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Sensors for Food Safety and Quality Assessment—Volume II
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Sensors for Food Safety and Quality Assessment—Volume II

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".

Deadline for manuscript submissions: closed (25 June 2024) | Viewed by 696

Special Issue Editors

Dr. Yujie Wang

E-Mail Website
Guest Editor
State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
Interests: optical sensing technology; computer vision; electronic nose; electronic tongue; food quality and safety assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Mingfei Pan

E-Mail Website
Guest Editor
College of Food Science and Engineering, Tianjin University of Science & Technology, Tianjin 300457, China
Interests: food science; food nutrition and safety; pesticide and veterinary drug testing; food harmful substances analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Yang Song

E-Mail Website
Guest Editor
Tianjin Key Laboratory of Animal and Plant Resistance, Tianjin Normal University, Tianjin, China
Interests: nanomaterials; sensors; food safety; food analysis

Special Issue Information

Dear Colleagues,

As the world's population and the popularity of healthy eating grow, so too have the consumer demands for food quality and safety. These demands have led food-focused researchers to develop advanced analytical methods and sensors enabling the rapid assessment of food safety and quality. This Special Issue will focus on such advanced sensors, including, but not limited to: optical sensors (infrared spectroscopy, Raman spectroscopy); computer vision; hyperspectral imaging; and bionic sensing technologies (electronic nose, electronic tongue). Papers on novel approaches for sensor data analysis based on strategies such as big data and deep learning are also welcome.

With this Special Issue, we hope to present recent developments in sensors for food quality and safety assessment to promote the advancement of food analysis methods and data handling. Original research articles, reviews and short communications will all be accepted.

Dr. Yujie Wang
Dr. Mingfei Pan
Dr. Yang Song
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 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. Foods 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 2900 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

  • food safety and quality
  • advanced sensor
  • optical sensing
  • computer vision
  • non-destructive testing sensor
  • bionic technology
  • surface-enhanced Raman spectroscopy
  • chemometrics

Published Papers (1 paper)

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Research

20 pages, 2410 KiB  
Article
Detection of Anthocyanins in Potatoes Using Micro-Hyperspectral Images Based on Convolutional Neural Networks
by Fuxiang Wang, Qiying Li, Weigang Deng, Chunguang Wang and Lei Han
Foods 2024, 13(13), 2096; https://doi.org/10.3390/foods13132096 - 1 Jul 2024
Viewed by 456
Abstract
The color potato has the function of both a food and vegetable. The color potato not only contains various amino acids and trace elements needed by the human body but also contains anthocyanins. Anthocyanins have many functions, such as antioxidation, inflammation inhibition, vision [...] Read more.
The color potato has the function of both a food and vegetable. The color potato not only contains various amino acids and trace elements needed by the human body but also contains anthocyanins. Anthocyanins have many functions, such as antioxidation, inflammation inhibition, vision improvement, and cancer prevention, so colored potatoes are deeply loved by consumers and have good market prospects. However, at present, the detection of anthocyanin content in color potatoes mainly depends on chemical methods, which are time-consuming and laborious, so it is necessary to study a fast and accurate detection method. In this study, microscopic hyperspectral equipment was used to collect the spectral information of the outer skin and inner skin of potatoes. The original spectrum, pretreatment spectrum, and characteristic spectrum variables of the outer skin and inner skin were predicted by the convolution neural network (CNN) algorithm and partial least squares regression (PLS) algorithm, respectively, and the performance of the model was evaluated by the prediction set correlation coefficient (Rp), prediction set root mean square error (RMSEP), correction set correlation coefficient (Rc), correction set root mean square error (RMSEC), and residual prediction deviation (RPD). The results revealed that the inner skin Raw + CNN model constructed under raw spectral data is optimal with Rc = 0.9508, RMSEC = 0.0374%, Rp = 0.9461, RMSEP = 0.2361% and RPD = 4.4933. The inner skin Savitzky-Golay (SG) + Detrend (DET) + CNN model constructed from pre-processed spectral data is optimal with Rc = 0.9499, RMSEC = 0.0359%, Rp = 0.9439, RMSEP = 0.2384%, RPD = 4.6516. The inner skin DET + competitive adaptive reweighted sampling (CARS) +CNN model constructed from the feature-based spectral data was optimal with Rc = 0.9527, RMSEC = 0.0708%, Rp = 0.9457, RMSEP = 0.2711%, and RPD = 4.1623. It can be seen that the Rp, RMSEP, Rc, RMSEC, and RPD values for modeling the spectral information of the inner skin were higher than those of the outer skin under the three different spectral data. The prediction accuracy of the model built by the CNN algorithm was better than the conventional algorithm PLS, the application of the CNN algorithm in inner skin can achieve accurate prediction of anthocyanin content in potato. Full article
(This article belongs to the Special Issue Sensors for Food Safety and Quality Assessment—Volume II)
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