by ferment replicate nested into strain, presentation replicate nested into strain, and ferment replicate were assessed, treating judge as a random effect. Following ANOVA, a protected HSD value was calculated using the mean square term of the judge × strain interaction at a 95% confidence level for attributes with a significant (p < 0.05) treatment effect. To explore the relationship between wine chemical composition and sensory attributes, PLS-R was conducted for each wine replicate, as described in [p < 0.10) signalled a treatment effect or a high F-ratio was found indicating potential treatment effects which may have been overshadowed by judge, fermentation, or presentation replicate variation.Minitab 19  was used for statistical analysis of the compositional data which were analysed by one-way analysis of variance (ANOVA). Multiple comparisons of the analyte concentration with respect to treatment were undertaken using Tukey’s honestly significant difference (HSD) test , and ribed in  with somThis study confirmed that the higher alcohol overproduction phenotype of five variants derived from the commercial wine strain AWRI796 is maintained in pilot-scale white winemaking conditions. This overproduction was associated with meaningful changes in wine sensory profiles, especially after some period of bottle storage. The effect of these strains on wine chemical composition was not just limited to the overproduction of 2-PE but also to an increase in the concentration of the higher alcohols TyrOH and/or TOL and to the formation of VSCs. Associations between these compounds and ‘sweet’, ‘sour’ and ‘bitter’ tastes, and ‘cooked vegetable/potato aroma’, were identified. These results highlight the intricate connections between the metabolism of aromatic amino acids and the sulfur-containing amino acid methionine during fermentation, ultimately influencing wine flavour.The various yeast strains isolated in this study provide novel tools for winemakers to adjust and preserve wine style. In particular, AWRI2965 has excellent potential as a white wine winemaking yeast, imparting accentuated and lasting rose/floral aromas to wines.3H, needs to be elucidated along with the physico-chemical conditions such as pH, temperature, storage time, and SO2 concentration, which might influence the equilibrium between these compounds in the finished wine.More research will be needed to understand the compositional drivers of bitterness. In particular, the role of higher alcohols derived from the metabolism of aromatic amino acids TyrOH, TOL, and its sulfonated derivative TOL-SO


Cichorium are known for their therapeutic and nutraceutical properties determined by a wealth of phytochemical substances contained in the whole plant. The aim of this paper was to characterize the metabolic profiles of local Salento chicory (Cichorium intybus L.) varieties  in order to describe their metabolites composition together with possible bioactivity and health beneficial properties. Methods: The investigation was performed by 1H-NMR spectroscopy and Multivariate Analysis (MVA), by which the metabolic profiles of the samples were easily obtained and compared. Results: The supervised Partial Least Squares Discriminant Analysis (PLS-DA) analysis showed as “Bianca” and “Galatina” samples grouped together separated by “Leccese” and “Otranto” varieties. A different content of free amino acids and organic acids was observed among the varieties. In particular a high content of cichoric and monocaffeoyl tartaric acid was observed for the “Leccese” variety. The presence of secondary metabolites adds significant interest in the investigation of Cichorium inthybus, as this vegetable may benefit human health when incorporated into the diet. Conclusions: The 1H-NMR (Nuclear Magnetic Resonance Spectroscopy) based characterization of Salento chicory varieties allowed us to determine the potential usefulness and nutraceutical properties of the product, also providing a method to guarantee its authenticity on a molecular scale.Background: Plants of genus  Cichorium intybus L., 1753) is a perennial herbaceous plant of the Asteraceae family, native of Mediterranean and middle Asia regions [To date, several organizations such as the World Health Organization (WHO) and Food and Agriculture Organization (FAO) promote increasing intake of vegetables in human nutrition , with im regions  and wide regions ,9. SouthCichorium intybus can be divided according to the purpose and use for which it was cultivated [ltivated : “root” ltivated . The “Raltivated ,11These vegetables can be used raw for the preparation of salads or cooked for the realization of first courses and/or side dishes. Among the various types, different in biological and morphological characteristics, the “Catalogna” group includes various categories that have a “head” integrated by numerous sprouts. They are similar in appearance to the asparagus shoots which results almost everywhere also in the denomination of “asparagus chicory” ,12.Cichorium: