The cold chain is responsible for the preservation and transportation of perishable foods in the proper temperature range to slow biological decay processes and deliver safe and high‐quality foods to ...consumers. Studies show that the efficiency of the cold chain is often less than ideal, as temperature abuses above or below the optimal product‐specific temperature range occur frequently, a situation that significantly increases food waste and endangers food safety. In this work, field studies on time–temperature conditions at each critical stage of the cold chain are reviewed to assess the current state of commercial cold chains. Precooling, ground operations during transportation, storage during display at retail and in domestic refrigerators, and commercial handling practices are identified and discussed as the major weaknesses in the modern cold chain. The improvement in efficiency achieved through the measurement, analysis, and management of time–temperature conditions is reviewed, along with the accompanying technical and practical challenges delaying the implementation of such methods. A combination of prospective experimental and modeling research on precooling uniformity, responsive food inventory management systems, and cold chains in developing countries is proposed for the improvement of the cold chain at the global scale.
Reducing food losses by intelligent food logistics Jedermann, Reiner; Nicometo, Mike; Uysal, Ismail ...
Philosophical transactions - Royal Society. Mathematical, Physical and engineering sciences/Philosophical transactions - Royal Society. Mathematical, physical and engineering sciences,
06/2014, Letnik:
372, Številka:
2017
Journal Article
Recenzirano
Odprti dostop
The need to feed an ever-increasing world population makes it obligatory to reduce the millions of tons of avoidable perishable waste along the food supply chain. A considerable share of these losses ...is caused by non-optimal cold chain processes and management. This Theme Issue focuses on technologies, models and applications to monitor changes in the product shelf life, defined as the time remaining until the quality of a food product drops below an acceptance limit, and to plan successive chain processes and logistics accordingly to uncover and prevent invisible or latent losses in product quality, especially following the first-expired-first-out strategy for optimized matching between the remaining shelf life and the expected transport duration. This introductory article summarizes the key findings of this Theme Issue, which brings together research study results from around the world to promote intelligent food logistics. The articles include three case studies on the cold chain for berries, bananas and meat and an overview of different post-harvest treatments. Further contributions focus on the required technical solutions, such as the wireless sensor and communication system for remote quality supervision, gas sensors to detect ethylene as an indicator of unwanted ripening and volatile components to indicate mould infections. The final section of this introduction discusses how improvements in food quality can be targeted by strategic changes in the food chain.
Ubiquitous sensor networks collecting real-time data have been adopted in many industrial settings. This paper describes the second stage of an end-to-end system integrating modern hardware and ...software tools for precise monitoring and control of soil conditions. In the proposed framework, the data are collected by the sensor network distributed in the soil of a commercial strawberry farm to infer the ultimate physicochemical characteristics of the fruit at the point of harvest around the sensor locations. Empirical and statistical models are jointly investigated in the form of neural networks and Gaussian process regression models to predict the most significant physicochemical qualities of strawberry. Color, for instance, either by itself or when combined with the soluble solids content (sweetness), can be predicted within as little as 9% and 14% of their expected range of values, respectively. This level of accuracy will ultimately enable the implementation of the next phase in controlling the soil conditions where data-driven quality and resource-use trade-offs can be realized for sustainable and high-quality strawberry production.
Temperature-controlled closed-loop systems are vital to the transportation of produce. By maintaining specific transportation temperatures and adjusting to environmental factors, these systems delay ...decomposition. Wireless sensor networks (WSN) can be used to monitor the temperature levels at different locations within these transportation containers and provide feedback to these systems. However, there are a range of unique challenges in WSN implementations, such as the cost of the hardware, implementation difficulties, and the general ruggedness of the environment. This paper presents the novel results of a real-life application, where a sensor network was implemented to monitor the environmental temperatures at different locations inside commercial temperature-controlled shipping containers. The possibility of predicting one or more locations inside the container in the absence or breakdown of a logger placed in that location is explored using combinatorial input-output settings. A total of 1016 machine learning (ML) models are exhaustively trained, tested, and validated in search of the best model and the best combinations to produce a higher prediction result. The statistical correlations between different loggers and logger combinations are studied to identify a systematic approach to finding the optimal setting and placement of loggers under a cost constraint. Our findings suggest that even under different and incrementally higher cost constraints, one can use empirical approaches such as neural networks to predict temperature variations in a location with an absent or failed logger, within a margin of error comparable to the manufacturer-specified sensor accuracy. In fact, the median test accuracy is 1.02 degrees Fahrenheit when using only a single sensor to predict the remaining locations under the assumptions of critical system failure, and drops to as little as 0.8 and 0.65 degrees Fahrenheit when using one or three more sensors in the prediction algorithm. We also demonstrate that, by using correlation coefficients and time series similarity measurements, one can identify the optimal input-output pairs for the prediction algorithm reliably under most instances. For example, discrete time warping can be used to select the best location to place the sensors with a 92% match between the lowest prediction error and the highest similarity sensor with the rest of the group. The findings of this research can be used for power management in sensor batteries, especially for long transportation routes, by alternating standby modes where the temperature data for the OFF sensors are predicted by the ON sensors.
Members of the SAR11 order Pelagibacterales dominate the surface oceans. Their extensive diversity challenges emerging operational boundaries defined for microbial 'species' and complicates efforts ...of population genetics to study their evolution. Here, we employed single-amino acid variants (SAAVs) to investigate ecological and evolutionary forces that maintain the genomic heterogeneity within ubiquitous SAR11 populations we accessed through metagenomic read recruitment using a single isolate genome. Integrating amino acid and protein biochemistry with metagenomics revealed that systematic purifying selection against deleterious variants governs non-synonymous variation among very closely related populations of SAR11. SAAVs partitioned metagenomes into two main groups matching large-scale oceanic current temperatures, and six finer proteotypes that connect distant oceanic regions. These findings suggest that environmentally-mediated selection plays a critical role in the journey of cosmopolitan surface ocean microbial populations, and the idea 'everything is everywhere but the environment selects' has credence even at the finest resolutions.
