Mushrooms bio-residues valorisation: Optimisation of ergosterol extraction using response surface methodology

Pleasecitethisarticleinpressas:Silva,A.R.,etal., Mushroomsbio-residuesvalorisation:Optimisationofergosterolextractionusingresponse ContentslistsavailableatScienceDirect

Food

and

Bioproducts

Processing

jo u r n al ho m e p a g e :w w w . e l s e v i e r . c o m / l o c a t e / f b p

Mushrooms

bio-residues

valorisation:

Optimisation

of

ergosterol

extraction

using

response

surface

methodology

Ana

Rita

Silva

_,

_Taofiq

_Oludemi

_,

_Cristina

_Costa

_,

_Joana

_Barros

_,

Inês

Ferreira

_,

_João

_Nunes

_,

_Miguel

_A.

_Prieto

_,

_Jesús

_{Simal-Gandara}

_,

Lillian

Barros

_,

_Isabel

_C.F.R.

_Ferreira

a_{CentrodeInvestigac¸ãodeMontanha(CIMO),InstitutoPolitécnicodeBraganc¸a,CampusdeSantaApolónia,}

5300-253Braganc¸a,Portugal

b_{DepartamentodeCienciasFarmacéuticas,FacultaddeFarmacia,CIETUS-IBSAL,UniversidaddeSalamanca,}

37007Salamanca,Spain

c_{CentreBioR&DUnit,AssociationBLC3–TechnologyandInnovationCampus,RuaNossaSenhoradaConceic¸ão,}

n2,3405-155OliveiradoHospital,Portugal

d_{NutritionandBromatologyGroup,DepartmentofAnalyticalandFoodChemistry,FacultyofScience,Universityof}

Vigo–OurenseCampus,Ourense,Spain

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received23February2020 Receivedinrevisedform27March 2020 Accepted15April2020 Availableonlinexxx Keywords: Mushrooms Bio-residues Heat-assistedextraction Ergosterol

Responsesurfacemethodology

a

b

s

t

r

a

c

t

Thebio-residuesofPleurotusostreatus,Agrocybecylindracea,andPleurotuseryingiiwere stud-iedassustainablesourcesofergosterol.Itsextractionwasperformedbyaheat-assisted extractiontechniqueandoptimisedusingresponsesurfacemethodology.Theresponses were:extractionyieldoftheresidualmaterial(R)fromthemushroombio-residuesdried weight(%),thequantificationofergosterolintheMdw(mgE/100gMdw),andintheR(mg E/gR).ThemostfeasibleresponsesforindustrialtransferencewereobtainedforP.ostreatus

bio-residues,attheoptimalconditionsof65.6minat30◦Cand43.7minat90◦Cproducing 43.72mgE/gRand290.90mgE/100gMdw,respectively.Themodelsatisfactorilyfittedthe experimentaldataforallresponses,thusimplyingagoodagreementbetweenthe experi-mentalvaluesandthosepredictedbythemodel.Thestudyproposesasimpleandefficient methodtoproduceanergosterolrichextractfrommushroombio-residues.

©2020InstitutionofChemicalEngineers.PublishedbyElsevierB.V.Allrightsreserved.

1.

Introduction

Edible,medicinal,andwildmushroomsarethethreemajor components of the global mushroom industry, recently accounted for US$ 38.13 billion,and expanding ata com-poundannualgrowthrate(CAGR)of7.9%from2018to2026 (ResearchandMarkets,2018;Royseetal.,2017).Europeisthe keybio-economymarketandholdsthelargestrevenueshare intheglobalmushroomindustryduetothehighdemandfor

∗ _{Correspondingauthors.}

E-mailaddresses:lillian@ipb.pt(L.Barros),iferreira@ipb.pt(I.C.F.R.Ferreira).

freshandcannedmushroomsinSpain,Netherlands,France, andGermany(BoinandNunes,2018;ResearchandMarkets, 2018).Duringmushroom’sprocessing,alargeamountof bio-residues are generated: volva and the bottom part of the stems due to their tough texture, the substrate used for mushroomgrowth,andthespecimensthatdonotfitthe mar-ketrequirements(irregulardimensions/shape/colour)(Heleno et al.,2016b;Taoetal., 2004).Dependingonthesizeofthe mushroomindustry,anexpressivevolume(20–35%inweight

https://doi.org/10.1016/j.fbp.2020.04.005

offreshmushrooms)ofbio-residuesisoftendiscarded,even though their content in biomolecules are not necessarily compromised(Heleno etal., 2016b). Eachperson consumes approximately5kgofmushroomperyear,andsince mush-roomproduction/consumptionratesareexpectedtoincrease inthenextyears,itsassociatedproblemsarelikelytofollow thetrend(Royseetal., 2017).Thesehigh-valueunexploited residuesdemandindustries todealwithits environmental impactandmanagingcosts(GrimmandWösten,2018;Leiva etal.,2015).

