scholarly journals A Multi-Model Approach to Implement a Dynamic Shelf Life Criterion in Meat Supply Chains

Foods ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 2740
Author(s):  
Antonia Albrecht ◽  
Maureen Mittler ◽  
Martin Hebel ◽  
Claudia Waldhans ◽  
Ulrike Herbert ◽  
...  
Keyword(s):  
Shelf Life ◽  
Real Time ◽  
Ph Value ◽  
Gompertz Model ◽  
Quality Parameters ◽  
Viable Count ◽  
Short Sales ◽  
Time Dynamic ◽  
Sensory Data ◽  
Model Approach

The high perishability of fresh meat results in short sales and consumption periods, which can lead to high amounts of food waste, especially when a fixed best-before date is stated. Thus, the aim of this study was the development of a real-time dynamic shelf-life criterion (DSLC) for fresh pork filets based on a multi-model approach combining predictive microbiology and sensory modeling. Therefore, 647 samples of ma-packed pork loin were investigated in isothermal and non-isothermal storage trials. For the identification of the most suitable spoilage predictors, typical meat quality parameters (pH-value, color, texture, and sensory characteristics) as well as microbial contamination (total viable count, Pseudomonas spp., lactic acid bacteria, Brochothrix thermosphacta, Enterobacteriaceae) were analyzed at specific investigation points. Dynamic modeling was conducted using a combination of the modified Gompertz model (microbial data) or a linear approach (sensory data) and the Arrhenius model. Based on these models, a four-point scale grading system for the DSLC was developed to predict the product status and shelf-life as a function of temperature data in the supply chain. The applicability of the DSLC was validated in a pilot study under real chain conditions and showed an accurate real-time prediction of the product status.

scholarly journals Kualitas Tuna Kaleng dari Perairan Aceh yang Disterilisasi dengan Pressure Canner

2021 ◽  
Vol 16 (1) ◽  
pp. 73
Author(s):  
Sri Haryani Anwar ◽  
Rosa Wildatul Hifdha ◽  
Syarifah Rohaya ◽  
Hafidh Hasan
Keyword(s):  
Shelf Life ◽  
Palm Oil ◽  
Ph Value ◽  
Quality Parameters ◽  
Consumer Acceptance ◽  
Plate Count ◽  
Canned Tuna ◽  
Total Plate Count ◽  
Perishable Commodity

Ikan tuna termasuk komoditi yang mudah rusak sehingga perlu diolah untuk memperpanjang umur simpan, salah satu caranya dengan pengalengan. Penelitian tentang pengalengan tuna dari perairan Aceh belum pernah dilakukan. Oleh karena itu, penelitian ini bertujuan untuk mempelajari kualitas tuna kaleng yang disterilisasi menggunakan alat pressure canner berkapasitas 24L dengan memvariasikan suhu dan lama sterilisasi (suhu 121°C selama 20 menit dan suhu 115°C selama 50 menit) serta jenis medium (larutan garam dan minyak kelapa sawit). Ikan tuna yang dikalengkan diperoleh dari perairan Aceh. Parameter kualitas bahan baku yang diuji pada tuna segar adalah kadar histamin, angka lempeng total (ALT) dan pH. Sementara itu, parameter kualitas yang diuji pada tuna kaleng adalah ALT, pH, kandungan logam berat (timbal dan merkuri) serta tingkat penerimaan konsumen melalui uji organoleptik (hedonik). Hasil penelitian menunjukkan bahwa ALT tuna kaleng pada semua perlakuan <1x101 koloni/g, sedangkan kandungan timbal (Pb) <0,0001 mg/kg dan merkuri (Hg) berkisar antara 0,29-0,58 mg/kg. Hasil uji hedonik menunjukkan bahwa panelis secara umum dapat menerima kedua jenis produk tuna kaleng, namun panelis lebih menyukai rasa tuna kaleng dalam larutan garam serta warna tuna kaleng dalam minyak kelapa sawit. Hasil penelitian ini menyarankan pengalengan tuna sebaiknya dilakukan pada suhu 121°C selama 20 menit.ABSTRACTTuna is a perishable commodity thus it needs to be preserved to prolong its shelf life. The Canning process is one of the solutions to increase tuna shelf life at room temperature. Research on the tuna canning processes from Aceh waters has never been reported. Therefore, this research aimed to investigate the quality of canned tuna which was sterilized using a 24L pressure canner with varying the temperature and duration of sterilization (121°C for 20 minutes and 115°C for 50 minutes) and the type of medium (brine and palm oil). The fresh tuna used for canning was caught from Aceh water. The quality parameters evaluated for fresh tuna were histamine levels, total plate count (TPC), and pH. Meanwhile, the parameters tested on the quality of the canned tuna were TPC, pH value, heavy metals lead (Pb) and mercury (Hg) contamination, and levels of consumer acceptance through organoleptic tests (hedonic). The results indicated that the TPC values for all canned tuna were <1x101 cfu/g, the metal contaminations were <0.0001 mg/kg for Pb and in the range of 0.29-0.58 mg/kg for Hg. The hedonic tests proved that although all the panelists accepted these two types of canned tuna, they prefer the taste of canned tuna in a salt solution and the color of canned tuna in palm oil. This research suggests that the sterilization process for canned tuna using a 24L pressure canner should be carried out at 121°C for 20 min.

