תפריט נגישות
ניגודיות עדינהניגודיות גבוהה
מונוכרום
הדגשת קישורים
חסימת אנימציה
פונט קריא
סגור
איפוס הגדרות נגישות
להורדת מודול נגישות חינם תפריט נגישותניגודיות עדינהניגודיות גבוההמונוכרוםהדגשת קישוריםחסימת אנימציהפונט קריאסגוראיפוס הגדרות נגישותלהורדת מודול נגישות חינםSave all to author list
The optimal temperature for postharvest storage of 'Rustenburg' navel oranges is 5 ºC, and any deviations from this temperature may harm fruit quality. The goals of the current study were to examine the effects of different temperature interruptions on the quality of stored 'Rustenburg' navel oranges, and to develop a dynamic fruit-quality prediction model that takes into account any deviations from the optimal storage temperature. The temperature interruptions included exposures to a temperature of 22 ºC for different lengths of time (1, 3 or 7 d) at different time points during the cold-storage period (after 4, 8 or 12 w). The experiment included 126 cartons of 30 fruit each, which were stored under different temperature-interruption regimes. Quality evaluations were conducted every 4 w during a prolonged storage period of up to 24 w. Storage time appeared to be the most important feature affecting fruit quality, followed by the length and the timing of the temperature interruptions. Statistical analysis revealed that storage time significantly affected most of the examined quality parameters; whereas the duration and timing of the temperature interruptions significantly affected fruit weight loss, firmness, internal dryness, shriveling, flavor and quality-acceptance scores. Overall, the longer the temperature interruptions and the later during storage they were applied, the more harmful they were for fruit quality. The collected data was further used to develop dynamic quality-prediction models. It was found that an extreme gradient boosting (XGBoost) model allowed the prediction of fruit acceptance score on a scale of 1–5 with a root mean square error (RMSE) of 0.233 and R2 of 0.876. The XGBoost model also effectively predicted fruit quality at various fictive data points within and outside the tested data set, including different durations and timings of temperature interruptions during the cold-storage period. Dynamic quality-prediction models that consider storage interruptions will assist in assuring produce quality and reducing food losses.
Save all to author list
The optimal temperature for postharvest storage of 'Rustenburg' navel oranges is 5 ºC, and any deviations from this temperature may harm fruit quality. The goals of the current study were to examine the effects of different temperature interruptions on the quality of stored 'Rustenburg' navel oranges, and to develop a dynamic fruit-quality prediction model that takes into account any deviations from the optimal storage temperature. The temperature interruptions included exposures to a temperature of 22 ºC for different lengths of time (1, 3 or 7 d) at different time points during the cold-storage period (after 4, 8 or 12 w). The experiment included 126 cartons of 30 fruit each, which were stored under different temperature-interruption regimes. Quality evaluations were conducted every 4 w during a prolonged storage period of up to 24 w. Storage time appeared to be the most important feature affecting fruit quality, followed by the length and the timing of the temperature interruptions. Statistical analysis revealed that storage time significantly affected most of the examined quality parameters; whereas the duration and timing of the temperature interruptions significantly affected fruit weight loss, firmness, internal dryness, shriveling, flavor and quality-acceptance scores. Overall, the longer the temperature interruptions and the later during storage they were applied, the more harmful they were for fruit quality. The collected data was further used to develop dynamic quality-prediction models. It was found that an extreme gradient boosting (XGBoost) model allowed the prediction of fruit acceptance score on a scale of 1–5 with a root mean square error (RMSE) of 0.233 and R2 of 0.876. The XGBoost model also effectively predicted fruit quality at various fictive data points within and outside the tested data set, including different durations and timings of temperature interruptions during the cold-storage period. Dynamic quality-prediction models that consider storage interruptions will assist in assuring produce quality and reducing food losses.
