Researchers Use Mathematical Models to Optimize Olive Oil Production

A group of researchers from Spain’s University of Jaén have devel­oped new math­e­mat­i­cal mod­els that can help to pre­dict the qual­ity of extra vir­gin olive oil and opti­mize its pro­duc­tion.

The researchers devel­oped these mod­els using response sur­face method­ol­ogy (RSM), which explores the rela­tion­ship between inde­pen­dent and depen­dent vari­ables involved in the pro­duc­tion process and is often used to max­i­mize the yield of a spe­cific sub­stance.

“RSM allows us to obtain infor­ma­tion that we will miss when employ­ing clas­si­cal method­ol­ogy,” Francisco Espínola Lozano, a pro­fes­sor at the University of Jaén and lead researcher on the study, told Olive Oil Times. ​“Its advan­tage is that one can study the com­bined per­for­mance of sev­eral vari­able fac­tors, mea­sur­ing the influ­ence of each and the inter­ac­tion exist­ing among them all.”

Espínola Lozano said that the study explored, for the first time, a com­bined research of four tech­no­log­i­cal fac­tors: the size of the sieve and of the ham­mer mill that grinds the olives; the time and the tem­per­a­ture at which the olive paste is malaxed; and the doses of tech­no­log­i­cal adju­vant used to improve the per­for­mance of the olive.

“Talc and cao­lit­ica clay are already autho­rized as tech­no­log­i­cal adju­vants, but we used cal­cium car­bon­ate and had bet­ter results,” he said. ​“This rep­re­sents a promis­ing research line.”

According to Espínola Lozano, the use of dif­fer­ent math­e­mat­i­cal mod­els dur­ing the inves­ti­ga­tion allowed the researchers to dis­cover the rela­tion­ship between these tech­no­log­i­cal and agro­nomic fac­tors. For this rea­son, the mod­els can deter­mine the effects that olive vari­ety, degree of ripeness, kind of cul­ti­va­tion (tra­di­tional, inten­sive, super inten­sive) and the use or lack of irri­ga­tion will have on the oil pro­duc­tion process.

By apply­ing RSM to a pre­vi­ously designed sta­tis­ti­cal exper­i­ment, the researchers also devel­oped mod­els that can pre­dict the effect of the vari­a­tion cer­tain tech­no­log­i­cal aspects have on the oil.

An exam­ple of this is the model’s abil­ity to increase or reduce cer­tain phe­no­lic com­pounds with antiox­i­dant and anti-inflam­ma­tory prop­er­ties, such as oleo­can­thal, allow­ing for the cre­ation of prod­ucts with spe­cific traits and vari­ances, both in terms of fla­vor and health prop­er­ties.

“In addi­tion to yields, we stud­ied reg­u­lated qual­ity para­me­ters and the con­tents of phe­no­lic com­pounds (nat­ural antiox­i­dants) and volatile con­tents, which are respon­si­ble for aro­mas, eval­u­at­ing more than 30 responses,” Espínola Lozano said.

He added that the math­e­mat­i­cal mod­els used depend on the oil trait that researchers want to improve.

These new mod­els not only help pre­dict the qual­ity of olive oils, but also facil­i­tate the automa­tion of olive oil pro­duc­tion at the mills. No spe­cific tech­no­log­i­cal adap­ta­tion seems to be required, only the appli­ca­tion of the math­e­mat­i­cal mod­els adapted to the olives processed at the mill.

In case a mill decides to man­age its pro­duc­tion in a sys­tem­atic and auto­mated man­ner, the math­e­mat­i­cal mod­els can be included in the soft­ware that is used.

Espínola Lozano empha­sized that mills cur­rently han­dle olive pro­duc­tion more as an artis­tic under­tak­ing, which trusts the expe­ri­ence and intu­ition of olive mas­ters, rather than as a process that must be han­dled with a sci­en­tific and tech­no­log­i­cal approach.

In his view, it is impor­tant that the com­pa­nies involved in olive pro­duc­tion learn about the many pos­si­bil­i­ties sci­en­tific research opens and max­i­mize their advan­tages.

This view­point is likely shared by the Junta de Andalucía, whose Council of Innovation, Science and Enterprises funded the project.

• Europapress


• University of Jaén