Applicability of an enzymatic time temperature integrator as a quality indicator for mushrooms in the distribution chain

Bobelyn, Els; Hertog, Maarten; Nicolaı̈, Bart

doi:10.1016/j.postharvbio.2006.05.011

Cited by 95 publications

(59 citation statements)

References 24 publications

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“…Kinetic study of food products, especially horticultural produce is important due to the fact that the quality of food product is strongly dependent on their temperature exposure history, from production through distribution and storage to consumption (Bobelyn et al, 2006). The Arrhenius relation is a model widely used to describe quality loss and the effect of temperature on different physico-chemical properties (Olivera et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Kinetics of Mango Fruits (Mangifera indica cv. ‘Nam Dok Mai Si Thong’) Quality Changes during Storage at Various Temperatures

Noiwan¹,

Suppakul²,

Joomwong³

et al. 2017

JAS

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Effect of various storage temperatures (13, 20, 27 and 34 °C) on biochemical, physical and physiological changes of mango fruits (Mangifera indica cv. 'Nam Dok Mai Si Thong') was investigated. Mangoes stored at low temperature revealed a decrease in their respiration rates. The lower respiration rates delayed ripening, ethylene production, weight loss, peel and flesh color changes, firmness, and total soluble solid content (SSC) as well as titratable acidity (TA). A second-order kinetic model, a Gaussian model, and a first-order kinetic model fitted well with response quality parameters on firmness, SSC and TA. The Arrhenius function was used to calculate the activation energies (Ea) of mango qualities including firmness, SSC and TA, which were 46.45, 43.05 and 54.22 kJ mol -1 , respectively. These activation energies indicate a moderate temperature sensitivity of ripeness response and represent a good predictive tool for mango quality estimation along the food supply chain. The data reveal that stored mango fruit at 13 °C effectively prolongs the quality attributes and extends the shelf life of mango fruit.

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Section: Introductionmentioning

confidence: 99%

Kinetics of Mango Fruits (Mangifera indica cv. ‘Nam Dok Mai Si Thong’) Quality Changes during Storage at Various Temperatures

Noiwan¹,

Suppakul²,

Joomwong³

et al. 2017

JAS

View full text Add to dashboard Cite

show abstract

“…365-369). The knowledge gained from those studies using TTIs in postharvest handling provides us with the basis for incorporation of the following heuristics into NN models [8,9].…”

Section: Description Of Neural Networkmentioning

confidence: 99%

“…Some of these consumer readable TTI tags were evaluated with greenhouse-grown lettuce in a previous study, and Vitsab was found to be most suitable for lettuce [8]. There are certain problems that need to be corrected before TTI can be used [9]. For example, manual activation of the TTIs may cause variability in the mixing of the lipid substrate and the enzyme.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Modeling to Predict Shelf Life of Greenhouse Lettuce

Lin

Block

2009

Algorithms

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Greenhouse-grown butter lettuce (Lactuca sativa L.) can potentially be stored for 21 days at constant 0°C. When storage temperature was increased to 5°C or 10°C, shelf life was shortened to 14 or 10 days, respectively, in our previous observations. Also, commercial shelf life of 7 to 10 days is common, due to postharvest temperature fluctuations. The objective of this study was to establish neural network (NN) models to predict the remaining shelf life (RSL) under fluctuating postharvest temperatures. A box of 12 -24 lettuce heads constituted a sample unit. The end of the shelf life of each head was determined when it showed initial signs of decay or yellowing. Air temperatures inside a shipping box were recorded. Daily average temperatures in storage and averaged shelf life of each box were used as inputs, and the RSL was modeled as an output. An R 2 of 0.57 could be observed when a simple NN structure was employed. Since the "future" (or remaining) storage temperatures were unavailable at the time of making a prediction, a second NN model was introduced to accommodate a range of future temperatures and associated shelf lives. Using such 2-stage NN models, an R 2 of 0.61 could be achieved for predicting RSL. This study indicated that NN modeling has potential for cold chain quality control and shelf life prediction.

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“…Food degradation is related to environmental conditions (such as temperature and humidity), intrinsic properties (such as microbial contamination, and composition) and the time that food products are exposed to these conditions (Van der Vorst et al, 2007). Several studies have been conducted on food quality decay modeling and time-temperature impact of the quality throughout the distribution (Sloof et al, 1996;Taoukis and Labuza, 1999;Schouten et al, 2006;Bobelyn et al, 2006). Van Donk et al (2008) suggests that existing logistics theories, frameworks and methodologies have weaknesses in a food supply chain context.…”

Section: Introductionmentioning

confidence: 99%