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Advances in Odor Biosensors Employing Biological Principles or Components
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Advances in Odor Biosensors Employing Biological Principles or Components

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 1041

Special Issue Editor

Dr. Debajit Saha

E-Mail Website
Guest Editor
Biomedical Engineering and Institute for Quantitative Health Science & Engineering, Michigan State University, East Lansing, MI, USA
Interests: forward-engineered biosensors; insect olfaction in disease detection; neuromorphic sensing; non-invasive cancer detection; breath analysis

Special Issue Information

Dear Colleagues,

There is a large demand for volatile chemical sensors that leverage biological principles and attain higher sensitivity and selectivity for complex odor sensing in natural environments. Biological olfaction has optimized the odor coding principles over millions of years of evolution and several biological principles, including, cross-selective chemosensory array, combinatorial encoding schemes, and sparse decoding approaches, are routinely implemented in portable electronic-nose sensors, which are engineered odor sensors that employ biological principles of one-shot odor recognition.

In this Special Issue, advances of odor biosensors that implement various aspects of biological olfaction will be covered. This will include odor biosensors that (a) employ biological computational rules in engineered odor sensors, (b) incorporate biological sensory components in engineering platforms as hybrid sensing devices, (c) directly harness biological olfactory sensory systems to perform odor sensing, and (d) demonstrate the applications and limits of biological odor sensors (e.g., canine and insects) in disease diagnosis and homeland security applications.

Dr. Debajit Saha
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neuromorphic sensing
  • electronic nose
  • combinatorial coding
  • biohybrid sensor
  • canine sensing
  • insect olfaction
  • complex–odor mixture
  • disease detection
  • explosive detection
  • sensing in natural environments

Published Papers (1 paper)

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Research

13 pages, 2350 KiB  
Article
The Identification of Bee Comb Cell Contents Using Semiconductor Gas Sensors
by Beata Bąk, Jakub Wilk, Piotr Artiemjew, Maciej Siuda and Jerzy Wilde
Sensors 2023, 23(24), 9811; https://doi.org/10.3390/s23249811 - 14 Dec 2023
Cited by 1 | Viewed by 789
Abstract
Beekeeping is an extremely difficult field of agriculture. It requires efficient management of the bee nest so that the bee colony can develop efficiently and produce as much honey and other bee products as possible. The beekeeper, therefore, must constantly monitor the contents [...] Read more.
Beekeeping is an extremely difficult field of agriculture. It requires efficient management of the bee nest so that the bee colony can develop efficiently and produce as much honey and other bee products as possible. The beekeeper, therefore, must constantly monitor the contents of the bee comb. At the University of Warmia and Mazury in Olsztyn, research is being carried out to develop methods for efficient management of the apiary. One of our research goals was to test whether a gas detector (MCA-8) based on six semiconductor sensors—TGS823, TGS826, TGS832, TGS2600, TGS2602, and TGS2603 from the company FIGARO—is able to recognize the contents of bee comb cells. For this purpose, polystyrene and wooden test chambers were created, in which fragments of bee comb with different contents were placed. Gas samples were analyzed from an empty comb, a comb with sealed brood, a comb with open brood, a comb with carbohydrate food in the form of sugar syrup, and a comb with bee bread. In addition, a sample of gas from an empty chamber was tested. The results in two variants were analyzed: (1) Variant 1, the value of 270 s of sensor readings from the sample measurement (exposure phase), and (2) Variant 2, the value of 270 s of sensor readings from the sample measurement (measurement phase) with baseline correction by subtracting the last 600 s of surrounding air measurements (flushing phase). A five-time cross-validation 2 (5xCV2) test and the Monte Carlo cross-validation 25 (trained and tested 25 times) were performed. Fourteen classifiers were tested. The naive Bayes classifier (NB) proved to be the most effective method for distinguishing individual classes from others. The MCA-8 device brilliantly differentiates an empty comb from a comb with contents. It differentiates better between an empty comb and a comb with brood, with results of more than 83%. Lower class accuracy was obtained when distinguishing an empty comb from a comb with food and a comb with bee bread, with results of less than 73%. The matrix of six TGS sensors in the device shows promising versatility in distinguishing between various types of brood and food found in bee comb cells. This capability, though still developing, positions the MCA-8 device as a potentially invaluable tool for enhancing the efficiency and effectiveness of beekeepers in the future. Full article
(This article belongs to the Special Issue Advances in Odor Biosensors Employing Biological Principles or Components)
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