Shelf-life prediction models for ready-to-eat fresh cut salads: Testing in real cold chain

2017 ◽  
Vol 240 ◽  
pp. 131-140 ◽  
Author(s):  
Theofania Tsironi ◽  
Efimia Dermesonlouoglou ◽  
Marianna Giannoglou ◽  
Eleni Gogou ◽  
George Katsaros ◽  
...  
Keyword(s):  
Shelf Life ◽  
Life Prediction ◽  
Prediction Models ◽  
Cold Chain ◽  
Shelf Life Prediction ◽  
Fresh Cut

scholarly journals Quality Characteristics Analysis and Remaining Shelf Life Prediction of Fresh Tibetan Tricholoma matsutake under Modified Atmosphere Packaging in Cold Chain

Foods ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 136 ◽  
Author(s):  
Zetian Fu ◽  
Shuang Zhao ◽  
Xiaoshuan Zhang ◽  
Martin Polovka ◽  
Xiang Wang
Keyword(s):  
Shelf Life ◽  
Quality Indicators ◽  
Life Prediction ◽  
Modified Atmosphere Packaging ◽  
Quality Characteristics ◽  
Sensory Characteristics ◽  
Cold Chain ◽  
Tricholoma Matsutake ◽  
Modified Atmosphere ◽  
Shelf Life Prediction

Tricholoma matsutake (T. matsutake) growing in Tibet is very popular for its high economic and medicinal value, but fresh T. matsutake has an extremely short shelf life. The shelf life of T. matsutake is complex, influenced by product characteristics, surrounding environmental conditions, and spoilage development. The objective of this work was to study the quality characteristics of fresh T. matsutake during its shelf life period in modified atmosphere packaging (MAP) conditions and establish its remaining shelf life prediction models in a cold chain. In this study, we measured and analyzed quality indicators of fresh T. matsutake, including hardness (cap, stipe), color, odor of sensory characteristics, pH, soluble solids content (SSC), and moisture content (MC) of physical and chemical characteristics under the temperature condition of 4 °C and relative humidity (RH) of 90%. The sensory evaluation results showed that the odor indicator in sensory characteristics was more sensitive to the freshness of T. matsutake. The changes of pH, SSC, and MC were divided into three periods to analyze the physiological changes of T. matsutake. The cap spread process could affect the changes of pH, SSC, and MC in period S1, and they changed gradually in period S2. In the period S3, they changed complicatedly because of deterioration. The remaining shelf life prediction model of T. matsutake was established by the back propagation (BP) neural network method to quantify the relationship between the quality indicators and the remaining shelf life. The shelf life characteristics are complex, which were optimized by correlation analysis. Significant benefits of this work are anticipated on the transportation and preservation of fresh T. matsutake to the market and the reduction of its losses in the postharvest chain.

Storage time assessment and shelf-life prediction models for postharvest Agaricus bisporus

LWT ◽  
2019 ◽  
Vol 101 ◽  
pp. 360-365 ◽  
Author(s):  
Yuan Song ◽  
Qiuhui Hu ◽  
Yuanyue Wu ◽  
Fei Pei ◽  
Benard Muinde Kimatu ◽  
...  
Keyword(s):  
Shelf Life ◽  
Agaricus Bisporus ◽  
Life Prediction ◽  
Storage Time ◽  
Prediction Models ◽  
Shelf Life Prediction ◽  
Time Assessment

scholarly journals Temperature Dependence of Oxidation Kinetics of Extra Virgin Olive Oil (EVOO) and Shelf-Life Prediction

Foods ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 295 ◽  
Author(s):  
Lanfranco Conte ◽  
Andrea Milani ◽  
Sonia Calligaris ◽  
Pierangela Rovellini ◽  
Paolo Lucci ◽  
...  
Keyword(s):  
Temperature Dependence ◽  
Shelf Life ◽  
Olive Oil ◽  
Life Prediction ◽  
Prediction Models ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Shelf Life Prediction ◽  
Conjugated Trienes ◽  
Increasing Temperature