hepatoprotective, anti-inflammatory, antioxidant, sedative, immunological, reproductive, gastro-protective, antidiabetic, and analgesic [Within the Apulian territory they are known with different local names, such as “Catalogna puntarelle”, “Brindisina”, “Bianca”, “Galatina”, “Pugliese”, “Leccese”, “Otrantina”, and “Molfettese” . Thanks nalgesic . Severalnalgesic . It was nalgesic ,17,18,19nalgesic ,16,17,18nalgesic . For thinalgesic ,22. Thisnalgesic . Originanalgesic . NMR spenalgesic . NMR-basnalgesic . Plant mnalgesic .In this work, a NMR-based metabolomic approach was applied to four local varieties of chicory with the aim of improving the knowledge on their nutraceutical value; by identifying the bioactive compounds present, and therefore of the possible benefits for human health related to its consumption.A total of 36 chicory samples (edible leaves and stems) were obtained from 4 chicory local Salento varieties, “Bianca”, “Galatina”, “Leccese”, and “Otranto” . The locChicory samples were supplied by local farms in the Salento peninsula (province of Lecce) within the framework of the project “Biodiversity of Apulian vegetable species”  . Chicory3OD and 0.75 mL of KH2PO4 buffer in D2O (pH 5.9) containing 0.05% w/v TSP-d4  were added to each sample. The content of the eppendorf tubes was mixed thoroughly with a vortex mixer at room temperature for 1 min and then sonicated for 10 min at room temperature. Samples were spun down in a microcentrifuge at 17,000 g for 20 min at 4 °C; then, 700 μL of the supernatant were filled into a 5 mm NMR tube.Samples were prepared according to the experimental procedure as reported in literature . Briefly1H observation, equipped with a TCI cryoprobe incorporating a z axis gradient coil and automatic tuning-matching (ATM). Experiments were acquired at 300 K in automation mode after loading individual samples by a Bruker Automatic Sample Changer, interfaced with the software IconNMR (Bruker). For each sample a 1D sequence with pre-saturation and composite pulse for selection (zgcppr Bruker standard pulse sequence) was acquired, with 16 transients, 16 dummy scans, 5 s relaxation delay, size of fid of 64K data points, a spectral width of 12,019.230 Hz (20.0276 ppm) and an acquisition time of 2.73 s. The resulting FIDs were multiplied by an exponential weighting function corresponding to a line broadening of 0.3 Hz before Fourier transformation, automated phasing and baseline correction. Metabolite identification were based on 1H and 13C assignment by 1D and 2D omo and heteronuclear experiments and by comparison with literature data [1H NMR spectra were converted to a suitable form for multivariate analysis by segmentation into small rectangular bins (buckets) and integration by Amix 3.9.15  software. Bucketing was performed within 10.00–0.5 ppm region, excluding the residual non-deuterated water (4.9–4.7 ppm) and methanol (3.40–3.30 ppm) signals. In order to account for small pH or concentration signals shift, the width of each bucket was fixed to 0.04 ppm. The total sum normalization was then performed in order to minimize small differences due to metabolites concentration and/or experimental conditions among samples [All measurements were performed on a Bruker Avance III 600 Ascend NMR spectrometer , operating at 600.13 MHz for ure data ,32. NMR  samples . The Par2 and Q2 parameters. These parameters show completely different behavior with the model complexity increase. The R2X, R2Y, and Q2, describing the total variation in X, the variation in the response variable Y and the predictive ability of the models, respectively, were calculated [Software Simca-P version 14  was used to accomplish multivariate statistical analysis. In particular, PCA , PLS-DA  and OPLS-DA Orthogonal Partial Least Squares Discriminant Analyses) were performed. The commonly used unsupervised analysis (PCA) shows the systematic variation in a data matrix X formed by rows (the considered observations), and columns (the variables which describe the samples) ,35. PLS-lculated . The rellculated ,42. Resulculated .All chemical reagents for analysis were of analytical grade. Deuterium oxide (99.9 atom %D) containing 0.05% wt 3-(trimethylsilyl)propionic-2,2,3,3 d4 acid sodium salt (TSP), Potassium phosphate monobasic were purchased from Armar Chemicals . Methanol-d4 (99.9 atom %D), potassium phosphate monobasic were purchased from CARLO ERBA Reagents . 