Effect of Freshness Protection Package on Storage and Preservation of Chinese Cabbage

The effect of freshness protection package on Chinese cabbage placed in the self-made shelf in ventilated storage was investigated, and the physiological-qual-ity indexes change were determined during the storage period. The results showed that the losses of weight and vitamin C were significantly reduced. The accumula-tion of crude fiber was effectively inhibited. But protein content was maintained at a low level and the rotting more serious of Chinese cabbage at the end of the stor-age packaged with the fresh keeping bags.

Effects of Salinity and Freezing on Freshness of Crucian

Salt tolerance and freeze preservation of crucian are always important concerns of people. There are rare reports on effect of different salinity on fresh- ness and quality. However, water salinity will directly affect osmotic pressure in fish, thereby affecting meat quality. In this study, the tolerance of crucian to different salinities （1‰, 2‰, 5‰, 8‰, 10‰ and 15‰） was investigated. In addition, the ef- fect of different salinity on freezing freshness and quality of crucian was studied. The results showed that the tolerance threshold of crucian to salinity was about 8‰, and the freezing quality of crucian in water with salinity of ‰ was significantly better than that in fresh water.

Electronic Nose with an Air Sensor Matrix for Detecting Beef Freshness

China is one of the largest meat producing countries in the wodd. With the growing concern for food safety more attention has been paid to meat quality. The application of conventional test methods for meat quality is limited by many factors, and subjectiveness, such as longer time to prepare samples and to test. A sensor matrix was constructed with several separate air sensors, and tests were conducted to detect the freshness of the beef. The results show that the air sensors TGS2610, TGS2600, TGS2611, TGS2620 and TGS2602 made by Tianjin Figaro Electronic Co, Ltd could be used to determine the degree of freshness but TGS2442 is not suitable. This study provides a foundation for designing and making an economical and practical detector for beef freshness.

Application of Lactic Acid Bacteria(LAB) in Freshness Keeping of Tilapia Fillets as Sashimi

Aquatic products are extremely perishable food commodities. Developing methods to keep the freshness of fish represents a major task of the fishery processing industry. Application of Lactic Acid Bacteria(LAB) as food preservative is a novel approach. In the present study, the possibility of using lactic acid bacteria in freshness keeping of tilapia fillets as sashimi was examined. Fish fillets were dipped in Lactobacillus plantarum 1.19(obtained from China General Microbiological Culture Collection Center) suspension as LAB-treated group. Changes in K-value, APC, sensory properties and microbial flora were analyzed. Results showed that LAB treatment slowed the increase of K-value and APC in the earlier storage, and caused a smooth decrease in sensory score. Gram-negative bacteria dominated during refrigerated storage, with Pseudomonas and Aeromonas being relatively abundant. Lactobacillus plantarum 1.19 had no obvious inhibitory effect against these Gram-negatives. However, Lactobacillus plantarum 1.19 changed the composition of Gram-positive bacteria. No Micrococcus were detected and the proportion of Staphylococcus decreased in the spoiled LAB-treated samples. The period that tilapia fillets could be used as sashimi material extended from 24 h to 48 h after LAB treatment. The potential of using LAB in sashimi processing was confirmed.

Fruits and Vegetables Freshness Categorization Using Deep Learning

The nutritional value of perishable food items,such as fruits and vegetables,depends on their freshness levels.The existing approaches solve a binary class problem by classifying a known fruit\vegetable class into fresh or rotten only.We propose an automated fruits and vegetables categorization approach that first recognizes the class of object in an image and then categorizes that fruit or vegetable into one of the three categories:purefresh,medium-fresh,and rotten.We gathered a dataset comprising of 60K images of 11 fruits and vegetables,each is further divided into three categories of freshness,using hand-held cameras.The recognition and categorization of fruits and vegetables are performed through two deep learning models:Visual Geometry Group(VGG-16)and You Only Look Once(YOLO),and their results are compared.VGG-16 classifies fruits and vegetables and categorizes their freshness,while YOLO also localizes them within the image.Furthermore,we have developed an android based application that takes the image of the fruit or vegetable as input and returns its class label and its freshness degree.A comprehensive experimental evaluation of proposed approach demonstrates that the proposed approach can achieve a high accuracy and F1score on gathered FruitVeg Freshness dataset.The dataset is publicly available for further evaluation by the research community.

