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.

Volatile aroma compounds of sugarcane molasses as affected by spray drying at low and high temperature

LWT ◽  
2021 ◽  
pp. 111288
Author(s):  
Katarzyna Samborska ◽  
Radosław Bonikowski ◽  
Danuta Kalemba ◽  
Alicja Barańska ◽  
Aleksandra Jedlińska ◽  
...  
Keyword(s):  
High Temperature ◽  
Spray Drying ◽  
Aroma Compounds ◽  
Sugarcane Molasses ◽  
Volatile Aroma ◽  
Volatile Aroma Compounds

Extraction of volatile aroma compounds from toasted oak wood using pulsed electric field

2021 ◽  
Author(s):  
George V. Ntourtoglou ◽  
Foteini Drosou ◽  
Yang Enoch ◽  
Evangelia A. Tsapou ◽  
Eleni Bozinou ◽  
...  
Keyword(s):  
Electric Field ◽  
Pulsed Electric Field ◽  
Aroma Compounds ◽  
Oak Wood ◽  
Volatile Aroma ◽  
Volatile Aroma Compounds

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.

scholarly journals Supermarket Sales Prediction Using Regression

2021 ◽  
Vol 10 (2) ◽  
pp. 1153-1157
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Customer Data ◽  
Sales Data ◽  
Online Marketplace ◽  
Sales Prediction ◽  
The Future

Sales forecasting is an important when it comes to companies who are engaged in retailing, logistics, manufacturing, marketing and wholesaling. It allows companies to allocate resources efficiently, to estimate revenue of the sales and to plan strategies which are better for company’s future. In this paper, predicting product sales from a particular store is done in a way that produces better performance compared to any machine learning algorithms. The dataset used for this project is Big Mart Sales data of the 2013.Nowadays shopping malls and Supermarkets keep track of the sales data of the each and every individual item for predicting the future demand of the customer. It contains large amount of customer data and the item attributes. Further, the frequent patterns are detected by mining the data from the data warehouse. Then the data can be used for predicting the sales of the future with the help of several machine learning techniques (algorithms) for the companies like Big Mart. In this project, we propose a model using the Xgboost algorithm for predicting sales of companies like Big Mart and founded that it produces better performance compared to other existing models. An analysis of this model with other models in terms of their performance metrics is made in this project. Big Mart is an online marketplace where people can buy or sell or advertise your merchandise at low cost. The goal of the paper is to make Big Mart the shopping paradise for the buyers and a marketing solutions for the sellers as well. The ultimate aim is the complete satisfaction of the customers. The project “SUPERMARKET SALES PREDICTION” builds a predictive model and finds out the sales of each of the product at a particular store. The Big Mart use this model to under the properties of the products which plays a major role in increasing the sales. This can also be done on the basis hypothesis that should be done before looking at the data

Modeling the Partition of Volatile Aroma Compounds from a Cloud Emulsion

2002 ◽  
Vol 50 (7) ◽  
pp. 1985-1990 ◽  
Author(s):  
Michelle E. Carey ◽  
Tom Asquith ◽  
Robert S. T. Linforth ◽  
Andrew J. Taylor
Keyword(s):  
Aroma Compounds ◽  
Volatile Aroma ◽  
Volatile Aroma Compounds

scholarly journals Batch distillation of spirits: experimental study and simulation of the behaviour of volatile aroma compounds

2019 ◽  
Vol 125 (2) ◽  
pp. 268-283 ◽  
Author(s):  
Adrien Douady ◽  
Cristian Puentes ◽  
Pierre Awad ◽  
Martine Esteban-Decloux
Keyword(s):  
Experimental Study ◽  
Aroma Compounds ◽  
Batch Distillation ◽  
Volatile Aroma ◽  
Volatile Aroma Compounds

scholarly journals Influence of media composition and temperature on volatile aroma production by various wine yeast strains

2008 ◽  
Vol 26 (No. 5) ◽  
pp. 376-382 ◽  
Author(s):  
V. Petravić Tominac ◽  
K. Kovačević Ganić ◽  
D. Komes ◽  
L. Gracin ◽  
M. Banović ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Synthetic Medium ◽  
Aroma Compounds ◽  
Small Scale ◽  
Wine Yeasts ◽  
Wine Production ◽  
Yeast Strains ◽  
Aroma Production ◽  
Volatile Aroma ◽  
Volatile Aroma Compounds

Volatile aroma compounds production by two autochthonous <I>Saccharomyces cerevisiae</I> strains, isolated from Istria region, and three other yeast strains (<I>Saccharomyces bayanus</I> and two commercial <I>Saccharomyces cerevisiae</I> wine yeasts) was investigated on a small scale using synthetic VP4 medium and Graševina must at 12 and 20°C. The results obtained by gas chromatography analyses were compared with the aroma production properties of the native microflora, remaining after Graševina must sulphiting. In both media and at both temperatures, the wine yeasts investigated showed different metabolic profiles regarding the tested volatile aroma compounds, which should be taken in consideration for autochthonous wine production. Although the synthetic medium proved to be appropriate for the investigation of the fermentative properties, the determination of secondary aroma production by wine yeasts has to be conducted by must fermentation or possibly by fermentation of another synthetic medium whose composition would be more similar to must.

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