1H NMR typical spectra revealed a complex pattern of signals due to the presence of different classes of metabolites. In the low frequency field region of the spectra (0.5–3.00 ppm) the presence of amino acids, fatty acids chains from cell membrane [C. intybus. The contribute to the organoleptic properties as well as the physiological role for malic [β glucose, and α fructofuranose, β fructofuranose, and fructopyranose signals were observed. The detection of β fructofuranose signals could be also ascribable to inulin fructofuranose units [1H NMR spectrum, the 2D NMR 1H-13C hsqc NMR spectra were used in the assignments of these signals. Moreover, signals of choline and tartrate were also observed. Choline’s role in human health begins prenatally and extends into adulthood and old age [1H-13C hsqc spectrum   a. The S = 0.842) b. On the2X = 0.948, R2Y = 0.996; Q2 = 0.995). The S line plot for the model indicated the presence of higher relative content of aromatic amino acid phenylalanine and formate for “Otranto” varieties (Another pair wise OPLS-DA analysis was then performed by comparing the metabolic profiles of “Leccese” and “Otranto” in the aromatic region. Moreover, in this case, the resulting OPLS-DA model a showed arieties b. On theC. intybus was also determined by quantitative comparison of the buckets corresponding to selected distinctive unbiased NMR signals (The variation in discriminating metabolites content for the different local varieties of  signals . In part signals . A signi signals .C. intybus local varieties, by 1H NMR based metabolomics, allowed to determine their potential usefulness as well as to define specific qualitative-nutritional characteristics. The obtained results could improve the knowledge on these local varieties also highlighting the presence of compounds with nutraceutical activities such as chicoric acid. Further investigations are required to fully achieve a complete characterization of the studied chicory varieties. Nevertheless the acquisition of preliminary data suitable for a possible origin certification for these local products could be considered as a further outcome of this study.The here reported characterization of


Zea mays L.) grain yield over the last century. Here we show that both RSA and yield have changed with decades of maize breeding, but not the crop water uptake. Results from X-ray phenotyping in controlled environments showed that single cross (SX) hybrids have smaller root systems than double cross (DX) hybrids for root diameters between 2465 µm and 181µm (P<0.05). Soil water extraction measured under field conditions ranged between 2.6 mm d–1 and 2.9 mm d–1 but were not significantly different between SX and DX hybrids. Yield and yield components were higher for SX than DX hybrids across densities and irrigation (P<0.001). Taken together, the results suggest that changes in RSA were not the cause of increased water uptake but an adaptation to high-density stands used in modern agriculture. This adaptation may have contributed to shift in resource allocation to the ear and indirectly improved reproductive resilience. Advances in root physiology and phenotyping can create opportunities to maintain long-term genetic gain in maize, but a shift from ideotype to crop and production system thinking will be required.Because plants capture water and nutrients through roots, it was proposed that changes in root systems architecture (RSA) might underpin the 3-fold increase in maize ( Emerging opportunity to continue long-term genetic gain in maize yield in the US corn belt by improving the balance between canopy, root, and reproductive growth and development. Changes in root system architecture (RSA) were implicated in the determination of long-term yield improvement of maize in the US corn belt. In the USA, long-term crop improvement in temperate maize has resulted from pedigree breeding combined with reciprocal recurrent selection to improve hybrid performance , and theAs elucidated in Darwinian agriculture theory, a common plant adaptation to cultivated systems is the reduction of intraspecific competitive ability  are expeThe consequence of the root phenotypes on crop water status and drought tolerance depend on the capacity of the root system to supply the water demand established by the crop leaf area (–3) as the growth substrate. Each container was placed in structural pods that held eight plants. The dripper assembly system for each container consisted of four equally spaced pressure-compensated dripper heads  operating on a 20/270 s on/off cycle at ~1.5 l h–1. A bank of metal halide lamps provided 400 µmol m–2 s–2 illumination on a 14/10 h day/night cycle. The temperature regime was 35 °C/24 °C for the day/night cycle.Root phenomics for RSA were conducted on a sequence of ERA maize hybrids  at the PA custom X-ray system developed by the PSC was used to image roots growing in polystyrene containers . The expEach root system was imaged twice—once at the V6 developmental stage and once at the V8 stage. Images were analyzed using RhizoTraits, version 1, a custom software developed by the PSC to extract root traits from X-ray images. RhizoTraits is built off ImageJ ; Fig. 2.