A Cooperative Schema between Web Sever and Search Engine for Improving Freshness of Web Repository

Because the web is huge and web pages are updated frequently, the index maintained by a search engine has to refresh web pages periodically. This is extremely resource consuming because the search engine needs to crawl the web and download web pages to refresh its index, Based on present technologies of web refreshing, we present a cooperative schema between web server and search engine for maintaining freshness of web repository. The web server provides metadata defined through XML standard to describe web sites. Before updating the web page the crawler visits the meta-data files. If the meta-data indicates that the page is not modified, then the crawler will not update it. So this schema can save bandwidth resource. A primitive model based on the schema is implemented. The cost and efficiency of the schema are analyzed.

Developing a Machine Vision System Equipped with UV Light to Predict Fish Freshness Based on Fish-Surface Color

This study assessed the feasibility of developing a machine vision system equipped with ultraviolet (UV) light, using changes in fish-surface color to predict aerobic plate count (APC, a standard freshness indicator) during storage. The APC values were tested and images of the fish surface were taken when fish were stored at room temperature. Then, images’ color-space conversion among RGB, HSV, and L*a*b* color spaces was carried out and analyzed. The results revealed that a* and b* values from the UV-light image decreased linearly during storage. A further regression analysis of these two parameters with APC value demonstrated a good exponential relationship between the a* value and the APC value (R2 = 0.97), followed by the b* (R2 = 0.85). Therefore, our results suggest that the change in color of the fish surface under UV light can be used to assess fish freshness during storage.

Development,Characterization and Application of a Three-Layer Intelligent pH-Sensing Indicator Based on Bromocresol Green(BCG)for Monitoring Fish Freshness

As a novel food quality monitoring technology,intelligent freshness indicator has received wide attention in recent years.However,its poor safety and stability are the main problems hindering its practical application.Hence a new pH-sensing indicator based on bromocresol green(BCG)was developed in this study for nondestructive and real-time monitoring the freshness of marine fishes.The indicator was designed with a three-layer structure,using the polytetrafluoroethylene(PTFE)membrane with high hydrophobicity and air permeability as the inner layer to isolate the moisture in the package,BCG-coated filter paper as the colorchanging layer to indicate the freshness of fish,and a transparent unidirectional permeable(TUP)membrane with moisture resistance as the out layer to isolate the moisture in the environment.This contributed to weaken the influence of humidity and prevent dye migration,so as to improve the accuracy and safety of the indicator.Therefore,a highly sensitive and distinguished color variation response to trimethylamine(TMA)standard solution with different concentrations was observed on the indicator.Additionally,the indicator showed a high color stability at different storage temperatures up to 14 days with total color differences(ΔE)less than 5.0.The indicator presented visible color variations from yellow to green then eventually to blue when applied to monitor the freshness of sea bass and salmon stored at 4℃,implying that fish was spoiled.Meanwhile,indicatorsΔE value was significantly positively correlated with total volatile basic nitrogen(TVB-N)and total viable count(TVC)in sea bass and salmon samples.Thus,the pH-sensing indicator can be applied as a cost-effective and promising intelligent indicator for monitoring fish freshness.

A New Method for the Non-destructive Determination of Fish Freshness by Nuclear Imaging

A new method for determining the freshness of harvested carp using nuclear imaging technology is presented.This research attempted to determine the freshness of carp by measuring the radioactive area increase (RAI) value of fish.The carp samples were maintained under refrigerated condition for 12d, and RAI and total volatile base nitrogen (T-VBN)values were determined during the storage. The results showed that the RAI value increased with the T-VBN content duringthe storage. Also, there is a significant correlation between T-VBN content and RAI value of the carp kept in refrigeratedcondition. The results of this study suggest that the RAI value could be utilized to evaluate rapidly the freshness of carp.