Recentresearchdatademonstratesthattheconsumption ofabout1.6–2g/dofsterolsandstanolsafterameal, prefer-ablydissolvedinafatrichfoodmatrix,iseffectiveinlowering LDLcholesterollevelsby10%(MarangoniandPoli,2010).Edible mushroomsalsocontainsterolswithstructuralsimilaritiesto phytosterolsandcholesterol.Ergosterol (ergosta-5,7,22-trien-3␤-ol)isthe majorsterolofmushroomhyphalmembranes (approx. 80% ofthe sterols w/w) followed by other deriva-tivessuchasergosta-5,8,22-trien-3-ol,ergosta-7,22-dien-3-ol, ergosta-5,7-dien-3-ol,andergosta-7-en-3-ol(fungisterol)( Gil-Ramírezetal.,2013).Schneideretal.(Schneideretal.,2011) investigatedthe cholesterolloweringpropertiesofPleurotus ostreatus mushroomdietin humans forthe first time dur-ing aplacebo study.The beneficial effectson blood serum parameters were hypothetically attributed tothe presence of ergosterol, linoleic acid, and ergosta-derivatives, which showednotableactivityinoxygenradicalabsorbance capac-ityand cyclooxygenaseinhibitionassays invitro(Schneider et al., 2011). Later, a 2014 study reported that ergosterol-enrichedfractionsobtainedfromAgaricusbisporusmoderately reducedcholesterolfrom mixedfood micelles(DMMs) dur-ing gastrointestinal digestion (Gil-Ramírez et al., 2013). In thefollowingstudy,thesamefractionssignificantlyreduced hepatic triglyceride levels and alteredmRNA expressionof cholesterol-relatedgenes,revealingapotentialeffectagainst hepaticsteatosis(Gil-Ramírezetal.,2016).Hence,duetoits well-knownanti-inflammatory(Maetal.,2013;Taofiqetal., 2016c),anti-cancer (He et al., 2018; Kang et al., 2015), and anti-tyrosinase(Taofiqetal.,2016a,2016b)activities,together withitsuseinnewdrugformulationsassociatedwith antibi-otics(Tintinoetal.,2017),thereisanincreasinginterest in ergosterol.Ergosterolpresenceandconcentrationarespecie dependentandinfluencedbymanyotherfactors(Helenoetal., 2016a;Kala ˇc, 2009; Mattilaet al., 2002). Tounderstand the health-promotingeffectsofergosterol-richextractsobtained frommushroomsbio-residues,studiestomaximiseits recov-ery needtobeconducted(Corrêa etal., 2018; Lópezet al., 2018;Valverdeetal.,2015).Whenstudyingaproductive pro-cess, such as extraction, the interferences can be globally assessedbyapplyingamathematicalmodellingtooptimise theinteractionsbetweenexperimentalvariablesand, simul-taneously,predictthemostefficientconditions(Albuquerque etal.,2016;Rorizetal.,2017).Thepresentworkwasdeveloped withindifferentinterfaces(SMEandscientificand technolog-icalentities),aimedatoptimisingergosterolextractionfrom mushroom bio-residues using conventional heat assisted extraction(HAE),andevaluatingthecombinedeffectsof tem-peratureandtimeonthefinalyieldusingresponsesurface methodology(RSM)(Lópezetal.,2018;Mu ˜niz-Márquezetal., 2019;SarabiaandOrtiz,2009).Thestudiedspecieswere Pleu-rotusostreatus,Pleurotuseryngii,andAgrocybecylindraceae,three mushroomswidelyconsumedintheworld(BoinandNunes, 2018).

2.

Material

and

methods

2.1. Mushroomsamples

Pleurotuseryngii(PER),Agrocybecylindraceae (ACR)and Pleuro-tusostreatus (POR)mushroomswere cultivatedbyMicNatur (SME,SpinoffofBLC3–OliveiradoHospital),basedon con-trolled system, andits bio-residues(volva,the bottompart ofthestems,andthespecimensthatdonotfitthemarket requirements)driedinacirculationovenat40◦C,milledtoa finepowder(∼40mesh),vacuumpacked,andstoredatroom temperatureforfurtheranalysis.

2.2. Standardsandreagents

Methanolandacetonitrile(HPLCgrade)werepurchasedfrom FisherScientific(Lisbon,Portugal)andergosterol(E6510;CAS number:57-87-4)wasacquiredfromSigma-Aldrich(St.Louis, MO,USA).Allother chemicalswereofanalyticalgradeand obtained from common sources. Milli-Q® _{was the water}

utilisedinalltheperformedassays(TGIPureWaterSystems, Greenville,SC,USA).

2.3. Heat-assistedextraction(HAE)ofergosterol(E) Thedriedsamples(600mg)wereplacedinavialwith20mL ofethanol,followingasolid/liquidratio(S/L)of30g/L.Thevial containingthemixturewasthenpositionedinathermostatic waterbathwithinaspecifictimeinterval(continuous elec-tromagneticstirring).Thepowderedsampleswereextracted according different time (t) and temperature (T) intervals, 10–150minand30–90◦C,respectively.TheRSMdesign com-prises16experimentalrunswith4centrepoints,previously plannedandbasedonliteratureresearchandgroup experi-ence(Helenoetal.,2016b;Lópezetal.,2018;Vieiraetal.,2017). Afterextraction,sampleswerefiltered,supernatantcarefully collected,andthedryweightobtainedtodeduceextraction yield.