–2) and low (3 plants m–2) planting density treatment levels using drip tapes installed 20 cm belowground. The experiment was planted in four-row plots on 7 November 2014 and it was harvested on 1 April 2015. Rows were 4.2 m long and spaced by 0.76 cm. For prior research  considered as samples taken from a broader population of the hybrid class (c),Total root length and plant height from Experiment 1 were modeled within a linear mixed-effects model framework with the objective to test cross type contrasts between DX and SX hybrids, with named hybrids , at growth stage j. In this model, cross type , growth stage (gj), SC (sk), and all two-way and three-way interactions were considered as fixed effects; hybrid [δ h(i)] served as random effect. The residual term is ijkl ~N, which means that for each level of cross type, growth stage, and size class, a unique variance component σ2ilk is fitted in the mixed model. For the plant height variable, a linear mixed-effects model was fitted,where Tijl represents plant height for plant l of cross type i at growth stage j. Other notations are the same as described for total root length. Cross type, growth stage, and their interaction were considered fixed effects; genotype served as random effect. The residual term of this model is ijl ~N, where σ2il represents the residual variance term among plants for cross type i at growth stage j, allowing each level of cross type and growth stage to have a unique residual variance component.where To test for heterogeneity of plant to plant variation between SX and DX hybrids under different growth stages or root SCs, a likelihood ratio method was applied using two nested mixed-effects models. For the plant height trait, a cross type fixed effect and a hybrid (nested in cross type) random effect are included in both models.il ~N. For the reduced model (M2), a single residual variance parameter is used for all observations, il ~N. The P-value of the likelihood ratio test was calculated asFor the full model (M1), the residual variance parameter depends on the level of cross type, f=df(M1)–df(M2). A similar approach was implemented to test plant to plant variation among cross types for root-level traits. For a given growth stage, the full model (M1),where ∆dikl ~N and the plant random effect pl(i) ~N . Parameters and fitted curves were estimated for each root SC .where the hybrid random effect δ Reproductive and vegetative traits from Experiment 2 were analyzed within a generalized linear mixed-effects model framework to test for differences between cross type DX and SX. For the traits with continuous numeric values, a Gaussian model with identity link was used. For the traits with count or fraction values (e.g. proportion of barren plants), a binomial model with logistic link was used. In the generalized linear mixed-effects model, cross type, plant population, location, and their interactions were considered fixed effects, while field spatial factor defined as row and columns, and named hybrid nested in cross type  were conci is cross type (DX or SX) effect, bj is plant population effect, lk is irrigation treatment effect, (cb)ij, (cl)ik, (bl)jk, and (cbl)ijk are the two factor and three factor interaction effects between cross type, population, and irrigation, α x,l and β y,l are row and column effects at each irrigation treatment with α x,l ~N and β y,l ~N, δ h(i) is the named hybrid random effect with δ h(i) ~N, and σ2l(α), σ2l(β), and σ2i(δ) are variance parameters for the three random effects in the model.where y was total available soil water (mm) and the independent variable was days after planting (x),A generalized additive model with integrated smoothness was applied to analyze the effect of cross type, plant population, and total depth (800 mm or 1000 mm) on the temporal dynamics of soil water content for Experiment 2. The dependent variable ci), plant population (pj), total depth (dk), and their interactions served as the parametric terms. The functions f1, f2, and f3, are the smoothing terms by cross type, plant population, and total depth, respectively. Cubic regression spline bases with dimensions of 20 were used to fit the smoothing function f1, f2, and f3. All the non-parametric smoothing terms estimated here were centered at 0. In this model, each parametric term represents the overall magnitude of a certain fixed effect, while each smoothing term represents the pattern of the curve under each specific level of the corresponding factor.where hybrid cross (DX or SX)  for roots of diameters between 725–2465 µm and 181–616 µm .Results from the root morphology and water uptake experiments indicate that selection improved root system efficiency but not total soil water capture. SX hybrids had a smaller RSA per plant when measured in growth chambers , but theectively , F. Diffectively , 2005. Dectively , an estiThe difference in soil moisture between low and high density plant populations was 17±2.8 for DX hybrids and 13±2.8 mm for SX hybrids when the soil moisture reached a minimum value. This result suggests that plant population is the main controlling factor of root occupancy and water capture, and that there was enough water in the soil column to quantify differences in soil water capture due to variation in RSA if differences were present , F. At lP<0.001). Yield of DX hybrids decreased with increasing barrenness, and barrenness increased with increasing density and WD (y=528(±48)–67.26(±19.03)×x, r=0.81]. The lower absolute correlation between kernels per ear and ASI relative to yield and ASI (|r|=0.81 versus |r|=0.58) is associated with compensation between kernel weight and number within fertile ears . Exploring adjacent spaces in the adaptation landscape (The feedback between genetics and agronomy, and the evidence that the impact of root phenomics and selection on yield within breeding programs have been slow (


