Preemptive identification of optimum fermentation time for black tea using electronic nose

By N. Bhattacharya, B. Tudu, A. Jana, D. Ghosh, Rajib Bandhopadhyaya, Manabendra Bhuyan. 2008

72 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Food Science

View Paper: https://www.semanticscholar.org/paper/51c0d7b0d9356b390b408f6a45d8ae9a1a419097

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Lars Karlsson
2. Martin Längkvist
3. Amy Loutfi

In Biomedical Engineering

1. Lars Karlsson
2. Martin Längkvist
3. Amy Loutfi

Citations in the corpus

• Performance Evaluation of a Novel iTongue for Indian Black Tea Discrimination (2010, unknown): “A number of efforts have been made to classify different beverages including tea using gas sensor array (E-Nose) [14-16] and electrochemical techniques such as Cyclic Voltametry, Potentiometry and Conductivity [17, 18].”
• A review of unsupervised feature learning and deep learning for time-series modeling (2014, background): “smell of spices [133], ternary mixtures [124], optimum fermentation time for black tea [12], and vintages of wine [130].”