2.4. Ergosterolquantification

Driedextractswerere-dissolvedinmethanolatafinal con-centrationof20mg/mLandfilteredthrougha0.22␮mnylon disposablefilter.Eidentificationandquantificationwas per-formed usinga HPLC-UVequipment,anintegrated system composedbyapump(Knauer,Smartlinesystem1000,Berlin, Germany),adegassersystem (Smartlinemanager5000),an auto-sampler (AS-2057 Jasco, Easton, MD, USA), and a UV detector(KnauerSmartline2500)aspreviouslydescribedby Barreiraetal.(2014).Theseparationwasperformedat280nm usinganInertsil100AODS-3reversed-phasecolumn(Barreira etal.,2014)andacetonitrile/methanol(70:30,v/v)asmobile phase. Ewas quantified usinga calibrationcurve obtained fromacommercial.DataanalysiswasperformedusingClarity 2.4Software(DataApex).

2.5. Responsesurfacemethodology(RSM)

2.5.1. Experimentaldesign

TheRSMdesignwasusedtoexploretheinteractionsbetween independent(tandT)anddependentvariables (Y1, Y2, and

Y3)oftheexperimentusingasmallnumberofcombinations.

Thecombinedeffectoftheindependentvariableswas stud-ied usinganorthogonal rotatabledesignof5levels coding

Pleasecitethisarticleinpressas:Silva,A.R.,etal., Mushroomsbio-residuesvalorisation:Optimisationofergosterolextractionusingresponse thepossibleinteractionsbetweentandT(RSM

mathemati-calmodeldoesnotusevalues,allvariableswerecoded),which compromisesatotalof16experimentalruns.Theunexpected variability ofthe obtained responseswas reduced through experimentalrunsrandomisation.

2.5.2. Responsesformatvaluestopresenttheresults

Theresponseswereexpressedintheformofthreedependent variables(Y):Y1,the%ofextractedmaterial(R);Y2,inmgof

Eobtainedintheextractresidue(mgE/gR),appliedto eval-uatetheEpurityintheextracts;andY3,inmgofEin100g

ofmushroomdryweightmaterial(mgE/100gMdw),usedto analysethedifferentEextractionyields.

2.5.3. Mathematicalmodel

Tofurtheranalysetheobtainedexperimentalvalues,a polyno-mialmodelincludinglinear,quadraticandinteractiveeffects wasused: Y=b0+ n









whereYwastheresponse(dependentvariable)tobemodelled;

Xi andXj,theindependentvariables,b0,theconstant coeffi-cient;bi,thecoefficientoflineareffect;bij,thecoefficientof interactioneffect;bii,thecoefficientsofquadraticeffect;and

n,thenumberofvariables.

2.6. Variableoptimisationformaximumresponse Tosolvethenon-linearproblemsofthepredictivemodel,a maximisedprocessedwasperformedaspreviouslydescribed byAlbuquerqueetal.(2016).Certainlimitationswereimposed (i.e.,tlowerthan0)toavoidvariableswithunnaturaland unre-alisticphysicalconditions.

2.7. Numericalmethodsandstatisticalanalysis

AsextensivelydescribedbyPinelaetal.(2017,2018),fitting procedures,coefficientestimates,andstatisticalcalculations were performed as follows: (a) to minimise the sum of thequadraticdifferencesbetweentheexperimentalandthe model-predicted values, the coefficient measurement was performedusingthenonlinear least-square(quasi-Newton) methodprovidedbythemacro“Solver”ofMicrosoftExcel;(b) todeterminetheparametricconfidenceintervals,the coeffi-cientsignificancewasevaluatedusing‘SolverAid’.Tosimplify the model, the non-significant terms (p-value>0.05) were dropped;andfinally;(c)thefollowingstatisticalassessment criteriawereperformedforverifiedthemodeladequacy:(i) theFisherF-test(˛=0.05)wasusedtodeterminewhetherthe modelsdescribeproperlytheobserveddata;(ii)the‘SolverStat’ macroassessedtheparameterandmodelprediction uncer-tainties;and(iii)theproportionofvariabilityofthedependent variableexplainedbythemodelwasachievedusingR2_value

(Taofiqetal.,2019).

3.

