scholarly journals Beer Taste Detection Based on Electronic Tongue

2020 ◽  
Vol 32 (9) ◽  
pp. 2949
Author(s):  
Hongmei Zhang ◽  
Guangyu Zou ◽  
Wanru Liu ◽  
Zheng Zhou
Keyword(s):  
Electronic Tongue ◽  
Taste Detection

Electronic tongue and their applications in food industry

2017 ◽  
Vol 8 (1and2) ◽  
pp. 98-102
Author(s):  
BHUPINDER SINGH ◽  
PRIYANKA HANDA
Keyword(s):  
Food Industry ◽  
Electronic Tongue

scholarly journals A closed-loop optogenetic screen for neurons controlling feeding in Drosophila

2021 ◽  
Author(s):  
Celia K S Lau ◽  
Meghan Jelen ◽  
Michael D Gordon
Keyword(s):  
Drosophila Melanogaster ◽  
Expression Pattern ◽  
Sensory Neurons ◽  
Closed Loop ◽  
Higher Order ◽  
Taste Detection ◽  
Proof Of Principle ◽  
Feeding Circuits

Abstract Feeding is an essential part of animal life that is greatly impacted by the sense of taste. Although the characterization of taste-detection at the periphery has been extensive, higher order taste and feeding circuits are still being elucidated. Here, we use an automated closed-loop optogenetic activation screen to detect novel taste and feeding neurons in Drosophila melanogaster. Out of 122 Janelia FlyLight Project GAL4 lines preselected based on expression pattern, we identify six lines that acutely promote feeding and 35 lines that inhibit it. As proof of principle, we follow up on R70C07-GAL4, which labels neurons that strongly inhibit feeding. Using split-GAL4 lines to isolate subsets of the R70C07-GAL4 population, we find both appetitive and aversive neurons. Furthermore, we show that R70C07-GAL4 labels putative second-order taste interneurons that contact both sweet and bitter sensory neurons. These results serve as a resource for further functional dissection of fly feeding circuits.

scholarly journals Estimating hydroxytyrosol-tyrosol derivatives amounts in cv. Cobrançosa olive oils based on the electronic tongue analysis of olive paste extracts

LWT ◽  
2021 ◽  
Vol 147 ◽  
pp. 111542
Author(s):  
Ítala M.G. Marx ◽  
Susana Casal ◽  
Nuno Rodrigues ◽  
Ana C.A. Veloso ◽  
José A. Pereira ◽  
...  
Keyword(s):  
Electronic Tongue ◽  
Olive Oils

Quality-specific differences in rat taste detection performance as a function of stimulus volume

1991 ◽  
Vol 50 (4) ◽  
pp. 711-716 ◽  
Author(s):  
Gary M. Brosvic ◽  
Gerald S. Hecht ◽  
Susan LaHaye ◽  
M.Michelle Rowe ◽  
Judith M. Risser ◽  
...  
Keyword(s):  
Detection Performance ◽  
Taste Detection

scholarly journals Effects of Sugar Cane Molasses Addition on the Fermentation Quality, Microbial Community, and Tastes of Alfalfa Silage

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 355
Author(s):  
Runbo Luo ◽  
Yangdong Zhang ◽  
Fengen Wang ◽  
Kaizhen Liu ◽  
Guoxin Huang ◽  
...  
Keyword(s):  
Sugar Cane ◽  
Ph Value ◽  
Electronic Tongue ◽  
Water Soluble ◽  
Soluble Carbohydrates ◽  
Cane Molasses ◽  
Fermentation Quality ◽  
Alfalfa Silage ◽  
Additive Control

The objective was to study the effects of sugar cane molasses addition on the fermentation quality and tastes of alfalfa silage. Fresh alfalfa was ensiled with no additive (Control), 1% molasses (M1), 2% molasses (M2), and 3% molasses (M3) for 206 days. The chemical composition and fermentation characteristics of the alfalfa silages were determined, the microbial communities were described by 16S rRNA sequencing, and the tastes were evaluated using an electronic tongue sensing system. With the amount of added molasses (M), most nutrition (dry matter and crude protein) was preserved and water-soluble carbohydrates (WSC) were sufficiently used to promote the fermentation, resulting in a pH reduction from 5.16 to 4.48. The lactic acid (LA) content and LA/acetic acid (AA) significantly increased, indicating that the fermentation had turned to homofermentation. After ensiling, Enterococcus and Lactobacillus were the dominant genus in all treatments and the undesirable microbes were inhibited, resulting in lower propionic acid (PA), butyric acid (BA), and NH3-N production. In addition, bitterness, astringency, and sourness reflected tastes of alfalfa silage, while umami and sourness changed with the amount of added molasses. Therefore, molasses additive had improved the fermentation quality and tastes of alfalfa silage, and the M3 group obtained the ideal pH value (below 4.5) and the best condition for long-term preservation.

scholarly journals Application of Artificial Intelligence in Food Industry—a Guideline

2021 ◽  
Author(s):  
Nidhi Rajesh Mavani ◽  
Jarinah Mohd Ali ◽  
Suhaili Othman ◽  
M. A. Hussain ◽  
Haslaniza Hashim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Food Industry ◽  
Near Infrared ◽  
Vision System ◽  
Electronic Tongue ◽  
World Population ◽  
Computer Vision System ◽  
The Past

AbstractArtificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.

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