scholarly journals Sniff Species: SURMOF-Based Sensor Array Discriminates Aromatic Plants beyond the Genus Level

Chemosensors ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 171
Author(s):  
Salih Okur ◽  
Chun Li ◽  
Zejun Zhang ◽  
Sahi Vaidurya Pratap ◽  
Mohammed Mohammed Sarheed ◽  
...  
Keyword(s):  
Low Cost ◽  
Classification Model ◽  
Data Sets ◽  
Electronic Noses ◽  
Linear Discriminant ◽  
Genus Level ◽  
Consumer Safety ◽  
Efficient Alternative ◽  
Cost Efficient ◽  
Repeated Test

Lamiaceae belong to the species-richest family of flowering plants and harbor many species that are used as herbs or in medicinal applications such as basils or mints. The evolution of this group has been driven by chemical speciation, mainly volatile organic compounds (VOCs). The commercial use of these plants is characterized by adulteration and surrogation to a large extent. Authenticating and discerning this species is thus relevant for consumer safety but usually requires cumbersome analytics, such as gas chromatography, often coupled with mass spectroscopy. Here, we demonstrate that quartz-crystal microbalance (QCM)-based electronic noses provide a very cost-efficient alternative, allowing for fast, automated discrimination of scents emitted from the leaves of different plants. To explore the range of this strategy, we used leaf material from four genera of Lamiaceae along with lemongrass, which is similarly scented but from an unrelated outgroup. To differentiate the scents from different plants unambiguously, the output of the six different SURMOF/QCM sensors was analyzed using machine learning (ML) methods together with a thorough statistical analysis. The exposure and purging of data sets (four cycles) obtained from a QCM-based, low-cost homemade portable e-Nose were analyzed using a linear discriminant analysis (LDA) classification model. Prediction accuracy with repeated test measurements reached values of up to 0%. We show that it is possible not only to discern and identify plants at the genus level but also to discriminate closely related sister clades within a genus (basil), demonstrating that an e-Nose is a powerful device that can safeguard consumer safety against dangers posed by globalized trade.

scholarly journals Sniff species - SURMOF based sensor array discriminate aromatic plants beyond the genus level

2021 ◽  
Author(s):  
Salih Okur ◽  
Chun Li ◽  
Zejun Zhang ◽  
Sahi Vaidurya Pratap ◽  
Mohammed Mohammed Sarheed ◽  
...  
Keyword(s):  
Low Cost ◽  
Classification Model ◽  
Aromatic Plants ◽  
Electronic Noses ◽  
Linear Discriminant ◽  
Genus Level ◽  
Consumer Safety ◽  
Volatile Organic ◽  
Efficient Alternative ◽  
Cost Efficient

The Lamiaceae belong to the species-richest families of flowering plants and harbor many species used as herbs or for medicinal applications, such as Basils or Mints. Evolution of this group has been driven by chemical speciation, mainly of Volatile Organic Compounds (VOCs). The commercial use of these plants is characterized by a large extent of adulteration and surrogation. To authenticate and discern the species, is, thus, relevant for consumer safety, but usually requires cumbersome analytics, such as Gas Chromatography, often to be coupled with Mass Spectroscopy. We demon-strate here that quartz-crystal microbalance (QCM)-based electronic noses provide a very cost-efficient alternative, allowing for a fast, automated discrimination of scents emitted from leaves of different plants. To explore the range of this strategy, we used leaf material from four genera of Lamiaceae along with Lemongrass as similarly scented, but non-related outgroup. In order to unambiguously differentiate the scents from the different plants, the output of the 6 different SURMOF/QCM sensors was analyzed using machine learning (ML) methods, together with a thorough statistical analysis. The exposure and purging datasets (4 cycles) obtained from a QCM-based, low-cost homemade portable e-Nose were analyzed with Linear Discriminant Analysis (LDA) classification model. Prediction accuracies with repeating test measurements reached values of up to 90%. We show that it is not only possible to discern and identify plants on the genus level, but even to discriminate closely related sister clades within a genus (Basil), demonstrating that e-Noses are a powerful technology to safeguard consumer safety against the challenges of globalized trade.

scholarly journals A Low-Cost Contactless Overhead Micrometer Surface Scanner

2021 ◽  
Vol 11 (14) ◽  
pp. 6274
Author(s):  
Xenophon Zabulis ◽  
Panagiotis Koutlemanis ◽  
Nikolaos Stivaktakis ◽  
Nikolaos Partarakis
Keyword(s):  
Image Registration ◽  
Low Cost ◽  
Image Acquisition ◽  
Design And Implementation ◽  
Planar Surfaces ◽  
Surface Mosaics ◽  
Efficient Alternative ◽  
Cost Efficient

The design and implementation of a contactless scanner and its software are proposed. The scanner regards the photographic digitization of planar and approximately planar surfaces and is proposed as a cost-efficient alternative to off-the-shelf solutions. The result is 19.8 Kppi micrometer scans, in the service of several applications. Accurate surface mosaics are obtained based on a novel image acquisition and image registration approach that actively seeks registration cues by acquiring auxiliary images and fusing proprioceptive data in correspondence and registration tasks. The device and operating software are explained, provided as an open prototype, and evaluated qualitatively and quantitatively.