Results

and

discussion

3.1. Preliminaryselectionoftherelevantvariablesand instrumentalparameterstocentreexperimentaldomains beforeRSMapplication

The selectionof anadequate extractionmethod and opti-malindependentvariableshavegainedparticularrelevance due toits directinterferenceinyieldand/orquality ofthe extracts(Cásedasetal.,2017;Chematetal.,2017;Khadhraoui et al., 2018). The characteristics of mushroommatrices, E,

andethanol,aswellastheresistanceofEtocertainranges of T, make heat assisted extraction a valid methodology forEextraction. Therefore, atotalof16experimentalruns wereutilisedtooptimisetheextractionofEfrommushroom bio-residues.HAEtechniquetogetherwiththe16conditions were selected based on literature evidence, group experi-enceattestingtheefficiencyofthismethodforEextraction, aswellasthepreservationofits bioactivepropertieswhen submittedtoheat(Helenoetal.,2016b).Theprocedure con-sistsofmixingthesolid(mushroompowder)withasolvent (ethanol) followedby stirringfor acertain period oft ata specific T. Itis a simpletechnique with low requirements interms ofequipment andcosts, beingeasilyscaledupat industriallevel. Fortheselectionofanappropriatesolvent, theuseoftoxicorhazardousorganicsolventswasavoided. Together with being a more green approach when com-paredtobenzene,cyclohexane,anddichloromethane,ethanol proved tobeoneofthemostefficientsolventstoextractE

(676±3mg/100gdw)duringapreviousstudy(Helenoetal., 2016a),followed bylimonene(261±11mg/100gdw)and n-hexane(186.1±0.3mg/100gdw).

Insummary,theefficiencyofHAEEextractionfrom dif-ferentmushroombio-residueswasevaluatedusingRSMina CCCDwithfivelevelvaluesper variable.Thismultivariable fittingprovidesastrongsolutiontominimisethe experimen-talerrorsusingashortnumberoftrials,whileoptimisingthe variableconditionsassistedbyempiricalmathematical mod-els,whichpredictthemaximumextractionyield(Vieiraetal., 2017).

3.2. MathematicalmodelsderivedfromRSM(CCCD) usingtwodefinedvariablesandstatisticalassessment Beforeoptimisation,thecompoundspresentintheextracts were analysed by HPLC-UV; E, one of the main sterols compounds present in edible mushrooms and the major sterolpresentinitshyphalmembraneswasscreenedusing HPLC-UV.TheHAEresultsforthethreetestedmushroom bio-residuesareshowninTable1.Tofurtheranalysetheobtained experimental values, a polynomial model including linear, quadratic,andinteractiveeffectswasapplied.Byfittingthe second-orderpolynomialmodel(Eq.(1))totheobtained exper-imentalresponses,theparametricvalueswereestimatedand presentedinTable2.Thosecoefficients,whichshowed con-fidenceintervalvaluesof˛=0.05higherthantheparameter valuewasconsiderasnon-significant(ns)andwerenotused formodeldevelopment.

3.3. ResponsepatternsderivedfromtheRSMmodel As shown in Table2, apart from POR-Y1 (≈79%) and

Table1–ExperimentalRSMresultsfortheoptimisationofthetwoindependentvariablesinvolved(X1andX2)inthe

extractionofEfromthreedifferentmushroombio-residuesofPleurotuseryngii(PER),Agrocybecylindraceae(ACR)and Pleurotusostreatus(POR)species.Threeresponsevalueswereassessed(Y1,yieldin%;Y2,mgE/gR;andY3mgE/100gM

dw).Naturalvalues,variables,andtheirrangesaredisplayed.

Experimentaldesign Experimentalresponses

X1(t,min) X2(T,◦C) Pleurotuseryngii(PER) Agrocybecylindraceae(ACR) Pleurotusostreatus(POR)

Y1 Y2 Y3 Y1 Y2 Y3 Y1 Y2 Y3 −1(30.5) −1(38.8) 6.14 29.65 182.04 4.69 47.85 224.62 5.40 14.08 76.07 −1(30.5) 1(81.2) 9.65 17.58 169.68 7.78 46.13 359.00 11.60 22.93 265.87 1(129.5) −1(38.8) 6.93 29.70 205.88 2.43 58.10 140.91 6.03 11.35 68.48 1(129.5) 1(81.2) 13.28 13.00 172.62 7.93 41.48 329.04 7.42 25.15 186.66 −1.41(10) 0(60) 6.18 17.90 110.61 5.31 58.20 308.92 10.13 22.33 226.19 1.41(150) 0(60) 8.28 22.23 184.10 6.73 45.15 304.00 7.52 10.70 80.52 0(80) −1.41(30) 5.96 25.58 152.34 4.50 59.80 269.20 4.53 46.23 209.63 0(80) 1.41(90) 16.89 10.65 179.85 7.50 40.33 302.27 10.53 21.25 223.78 −1.41(10) −1.41(30) 5.13 30.40 156.04 3.94 60.23 237.52 3.93 48.88 191.92 −1.41(10) 1.41(90) 13.05 13.05 170.27 7.71 42.60 328.61 11.74 23.95 281.19 1.41(150) −1.41(30) 5.94 32.30 191.76 6.99 67.08 468.86 5.42 42.55 230.63 1.41(150) 1.41(90) 12.19 16.13 196.61 8.04 41.83 336.13 14.75 13.50 199.16 0(80) 0(60) 10.33 19.03 196.57 6.11 52.80 322.76 9.00 30.08 270.63 0(80) 0(60) 11.41 15.25 173.96 5.92 51.63 305.48 8.46 34.00 287.62 0(80) 0(60) 11.38 15.78 179.54 5.78 51.10 295.49 8.65 31.10 269.00 0(80) 0(60) 11.13 18.25 203.18 5.95 47.33 281.43 8.85 32.43 287.02

