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果蔬货架期估算的实时监测系统。

Real-Time Monitoring System for Shelf Life Estimation of Fruit and Vegetables.

机构信息

Systems and Electronics Division Group. ETSII. Universidad Politécnica de Cartagena, 30202 Cartagena, Spain.

Postharvest and Refrigeration Group. ETSIA. Institute of Vegetal Biotechnology. Universidad Politécnica de Cartagena, 30202 Cartagena, Spain.

出版信息

Sensors (Basel). 2020 Mar 27;20(7):1860. doi: 10.3390/s20071860.

DOI:10.3390/s20071860
PMID:32230866
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7180900/
Abstract

The control of the main environmental factors that influence the quality of perishable products is one of the main challenges of the food industry. Temperature is the main factor affecting quality, but other factors like relative humidity and gas concentrations (mainly CH, O and CO) also play an important role in maintaining the postharvest quality of horticultural products. For this reason, monitoring such environmental factors is a key procedure to assure quality throughout shelf life and evaluate losses. Therefore, in order to estimate the quality losses that a perishable product can suffer during storage and transportation, a real-time monitoring system has been developed. This system can be used in all post-harvest steps thanks to its Wi-Fi wireless communication architecture. Several laboratory trials were conducted, using lettuce as a model, to determine quality-rating scales during shelf life under different storage temperature conditions. As a result, a multiple non-linear regression (MNLR) model is proposed relating the temperature and the maximum shelf life. This proposed model would allow to predict the days the commodities will reduce their theoretical shelf-life when an improper temperature during storage or in-transit occurs. The system, developed as a sensor-based tool, has been tested during several land transportation trips around Europe.

摘要

控制影响易腐产品质量的主要环境因素是食品工业的主要挑战之一。温度是影响质量的主要因素,但其他因素,如相对湿度和气体浓度(主要为 CH、O 和 CO),在保持园艺产品采后质量方面也起着重要作用。因此,监测这些环境因素是保证整个货架期质量和评估损失的关键程序。因此,为了估计易腐产品在储存和运输过程中可能遭受的质量损失,开发了一种实时监测系统。该系统得益于其 Wi-Fi 无线通信架构,可用于所有采后步骤。使用生菜作为模型进行了多次实验室试验,以确定在不同储存温度条件下货架期内的质量评级标准。结果,提出了一个与温度和最大货架期相关的多元非线性回归 (MNLR) 模型。当储存或运输过程中温度不当导致商品减少理论货架期时,该模型可用于预测商品减少理论货架期的天数。该系统作为基于传感器的工具开发,已在欧洲各地的几次陆地运输旅行中进行了测试。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48b9/7180900/a4eec716c5d5/sensors-20-01860-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48b9/7180900/3b235e00805e/sensors-20-01860-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48b9/7180900/47fc3ab40fc2/sensors-20-01860-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48b9/7180900/cd9132e30e62/sensors-20-01860-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48b9/7180900/e0869add019f/sensors-20-01860-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48b9/7180900/4f571c1785c2/sensors-20-01860-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48b9/7180900/eef44964d3b8/sensors-20-01860-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48b9/7180900/5bceec776bf1/sensors-20-01860-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48b9/7180900/a4eec716c5d5/sensors-20-01860-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48b9/7180900/3b235e00805e/sensors-20-01860-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48b9/7180900/47fc3ab40fc2/sensors-20-01860-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48b9/7180900/cd9132e30e62/sensors-20-01860-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48b9/7180900/e0869add019f/sensors-20-01860-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48b9/7180900/4f571c1785c2/sensors-20-01860-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48b9/7180900/eef44964d3b8/sensors-20-01860-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48b9/7180900/5bceec776bf1/sensors-20-01860-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48b9/7180900/a4eec716c5d5/sensors-20-01860-g008.jpg

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Impact of transportation, storage, and retail shelf conditions on lettuce quality and phytonutrients losses in the supply chain.运输、储存及零售货架条件对供应链中生菜品质和植物营养素损失的影响
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