Sorting System Research Articles

Selectable encapsulated cell quantity in droplets via label-free electrical screening and impedance-activated sorting.

Single-cell encapsulation in droplets has become a powerful tool in immunotherapy, medicine discovery, and single-cell analysis, thanks to its capability for cell confinement in picoliter volumes. However, the purity and throughput of single-cell droplets are limited by random encapsulation process, which resuts in a majority of empty and multi-cells droplets. Herein we introduce the first label-free selectable cell quantity encapsulation in droplets sorting system to overcome this problem. The system utilizes a simple and reliable electrical impedance based screening (98.9% of accuracy) integrated with biocompatible acoustic sorting to select single-cell droplets, achieving 90.3% of efficiency and up to 200​Hz of throughput, by removing multi-cells (∼60% of rejection) and empty droplets (∼90% of rejection). We demonstrate the use of the droplet sorting to improve the throughput of single-cell encapsulation by ∼9-fold compared to the conventional random encapsulation process.

Further Perspective of Machine Vision in Industrial Robot Systems

With the rapid development of automation systems and artificial intelligence, industrial robots have played significant roles in automated production processes. Due to the improvement of computer chips, more and more vision algorithms are able to run in industrial robot systems. Increasingly, robotic systems based on visual recognition are replacing those based on traditional sensors. However, there is much work to be done in real situations. This research focuses on the application of machine vision in the field of industrial robot systems. It first gives the overall summary of industrial robot systems and machine vision as well as their applications. Then it gives an example of a sorting system and expounds its strategy. Finally, it turns out that machine vision can be widely used in industrial robot systems because of its excellent performance, although there are still existing problems to be solved in it. Those problems may be solved by further development of algorithms.

Sistem Identifikasi Kesegaran dan Jenis Ikan dengan Metode K-Nearest Neighbor Berdasarkan Citra Mata dan Bentuk Ikan

Fish is a food commodity that needs attention, to improve the quality of food production, especially the fish itself. The level of freshness of fish greatly affects the quality of food production both at the household and industrial levels, as well as determining the feasibility of the fish for processing and consumption. Currently, to determine the level of quality of fish freshness, it is still done conventionally by humans, while those who have used tools but still have deficiencies in both the level of accuracy and also the features they have are still small. In this study, a tool or system design was carried out that could identify the freshness level of a fish based on eye images taken using a webcam camera or the like as input from data to be processed using image processing. In addition, the system is given additional features to be able to identify the type of fish. So that this additional feature can help facilitate identification all at once. To classify the method used is the K-Nearest Neighbor method. The results of the data processing will be displayed in the form of a sorting system for output. In the research results obtained from the system this time from 280 datasets for identification of freshness were tested on 50 images with a success rate of 96% for fresh and 84% for rotten. While the results of identification of fish species from 50 images of test data from three types of fish obtained a success rate of 97.7% with a value of k = 5.

Assessing Italian household preferences for waste sorting systems: The role of environmental awareness, socioeconomic characteristics, and local contexts

Efficient Waste Management Systems (WMS) depend on citizens’ willingness to sort waste and to cooperate for its improved provision. Thus, it is essential to understand what attributes individuals value the most and what drives WMS preference heterogeneity. In this paper, we investigate how individuals evaluate WMS attributes (e.g., the number of waste sorting categories, the introduction of textile sorting, and the frequency of collection per week) and how the local contexts, socioeconomics, and environmental values shape their preferences. For this purpose, we conducted a discrete choice experiment on a sample representative of the Italian population interviewed through an online panel survey. We collected information on individual’s preferences for WMS attributes, environmental awareness, and socioeconomic characteristics. We analyzed the data using hybrid mixed choice models, which allowed us to integrate environmental awareness values into willingness to pay estimations. We found that individuals are willing to pay for waste sorting, even if this implies more effort on their part and increased storage space. In addition, we found that the extent to which individuals support WMS improvements strongly depends on their environmental values, experiences (which are shaped by their local context), and socioeconomic factors. Our results suggest that to increase recycling rates and citizen satisfaction, there is a need for context-specific WMS designs that acknowledge the observed heterogeneity of preferences.

Application of a Magnetic Platform in α6 Integrin-Positive iPSC-TM Purification.