Table2–Thestatisticaldetailsandparametricresultsofthesecond-orderpolynomialequationofEq.(1)forthethree mushroombio-residues(Pleurotuseryngii(PER),Agrocybecylindraceae(ACR),andPleurotusostreatus(POR))speciesare presented.Thecodedvalueshavestatisticalsignificanceat˛=0.05.

b0 b1(t) b2(T) b11(t2_{) b22(T}2_{) b12(tT) R}2 Y1 PER 9.25±0.61 0.53±0.36 2.54±0.35 −1.10±0.45 0.33±0.30 ns 0.8308 ACR 5.68±0.45 0.25±0.07 1.23±0.26 0.20±0.17 0.19±0.15 −0.15±0.06 0.8823 POR 8.32±0.63 ns 2.52±0.37 0.33±0.17 −0.27±0.16 ns 0.7912 Y2 PER 37.79±2.01 ns −9.83±1.32 3.41±1.56 ns −1.12±0.58 0.8308 ACR 50.70±1.06 0.35±0.26 −6.66±1.05 ns ns −1.51±0.94 0.7739 POR 26.87±4.37 −2.45±1.58 −5.56±2.56 −4.22±3.28 4.31±.22 ns 0.8168 Y3 PER 336.23±12.55 17.19±8.35 16.26±8.16 ns −17.71±9.46 −10.19±7.30 0.8470 ACR 279.57±2.97 14.77±5.14 19.40±3.64 14.05±6.65 6.59±2.13 −19.69±4.71 0.8890 POR 239.15±25.03 −20.23±4.79 25.61±7.46 −28.88±5.95 ns −15.65±5.72 0.8825 ns:non-significantcoefficient;R2_{:correlationcoefficient.}

mushroombio-residuesweresuccessfullyexplainedby tem-perature and time collectively. The parametric coefficients (Table2)obtainedafterthemathematicalmodelapplication providespecificinformationoftheimpactofthe two inde-pendentvariablesandtheirantagonist/synergeticimplication inthefinalresponses.Thelineareffectplayedanimportant roleinallthethreemushroombio-residuesextractions.Apart from PER-Y2 and POR-Y1 responses, thelinear effect twas

significantinalltheothercases,beingPER-Y3theresponse

wherethadthebiggestpositiveimpactandPOR-Y3thebiggest

negativeimpact in the final responses. Contrarily, the lin-eareffectofT wassignificant inallthe threeresponsesof thedifferentmushroombio-residues,wherePOR-Y3showed

thehighestpositive(25.61±7.46)andPER-Y2themost

nega-tive(−9.83±1.32)impact.Thepresenceofpositivequadratic effectsimpliesthatalmostallresponsesshowednon-linear patterns,whichisdirectlyrelatedwiththenatureofthe cho-senvariables.Inderivedmathematicalmodels,responsesare studied separately and the determination ofthe optimum independentvariablesisnotpossible.Interactiveparametric valuescanactasacontrolformultivariableanalysis(RSM) toproperlydeterminethebestextractionconditions.

Regard-ingtTinteractiveeffectinY1response(%yield),PERandPOR

mushroombio-residuesshowednon-significantinteraction, contrarilytoACR,wherethisinteractionhadanegativeimpact inthefinalresponse(−0.15±0.006).ForY2(mgE/gR),tThada

negativeimpactforPERandACRfinalresponse(−1.12±0.58 and−1.51±0.94,respectively),andanon-significanttoPOR bio-residuesfinalresponse.RegardingY3(mgE/100gMdw),

tTinteractionhadanegativeimpactinalltheresponsesof thedifferentmushroombio-residues.Inconclusion,a non-linearmultivariableanalysiswasneededinordertofullyfit theexperimentalresults.

Although with longer extraction periods (150min), PER and ACR presentedsimilarEyieldtoPOR (65.6min),60.75, 63.62,and43.72mgE/gR,respectively.PER-Y2graphical

rep-resentationhighlightsthatat30◦C,theoptimalresponsecan beobtainedwithbothshortandlongperiodsofextraction. Thelinearparametricvaluesofthesecond-orderpolynomial equationfortimeandtemperatureofPER-Y2,non-significant

and−9.83±1.32,respectively,showthenegativeimpactthat temperature has in Y2 response. Contrarily, the quadratic

effect of time (t2_{: 3.41±1.56) has a positive impact in the}

Pleasecitethisarticleinpressas:Silva,A.R.,etal., Mushroomsbio-residuesvalorisation:Optimisationofergosterolextractionusingresponse ofT2_{,confirms thattimeisthe mainoptimalindependent}

variable to be considered for an optimalPER-Y2 response.