Producers have to guarantee the extra virgin olive oil (EVOO) quality characteristics reported in the Regulation (CEE) 2568/91 throughout the product shelf-life (SL). Unfortunately, due to the development of oxidative reactions, some quality indices change during storage leading to a progressive deterioration of EVOO quality. To avoid the risk of product downgrading in the virgin oil category, the development of effective shelf-life prediction models is extremely important for the olive oil industry. In this research, the accelerated shelf-life testing (ASLT) protocol was applied to evaluate the temperature dependence of selected oxidation indexes as well as to develop a shelf-life predictive model. The evolution of conventional (peroxide value, K232, K270, polyphenols, tocopherols and hexanal) and unconventional parameters (conjugated trienes and pyropheophytin a) was monitored in bottled EVOO stored in the dark at increasing temperature (25, 40, 50 and 60 °C). Accordingly, for well-packed products with reduced oxygen in headspace, the best shelf-life index allowing the ability to predict EVOO SL turned out to be K270. In addition, pyropheophytin a (%) has been shown to be more sensitive to temperature changes than the secondary oxidation indices, thus suggesting its use as a freshness indicator for storage temperatures higher than 25 °C.

scholarly journals Shelf-life Prediction Modeling and Physicochemical Changes of Canned African Giant Snail (Achachatina achatina) Based Products during Storage Using Sensory and Kinetic Data

2021 ◽  
Vol 10 (2) ◽  
pp. 11
Author(s):  
Elizabeth Ugbede-Ohuoba ◽  
Thomas M. Okonkwo ◽  
Iro Nkama ◽  
J. I. Eze ◽  
Samaila James
Keyword(s):  
Shelf Life ◽  
Kinetic Data ◽  
Life Prediction ◽  
Prediction Models ◽  
Overall Acceptability ◽  
Death Time ◽  
Physicochemical Changes ◽  
Shelf Life Prediction ◽  
Fat Hydrolysis ◽  
Thermal Death Time

Canned African giant snails (brine, egusi and sauce) of low acidity (pH 4.5) were produced using Thermal Death Time (TDT) techniques. Sixty cans of each product were stored for 6 months at normal temperature (30oC). The physicochemical and sensory properties of these products were evaluated monthly. And also, 28 cans of each product were equally evaluated at accelerated storage temperatures stress (50, 60, 70 & 80oC) for 6 weeks. Changes in pH and overall acceptability were described by first and second order kinetics respectively. The extent of proteolysis, peroxidase activities, and extent of fat hydrolysis i.e peroxide and thiobabituric acid values did not change significantly (P > 0.05) during storage. Regression analysis was used to fit models for Gibbs free energy of activation for physicochemical changes as a function of temperature and to predict models for overall acceptability as a function of pH. Shelf-life prediction models were fitted based on sensory and pH kinetic data. Shelf-lives of 27.6 months, 27-25 months and 18-16.8 months were estimated for snail in brine (SIB) snail in egusi (SES) and snail in sauce (SIS) respectively during storage at 30OC.

scholarly journals Shelf Life of Extra Virgin Olive Oil and Its Prediction Models

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xueqi Li ◽  
Selina C. Wang
Keyword(s):  
Shelf Life ◽  
Olive Oil ◽  
Real Time ◽  
Life Prediction ◽  
Prediction Models ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Alpha Tocopherol ◽  
Shelf Life Prediction ◽  
And Storage

Extra virgin olive oil (EVOO), with high unsaturation degree (oleic acid, linoleic acid, and linolenic acid), is prone to oxidation during production and storage even with the presence of abundant antioxidants (e.g., phenolic compounds, alpha-tocopherol, and chlorophyll). The level of oxidation degradation is greatly affected by the EVOO chemical composition (free fatty acids, saturated and unsaturated fat ratio, total phenol content, etc.) and storage conditions (packaging material, oxygen, temperature, and light). With the increasing demand on qualitative acceptability and food safety of an EVOO product, consumers rely heavily on “shelf life” as a good indicator. Hence, it is critical for olive oil producers to provide accurate and practical information on shelf-life prediction. This review analyzes ten shelf-life prediction models that used various parameters and approaches for model establishment. Due to the complexity of chemical interactions between oil phase and environment under real-time storage and rapid accelerated testing conditions, further investigation is needed to scrutinize and minimize the discrepancies between real-time shelf life and predicted shelf life of EVOO products.

scholarly journals Shelf life prediction of carica seeds powder using accelerated method

2021 ◽  
Vol 653 (1) ◽  
pp. 012055
Author(s):  
S D Astuti ◽  
S Lestari ◽  
Erminawati ◽  
S Widarni ◽  
G Wijanarko ◽  
...  
Keyword(s):  
Shelf Life ◽  
Life Prediction ◽  
Shelf Life Prediction
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