scholarly journals Assessment of Smoke Contamination in Grapevine Berries and Taint in Wines Due to Bushfires Using a Low-Cost E-Nose and an Artificial Intelligence Approach

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5108 ◽  
Author(s):  
Sigfredo Fuentes ◽  
Vasiliki Summerson ◽  
Claudia Gonzalez Viejo ◽  
Eden Tongson ◽  
Nir Lipovetzky ◽  
...  
Keyword(s):  
Low Cost ◽  
Field Trials ◽  
Smoke Exposure ◽  
High Density ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Test Model ◽  
Volatile Phenols ◽  
Experimental Treatments ◽  
Harvest Model

Bushfires are increasing in number and intensity due to climate change. A newly developed low-cost electronic nose (e-nose) was tested on wines made from grapevines exposed to smoke in field trials. E-nose readings were obtained from wines from five experimental treatments: (i) low-density smoke exposure (LS), (ii) high-density smoke exposure (HS), (iii) high-density smoke exposure with in-canopy misting (HSM), and two controls: (iv) control (C; no smoke treatment) and (v) control with in-canopy misting (CM; no smoke treatment). These e-nose readings were used as inputs for machine learning algorithms to obtain a classification model, with treatments as targets and seven neurons, with 97% accuracy in the classification of 300 samples into treatments as targets (Model 1). Models 2 to 4 used 10 neurons, with 20 glycoconjugates and 10 volatile phenols as targets, measured: in berries one hour after smoke (Model 2; R = 0.98; R2 = 0.95; b = 0.97); in berries at harvest (Model 3; R = 0.99; R2 = 0.97; b = 0.96); in wines (Model 4; R = 0.99; R2 = 0.98; b = 0.98). Model 5 was based on the intensity of 12 wine descriptors determined via a consumer sensory test (Model 5; R = 0.98; R2 = 0.96; b = 0.97). These models could be used by winemakers to assess near real-time smoke contamination levels and to implement amelioration strategies to minimize smoke taint in wines following bushfires.

scholarly journals Detection of smoke-derived compounds from bushfires in Cabernet-Sauvignon grapes, must, and wine using Near-Infrared spectroscopy and machine learning algorithms

OENO One ◽  
2020 ◽  
Vol 54 (4) ◽  
pp. 1105-1119
Author(s):  
Vasiliki Summerson ◽  
Claudia Gonzalez Viejo ◽  
Damir D. Torrico ◽  
Alexis Pang ◽  
Sigfredo Fuentes
Keyword(s):  
Near Infrared ◽  
Smoke Exposure ◽  
High Density ◽  
Machine Learning Algorithms ◽  
Exposure Model ◽  
Volatile Phenols ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Harvest Model ◽  
Absorbance Spectra

The number and intensity of wildfires are increasing worldwide, thereby raising the risk of smoke contamination of grapevine berries and the development of smoke taint in wine. This study aimed to develop five artificial neural network (ANN) models from berry, must, and wine samples obtained from grapevines exposed to different levels of smoke: (i) Control (C), i.e., no misting or smoke exposure; (ii) Control with misting (CM), i.e., in-canopy misting, but no smoke exposure; (iii) low-density smoke treatment (LS); (iv) high-density smoke treatment (HS) and (v) a high-density smoke treatment with misting (HSM). Models 1, 2, and 3 were developed using the absorbance values of near-infrared (NIR) berry spectra taken one day after smoke exposure to predict levels of 10 volatile phenols (VP) and 18 glycoconjugates in grapes at either one day after smoke exposure (Model 1: R = 0.98; R2 = 0.97; b = 1) or at harvest (Model 2: R = 0.98; R2 = 0.97; b = 0.97), as well as six VP and 17 glycoconjugates in the final wine (Model 3: R = 0.98; R2 = 0.95; b = 0.99). Models 4 and 5 were developed to predict the levels of six VP and 17 glycoconjugates in wine. Model 4 used must NIR absorbance spectra as inputs (R = 0.99; R2 = 0.99; b = 1.00), while Model 5 used wine NIR absorbance spectra (R = 0.99; R2 = 0.97; b = 0.97). All five models displayed high accuracies and could be used by grape growers and winemakers to non-destructively assess at near real-time the levels of smoke-related compounds in grapes and/or wine in order to make timely decisions about grape harvest and smoke taint mitigation techniques in the winemaking process.