The emergence of induced pluripotent stem cell (iPSC) technology has provided a new approach to regenerating decellularized trabecular meshwork (TM) in glaucoma. We have previously generated iPSC-derived TM (iPSC-TM) using a medium conditioned by TM cells and verified its function in tissue regeneration. Because of the heterogeneity of iPSCs and the isolated TM cells, iPSC-TM cells appear to be heterogeneous, which impedes our understanding of how the decellularized TM may be regenerated. Herein, we developed a protocol based on a magnetic-activated cell sorting (MACS) system or an immunopanning (IP) method for sorting integrin subunit alpha 6 (ITGA6)-positive iPSC-TM, an example of the iPSC-TM subpopulation. We first analyzed the purification efficiency of these two approaches by flow cytometry. In addition, we also determined cell viability by analyzing the morphologies of the purified cells. To conclude, the MACS-based purification could yield a higher ratio of ITGA6-positive iPSC-TM and maintain a relatively higher cell viability than the IP-based method, allowing for the preparation of any iPSC-TM subpopulation of interest and facilitating a better understanding of the regenerative mechanism of iPSC-based therapy.

Evaluation of a new apple in-field sorting system for fruit singulation, rotation and imaging

Automated, computer-imaging based in-field grading and sorting of apples at the time of harvest has the potential to help growers achieve significant cost savings in postharvest storage and packing. Singulation and rotation of fruit are essential steps for a computer imaging-based grading/sorting system. We have recently developed a novel compact, low-cost in-field apple sorting system, which consists of pairs of multi-stage helical conveyance drives to singulate, rotate and advance apples so that they can be inspected by a computer vision unit for quality grading. This study was aimed at evaluating the singulating and rotating performance of the sorting system for automatic inspection and grading of apples, in terms of conveying speed, fruit size, and the percentage of apple exposure area for imaging. Two apple varieties (i.e., ‘Golden Delicious’ and ‘Delicious’) were used for experimental evaluations. To determine the rotational behavior of apples, each quadrant of the apple surface was painted with different colors. Images were acquired for the apples at 15 frames per second and then automatically segmented for the estimation of exposed areas in terms of pixels. It was found that the number of images acquired for each fruit varied from 24 to 9 images when the speed of the sorting system was changed from 1 to 3 apples per second per lane. The singulating and rotating mechanism (SRM) provided continuous and relatively even rotation of the apples while conveying, which was confirmed by cumulative percentages of the four colored areas of ‘Golden Delicious’ and ‘Delicious’ apples at the three sorting speeds. At the sorting speed of 1 apple/s, the entire surface of each fruit was imaged for more than 4 times, while at 3 apples/s, the surface of the apples was imaged for at least 1.4 times. Hence, the sorting system with 3 sorting lanes is able to perform in-field quality grading of apples at a throughput of 9 or more apples/s.

Label-free droplet-based bacterial growth phenotype screening by a mini integrated microfluidic platform

Rapid and accurate microbial screening, which is important in industrial breeding, enzyme directed evolution and synthetic biology, is currently time-consulting. Fluorescence-activated droplet sorting (FADS) system offers a promising alternative for microbial screening with high-throughput analysis. However, pre-labeling steps are required before use. Here, a low-cost, label-free droplet-based mini integrated microfluidic platform is developed for bacterial growth phenotype screening based on the difference in droplet autofluorescence properties. The platform integrates the main functions into a box, in which droplets are passed through the detection area and transmitted the signal of the light directly to the photomultiplier tubes (PMT) through the optical fiber inserted in a specially designed chip. After optimizing the chip structure and parameter, we firstly verify the capabilities of this platform for bacteria automatic counting. Then, label-free counting and sorting of the cultured bacteria in droplet have been well performed in this platform with the support of a voltage amplifier. We also employee this platform to determine the growth phenotype for microbial strain in droplets and screen the fast-growth bacteria in an automatic way. The label-free droplet-based platform provides an automated method for rapid bacteria growth phenotype screening, which can be further employed in high quality industrial strains screening, antibiotic resistance and directed evolution.

Automatic Detection of Small Sample Apple Surface Defects Using ASDINet.

The appearance quality of apples directly affects their price. To realize apple grading automatically, it is necessary to find an effective method for detecting apple surface defects. Aiming at the problem of a low recognition rate in apple surface defect detection under small sample conditions, we designed an apple surface defect detection network (ASDINet) suitable for small sample learning. The self-developed apple sorting system collected RGB images of 50 apple samples for model verification, including non-defective and defective apples (rot, disease, lacerations, and mechanical damage). First, a segmentation network (AU-Net) with a stronger ability to capture small details was designed, and a Dep-conv module that could expand the feature capacity of the receptive field was inserted in its down-sampling path. Among them, the number of convolutional layers in the single-layer convolutional module was positively correlated with the network depth. Next, to achieve real-time segmentation, we replaced the flooding of feature maps with mask output in the 13th layer of the network. Finally, we designed a global decision module (GDM) with global properties, which inserted the global spatial domain attention mechanism (GSAM) and performed fast prediction on abnormal images through the input of masks. In the comparison experiment with state-of-the-art models, our network achieved an AP of 98.8%, and a 97.75% F1-score, which were higher than those of most of the state-of-the-art networks; the detection speed reached 39ms per frame, achieving accuracy-easy deployment and substantial trade-offs that are in line with actual production needs. In the data sensitivity experiment, the ASDINet achieved results that met the production needs under the training of 42 defective pictures. In addition, we also discussed the effect of the ASDINet in actual production, and the test results showed that our proposed network demonstrated excellent performance consistent with the theory in actual production.

