Mealiness is a textural attribute related to internal fruit disorder that is characterized by the combination of abnormal softness of the fruit and absence of free juiciness in the mouth when eaten by the consumer. Timeresolved laser reflectance spectroscopy was used as a tool to determine mealiness. This new technique in agrofood research may provide physical and chemical information independently and simultaneously, which is relevant to characterize mealiness. Using visible and near infrared lasers as light sources, time-resolved laser reflectance spectroscopy was applied to Golden Delicious and Cox apples (n = 90), to characterize batches of untreated samples and samples that were stored under conditions that promote the development of mealiness (20C & 95% RH). The collected database was clustered into different groups according to their instrumental test values. The optical coefficients were used as explanatory variables to build discriminant functions for mealiness. The performance of the classification models created ranged from 47 to 100% of correctly identified mealy versus nonmealy apples.
Mealiness detection In apples using time resolved reflectance spectroscopy
R Cubeddu;A Pifferi;P Taroni;G Valentini;
2005
Abstract
Mealiness is a textural attribute related to internal fruit disorder that is characterized by the combination of abnormal softness of the fruit and absence of free juiciness in the mouth when eaten by the consumer. Timeresolved laser reflectance spectroscopy was used as a tool to determine mealiness. This new technique in agrofood research may provide physical and chemical information independently and simultaneously, which is relevant to characterize mealiness. Using visible and near infrared lasers as light sources, time-resolved laser reflectance spectroscopy was applied to Golden Delicious and Cox apples (n = 90), to characterize batches of untreated samples and samples that were stored under conditions that promote the development of mealiness (20C & 95% RH). The collected database was clustered into different groups according to their instrumental test values. The optical coefficients were used as explanatory variables to build discriminant functions for mealiness. The performance of the classification models created ranged from 47 to 100% of correctly identified mealy versus nonmealy apples.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
