‘Ataulfo’ mango maturity index prediction using the AS7262 spectral sensor

Cano, David Vargas;

Schlam, Federico Félix Hahn;

De La O, José Luis Rodríguez;

Priego, Alejandro Facundo Barrientos;

Abstract ‘Ataulfo’ mango is highly produced in Mexico, being harvested when it reaches its physiological maturity. This process takes at least another month for the fruit to reach consumption maturity. Warehouses and markets present important losses as the ready-to-eat status is unknown. Maturity status is determined by measuring slow and destructive physicochemical variables. An optical device based on the AS7262 spectral sensor was connected to the ESP32 microcontroller and measurements were correlated with soluble solids content (SSC), dry matter (DM) and firmness of mangoes obtained from the local market. Data analysis was carried out by partial least squares (PLS) regression, classification, regression tree (CART) and random forest (RF) models. With PLS, SST and firmness were predicted with R2 of 0.61 and 0.76, respectively. The root mean squared error of prediction (RMSEP) was 0.91 for SSC and 0.67 for firmness. With the CART model, classification accuracy was 90% for SSC and 87% for firmness of intact mango fruits.Cano, David Vargas;

Schlam, Federico Félix Hahn;

De La O, José Luis Rodríguez;

Priego, Alejandro Facundo Barrientos;

Abstract ‘Ataulfo’ mango is highly produced in Mexico, being harvested when it reaches its physiological maturity. This process takes at least another month for the fruit to reach consumption maturity. Warehouses and markets present important losses as the ready-to-eat status is unknown. Maturity status is determined by measuring slow and destructive physicochemical variables. An optical device based on the AS7262 spectral sensor was connected to the ESP32 microcontroller and measurements were correlated with soluble solids content (SSC), dry matter (DM) and firmness of mangoes obtained from the local market. Data analysis was carried out by partial least squares (PLS) regression, classification, regression tree (CART) and random forest (RF) models. With PLS, SST and firmness were predicted with R2 of 0.61 and 0.76, respectively. The root mean squared error of prediction (RMSEP) was 0.91 for SSC and 0.67 for firmness. With the CART model, classification accuracy was 90% for SSC and 87% for firmness of intact mango fruits. Leer más