Eliciting effective tumor immunity against ovarian cancer by cancer stem cell vaccination

Advanced ovarian cancer (OC) patients have limited benefit from current relevant cytotoxic and targeted therapies following debulking surgery. Therefore, new therapeutic strategies are in urgent need. Immunotherapy has shown great potential in tumor treatment, especially in tumor vaccine development. The study objective was to evaluate the immune effects of cancer stem cells (CSCs) vaccines on OC. The CD44+CD117+CSCs were isolated from human OC HO8910 and SKOV3 cells using the magnetic cell sorting system; the cancer stem-like cells were selected from murine OC ID8 cell by no-serum formed sphere culture. The CSC vaccines were prepared by freezing and thawing these CSCs, which were then injected into mice followed by challenging the different OC cells. The in vivo antitumor efficacy of CSC immunization revealed the vaccines were capable of significantly provoking immune responses to autologous tumor antigens in vaccinated mice as the mice were found to have markedly inhibited tumor growth, prolonged survival, and decreased CSC counts in OC tissues when compared to mice without the CSC vaccination. The in vitro cytotoxicities of immunocytes toward SKOV3, HO8910 and ID8 cells indicated a significant killing efficacy compared with the controls. However, the antitumor efficacy was remarkably reduced whilst the mucin-1 expression in CSC vaccines was down-regulated by small interfering RNA. Overall, findings from this study provided the evidence that has deepened our understanding of CSC vaccine immunogenicity and anti-OC efficacy, particularly for the role of dominant antigen mucin-1. It is possible to turn the CSC vaccine into an immunotherapeutic approach against ovarian cancer.

Fish Detection and Classification for Automatic Sorting System with an Optimized YOLO Algorithm

Automatic fish recognition using deep learning and computer or machine vision is a key part of making the fish industry more productive through automation. An automatic sorting system will help to tackle the challenges of increasing food demand and the threat of food scarcity in the future due to the continuing growth of the world population and the impact of global warming and climate change. As far as the authors know, there has been no published work so far to detect and classify moving fish for the fish culture industry, especially for automatic sorting purposes based on the fish species using deep learning and machine vision. This paper proposes an approach based on the recognition algorithm YOLOv4, optimized with a unique labeling technique. The proposed method was tested with videos of real fish running on a conveyor, which were put randomly in position and order at a speed of 505.08 m/h and could obtain an accuracy of 98.15%. This study with a simple but effective method is expected to be a guide for automatically detecting, classifying, and sorting fish.

An online automatic sorting system for defective Ginseng Radix et Rhizoma Rubra using deep learning

ObjectiveTo establish a deep-learning architecture based on faster region-based convolutional neural networks (Faster R-CNN) algorithm for detection and sorting of red ginseng (Ginseng Radix et Rhizoma Rubra) with internal defects automatically on an online X-ray machine vision system. MethodsA Faster R-CNN based classifier was trained with around 20 000 samples with mean average precision value (mAP) of 0.95. A traditional image processing method based on feedforward neural network (FNN) obtained a bad performance with the accuracy, recall and specificity of 69.0%, 68.0%, and 70.0%, respectively. Therefore, the Faster R-CNN model was saved to evaluate the model performance on the defective red ginseng online sorting system. ResultsAn independent set of 2 000 red ginsengs were used to validate the performance of the Faster R-CNN based online sorting system in three parallel tests, achieving accuracy of 95.8%, 95.2% and 96.2%, respectively. ConclusionThe overall results indicated that the proposed Faster R-CNN based classification model has great potential for non-destructive detection of red ginseng with internal defects.