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 Glycosylation of Volatile Phenols in Grapes Following Pre-Harvest (On-Vine) vs. Post-Harvest (Off-Vine) Exposure to Smoke

Molecules ◽  
2021 ◽  
Vol 26 (17) ◽  
pp. 5277
Author(s):  
Julie A. Culbert ◽  
WenWen Jiang ◽  
Renata Ristic ◽  
Carolyn J. Puglisi ◽  
Elizabeth C. Nixon ◽  
...  
Keyword(s):  
Sensory Analysis ◽  
Field Trials ◽  
Smoke Exposure ◽  
Cabernet Sauvignon ◽  
Financial Viability ◽  
Volatile Phenols ◽  
Post Harvest ◽  
Volatile Phenol ◽  
Phenol Glycosides

Taint in grapes and wine following vineyard exposure to bushfire smoke continues to challenge the financial viability of grape and wine producers worldwide. In response, researchers are studying the chemical, sensory and physiological consequences of grapevine smoke exposure. However, studies involving winemaking trials are often limited by the availability of suitable quantities of smoke-affected grapes, either from vineyards exposed to smoke or from field trials involving the application of smoke to grapevines. This study compared the accumulation of volatile phenol glycosides (as compositional markers of smoke taint) in Viognier and Cabernet Sauvignon grapes exposed to smoke pre- vs. post-harvest, and found post-harvest smoke exposure of fruit gave similar levels of volatile phenol glycosides to fruit exposed to smoke pre-harvest. Furthermore, wines made from smoke-affected fruit contained similar levels of smoke-derived volatile phenols and their glycosides, irrespective of whether smoke exposure occurred pre- vs. post-harvest. Post-harvest smoke exposure therefore provides a valid approach to generating smoke-affected grapes in the quantities needed for winemaking trials and/or trials that employ both chemical and sensory analysis of wine.

scholarly journals Digital Smoke Taint Detection in Pinot Grigio Wines Using an E-Nose and Machine Learning Algorithms Following Treatment with Activated Carbon and a Cleaving Enzyme

Fermentation ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 119
Author(s):  
Vasiliki Summerson ◽  
Claudia Gonzalez Viejo ◽  
Damir D. Torrico ◽  
Alexis Pang ◽  
Sigfredo Fuentes
Keyword(s):  
Machine Learning ◽  
Activated Carbon ◽  
Low Cost ◽  
Aroma Compounds ◽  
Machine Learning Algorithms ◽  
Enzyme Treatment ◽  
Mean Values ◽  
Volatile Aroma ◽  
Carbon Treatment ◽  
Volatile Aroma Compounds

The incidence and intensity of bushfires is increasing due to climate change, resulting in a greater risk of smoke taint development in wine. In this study, smoke-tainted and non-smoke-tainted wines were subjected to treatments using activated carbon with/without the addition of a cleaving enzyme treatment to hydrolyze glycoconjugates. Chemical measurements and volatile aroma compounds were assessed for each treatment, with the two smoke taint amelioration treatments exhibiting lower mean values for volatile aroma compounds exhibiting positive ‘fruit’ aromas. Furthermore, a low-cost electronic nose (e-nose) was used to assess the wines. A machine learning model based on artificial neural networks (ANN) was developed using the e-nose outputs from the unsmoked control wine, unsmoked wine with activated carbon treatment, unsmoked wine with a cleaving enzyme plus activated carbon treatment, and smoke-tainted control wine samples as inputs to classify the wines according to the smoke taint amelioration treatment. The model displayed a high overall accuracy of 98% in classifying the e-nose readings, illustrating it may be a rapid, cost-effective tool for winemakers to assess the effectiveness of smoke taint amelioration treatment by activated carbon with/without the use of a cleaving enzyme. Furthermore, the use of a cleaving enzyme coupled with activated carbon was found to be effective in ameliorating smoke taint in wine and may help delay the resurgence of smoke aromas in wine following the aging and hydrolysis of glycoconjugates.

Otta Seals and Gravseals as Low-Cost Surfacing Alternatives for Low-Volume Roads: Experiences in South Africa

2003 ◽  
Vol 1819 (1) ◽  
pp. 338-342 ◽  
Author(s):  
Simon Oloo ◽  
Rob Lindsay ◽  
Sam Mothilal
Keyword(s):  
South Africa ◽  
Low Cost ◽  
Cost Savings ◽  
Cost Effective ◽  
Field Trials ◽  
Low Volume Roads ◽  
Kwazulu Natal ◽  
Traffic Volumes ◽  
Low Volume ◽  
To Come

The geology of the northeastern part of the province of KwaZulu–Natal, South Africa, is predominantly alluvial with vast deposits of sands. Suitable gravel sources are hard to come by, which results in high graveling and regraveling costs brought about by long haul distances and accelerated gravel loss. Most gravel roads carry fewer than 500 vehicles per day of which less than 10% are heavy vehicles. The high cost of regraveling has led to consideration of upgrading such roads to surfaced standard, even though traffic volumes do not justify upgrading. Traditional chip seals are expensive and cannot be economically justified on roads that carry fewer than 500 vehicles per day. The KwaZulu–Natal Department of Transport is actively involved in efforts to identify cost-effective alternative surfacing products for low-volume roads. Field trials were conducted with Otta seals and Gravseals, which have been used successfully in other countries, as low-cost surfacing products for low-volume roads. The Otta seal is formed by placing graded aggregates on a relatively thick film of soft binder that, because of traffic and rolling, works its way through the aggregates. Gravseal consists of a special semipriming rubberized binder that is covered by a graded aggregate. Both Otta seals and Gravseals provide relatively flexible bituminous surfaces suitable for low-volume roads. Cost savings are derived mainly from the broad aggregate specifications, which allow for the use of marginal materials.