Therefore, the long extraction period required for PER-Y2,

150min, isnot suitable forindustrial applications. In con-trasttoPER-Y2,PORoptimaltimepredictedbythemodelis

43.72min,thisresultisconfirmedbythenegativeeffectofthe linearparametricvalues,t(−2.45±1.58)andT(−5.56±2.56), together with the positive quadratic effect oftemperature (T2_{: 4.31±3.22) and the negative effect oft}2 _{(−4.22±3.28).}

Thesevaluesconfirmthatthequadraticeffectoftemperature (T2_{)isthe mainoptimalindependentvariableforaY}

2-POR

optimum response, being more appropriate than PER for industrialpurposes.POR-Y3extractionledtothebestresult,

290.90mg E/100g M dw. Although POR-Y3 did not present

thehighestcontentofEextractedfromthethreemushroom bio-residues (ACR: 366.80mg E/100g M dw), in terms of E

purity,time and energy reductions, POR-Y3 optimal

condi-tionswereconsideredthemostfavourable.PER-Y3graphical

representationand independentvariables showatendency tolongextractionperiodsindependentlyofthetemperature applied,conditionnotoptimalforindustrialpurposes,where shorterperiodsforextractionaretheideal.Similarly,ACR-Y3

increasestogetherwithtimeandtemperature.Theoptimum independentvariables,time and temperature, predicted by the model for POR-Y3 are 43.7min at 90◦C.In conformity

with these values, Fig. 3 arrow tendency also shows that toobtain POR-Y3 optimalresponse, high temperaturesand

lowextractiontimearetheoptimalsolution.Theseresults, togetherwiththelinearparametricvaluesofthesecond-order polynomialequationfortimeandtemperature,−20.23±4.79 and 25.61±7.46, respectively, show the negative impact of timeandthegreatinfluenceoftemperatureinY3 response.

Thequadraticeffectoftime(t2_{:−28.88±5.95)hasanegative}

impactinthefinalY3responseand,togetherwiththenegative

interactiveeffect(tT:−15.65±5.72),andthenon-significance ofthequadraticeffectoftemperature(T2_{)confirmsthat}

tem-perature is the main optimal independent variable to be consideredforPOR-Y3optimalresponse.Forbothresponses

Y2anY3,PORwasthemostindustriallyappealingbio-residue

whencomparingthethreemushrooms.

Fig.1showstheHAEresultsforY1response,expressedas

extractionyield%.Fig.1isdividedinthreeunits, eachone correspondingtothedifferentresultsobtainedwiththethree mushroombio-residues.Additionally,eachunitisdividedinto foursections(AtoD).SectionAshowsthe3Dsurfaceplots for the two possible variable combinations.Section B is a two-dimensionalrepresentationofthefittingofEq.(1)tothe experimentaldata. Thebinary combinations between vari-ablesarepresentedwhentheexcludedvariableispositioned attheoptimumoftheexperimentaldomain.SectionC illus-tratesthecapabilitytopredicttheobtainedresultsandthe residualdistribution asafunctionofeach considered vari-ables. Takingthe distributionofthe residuals presentedin Fig.1asanexampleofalltheachievedresponses,itcanbe perceivedthattheyarearbitrarilyaroundzeroandnogroups ofvalues,orautocorrelations,wereobserved.

ForPER-Y1response,andascanbeobservedonthefirst

threedimensionalplot,theextractionyieldpercentagerises astemperatureincreases.Whencomparedtot,Tisthemore relevantofthe twoindependentvariables,this significance isalsoclearthroughtheobservationofthelinear paramet-ricvaluesofthesecond-orderpolynomialequationpresented onTable2,2.54±0.35and0.53±0.36,respectively.SectionC

showsalineofalmostperfectfitting,aconsequenceofthe abilityofmodeltodescribetheexperimentaldatareasonably well.Theresidualsarethedifferencebetweenthepredicted andtheexperimentalvaluesforthetwoanalysed indepen-dentvariables(tandT)andascanbeobserved,thedistribution islow,withminimumautocorrelation,novariablesmissing fromthemodel.

Regarding ACR and POR-Y1 responses, similarly to

PER-Y1,Tisthemorerelevantofthetwoindependentvariables.

For both responses, this was clearly observed through the analysisoftheparametricvaluesofthe second-order poly-nomialequationpresentedonTable2,wherethemodelgives smallsignificancetotinACR(0.25±0.07)finalresponseand non-significance forPOR.Inbothcases,subunitC showsa line of almost perfect fitting and the distribution between experimental and predicted values is low, with minimum autocorrelation.

Section D shows the individual 2D responses of the threedifferentmushroombio-residuesasafunctionofthe defined variables for HAE extraction technique. The dots highlightthelocationoftheoptimumvalue.Linesanddots are generated bythe theoretical second order polynomial. The properly optimised responses (Y1, Y2, Y3) display

pat-ternsinwhichtheamountofextractedcompoundsincreases to an optimum value and then decreases as a function of each of the assessed independent variables. Therefore, an absolute optimum can be found at one single point, allowing computingandconsequently leadingto the abso-lute maximum. For instance, all responses derived from the HAEproducedclearoptimalresponsesforallvariables, wherefewresponsesshowedtendenciestowardsanabsolute optimum.

