Fatty Acids and Minerals as Markers Useful to Classify Hass Avocado Quality: Ripening Patterns, Internal Disorders, and Sensory Quality

Abstract

Hass avocado quality varies by origin, season, and production practices. However, there is a lack of methodologies to guarantee that fruit reaching the market has consistent quality. The aim of this work was to identify predictive markers for quality management. Fruit samples produced under different nutrient management, elevation, date-to-harvest, and growth cycle conditions were analyzed. Dry matter, oil content, internal disorders, sensory attributes, minerals, and fatty acids were evaluated as quality variables. The results highlighted soil and weather differences among orchards. Nutrient management practices based on index balancing in some samples increased both productivity and fruit size. High variability was observed in the dry matter related to the age of the fruit at harvest. Ripening heterogeneity was very large in low-elevation orchards where the fruit was picked relatively early. High flesh mineral contents delayed fruit ripening. At low growing temperatures, more oleic and linoleic acids were present in fruits. The sensory texture and taste descriptors were affected by the fruit age and related to the flesh composition. Logistic, PLS-DA, and biplot models effectively represented the variabilities in the ripening pattern, composition, and sensory profile of avocado fruits and allowed the samples to be grouped according to the internal fruit quality.

Quantification of Dry Matter Content in Hass Avocado by Near-Infrared Spectroscopy (NIRS) Scanning Different Fruit Zones

“…Consequently, orchards that harvest Hass avocados within a short development period (162-176 days) tend to yield poor-quality fruits. Furthermore, a wide variation in dry matter content has been observed in commercial Hass avocado crops [37]. In Colombia, the DM content has been observed to range between 18 and 28% (120 days to 215 days of fruit development, respectively).…”

“…This deviation, however, did not significantly impact the overall quality. On the other hand, models with high SEP values (>2%) resulting in a prediction error of more than 30 days would have a substantial impact on quality, particularly in terms of underestimating the dry matter content [37]. This scenario would result in harvesting fruits with insufficient maturity levels, failing to meet market expectations.…”