Information Freshness-Oriented Trajectory Planning and Resource Allocation for UAV-Assisted Vehicular Networks

In this paper,multi-UAV trajectory planning and resource allocation are jointly investigated to improve the information freshness for vehicular networks,where the vehicles collect time-critical traffic information by on-board sensors and upload to the UAVs through their allocated spectrum resource.We adopt the expected sum age of information(ESAoI)to measure the network-wide information freshness.ESAoI is jointly affected by both the UAVs trajectory and the resource allocation,which are coupled with each other and make the analysis of ESAoI challenging.To tackle this challenge,we introduce a joint trajectory planning and resource allocation procedure,where the UAVs firstly fly to their destinations and then hover to allocate resource blocks(RBs)during a time-slot.Based on this procedure,we formulate a trajectory planning and resource allocation problem for ESAoI minimization.To solve the mixed integer nonlinear programming(MINLP)problem with hybrid decision variables,we propose a TD3 trajectory planning and Round-robin resource allocation(TTPRRA).Specifically,we exploit the exploration and learning ability of the twin delayed deep deterministic policy gradient algorithm(TD3)for UAVs trajectory planning,and utilize Round Robin rule for the optimal resource allocation.With TTP-RRA,the UAVs obtain their flight velocities by sensing the locations and the age of information(AoI)of the vehicles,then allocate the RBs to the vehicles in a descending order of AoI until the remaining RBs are not sufficient to support another successful uploading.Simulation results demonstrate that TTP-RRA outperforms the baseline approaches in terms of ESAoI and average AoI(AAoI).

Information freshness optimization of multiple status update streams in Internet of things:Generation rate control and service rate reservation

The Internet of things(IoT)has become a key infrastructure providing up-to-date and fresh information for policy analysis and decision-making of upper-layer applications.However,there are limited sensing and communication resources in IoT devices,which significantly affects the timeliness and freshness of the updated status.This work proposes two schemes,namely,the generation rate control and service rate reservation schemes,to improve the overall information freshness of multiple status update streams at the receiver.Specifically,using the recently proposed Age of Information(AoI)as the metric for evaluating information freshness,we characterized the overall information freshness,i.e.,the overall average AoI at the receiver for both schemes,by considering the urgency difference of status update and streams.Both schemes for status updates and streams,respectively,were formulated as two optimization problems.We proved that both problems are convex and the optimal generation and service rates for different streams are found by the standard convex optimization algorithm.Moreover,we proposed both approximate optimal generation and approximate optimal service rate for fast deployment in heavy and light load cases.Numerical results verify the theoretical findings and accuracy of the proposed approximate solutions,guiding the design and deployment of IoT.

Effect of supercooling storage applied with stepwise algorithm for fishes(salmon and olive flounder)and its freshness during extended storage

Supercooling storage reduces the temperature of a product by lowering its freezing point without phase transition and may extend its shelf life.However,it is difficult to maintain the supercooled state of food as it is thermodynamically metastable.A slow cooling rate and minimal fluctuation are essential for achieving stable supercooling storage.Therefore,a stepwise algorithm was adopted for supercooling storage in this study.Salmon and olive flounder were stored at 3℃(refrigeration),􀀀18℃(freezing),and􀀀2℃(supercooling)for 12 days.Samples were maintained in a supercooled state and unfrozen during the storage period.Samples stored after the supercooling treatment were superior with respect to drip loss and water holding capacity(WHC)compared to frozen samples,regardless of the type of sample.WHC and total volatile basic nitrogen values of olive flounder was higher than those in salmon owing to the higher water and protein content in olive flounder than in salmon.Moreover,the supercooled samples inhibited the increase in trimethylamine and volatile basic nitrogen levels.Microbial growth was slow.Thus,a stepwise algorithm for stable supercooled storage was achieved,which effectively preserved fish quality better than freezing and refrigeration storage.

Fabrication of cellulose acetate membrane for monitoring freshness of minimally processed pomegranate(Punica granatum)arils