3.4. Individualandglobalnumericaloptimal conditionsthatmaximisestheextraction

The optimalabsolute or relativeconditions that maximise the individual and global responses criteria presented in Figs.1and2(SectionsA,B,andD),andFig.3aredisplayed numericallyinTable3andgraphicallyinFigs.1and2section D.These valueswereobtainedthroughasimpleprocedure byinsertingrestrictionstotheexperimentalranges.PartAof Table3showstheindividualoptimalconditionsregardingHAE foralltheEcontentresponses(Y2andY3)and%ofobtained

extracts(Y3).

The most expressive Y1 response regarding the three

mushroombio-residueswasY1-PER(13.56%),followedbyPOR

(12.01%) and ACR (8.24%). For PER-Y1 individual response,

the optimumindependentvariableswere 91.9minat90◦C. When analysing the graphical representations A and Bof Fig. 1, regarding PER mushroom bio-residues, it is possi-ble conclude that using the same temperature (90◦C), and reducing theextractiontime(±50min),the %yieldoftotal extractisapproximatelythesame.InFig.3,thisconclusion becomesevenmoreevident,thearrowsshowthe%yield ten-dency,andinbothcases,itpointstolesstime(±50min)and highertemperatures(90◦C).ThoughtheoptimalPER-Y1

vari-ablespredictedbythemodelpointtoalongextractiontime, analysingthegraphical representationsandthe parametric valuesofthe secondarypolynomialequation,itispossible toconcludethatalongerexposurewillnotmakeamassive differenceintheextraction%yield.ACR-Y1showsexactlythe

samepatternasPER,althoughthemodelpredictslong extrac-tiontime.Whenanalysingthegraphicalrepresentations,itis

Fig.1–GraphicalresultsintermsofresponsevalueformatY1(%)forthreedifferentmushroombio-residuesofPleurotus

eryngii(PER),Agrocybecylindraceae(ACR)andPleurotusostreatus(POR)species.SectionA:Points(䊉)representthe

experimentalresultsdisplayedinTable1andthenetsurfacedenotesthetheoretical3Dresponsesurfacepredictedbythe modelasafunctionofeachindependentvariable.SectionB:Representsa2Dfittingoftheexperimentalresults.SectionC:

Representsthestatisticaldescriptionusingtwographicalcriteria:theabilitytosimulatethechangesoftheresponseand theresidualdistributionasafunctionofeachofthevariable.SectionD:Thedots( )representthelocationoftheoptimum valuegeneratedbyEq.(1).

Table3–OptimumconditionsandthecorrespondingresponsevaluesforRSMdeterminedforeachmushroom bio-residuesspecies:Pleurotuseryngii(PER),Agrocybecylindraceae(ACR)andPleurotusostreatus(POR).Theresultswere presentedusingthreeindividualresponsevalueformats(Y1,yieldin%;Y2,mgE/gR;andY3mgE/100gMdw).

Criteria Optimalvariableconditions Optimumresponse

X1:t(min) X2:T(◦C)

(A)Individualoptimalconditions PER Y1 91.9 90.0 13.56 % Y2 150.0 30.0 60.75 mgE/gR Y3 150.0 61.1 360.60 mgE/100gMdw ACR Y1 150.0 90.0 8.24 % Y2 150.0 30.0 63.62 mgE/gR Y3 10.0 90.0 366.80 mgE/100gMdw POR Y1 10.0 90.0 12.01 % Y2 65.6 30.0 43.72 mgE/gR Y3 43.7 90.0 290.90 mgE/100gMdw

(B)Globaloptimalconditions PER Y1 150.0 61.8 8.02 % Y2 43.63 mgE/gR Y3 360.58 mgE/100gMdw ACR Y1 150.0 90.0 8.24 % Y2 38.76 mgE/gR Y3 329.79 mgE/100gMdw POR Y1 50.4 90.0 11.47 % Y2 27.58 mgE/gR Y3 290.38 mgE/100gMdw

possibletoconcludethatareductioninextractiontimewill notdrasticallyinterfereinthefinalresponse.ForPOR-Y1,the optimumindependentvariables were 10minat90◦C,what

canbeconfirmedthroughtheanalysisofthegraphical repre-sentationsofthemodelforthismushroombio-residueandits correspondingparametricvaluespresentedonTable2.

Pleasecitethisarticleinpressas:Silva,A.R.,etal., Mushroomsbio-residuesvalorisation:Optimisationofergosterolextractionusingresponse Fig.2–GraphicalresultsintermsofresponsevalueformatY2(mgE/gR)forthreedifferentmushroombio-residuesof

Pleurotuseryngii(PER),Agrocybecylindraceae(ACR)andPleurotusostreatus(POR)species.SectionA:Points(䊉)representthe experimentalresultsdisplayedinTable1andthenetsurfacedenotesthetheoretical3Dresponsesurfacepredictedbythe modelasafunctionofeachindependentvariable.SectionB:Representsa2Dfittingoftheexperimentalresults.SectionC:

Representsthestatisticaldescriptionusingtwographicalcriteria:theabilitytosimulatethechangesoftheresponseand theresidualdistributionasafunctionofeachofthevariable.SectionD:Thedots( )representthelocationoftheoptimum valuegeneratedbyEq.(1).

