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Special Issue "Novel Contactless Sensors for Food, Beverage and Packaging Evaluation"

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

Deadline for manuscript submissions: 29 October 2021.

Special Issue Editors

Dr. Claudia Gonzalez Viejo
E-Mail Website
Guest Editor
Digital Agriculture Food and Wine, School of Agriculture and Food, Faculty of Veterinary and Agricultural Science, The University of Melbourne, Parkville, VIC 3010, Australia
Interests: food science and engineering; sensory science; computer vision; sensors; robotics; machine learning; artificial intelligence
Special Issues and Collections in MDPI journals
Dr. Sigfredo Fuentes
E-Mail Website
Guest Editor
Digital Agriculture Food and Wine, School of Agriculture and Food, Faculty of Veterinary and Agricultural Science, The University of Melbourne, Parkville VIC 3010, Australia
Interests: digital agriculture; food and wine sciences; plant physiology; remote sensing; climate change; robotics applied to agriculture and computer programming
Special Issues and Collections in MDPI journals
Dr. Damir Torrico
E-Mail Website
Guest Editor
Department of Wine, Food and Molecular Biosciences, Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln University, Lincoln 7647, New Zealand
Interests: sensory evaluation; consumer science; product development; statistical analysis
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Recent digital developments in food, beverage, and packaging analysis have been focused on using contactless sensors, such as remote sensing and biometrics, to assess the quality traits of produces, detect packaging defects during the production process, and understand perception through sensory analysis based on consumer biometrics. The implementation of these novel techniques is aiding the food, beverage, and packaging industries in the rapid, efficient, cost-effective, and reliable evaluation and assessment of products at any stage of the production chain for the early detection of potential faults within the process and consumer evaluation of the final product and packaging. 

This Special Issue will bring together high-quality papers focused on these technologies and their applications in the food and beverage industries, including packaging assessments. These contactless sensors incorporate electronic noses and tongues, the use of infrared thermal cameras, UV-Vis, mid- and near-infrared spectroscopy, and computer vision analysis, among others. These technologies may be applied to assess food, beverages, and packaging quality traits such as sensory attributes, physicochemical components, nutritional values, and defects, at any stage in the production chain, which aid in the quality assessment of products.

Dr. Claudia Gonzalez Viejo
Dr. Sigfredo Fuentes
Dr. Damir Torrico
Guest Editors

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 papers will be 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 2200 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

  • electronic nose
  • electronic tongue
  • infrared thermal cameras
  • computer vision (video and image analysis)
  • mid- and near-infrared spectroscopy
  • UV-Vis spectroscopy
  • hyperspectral images
  • artificial intelligence
  • machine learning

Published Papers (1 paper)

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Research

Open AccessArticle
Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity
Sensors 2021, 21(6), 2016; https://doi.org/10.3390/s21062016 - 12 Mar 2021
Viewed by 364
Abstract
Aroma is one of the main attributes that consumers consider when appreciating and selecting a coffee; hence it is considered an important quality trait. However, the most common methods to assess aroma are based on expensive equipment or human senses through sensory evaluation, [...] Read more.
Aroma is one of the main attributes that consumers consider when appreciating and selecting a coffee; hence it is considered an important quality trait. However, the most common methods to assess aroma are based on expensive equipment or human senses through sensory evaluation, which is time-consuming and requires highly trained assessors to avoid subjectivity. Therefore, this study aimed to estimate the coffee intensity and aromas using a low-cost and portable electronic nose (e-nose) and machine learning modeling. For this purpose, triplicates of nine commercial coffee samples with different intensity levels were used for this study. Two machine learning models were developed based on artificial neural networks using the data from the e-nose as inputs to (i) classify the samples into low, medium, and high-intensity (Model 1) and (ii) to predict the relative abundance of 45 different aromas (Model 2). Results showed that it is possible to estimate the intensity of coffees with high accuracy (98%; Model 1), as well as to predict the specific aromas obtaining a high correlation coefficient (R = 0.99), and no under- or over-fitting of the models were detected. The proposed contactless, nondestructive, rapid, reliable, and low-cost method showed to be effective in evaluating volatile compounds in coffee, which is a potential technique to be applied within all stages of the production process to detect any undesirable characteristics on–time and ensure high-quality products. Full article
(This article belongs to the Special Issue Novel Contactless Sensors for Food, Beverage and Packaging Evaluation)
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