Secondary analyses examining differences between education found that athletes reported higher enjoyment of concussion education immediately after participating in CC, as compared to CDC (p < 0.001). These findings demonstrate an additive benefit to implementing CC and CDC education in tandem, without decrement in enjoyment of concussion education after experiencing dual educations; in fact, enjoyment of concussion education improved after receiving education programs back to back. These educational programs appear to complement one another, and the results support the use of multi-modal concussion education to differentially target and maximize concussion reporting.Undiagnosed concussions increase the risk of additional concussion and persistent symptoms from concussion. Because there are no reliable objective markers of concussion, self-reporting of subjective and non-visible symptoms are critical to ensuring proper concussion management. For this reason, educational interventions target concussion reporting, but the majority of studies have examined the efficacy of single educational interventions or compared interventions to one another. This randomized crossover study sought to identify whether there was benefit to administering multiple concussion education programs in tandem, back to back. The study randomized 313 male high school football players to first receive CrashCourse concussion education (CC) or Centers for Disease Control and Prevention video concussion education (CDC) followed by crossover with the other education. Athlete concussion-reporting intention, attitudes, subjective norms, perceived behavioral control, and enjoyment of education were assessed at baseline and after each intervention. There were statistically significant improvements across all measures, both after single intervention and crossover (all  TPB posits that behavioral changes are signaled by behavioral intention (motivation), which is directly influenced by behavioral beliefs (attitudes about reporting), normative beliefs , and perceived behavioral control (understanding how to report).22Because of the complexities associated with measuring concussion-reporting behavior change, studies have evaluated the efficacy of concussion education programs using outcomes proximally related to behaviors, by either examining effects on concussion knowledge, or constructs derived from the theory of planned behavior TPB; .12,13,1523 Previous work demonstrates that TPB can be a useful predictive model for adolescent concussion-reporting behavior, with all three constructs  associated with concussion-reporting intention (motivation) and concussion reporting (behavior change).25 Few studies have directly compared the efficacy of different education programs; these studies demonstrated that athletes who experience different concussion education programs have variable improvement in concussion-reporting intention, concussion attitudes, subjective norms, and perceived behavioral control.26Over the past 30 years, TPB has been tested in a variety of contexts.15 Each educational intervention has a different format with different foci and, as a result, may have different strengths in influencing constructs related to the reporting decision. The benefit of administering multiple educational interventions has not been previously assessed. A common concern with implementing multiple interventions is that the learner may be overburdened, resulting in less satisfaction with an educational program, and thus less compliance.27 Additionally, different concussion education programs may be preferentially enjoyed by different learners; there is evidence that concussion education enjoyment may have a moderating effect on concussion reporting.28There may, however, be benefits to administering multiple concussion education programs to improve concussion reporting.29Over the past 10 years, the most commonly used concussion education resources in the United States have been created and provided by the Centers for Disease Control and Prevention (CDC). In 2010, the CDC partnered with the National Federation of State High School Associations (NFHS) to release the free “Concussion in Sports” course, which has been viewed >2.2 million times.30 A recent randomized controlled trial demonstrated that athletes randomized to CC reported greater intent to report concussion, more knowledge, and improved concussion-reporting attitudes when compared to CDC video and written materials.13Since launching in September 2018, the CrashCourse (CC) concussion education has been widely adopted, including by the majority of