This study examined the freshness of minimally processed pomegranate arils(MPPAs)under refrigeration conditions(5±1◦C)stored in a polypropylene(PP)container sealed with PP nanocomposite multi-layered film(PP-NMF).The freshness indicator was fabricated using cellulose acetate coated with a combination of colorimetric pH dyes,which included bromocresol green(BCG),bromothymol blue(BTB)and methyl red(MR)in a ratio of 6:9:15.The fabricated indicator labels were attached inside the package headspace and observed an increase in CO_(2)(0.1±0.01 to 0.9±0.03%v/v)and a decrease in O_(2)(19.6±0.01 to 18.8±0.03%v/v)during storage(0-18 days).Color change of indicator labels was mainly caused by headspace concentrations that changed from dark to colorless for fresh and spoiled pomegranate arils.The PP-NMF used in MPPAs has high barrier properties,extending its shelf life in the refrigerator to over 15 days.The pomegranate arils stored for different periods of time showed significant changes in their physicochemical parameters.Over storage days,aerobic mesophilic bacteria(AMB)and yeast and mold count(YMC)increased to 4.90±0.20 and 4.50±0.20 log CFU g􀀀1.An indicator developed for monitoring pomegranate arils stored in the refrigerator can detect freshness efficiently.

High-sensitivity intelligent packaging films harnessing rose anthocyanins and hydrophilic silica aerogel for visual food freshness monitoring

The enhancement of the sensitivity for anthocyanin-based indicator films in food freshness monitoring in real time is important for application.In this study,hydrophilic silica aerogel(SiO2 NA)was incorporated into corn starch(CS)/chitosan(CH)/rose anthocyanins(RACNs)-encapsulated potato amylopectin nanoparticles(APNPs)composite film to increase the sensitivity for shrimp freshness detection.The microstructure of films revealed that the gas absorption capacity was improved by amorphous SiO2 NA via hydrogen interactions.The pore size(1.74–5.60 times),pore volume(3.92–5.60 times),and specific surface area(2.21–2.34 times)of films increased with the addition of SiO2 NA.The sensing of NH3 and pH and the reversibility of films were also reinforced.Meanwhile,the pH-responsive films containing SiO2 NA changed visibly in color from purple–red to orange–gray and finally to gray,enabling effective monitoring of shrimp freshness during storage at 4°C.Thus,anthocyanin-based indicator films with improved sensitivity by adding SiO2 NA were successfully designed for monitoring shrimp freshness.

Colorimetric indicator based on zwitterionic anti-freezing hydrogel and alizarin for visual monitoring of salmon fillets freshness

Indicating hydrogels have shown the advantages of being fast, sensitive, and real-time in demonstrating the freshness of aquatic products due to their noticeable color change. In this work, an anti-freezing hydrogel embedded with pH-sensitive alizarin was developed as an original colorimetric indicator for monitoring the corruption of aquatic products at low temperatures. The anti-freezing hydrogel was prepared from 2-carboxyethyl acrylate, [2-(methacryloyloxy)ethyl]-trimethylammonium chloride, and 2-hydroxyethyl methacrylate monomers with triethylene glycol dimethacrylate as cross-linking agent. The hydrogel showed excellent anti-freezing capability, which was further incorporated with the pH-sensitive dye alizarin to gain the indicating capability. When applied in the indication of the freshness of salmon under low-temperature storage, it was found that the color changes of the indicator corresponded with the total volatile basic nitrogen contents of salmon, the correlation coefficient of which was 0.959 6. These results indicated the application potential of the hydrogels as freshness indicators during low-temperature storage or cold-chain transportation.

Determination of royal jelly freshness by ELISA with a highly specific anti-apalbumin 1,major royal jelly protein 1 antibody

Major royal jelly protein 1（MRJP1）, designated apalbumin 1, has been regarded as a freshness marker of royal jelly（RJ）. A MRJP1-specific peptide（IKEALPHVPIFD） identified by bioinformatics analysis of homologous members of the major royal protein family was synthesized and used to raise polyclonal anti-MRJP1 antibody（antiSP-MRJP1 antibody）. Western blot analysis showed that anti-SP-MRJP1 antibody only reacted with MRJP1 in RJ. In contrast, the previously reported antibody against recombinant MRJP1（anti-R-MRJP1 antibody） reacted with other members of MRJP family in RJ. Enzyme-linked immunosorbent assay（ELISA） using anti-SP-MRJP1 antibody demonstrated that MRJP1 content in RJ stored at 40 °C significantly degraded by 37.3%, 55.9%, 58.0%, 60.6%, 65.7%, 72.7%, and 73.1% at 7, 14, 21, 28, 35, 42, and 49 d, respectively, when compared with MRJP1 content in fresh RJ（0 d）. Optical density analysis of MRJP bands from sodium dodecyl sulfate-polyacrylamide gel electrophoresis（SDS-PAGE） profiles demonstrated that the degradation of MRJP1, MRJP2, MRJP3, and MRJP5 in RJ was strongly and positively correlated with the period of storage（P〈0.0001）. Our results indicated anti-SP-MRJP1 antibody was highly specific for MRJP1, and ELISA using the antibody is a sensitive and easy-to-use method to determine the freshness and authenticity of RJ.

