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

Fu, Zetian; Zhao, Shuang; Zhang, Xiaoshuan; Polovka, Martin; Wang, Xiang

doi:10.3390/foods8040136

Cited by 20 publications

(13 citation statements)

References 19 publications

(24 reference statements)

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“…Besides, enzyme-dependent nucleic acid detection involves harsh storage and reaction conditions of the enzyme. The enzyme-dependent inspection cycle is usually long, which forms an obvious contradiction with the short shelf life of imported cold-chain food [ 44 ]. It is necessary to contemplate whether methods using enzyme reagents are suitable for on-site screening of cold-chain foods.…”

Section: Characteristics For the Cold-chain Food Quarantinementioning

confidence: 99%

The Safety of Cold-Chain Food in Post-COVID-19 Pandemic: Precaution and Quarantine

Kong

et al. 2022

Foods

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Since the outbreak of coronavirus disease-19 (COVID-19), cold-chain food contamination caused by the pathogenic severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has attracted huge concern. Cold-chain foods provide a congenial environment for SARS-CoV-2 survival, which presents a potential risk for public health. Strengthening the SARS-CoV-2 supervision of cold-chain foods has become the top priority in many countries. Methodologically, the potential safety risks and precaution measures of SARS-CoV-2 contamination on cold-chain food are analyzed. To ensure the safety of cold-chain foods, the advances in SARS-CoV-2 detection strategies are summarized based on technical principles and target biomarkers. In particular, the techniques suitable for SARS-CoV-2 detection in a cold-chain environment are discussed. Although many quarantine techniques are available, the field-based quarantine technique on cold-chain food with characteristics of real-time, sensitive, specific, portable, and large-scale application is urgently needed.

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Section: Characteristics For the Cold-chain Food Quarantinementioning

confidence: 99%

The Safety of Cold-Chain Food in Post-COVID-19 Pandemic: Precaution and Quarantine

Kong

et al. 2022

Foods

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“…From Table 1, it was obvious that the two dielectric properties (Zs and θ) showed a strong correlation with firmness. A traditional back propagation (BP) neural network, which was multilayered and had several hidden layers in addition to input and output layers (Figure 4), was used to explain the correlation between them [33,34]. The original data of Zs and θ were set as the inputs, and the firmness was set as the output for the BP neural network established in this study.…”

Section: Correlation Matrixmentioning

confidence: 99%

A Comprehensive Peach Fruit Quality Evaluation Method for Grading and Consumption

Zhang

et al. 2020

Applied Sciences

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Featured Application: Based on principal component analysis (PCA), an evaluation and grading model for fresh peaches was developed to provide guidance for the selection of fresh peaches for the consumer market. The approach and findings presented here may be useful for effective evaluation and grading during real-world fruit production; they can potentially improve processing efficiency, reduce costs, and minimize waste in an automated quality evaluation system. Abstract: Peaches are a popular fruit appreciated by consumers due to their eating quality. Quality evaluation of peaches is important for their processing, inventory control, and marketing. Eleven quality indicators (shape index, volume, mass, density, firmness, color, impedance, phase angle, soluble solid concentration, titratable acidity, and sugar-acid ratio) of 200 peach fruits (Prunus persica (L.) Batsch "Spring Belle") were measured within 48 h. Quality indicator data were normalized, outliers were excluded, and correlation analysis showed that the correlation coefficients between dielectric properties and firmness were the highest. A back propagation (BP) neural network was used to predict the firmness of fresh peaches based on their dielectric properties, with an overall fitting ratio of 86.9%. The results of principal component analysis indicated that the cumulative variance of the first five principal components was 85%. Based on k-means clustering analysis, normalized data from eleven quality indicators in 190 peaches were classified into five clusters. The proportion of red surface area was shown to be a poor basis for picking fresh peaches for the consumer market, as it bore little relationship with the comprehensive quality scores calculated using the new grading model.

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“…Additionally, as a mathematical method with strong self-learning, self-adaptation, and other abilities [ 20 ], the BP neural network has been widely used in various food supply chains [ 21 , 22 ]. Whereas, conventional quality evaluation and the BP neural network have not realized the comprehensive evaluation and prediction of the critical influencing factors in the cold chain, resulting in indistinct and oversimplified final results [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Dynamic Quality Prediction of Chilled Chicken with Integrated IoT Flexible Sensing and Knowledge Rules Extraction

Stankovski

et al. 2022

Foods

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With the enhancement of consumers’ food safety awareness, consumers have become more stringent on meat quality. This study constructs an intelligent dynamic prediction model based on knowledge rules and integrates flexible humidity sensors into the non-destructive monitoring of the Internet of Things to provide real-time feedback and dynamic adjustments for the chilled chicken cold chain. The optimized sensing equipment can be attached to the inside of the packaging to deal with various abnormal situations during the cold chain, effectively improving the packaging effect. Through correlation analysis of collected data and knowledge rule extraction of critical factors in the cold chain, the established quality evaluation and prediction model achieved detailed chilled chicken quality level classification and intelligent quality prediction. The obtained results show that the accuracy of the prediction model is higher than 90.5%, and all the regression coefficients are close to 1.00. The relevant personnel (workers and cold chain managers) were invited to participate in the performance analysis and optimization suggestion to improve the applicability of the established prediction model. The optimized model can provide a more efficient theoretical reference for timely decision-making and further e-commerce management.

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

Cited by 20 publications

References 19 publications

The Safety of Cold-Chain Food in Post-COVID-19 Pandemic: Precaution and Quarantine

The Safety of Cold-Chain Food in Post-COVID-19 Pandemic: Precaution and Quarantine

A Comprehensive Peach Fruit Quality Evaluation Method for Grading and Consumption

Intelligent Dynamic Quality Prediction of Chilled Chicken with Integrated IoT Flexible Sensing and Knowledge Rules Extraction

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