BACHELOR THESIS: CHEMOMETRIC TOOLS IN THE STUDY AND OPTIMIZATION OF COCHINEAL DRY AND CARMINE OBTAINMENT

  • BACHELOR THESIS

     

    MARÍA CIFRIÁN HAS PRESENTED HER BACHELOR THESIS ON THE USE OF ANOVA, RANGE TEST AND EXPERIMENTAL DESIGN METHODOLOGY IN THE PROCESSES OF COCHINEAL DRY AND CARMINE OBTAINMENT

    As a result of the collaboration with Teno Osorio, from I.E.S. Teguise (Lanzarote), María Cifrián Tomé has presented her Bachelor thesis “Chemometric tools in the study and optimization of cochineal dry and carmine obtainment” on 8th July 2021. The development of this work started in March 2020 with cochineal from Lanzarote, but due to coronavirus pandemic it continued with data collected from literature.

    Cochineal (E-120) is a natural colourant obtained from the insect Dactylopius coccus Costa which is harvested from cactus plants in South America and the Canary Islands. Carminic acid is the main colouring agent in cochineal so, after collection and drying of insects, the extraction, and processing of carminic acid must be carried out. Therefore, it is interesting to know the effect of some controllable variables on certain characteristics of the cochineal. This issue has been addressed in this work by using analysis of variance (ANOVA), non-parametric range test and experimental design methodology.

    One and two-way analysis of variance and multiple range tests have been used to study the effect of temperature and drying time, and their interaction, on different responses (dry matter, wax, fat, protein, ash and carminic acid content in cochineal). Likewise, response surface designs have been fitted to predict those responses in any point within the experimental domain. Additionally, factorial designs have been used to investigate main and interaction effects of granulometry, temperature and time of extraction on percentage of carminic acid extracted, and of alum, time, and percentage of carminic acid for cochineal carmine precipitation on the yield for both Thorpe and French methods. Factorial experimental designs are more efficient than ANOVA to achieve this task.