Development of predictive models for egg freshness and shelf-life under different storage temperatures

The objective of the present study was to develop models for egg freshness and shelf-life predictions for the selected evaluation indicators including egg weight,Flaugh unit(HU),and albumen height.Experiments were carried out at different storage temperatures for a total period of 29-32 d.All data were collected and fitted in to Arrhenius equation for egg freshness,while the HU data were applied to a probability model for shelf-life prediction.The results showed that egg weight,albumen height,and HU decreased significantly,while albumen pH increased with the extension of storage time.The higher the storage temperature,the faster the egg quality decreased.In addition,the bias factor,accuracy factor,and the standard error of prediction were selected to verify the developed quality models.Maximum rescaled R-square statistic,the Hosmer-Lemeshow goodness-of-fit statistic,and the receiver operating characteristic curve were used to evaluate the goodness-of-fit of the developed probability model for the shelf-life of eggs,which indicated that the presented predictive models can be used to assess egg freshness and predict shelf-life during different storage temperatures.

Combining MobileNetV1 and Depthwise Separable convolution bottleneck with Expansion for classifying the freshness of fish eyes

Image classification using Convolutional Neural Network(CNN)achieves optimal perfor-mance with a particular strategy.MobileNet reduces the parameter number for learning features by switching from the standard convolution paradigm to the depthwise separable convolution(DSC)paradigm.However,there are not enough features to learn for identify-ing the freshness of fish eyes.Furthermore,minor variances in features should not require complicated CNN architecture.In this paper,our first contribution proposed DSC Bottle-neck with Expansion for learning features of the freshness of fish eyes with a Bottleneck Multiplier.The second contribution proposed Residual Transition to bridge current feature maps and skip connection feature maps to the next convolution block.The third contribu-tion proposed MobileNetV1 Bottleneck with Expansion(MB-BE)for classifying the freshness of fish eyes.The result obtained from the Freshness of the Fish Eyes dataset shows that MB-BE outperformed other models such as original MobileNet,VGG16,Densenet,Nasnet Mobile with 63.21%accuracy.

A statistical analysis of the freshness of postharvest leafy vegetables with application of water based on chlorophyll fluorescence measurement

Vegetable freshness is very important for both restaurant and home consumers.In market,sellers frequently apply water to leafy vegetables to make them not lose weight and look fresh;however,these vegetables may not be stored for a long time as they appear.After a time limit,they may be quickly rotten.It is thus meaningful to investigate early and simple detection tools to measure leafy vegetable freshness while they are frequently applied water in selling.In this work,three types of newly harvested leafy vegetables were bought from a local farmer market and stored in the air with room temperature and roots submerging in water.Chlorophyll a fluorescence(ChlF)from the vegetables was measured each half a day for three days.The obtained ChlF data were analyzed statistically and the correlation of ChlF parameters and vegetable freshness/storage time was obtained.The k-mean classification was also performed.It is found that Fo,Fj,Fm/Fo,and Fv/Fm can be used as an early detection tool to differentiate the freshness of leafy vegetables on which water is constantly applied in storage without visible difference.

A sensitive xanthine oxidase electrode with silk net for estimating fish freshness

An enzyme biosensor was constructed using a plate platinum electrode and immobilized xanthine oxidase (XOD). Only a very small quantity of enzyme was chemically immobilized on a special silk net. Hydrogen peroxide released during the enzymatic reaction was detected by the electrode at +0.65 V (vs. Ag/AgCl). The electrode was very sensitive to hypoxanthine and its detection limit was 1X10(-7) mol/L. When it was applied to the determination of fish freshness, the results agreed well with those obtained by traditional methods-determination of total volatile basic nitrogen (TVB-N) and microbial count. A range for estimating the freshness of river fish was suggested.