Concerningthe mg ofE/g R, the best Y2 response was

obtainedforACR(63.62mgE/gR),followedbyPER(60.75mg E/gR)andPOR(43.72mgE/gR).Forthethreemushroom bio-residues,thepredictedoptimumindependentvariablesforY2

responseareinaccordancewithallthegraphical representa-tions(SectionsAandBofFigs.1and3)andthecorrespondent parametricvalues(Table2).Althoughwithlongerextraction periods(150min),PER andACRhavesimilarEyieldtoPOR (65.6min),60.75,63.62,and43.72mgE/gR,respectively.

Finally,forY3;althoughverysimilar,thehighestpredicted

responsewaspresentedbyACR(366.80mgE/100gMdw), fol-lowedbyPER (360.60mgE/100g Mdw) andPOR (290.90mg E/100gMdw).AlthoughPER-Y3optimumresponsepresented

the longer extraction time (150min), the final E yield, mg E/100gMdw,wasvery similartothe othertwomushroom bio-residues, ACR (10min) and POR (43.7min), 366.80 and 290.90mg E/100gM dw,respectively. Despite the predicted optimumindependentvariablesforPERpointedalong extrac-tiontime,150min,atahightemperature(61.1◦C),itsgraphical representation (Fig. 3) shows a tendency (arrows) to long extractionperiodsindependentlyofthetemperatureapplied, confirmed bythe negative effect ofthe quadratic value of temperature(T2_{:−17.71±9.46).Eventhoughtheoptimum}

pre-dictedvalueforACR-Y3responsewas10min,Fig.3arrowalso

showsthatEresponse(mg)in100gofbio-residuesisdirectly proportionaltotheindependentvariables.Y3 responsewill

followthesametendencyastimeandtemperatureincrease.

Theoptimumindependentvariables,timeandtemperature, predictedbythemodelforPORare43.7minat90◦C. Accord-ingly,Fig.3arrowtendencyandthelinearparametricvalues ofthesecond-orderpolynomialequationforT(25.61±7.46) andt(−20.23±4.79),confirmedthatforanPOR-Y3optimum

response,hightemperaturesandlowextractiontimearethe optimalsolution.

The combined information of the three responses behaviourforallthemushroombio-residueswasdefinedin globalterms.Theglobaloptimumresultsforthethree mush-rooms bio-residues are presented in Table 3, section B. In globalterms,forPERandACR,alongextractiontime(150min) and high temperatures, 61.8 and 90.0◦C,respectively, were the optimumvaluespredictedbythemodelforanoptimal equilibrium between the three responses. Similarly, to the othertwomushroombio-residues,ahighextraction temper-ature(90.0◦C)wasrequiredtoattainPORoptimalequilibrium betweenthethreeresponsescontrarilytoashorterextraction time,50.4min.

Overall, HAE is a powerful extraction method that has proved to be efficient in terms of E extraction yield and extractpurity.Ethanolprovedtobeagood solvent,whatis in agreement with the results obtained during a previous study, wheretheperformanceofdifferent environmentally friendlysolventsforEextractionwascompared.Theeffect ofliquid-to-solidratioonEyieldhasbeenwellreported,with nosignificantimpactonEextractionyield.Thus,an

interme-Fig.3–GraphicalsummaryoftheRSMresultsfortheoptimisationofthetwomainvariablesinvolved(X1andX2)inthe

extractionofEfromthreemushroombio-residuesofPleurotuseryngii(PER),Agrocybecylindraceae(ACR)andPleurotus ostreatus(POR)speciesforthethreeresponsesassessed(Y1,yieldin%;Y2,mgE/gR;andY3mgE/100gMdw).

diateliquid-to-solidratioof30g/Lwasusedinallthepresent studyexperiments.

4.

Conclusions

RSMwassuccessfullyemployedtooptimiseEextractionand tofind the optimalindependentvariables.Y2 and Y3 were

consideredtheguidingresponsesintermsofoptimisationfor industrialtransference.Y2optimalindependentvariables

esti-matedbythemodelareinaccordancewithallthegraphical bio-residuesrepresentations.Theextractionprocess optimi-sationprovedtobeaviableoptionforobtainingergosterolrich extractsfrom mushroombio-residuesand bearinginmind theresultspresentedherein,largescaleheat-assisted extrac-tionprocessescanbedesignedforergosterolextractionand anopportunitytodevelopthecircular andsustainable bio-economyinmushroomvaluechain.

Conflict

of

interest

Theauthorsdeclarethatthereisnoconflictofinterest.

Acknowledgements

The authors are grateful to the Foundation for Science andTechnology(FCT,Portugal)forfinancialsupportthrough nationalfundsFCT/MCTES toCIMO (UIDB/00690/2020);this

workwasfundedbyFEDERthroughPOCI,withinthescopeof projectMicoBioExtract(POCI-01-0247-FEDER-033939). L. Bar-ros also thanks the national funding by FCT, P.I., through theinstitutionalscientificemploymentprogram-contractfor theircontracts.ToMICINNforthefinancialsupportforthe Ramón&CajalresearcherofM.A.Prieto.

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