U.S. youth football organizations , various U.S. Olympic Committees , medical organizations , and several U.S. high schools .The present study sought to evaluate the potential benefits of receiving dual concussion education programs, with persons randomized to first receive either CDC or CC, followed by crossover with the other education program. We hypothesized that athletes would improve in concussion-reporting intention, attitudes, subjective norms, perceived behavioral control, and enjoyment of education, after one educational intervention, with significantly greater gains after the second educational program. Secondary analyses were performed to evaluate differences in outcomes between the two programs.Participants were male athletes from seven high school football teams in Colorado. These schools were varied in terms of racial composition  and socioeconomic status . Participants were included if they were in attendance for practice on the study date and provided signed assent and parental consent. Participants were excluded if they did not complete >50% of the questionnaires. In total, 349 participants were screened for inclusion; 14 did not provide a signed parental consent and 13 were absent on the initial study date. Nine athletes were excluded because of incomplete participation. n = 313) completed study questionnaires and education on individual computers in July 2019. Participants first completed baseline study questionnaires before being randomized with a computer-based random number generator to be assigned to one of two groups: CC-First or CDC-First. CC-First (n = 146) received the CC concussion education curriculum followed by the CDC/NFHS “Concussion in Sports” video curricula. CDC-First (n = 167) received the CDC curriculum followed by CC. All athletes completed a post-education questionnaire immediately after exposure to the first educational curricula and another questionnaire immediately after exposure to the second educational curricula. Two research team members were present for the entirety of the administration to monitor students and were provided guidelines with escalating actions in case of disruptive behavior.All participants 3 non-profit organization, TeachAids, released a free video-based concussion education program. CC was created with a user-centered formative design process by iteratively incorporating feedback from hundreds of athletes, coaches, athletic trainers, education researchers, and scientific experts. The resulting 12-min video places the learner in a first-person game situation, where they experience a concussion and are presented with the choice to “Stay in the Game” or “Take a Knee” and be evaluated. The consequences of the learner's decision are further explored. Subsequently, the consequences of the alternate decision are shown, ensuring that learners are provided all information. Learners are taught to recognize concussion signs and symptoms, understand the importance of reporting, and seek medical attention for diagnosis and management. This narrative-driven interactive program is guided by near-peer Division I collegiate football players.29 The content is geared toward student-athletes, parents, and coaches. These materials engage learners by incorporating 25 min of videos and six slides, followed by nine questions, to teach learners concussion signs and symptoms, highlight the effects of sports-related concussion, and provides protocols to manage a suspected concussion including how to help players return to play safely after a concussion. The course is hosted by Michael Koester, MD, chair of the NFHS Sports Medicine Advisory Committee.31The CDC and NFHS partnered in 2010 to release the free “Concussion in Sports” online course, which was subsequently revised in 2018.33 Each question was individually chosen to ensure construct validity while maximizing assessment brevity. When multiple questions were asked related to a single construct, internal consistency was assessed, with Cronbach's alpha reported, and scores were averaged. The text of the specific questions asked of each athlete are below.All outcomes, except perceived behavioral control, were assessed based on athlete responses to questions on a 5-point Likert scale, from 1 = “strongly disagree” to 5 = “strongly agree,” using TPB-informed questions informed by past work.Athletes were asked to rate, “If I think I may have a concussion, I intend to report it to a medical professional ” and “if my teammates think they may have a concussion, they would report it to a medical professional ” .Athletes were asked to rate, “I understand the importance of telling someone if I suspect I have a concussion.”Athletes were asked to rate, “If I report what I suspect might be a concussion, my teammates would think I made the right decision”; “if I report what I suspect might be a concussion, my parents would think I made the right decision”; and “if I report what I suspect might be a concussion, my coaches would think I made the right decision” .34 Specifically, athletes