This paper presents a comparison of conventional and modern machine (deep) learning within the framework of anomaly detection in self-organizing networks. While deep learning has gained significant ...traction, especially in application scenarios where large volumes of data can be collected and processed, conventional methods may yet offer strong statistical alternatives, especially when using proper learning representations. For instance, support vector machines have previously demonstrated state-of-the-art potential in many binary classification applications and can be further exploited with different representations, such as one-class learning and data augmentation. We demonstrate for the first time, on a previously published and publicly available dataset, that conventional machine learning can outperform the previous state-of-the-art using deep learning by 15% on average across four different application scenarios. Our results further indicate that with nearly two orders of magnitude improvement in computational speed and an order of magnitude reduction in trainable parameters, conventional machine learning provides a robust alternative for 5G self-organizing networks especially when the execution and detection times are critical.
The Lawton Instrumental Activities of Daily Living (IADL) scale is the most widely used scale for the assessment of IADL in the elderly population. The aim of this study was to adapt the Lawton IADL ...Scale in Turkish and to investigate the validity and the reliability of the scale in older adults.
A total of 80 participants with a mean age of 71.6±5.8 years were included in the study. The independent living skills of the older adults were measured using Lawton IADL, Hodkinson Mental Test, Functional Independence Scale, Barthel Index, Katz Index, and visual analog scale. Lawton IADL was translated into Turkish, validated by professional reviewers, translated back into English, and then tested. Cronbach's alpha was used to measure reliability in a group of 34 participants and test-retest was performed 1 week after the first test. Pearson correlation analysis was used to show the relationship between Lawton IADL and other scales and indexes.
Internal consistency (Cronbach's alpha) value was 0.843 for the whole scale. The intraclass correlation coefficient value of the scale was 0.915.
These results confirm that the Turkish version of the Lawton IADL scale has excellent reliability and validity.
Deep clustering achieves unprecedented levels of accuracy with unsupervised feature extraction on rich datasets where the joint statistics of the latent space is learned via highly nonlinear ...compression. This paper has two separate contributions to this field. First, we conduct an extensive and first-of-its-kind empirical study on the statistical relationship between the clustering accuracy and image reconstruction quality of a state-of-the-art deep clustering topology in the form of a convolutional variational autoencoder (VAE) with a K-means back end. We change the latent variable z at the bottleneck of the network to create different latent dimensions and explore how clustering performance metrics and reconstruction metrics are statistically related. Secondly, based on our data-driven statistical findings, we also propose a novel cost function for the VAE which includes the structural similarity index measure to jointly optimize image quality and latent statistics for improved clustering. The preliminary results show significant increases in clustering accuracy of as much as 10.76% on two popular benchmark datasets. The TensorFlow implementation for the experimental framework can be found here: https://github.com/alla15747/IEEE-Comparitive-Study-VAE-Paper-(Python code will be available at the time of publication).
The paper presents a complete collection of data and its preliminary statistical analysis obtained from the first phase of a large soil study on how to improve strawberry production and achieve ...sustainable and high-quality harvests through sensor-assisted real-time field monitoring. Six real-time loggers were placed in an operational commercial strawberry farm in Central Florida for the entirety of a harvest season from soil preparation to planting to harvesting. Along with temporally high-resolution soil sensory measurements including water content, electrical conductivity, and temperature, strawberries were harvested from each of the six locations on three separate occasions for their objective physicochemical characteristics to be monitored and recorded in a food chemistry lab. The primary goal of this paper is to introduce the dataset to the food science and engineering research community and present the results of its preliminary statistical analysis in identifying which factors correlate with one another. Based on the findings of this paper, while there exists a weak correlation between the quality of the harvest and the water content of the soil immediately preceding it, there were several cases where statistically significant differences exist between the soil sensory measurements from different locations which did not replicate the same differences in their corresponding harvest qualities.
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Microvascular free flaps are preferred for most major head and neck reconstruction surgeries because of better functional outcomes, improved esthetics, and generally higher success rates. Numerous ...studies have investigated measures to prevent flap loss, but few have evaluated the optimal treatment for free flap complications. This study aimed to determine the complication rate after free flap reconstructions and discusses our management strategies. Medical records of 260 consecutive patients who underwent free flap reconstructions for head and neck defects between July 2006 and June 2010 were retrospectively reviewed for patient and surgical characteristics and postoperative complications. The results revealed that microvascular free flaps were extremely reliable, with a 3.5 % incidence of flap failure. There were 78 surgical site complications. The most common complication was neck wound infection, followed by dehiscence, vascular congestion, abscess, flap necrosis, hematoma, osteoradionecrosis, and brisk bleeding. Twenty patients with poor wound healing received hyperbaric oxygen therapy, which was ineffective in three patients who eventually experienced complete flap loss. Eleven patients with vascular congestion underwent medicinal leech therapy, which was effective. Among the 78 patients with complications, 44 required repeat surgery, which was performed for postoperative brisk bleeding in three. Eventually, ten patients experienced partial flap loss and nine experienced complete flap loss, with the latter requiring subsequent pectoralis major flap reconstruction. Microvascular free flap reconstruction represents an essential and reliable technique for head and neck defects and allows surgeons to perform radical resection with satisfactory functional results and acceptable complication rates.
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