A bi-factorial experimental design was considered to assess moisture variation of sweet potato-quinoa-kiwicha flakes (SP-Q-K) caused by the changes in the rotational speed and steam pressure of a rotary drum dryer (RDD). As it is a design with discrete variables, there is a limitation in the modeling and optimization thus techniques of Artificial Intelligence (AI): Artificial Neural Networks (ANN), Fuzzy Logic (FL) and Genetic Algorithms (GA), were applied, and their prediction ability evaluated. Due to the limitation of data for proper training, the ANN did not allow a correct prediction of the experimental data. Response Surface Methodology (RSM) was employed to obtain the relational equation among the experimental variables, which was used as the objective function with GA, and this allowed moisture optimization. Because of this, it is recommended to integrate RSM and GA into optimization studies. In this research the use of FL among variables, enabled us to get the best prediction adjustment of experimental values (R 2 = 0.99), with a mean absolute error of 0.6±0.66 %, setting a pressure value of 5 atm and a speed value of 6 rpm for flakes at 4.99 % humidity.

Potato is grown and consumed throughout the world for their particular calorific nutritional value and vitamin C, but it also contains phytochemicals (secondary metabolites) that have been studied and found to have positive effects in preventing degenerative diseases in human health, such as hypertensive activity, and also atherosclerosis, type 2 diabetes, liver fibrosis, Alzheimer's disease, macular degeneration and cancer. We aimed to identify and quantify phenolic compounds in potatoes grown on El Zuro (EZ) and Huayatan Alto (HA) in Santiago de Chuco, La Libertad, Peru. The field trials were carried out in EZ (altitude 3750 m.a.s.l.) and HA (altitude 3150 m.a.s.l.) employing organic and inorganic fertilization respectively. Extraction from the peel and flesh was obtained separately, with the following solution: 50% methanol, 50% deionized water and 0.5% acetic acid. The sample was injected into the system UPLC -MS / MS, using ESI ionization (Electrospray Ionization) and fifteen external reference standards. Thirteen metabolites were detected in the flesh and potato peel. The highest content of secondary metabolites (mg/100 g DW) were: Chlorogenic acid (476.82 ± 63.58), neochlorogenic acid (87.90 ± 19.42) caffeic acid (77.53 ± 14.49) and vanillin (11.52 ± 1.38). The PCA (Principal Components Analysis) scores show that the highest concentration of metabolites was found in the peel of both cultivars. We concluded that the native potato Huagalina contains the genes expressed in different biosynthetic pathways of the metabolites found in this study.

For over two decades, there has been an increasing interest in finding natural antioxidants, because they can protect the human body from free radicals and retard the progress of many chronic diseases. Phenolic compounds were identified and quantified in potato cooking water from freeze-dried slices with peel and from whole potato stored for 20 days. Extracts were obtained with an aqueous solution composed of 50% methanol and 0.5% acetic acid. Fifteen secondary metabolites were monitored using the Ultra Performance Liquid Chromatography system coupled to mass spectrometry (UPLC-MS / MS). A calibration curve (from 0.1 ng to 100 μg) was generated and the data was analyzed using the software "MassHunter Workstation" VB 06.00, the results were expressed as mg/100 g of sliced potato or raw potato. Principal Components Analysis (PCA) was performed using XLSTAT 2015 Software. The potato cooking water contains phytonutrients with potential antioxidant activity to prevent non-transmissible degenerative diseases. The metabolite content in the cooking water of the Huagalina native potato is directly related to the freshness of the product before cooking. Potato cooking water could be considered a neutraceutical food. However, further research is required to identify any other substances that can be harmful to health depending on the amount consumed.

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ResumenEl objetivo de este trabajo fue comparar la optimización del proceso de extracción de glucosinolatos totales de harina de maca (Lepidium meyenii) (EGTHM) utilizando SR por diseño Box-Behnken (SRBB) con el de AG, en función a x1: temperatura (ºC), x2: etanol (%), x3: relación solvente/materia prima y x4: tiempo de extracción (min). Se identificó y cuantificó los GT utilizando HPLC. Se evaluaron las variables (x1, x2, x3, x4) que influyen en la extracción utilizando un SRBB con el software Statistica y Wolfram Mathematica para los AG. Del desarrollo del SRBB se obtuvo una ecuación de segundo orden con R 2 = 0,74794, p = 1,88248E-10 << 0,05 con error absoluto medio de 11%; lo que indicó la consistencia del modelo. No fue posible obtener un valor óptimo de la EGTHM utilizando SRBB, por la existencia de dos zonas óptimas debido a la configuración de una superficie tipo silla. Empleando AG se obtuvo después de 2000 iteraciones el valor máximo de la función de 17,0986 μmol de GT/g de HM, el cual se alcanzó con 69,9783 ºC, 70,9540% de etanol, relación solvente/materia prima de 10,0488 en 90 min, lo que demuestra la aplicabilidad de los AG. Palabras clave: algoritmos genéticos, superficie de respuesta, optimización, secado, maca (Lepidium meyenii). AbstractThe aim this work was to compare the extraction process optimization of total glucosinolates of maca flour (Lepidium meyenii) (ETGMF) using RS for Box-Behnken (RSBB) Design with that of GA, according to x1: temperature (°C), x2: ethanol (%), x3: ratio solvent/raw material and x4: extraction time (min). TG were identified and quantified using HPLC. The variables (x1, x2, x3, x4) that influence their extraction were evaluated using a RSBB with the software Statistica and Wolfram Mathematica for the AG. From the development of the RSBB, a second order equation with R 2 = 0.74794, p = 1.88248E-10 << 0.05 with 11% average absolute error was obtained; it showed the consistency of the model. It was not possible to obtain an optimal value of the ETGMF using RSBB because of the existence of two optimal zones due to the configuration of a chair surface. After 2000 iterations using GA, the maximum value of the function of 17.0986 μmol of TG/g of MF was obtained, which was reached with 69.9783 °C, 70.9540 ethanol%, 10.0488 ratio solvent/raw material in 90 min, which demonstrates the applicability of the GA.