were asked to rate whether, “I can identify when I have a concussion”; “I can immediately tell a medical professional that I have a concussion”; and “I can eventually tell a medical professional that I have a concussion.” Scores were averaged to create a composite score .Perceived behavioral control was assessed based on athlete responses to three questions on a 100-point scale, from 1 = “cannot do” to 100 = “definitely can do,” adapted from previous work.Athletes were asked to rate, “I am satisfied with the concussion education I have received” and “I enjoyed the concussion education I have received” .t-tests (continuous measures) or chi-square  were used to assess demographic differences between athletes randomized to CC-First and CDC-First. Fisher's exact test was used in place of chi-square when expected cell values were <5. For primary and secondary outcomes, repeated measures analysis of variance (ANOVA), with one between-subjects factor (randomization group) and one within-subject factor . Post hoc testing examined pair-wise differences between time points. Secondary analyses via repeated-measures ANOVA examined differences between education formats by including one between-subjects factor (education type) and one within-subject factor (time point). When sphericity could not be assumed, Greenhouse-Geisser corrections were applied. Although these analyses assume normality, an advantage of repeated measures ANOVA is that it is robust to violations of normality, particularly with sample sizes >20. To account for multiple comparisons, false discovery rate p-values were calculated based on the Benjamini-Hochberg procedure and are presented for analyses of all primary or secondary outcomes. Analyses were performed using IBM SPSS Statistics  or R software .Independent-samples Demographic information is provided in p < 0.001), from 4.0 ± 0.8 to 4.4 ± 0.6, after the first educational intervention (p < 0.001) and significantly improved again to 4.6 ± 0.6 after the second educational intervention (p < 0.001). There was no difference between concussion education administered (p = 0.05).Athlete concussion-reporting intention significantly improved (p < 0.001) from 4.4 ± 0.7 to 4.6 ± 0.5 after the first educational intervention (p < 0.001) and significantly improved again to 4.7 ± 0.5 after the second educational intervention (p < 0.001). There was no difference between concussion education administered (p = 0.53).Attitudes about concussion reporting significantly improved (p < 0.001), from 4.3 ± 0.6 to 4.5 ± 0.5, after the first educational intervention (p < 0.001) and significantly improved again to 4.7 ± 0.5 after the second educational intervention (p < 0.001). There was no difference between concussion education administered (p = 0.53).Athlete subjective norms related to concussion reporting significantly improved (p < 0.001), from 68.8 ± 19.9 to 79.9 ± 17.7, after the first educational intervention (p < 0.001) and significantly improved again to 85.4 ± 16.2 after the second educational intervention (p < 0.001). There was no difference between concussion education administered (p = 0.53).Athlete perceived behavioral control about concussion reporting significantly improved (p < 0.001), from 3.5 ± 0.8 to 4.3 ± 0.6, after the first educational intervention (p < 0.001) and significantly improved again to 4.6 ± 0.6 after the second educational intervention (p < 0.001). There were significant differences by education administered and time (p = 0.01), with athletes reporting enjoyment of 4.5 ± 0.5 immediately after receiving CC and athletes reporting enjoyment of 4.4 ± 0.6 immediately after receiving CDC.Athlete enjoyment of concussion education significantly improved  and three questionnaires  in a single session, meaning that additional group differences could not be explored. As such, differences based on race, socioeconomic status, and other factors potentially relevant to concussion reporting could not be explored. However, the schools that athletes were recruited from represent a wide range of racial and socioeconomic diversity. Finally, both educational programs were video-based interventions; it is likely that mixing educational formats would have different results.This study showed that implementing two different educational programs in tandem results in greater improvements in TPB constructs related to concussion reporting, as compared to a single education alone. Additionally, contrary to concerns of fatigue after participating in multiple educational interventions, athletes reported greater enjoyment and satisfaction with the concussion education they received after both interventions in tandem. Given the largely invisible nature of concussion manifestation and the important role of athlete reports in concussion identification, concussion education plays an important role in improving concussion self-reporting. Future studies evaluating the optimal timing, setting, duration, and other contextual factors for repeat concussion education are warranted.