COIN SORTING AND COUNTING MACHINE

The design was entitled Automatic Coin Sorting Machine for it is to sort the Indian coin denomination. This was done to be able to help different Institutions that deal with large number of coins in their daily operation. Some of these are banks, churches, charitable institutions and the transport sector. The sorting part of the design is basically mechanical as it sorts the coin through their physical size then counts them as they pass through the Arduino. An automatic coin counting and sorting machine prototype is developed by using ARDUINO-UNO as the main controller. This work included design the display panel to show total quantity of each coin and total amount. The coin acceptor has been used as it acts as coin selector to determine the denomination of coins in sorting system is designed by using DC motor to hold and distributes coin to the corresponding slot. Key Words: Denomination, coin slot, rotating plate, microcontroller

PREDICTION OF PLASTIC-TYPE FOR SORTING SYSTEM USING DECISION TREE MODEL

Plastic is the most widely used inorganic material globally, but its hundred-year disintegration time can harm the environment. Polyethylene Terephthalate (PET/PETE), High-Density Polyethylene (HDPE), and Polypropylene are all commonly used plastics that have the potential to become waste (PP). An essential first step in the recycling process is sorting out plastic waste. A low-cost automated plastic sorting system can be developed by using digital image data in the red, green, and blue (RGB) color space as the dataset and predicting the type using learning datasets. This paper proposes the Decision Tree model to predict the three plastic-type sorting systems based on discretizing predictor variables into two and three categories. The resampling method of k-fold cross-validation with ten folds for less biased. Discretization of the predictor variables into three categories informs that the proposed decision tree model has higher performance compared to the two categories with an accuracy of 81.93 %, a recall-micro of 72.89 %, a recall-macro of 72.30 %, a specificity-micro of 86.45%, and the specificity-macro of 86.51%, respectively. The micro is determined by the number of decisions made for each object. In comparison, the macro is calculated based on the average decision made by each class.

Sorting of Fresh Tea Leaf Using Deep Learning and Air Blowing

The sorting of machine-picked fresh tea leaves after mechanized harvesting remains a challenge because of the complex morphological characteristics and physicochemical properties of fresh tea leaves. First, the recognition results of four types of models, namely, YOLOv5, YOLOv3, Fast RCNN, and SSD, were compared. It was found that YOLOv5, with guaranteed recognition accuracy, had a recognition speed of 4.7 ms/frame (about four times that of the second ranked YOLOv3). Therefore, this study presents a novel fresh tea leaf sorting system that provides rapid and high-precision multi-channel sorting for four grades of tea leaves using a tea leaf recognition model based on the You Only Look Once (YOLOv5) deep learning model. Subsequently, a raw dataset, consisting of 6400 target images of different grades and different moisture contents, was used to evaluate three different optimization methods. Among these, the Stochastic Gradient Descent (SGD) optimization method was found to provide the best model training results with an average recognition accuracy of 98.2%. In addition, the recognition efficacy of the recognition model was found to be positively correlated with the gradient coverage of tea’s moisture content in the training set. Theoretical analysis was then conducted, along with the experimental investigation of the air-blowing force on the fresh tea leaves in the sorting process, with 30° determined to be the optimal air-blowing angle. Finally, the overall results showed that the construction of the full moisture content training set enabled a model recognition accuracy of up to 88.8%, a recall of 88.4%, a recognition speed of 4.7 ms/frame, and an overall sorting accuracy of 85.4%. This result is promising for multi-channel sorting of fresh tea leaf grades in complex situations, and as such provides a strong basis for the application of tea leaf sorting equipment.

A rational way of sorting municipal solid waste

The article provides a brief overview of the most popular and promising methods of sorting municipal solid waste, attention is paid to automated systems for recognizing garbage objects during sorting, and in particular, their high cost with relatively low efficiency. The problem of using high-cost systems for recognizing waste objects by the payback factor with a low content of useful components in the total mass of garbage has been identified. A method of an automated garbage sorting system is proposed and described, in which a person is used as a system for recognizing garbage objects. This method significantly reduces the cost of electron-optical and tactile detectors that recognize garbage objects, while not requiring a huge database to compare 3-d images. At the same time, the recognition accuracy increases significantly, and the cost of the process of recognizing garbage objects on the conveyor is significantly reduced, which in general ensures a quick payback of the garbage sorting system. It is proposed that such a system be used as an intermediate stage in the improvement of image recognition systems for sorting garbage objects.. The approach of using the human ability to recognize as a more economical method to recognize waste objects when sorting them in robotic systems is proposed. At the same time, comfortable working conditions are provided for a person, a sorter operator, who recognizes and encodes each sorting object remotely from garbage using video cameras and a high-resolution monitor. A variant of such a sorting system using mechanotronics is described.

