Optimización de compuestos lipofílicos en tortillas de maíz pigmentado nativo obtenidas a partir de harinas por el proceso de extrusión cocción alcalina

The lime-cooking extrusion process depicts emerging technologies to making maize tortillas with the advantages of reducing energy, little water use, and not environmental deletions effluents. Multi-response optimization by response surface methodology (RSM) was a tool to optimize native pigmented ma...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Mora Rochin, Saraid, Menchaca-Armenta, Mariela, Milán-Noris, Ada Keila, Gutiérrez-Uribe, Janet Alejandra, Cueva-Rodríguez, Edith Oliva, Reyes-Moreno, Cuauhtémoc, Milán-Carrillo, Jorge
التنسيق: Online
اللغة:eng
منشور في: Universidad de Sonora 2021
الوصول للمادة أونلاين:https://biotecnia.unison.mx/index.php/biotecnia/article/view/1392
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الوصف
الملخص:The lime-cooking extrusion process depicts emerging technologies to making maize tortillas with the advantages of reducing energy, little water use, and not environmental deletions effluents. Multi-response optimization by response surface methodology (RSM) was a tool to optimize native pigmented maize lime-cooking extrusion process to obtain flours to develop tortillas with high lipophilic compounds. The effects of extrusion temperature (ET, 65–135 ºC) and screw speed (SS, 78–212 rpm) were investigated. The best extruded blue maize tortillas were selected over response variables: Linoleic acid (LA), Oleic acid (OA), Campesterol (CP), Stigmasterol (SP), and b-sitosterol (bSP), where the quadratic predictive developed models were adequate and reproducible inside the specified array of process factors. Appling desirability function, the optimum lime-cooking extrusion conditions to development extruded blue maize tortillas correspond to ET (119 °C), SS (78 rpm) and a global desirability value (D = 0.906). Values response variables obtained from the predictive models were compared from experimental tests, a close agreement between both values was observed. Hence, RSM is still convenient for optimization, particularly once used in mixture with other procedures.