Mango shelf-life modelling

被引：0

作者：

Chen, Y. ^{[1
]}

Joyce, D. C. ^{[2
,3
]}

White, N. A. ^{[4
]}

Bryant, P. ^{[5
]}

Valida, A. ^{[3
]}

Duong, H. V. ^{[5
]}

Macnish, A. J. ^{[5
]}

Smith, L. ^{[5
]}

机构：

[1] Ecosci Precinct, Dept Agr & Fisheries, Brisbane, Qld, Australia

[2] Gatton Res Facil, Dept Agr & Fisheries, Lawes, Australia

[3] Univ Queensland, Sch Agr & Food Sci, Gatton, Australia

[4] Leslie Res Facil, Dept Agr & Fisheries, Rockville, MD USA

[5] Maroochy Res Facil, Dept Agr & Fisheries, Nambour, Australia

来源：

XXXI INTERNATIONAL HORTICULTURAL CONGRESS, IHC2022: INTERNATIONAL SYMPOSIUM ON POSTHARVEST TECHNOLOGIES TO REDUCE FOOD LOSSES | 2023年 / 1364卷

关键词：

dry matter; harvest time; postharvest; prediction; supply chain; QUALITY; FRUIT;

D O I：

10.17660/ActaHortic.2023.1364.19

中图分类号：

F3 [农业经济];

学科分类号：

0202 ; 020205 ; 1203 ;

摘要：

Short shelf-life of fresh produce can disappoint wholesalers, retailers, and consumers, damage reputations, and lessen repeat purchases. With contemporary technologies, monitoring supply chain conditions from farm to retail is accessible to supply chain stakeholders. Thereby, agile decision-making in a 'first expired first out' context is enabled by real time data collection. Remaining shelf-life (RSL) models were developed for mango fruit using data collected in laboratory trials simulating realworld export air and sea freight conditions. Storage duration and temperature matrices covering a range of potential export scenarios and conditions were applied for 'R2E2' and ' Kensington Pride' (KP) mango cultivars. Harvest time and a phytosanitary treatment were also considered. Models were trained and validated with split data set (70 and 30%, respectively) and verified with real world shipment monitoring data. Simulation experiments showed that specific regression models were required to account for differences between cultivars and between harvest times ('early' or 'late'). Shelf-life prediction intervals ( PI) at the 90% confidence level were +/- 3.2 days for 'R2E2' and +/- 2.8 days for 'KP', respectively. Root mean square errors (RMSE) at 90% were 4.1 days for 'R2E2' and 4.8 days for 'KP'. Dry matter content at harvest as a co-variable inconsistently improved RSL predictions. A web-based dashboard and a mobile phone application were developed to demonstrate RSL modelling to industry.

引用

页码：151 / +

页数：8

共 28 条

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