Edge computing on TPU for brain implant signal analysis

The ever-increasing number of recording sites of silicon-based probes imposes a great challenge for detecting and evaluating single-unit activities in an accurate and efficient manner. Currently separate solutions are available for high precision offline evaluation and separate solutions for embedded systems where computational resources are more limited.We propose a deep learning-based spike sorting system, that utilizes both unsupervised and supervised paradigms to learn a general feature embedding space and detect neural activity in raw data as well as predict the feature vectors for sorting. The unsupervised component uses contrastive learning to extract features from individual waveforms, while the supervised component is based on the MobileNetV2 architecture. One of the key advantages of our system is that it can be trained on multiple, diverse datasets simultaneously, resulting in greater generalizability than previous deep learning-based models.We demonstrate that the proposed model does not only reaches the accuracy of current state-of-art offline spike sorting methods but has the unique potential to run on edge Tensor Processing Units (TPUs), specialized chips designed for artificial intelligence and edge computing. We compare our model performance with state of art solutions on paired datasets as well as on hybrid recordings as well. The herein demonstrated system paves the way to the integration of deep learning-based spike sorting algorithms into wearable electronic devices, which will be a crucial element of high-end brain–computer interfaces.

Approaches in Sustainable, Biobased Multilayer Packaging Solutions.

The depletion of fossil resources and the growing demand for plastic waste reduction has put industries and academic researchers under pressure to develop increasingly sustainable packaging solutions that are both functional and circularly designed. In this review, we provide an overview of the fundamentals and recent advances in biobased packaging materials, including new materials and techniques for their modification as well as their end-of-life scenarios. We also discuss the composition and modification of biobased films and multilayer structures, with particular attention to readily available drop-in solutions, as well as coating techniques. Moreover, we discuss end-of-life factors, including sorting systems, detection methods, composting options, and recycling and upcycling possibilities. Finally, regulatory aspects are pointed out for each application scenario and end-of-life option. Moreover, we discuss the human factor in terms of consumer perception and acceptance of upcycling.

Explainable AI for sensor-based sorting systems

Abstract Explainable artificial intelligence (XAI) can make machine learning based systems more transparent. This additional transparency can enable the use of machine learning in many different domains. In our work, we show how XAI methods can be applied to an autoencoder for anomaly detection in a sensor-based sorting system. The setup of the sorting system consists of a vibrating feeder, a conveyor belt, a line-scan camera and an array of fast-switching pneumatic valves. It allows the separation of a material stream into two fractions, realizing a binary sorting task. The autoencoder tries to mimic the normal behavior of the nozzle array and thus can detect abnormal behavior. The XAI methods are used to explain the output of the autoencoder. As XAI methods global and local approaches are used, which means we receive explanations for both a single result and the whole autoencoder. Initial results for both approaches are shown, together with possible interpretations of these results.

High-throughput sorting of two-color fluorescent-labeled zebrafish embryos

The zebrafish embryos were widely employed in genetics, development and drug discovery studies as miniatured animal models. Sorting of two-color fluorescent embryos is often required in large-scale experiments but it is challenging to manually sort with high efficiency. Here, we reported a high-throughput sorting system for two-color fluorescent zebrafish embryos. The embryos can be automatically loaded from a sample pool and sorted based on the average fluorescent intensity. The two-color fluorescent signals were split into two lines and detected by an area array camera. The system achieves the sorting of 100 embryos in less than 10[Formula: see text]min with an accuracy of greater than 95%.

Microbial Enzyme Biotechnology to Reach Plastic Waste Circularity: Current Status, Problems and Perspectives.

The accumulation of synthetic plastic waste in the environment has become a global concern. Microbial enzymes (purified or as whole-cell biocatalysts) represent emerging biotechnological tools for waste circularity; they can depolymerize materials into reusable building blocks, but their contribution must be considered within the context of present waste management practices. This review reports on the prospective of biotechnological tools for plastic bio-recycling within the framework of plastic waste management in Europe. Available biotechnology tools can support polyethylene terephthalate (PET) recycling. However, PET represents only ≈7% of unrecycled plastic waste. Polyurethanes, the principal unrecycled waste fraction, together with other thermosets and more recalcitrant thermoplastics (e.g., polyolefins) are the next plausible target for enzyme-based depolymerization, even if this process is currently effective only on ideal polyester-based polymers. To extend the contribution of biotechnology to plastic circularity, optimization of collection and sorting systems should be considered to feed chemoenzymatic technologies for the treatment of more recalcitrant and mixed polymers. In addition, new bio-based technologies with a lower environmental impact in comparison with the present approaches should be developed to depolymerize (available or new) plastic materials, that should be designed for the required durability and for being susceptible to the action of enzymes.