Understanding and Optimizing Hybrid SSD with High-Density and Low-Cost Flash Memory

2021 ◽  
Author(s):  
Liang Shi ◽  
Longfei Luo ◽  
Yina Lv ◽  
Shicheng Li ◽  
Changlong Li ◽  
...  
Keyword(s):  
Flash Memory ◽  
Low Cost ◽  
High Density

Machine Learning Models for Finger Bend Evaluation using Implemented Low cost Flex Sensor

2021 ◽  
Vol 9 (VI) ◽  
pp. 3605-3611
Author(s):  
Pratyush Kaware
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Low Cost ◽  
Learning Algorithms ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

In this paper a cost-effective sensor has been implemented to read finger bend signals, by attaching the sensor to a finger, so as to classify them based on the degree of bent as well as the joint about which the finger was being bent. This was done by testing with various machine learning algorithms to get the most accurate and consistent classifier. Finally, we found that Support Vector Machine was the best algorithm suited to classify our data, using we were able predict live state of a finger, i.e., the degree of bent and the joints involved. The live voltage values from the sensor were transmitted using a NodeMCU micro-controller which were converted to digital and uploaded on a database for analysis.

MONITORING AND CLASSIFYING THE STATE OF HARD DISKS USING RECURRENT NEURAL NETWORKS

2021 ◽  
pp. 36-43
Author(s):  
L. A. Demidova ◽  
A. V. Filatov
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Experimental Studies ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Dense Layer ◽  
Hard Disks ◽  
Destructive Testing ◽  
Data Set ◽  
State Classification

The article considers an approach to solving the problem of monitoring and classifying the states of hard disks, which is solved on a regular basis, within the framework of the concept of non-destructive testing. It is proposed to solve this problem by developing a classification model using machine learning algorithms, in particular, using recurrent neural networks with Simple RNN, LSTM and GRU architectures. To develop a classification model, a data set based on the values of SMART sensors installed on hard disks it used. It represents a group of multidimensional time series. At the same time, the structure of the classification model contains two layers of a neural network with one of the recurrent architectures, as well as a Dropout layer and a Dense layer. The results of experimental studies confirming the advantages of LSTM and GRU architectures as part of hard disk state classification models are presented.

Association of Snoring Characteristics with Predominant Site of Collapse of Upper Airway in Obstructive Sleep Apnoea Patients

SLEEP ◽  
2021 ◽  
Author(s):  
Arun Sebastian ◽  
Peter A Cistulli ◽  
Gary Cohen ◽  
Philip de Chazal
Keyword(s):  
Acoustic Analysis ◽  
Audio Signal ◽  
Upper Airway ◽  
Lateral Wall ◽  
Sleep Apnoea ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Individual Site ◽  
Obstructive Sleep ◽  
The Individual

Abstract Study objectives Acoustic analysis of isolated events and snoring by previous researchers suggests a correlation between individual acoustic features and individual site of collapse events. In this study, we hypothesised that multi-parameter evaluation of snore sounds during natural sleep would provide a robust prediction of the predominant site of airway collapse. Methods The audio signals of 58 OSA patients were recorded simultaneously with full night polysomnography. The site of collapse was determined by manual analysis of the shape of the airflow signal during hypopnoea events and corresponding audio signal segments containing snore were manually extracted and processed. Machine learning algorithms were developed to automatically annotate the site of collapse of each hypopnoea event into three classes (lateral wall, palate and tongue-base). The predominant site of collapse for a sleep period was determined from the individual hypopnoea annotations and compared to the manually determined annotations. This was a retrospective study that used cross-validation to estimate performance. Results Cluster analysis showed that the data fits well in two clusters with a mean silhouette coefficient of 0.79 and an accuracy of 68% for classifying tongue/non-tongue collapse. A classification model using linear discriminants achieved an overall accuracy of 81% for discriminating tongue/non-tongue predominant site of collapse and accuracy of 64% for all site of collapse classes. Conclusions Our results reveal that the snore signal during hypopnoea can provide information regarding the predominant site of collapse in the upper airway. Therefore, the audio signal recorded during sleep could potentially be used as a new tool in identifying the predominant site of collapse and consequently improving the treatment selection and outcome.

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