scholarly journals Cost-Effective Active Learning from Diverse Labelers

2017 ◽  
Author(s):  
Sheng-Jun Huang ◽  
Jia-Lve Chen ◽  
Xin Mu ◽  
Zhi-Hua Zhou
Keyword(s):  
Cost Effectiveness ◽  
Active Learning ◽  
Low Cost ◽  
Selection Criterion ◽  
Ground Truth ◽  
Cost Effective ◽  
Classification Model ◽  
Data Sets ◽  
Active Selection ◽  
The Cost

In traditional active learning, there is only one labeler that always returns the ground truth of queried labels. However, in many applications, multiple labelers are available to offer diverse qualities of labeling with different costs. In this paper, we perform active selection on both instances and labelers, aiming to improve the classification model most with the lowest cost. While the cost of a labeler is proportional to its overall labeling quality, we also observe that different labelers usually have diverse expertise, and thus it is likely that labelers with a low overall quality can provide accurate labels on some specific instances. Based on this fact, we propose a novel active selection criterion to evaluate the cost-effectiveness of instance-labeler pairs, which ensures that the selected instance is helpful for improving the classification model, and meanwhile the selected labeler can provide an accurate label for the instance with a relative low cost. Experiments on both UCI and real crowdsourcing data sets demonstrate the superiority of our proposed approach on selecting cost-effective queries.

Segmentation of Users of Social Networking Websites

2012 ◽  
Vol 40 (3) ◽  
pp. 401-414 ◽  
Author(s):  
Carlota Lorenzo-Romero ◽  
María-del -Carmen Alarcón-del -Amo
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Online Social Networks ◽  
Online Survey ◽  
Low Cost ◽  
Segmentation Analysis ◽  
Efficient Alternative ◽  
Cost Efficient ◽  
Patterns Of Interaction

The typology of networked consumers in The Netherlands presented in this study, was based on an online survey and obtained using latent segmentation analysis. This approach is based on the frequency with which users perform different activities, their sociodemographic variables, social networking experience, and patterns of interaction. The findings present new insights for marketing strategists wishing to use the communication potential of online social networks and for marketers willing to explore the potential of online networking as a low-cost, efficient alternative to traditional networking approaches. The findings also present researchers of social behavior with interesting insights into the role of online social networks as a platform for social interaction and communication.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

scholarly journals Prediction of Fruity Aroma Intensity and Defect Presence in Virgin Olive Oil Using an Electronic Nose

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2298
Author(s):  
Pablo Cano Marchal ◽  
Chiara Sanmartin ◽  
Silvia Satorres Martínez ◽  
Juan Gómez Ortega ◽  
Fabio Mencarelli ◽  
...  
Keyword(s):  
Olive Oil ◽  
Low Cost ◽  
Virgin Olive Oil ◽  
General Purpose ◽  
Quality Parameter ◽  
Lasso Regression ◽  
Virgin Olive Oils ◽  
Electronic Noses ◽  
Olive Oils ◽  
Instrumental Technique

The organoleptic profile of a Virgin Olive Oil is a key quality parameter that is currently obtained by human sensory panels. The development of an instrumental technique capable of providing information about this profile quickly and online is of great interest. This work employed a general purpose e-nose, in lab conditions, to predict the level of fruity aroma and the presence of defects in Virgin Olive Oils. The raw data provided by the e-nose were used to extract a set of features that fed a regressor to predict the level of fruity aroma and a classifier to detect the presence of defects. The results obtained were a mean validation error of 0.5 units for the prediction of fruity aroma using lasso regression; and 88% accuracy for the defect detection using logistic regression. Finally, the identification of two out of ten specific sensors of the e-nose that can provide successful results paves the way to the design of low-cost specific electronic noses for this application.

Learning emotional word embeddings for sentiment analysis

2021 ◽  
pp. 1-13
Author(s):  
Qingtian Zeng ◽  
Xishi Zhao ◽  
Xiaohui Hu ◽  
Hua Duan ◽  
Zhongying Zhao ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Research Problem ◽  
Emotional Word ◽  
Classification Model ◽  
Data Sets ◽  
Word Embeddings ◽  
Real World Data ◽  
Text Documents

Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models.

Fabrication of hydrophobic NiFe2O4@poly(DVB-LMA) sponge via Pickering emulsion template method for oil/water separation

Soft Matter ◽  
2021 ◽  
Author(s):  
Caimei Zhao ◽  
Lei Chen ◽  
Chuanming Yu ◽  
Binghua Hu ◽  
Haoxuan Huang ◽  
...  
Keyword(s):  
Oil Spills ◽  
Low Cost ◽  
Pickering Emulsion ◽  
Template Method ◽  
Water Separation ◽  
Environment Friendly ◽  
Oil Water Separation ◽  
Oil Water ◽  
Cost Efficient

Super-hydrophobic porous absorbent is a convenient, low-cost, efficient and environment-friendly material in the treatment of oil spills. In this work, a simple Pickering emulsion template method was employed to fabricate...

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