Control Model Constructing on Real-Time Dynamic Reversible Lane in Condition of Connected Vehicle

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Lina Mao ◽  
Wenquan Li ◽  
Pengsen Hu ◽  
Guiliang Zhou ◽  
Huiting Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Control Model ◽  
Connected Vehicle ◽  
Time Dynamic

Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning

TAPPI Journal ◽  
2019 ◽  
Vol 18 (11) ◽  
pp. 679-689
Author(s):  
CYDNEY RECHTIN ◽  
CHITTA RANJAN ◽  
ANTHONY LEWIS ◽  
BETH ANN ZARKO
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Predictive Analytics ◽  
Paper Industry ◽  
Quality Parameters ◽  
Fast Change ◽  
The Face ◽  
Model Tuning ◽  
Reducing Costs ◽  
Time Critical

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

scholarly journals Preliminary Insights in Sensory Profile of Sweet Cherries

Foods ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 612
Author(s):  
Vânia Silva ◽  
Sandra Pereira ◽  
Alice Vilela ◽  
Eunice Bacelar ◽  
Francisco Guedes ◽  
...  
Keyword(s):  
Sweet Cherry ◽  
Prunus Avium ◽  
Sugar Content ◽  
Principal Component ◽  
Titratable Acidity ◽  
Physicochemical Characteristics ◽  
Quality Parameters ◽  
Sensory Profile ◽  
Soluble Solids ◽  
Sensory Data

Sweet cherry (Prunus avium L.) is a fruit appreciated by consumers for its well-known physical and sensory characteristics and its health benefits. Being an extremely perishable fruit, it is important to know the unique attributes of the cultivars to develop cultivation or postharvest strategies that can enhance their quality. This study aimed to understand the influence of physicochemical characteristics of two sweet cherry cultivars, Burlat and Van, on the food quality perception. Several parameters (weight, dimensions, soluble solids content (SSC), pH, titratable acidity (TA), colour, and texture) were measured and correlated with sensory data. Results showed that cv. Van presented heavier and firmer fruits with high sugar content. In turn, cv. Burlat showed higher pH, lower TA, and presented redder and brightest fruits. The principal component analysis revealed an evident separation between cultivars. Van cherries stood out for their sensory parameters and were classified as more acidic, bitter, and astringent, and presented a firmer texture. Contrarily, Burlat cherries were distinguished as being more flavourful, succulent, sweeter, and more uniform in terms of visual and colour parameters. The results of the sensory analysis suggested that perceived quality does not always depend on and/or recognize the quality parameters inherent to the physicochemical characteristics of each cultivar.

scholarly journals IoT-enabled directed acyclic graph in spark cluster

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Jahwan Koo ◽  
Nawab Muhammad Faseeh Qureshi ◽  
Isma Farah Siddiqui ◽  
Asad Abbas ◽  
Ali Kashif Bashir
Keyword(s):  
Distributed Computing ◽  
Real Time ◽  
Directed Acyclic Graph ◽  
Random Access ◽  
Heterogeneous Data ◽  
The Body ◽  
Computing Environment ◽  
Time Data ◽  
Acyclic Graph ◽  
Sensory Data

Abstract Real-time data streaming fetches live sensory segments of the dataset in the heterogeneous distributed computing environment. This process assembles data chunks at a rapid encapsulation rate through a streaming technique that bundles sensor segments into multiple micro-batches and extracts into a repository, respectively. Recently, the acquisition process is enhanced with an additional feature of exchanging IoT devices’ dataset comprised of two components: (i) sensory data and (ii) metadata. The body of sensory data includes record information, and the metadata part consists of logs, heterogeneous events, and routing path tables to transmit micro-batch streams into the repository. Real-time acquisition procedure uses the Directed Acyclic Graph (DAG) to extract live query outcomes from in-place micro-batches through MapReduce stages and returns a result set. However, few bottlenecks affect the performance during the execution process, such as (i) homogeneous micro-batches formation only, (ii) complexity of dataset diversification, (iii) heterogeneous data tuples processing, and (iv) linear DAG workflow only. As a result, it produces huge processing latency and the additional cost of extracting event-enabled IoT datasets. Thus, the Spark cluster that processes Resilient Distributed Dataset (RDD) in a fast-pace using Random access memory (RAM) defies expected robustness in processing IoT streams in the distributed computing environment. This paper presents an IoT-enabled Directed Acyclic Graph (I-DAG) technique that labels micro-batches at the stage of building a stream event and arranges stream elements with event labels. In the next step, heterogeneous stream events are processed through the I-DAG workflow, which has non-linear DAG operation for extracting queries’ results in a Spark cluster. The performance evaluation shows that I-DAG resolves homogeneous IoT-enabled stream event issues and provides an effective stream event heterogeneous solution for IoT-enabled datasets in spark clusters.

scholarly journals Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1547
Author(s):  
Jian Sha ◽  
Xue Li ◽  
Man Zhang ◽  
Zhong-Liang Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Deep Learning ◽  
Real Time ◽  
Best Management Practices ◽  
Input Data ◽  
Management Practices ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Real Time Monitoring

Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

scholarly journals Dynamic Gesture Recognition System with Gesture Spotting Based on Self-Organizing Maps

2021 ◽  
Vol 11 (4) ◽  
pp. 1933
Author(s):  
Hiroomi Hikawa ◽  
Yuta Ichikawa ◽  
Hidetaka Ito ◽  
Yutaka Maeda
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Weight Vector ◽  
Recognition System ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Time Dynamic ◽  
System Input ◽  
Self Organizing ◽  
Gesture Spotting

In this paper, a real-time dynamic hand gesture recognition system with gesture spotting function is proposed. In the proposed system, input video frames are converted to feature vectors, and they are used to form a posture sequence vector that represents the input gesture. Then, gesture identification and gesture spotting are carried out in the self-organizing map (SOM)-Hebb classifier. The gesture spotting function detects the end of the gesture by using the vector distance between the posture sequence vector and the winner neuron’s weight vector. The proposed gesture recognition method was tested by simulation and real-time gesture recognition experiment. Results revealed that the system could recognize nine types of gesture with an accuracy of 96.6%, and it successfully outputted the recognition result at the end of gesture using the spotting result.

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