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Non-destructive Techniques for Sustainable Food Quality Evaluation
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Non-destructive Techniques for Sustainable Food Quality Evaluation

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Food".

Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 8636

Special Issue Editor

Dr. Katarzyna Włodarska

E-Mail Website
Guest Editor
Department of Technology and Instrumental Analysis, Institute of Quality Science, Poznań University of Economics and Business, 61-875 Poznań, Poland
Interests: food analysis; bioactive food components; spectroscopy; sensory evaluation; multivariate data analysis

Special Issue Information

Dear Colleagues,

Food quality evaluation involves a comprehensive assessment of various characteristics including chemical composition, physical and microbial properties, as well as sensory attributes such as appearance, texture, and flavor. The complexity of food requires the use of many conventional analytical methods including physical-chemical determinations and sensory analysis. Most of conventional methods are tedious, costly, time-consuming, and destructive because they usually require sample preparation procedures. These disadvantages make them frequently unsuitable for rapid analysis, especially for in-line quality assessment and large-scale industrial applications. Furthermore, the reagent and energy consumptions of these measurements have a negative impact on the environment. Thus, non-destructive approaches such as spectroscopy and spectral imaging techniques have become effective tools that provide analytical information in a more environmentally friendly way. The application of imaging and spectroscopy enables rapid and simple non-destructive measurements and the simultaneous determination of several parameters, so can therefore be used in the routine quality control of a large number of samples as an alternative to conventional analysis methods. Many studies have demonstrated the successful applications of various spectroscopic and spectral imaging techniques in combination with chemometrics for the qualitative and quantitative evaluation of numerous food products and for food process control. The use of these methods at different stages of the food production chain reduces the consumption of reagents and energy and should be promoted to support sustainable development.

The purpose of this Special Issue of Sustainability is to provide a forum where various findings and implementations can be presented, discussed, and promoted, and the future perspectives of emerging non-destructive tools can be considered. This Special Issue aims to collect up-to-date research papers and reviews referring to the development of new non-destructive methods. Application-oriented papers related to using non-invasive techniques in various sectors of the agri-food industry as well as at different stages of the food supply chain are also welcome.

Dr. Katarzyna Włodarska
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. Sustainability 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 2400 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 quality evaluation
  • sustainable food system
  • green analytical techniques
  • spectroscopy techniques
  • spectral imaging
  • chemometrics
  • application of non-destructive techniques

Published Papers (3 papers)

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Research

10 pages, 1414 KiB  
Article
The Application of Visible and Near-Infrared Spectroscopy Combined with Chemometrics in Classification of Dried Herbs
by Anna Dankowska, Agnieszka Majsnerowicz, Wojciech Kowalewski and Katarzyna Włodarska
Sustainability 2022, 14(11), 6416; https://0-doi-org.brum.beds.ac.uk/10.3390/su14116416 - 24 May 2022
Cited by 4 | Viewed by 1545
Abstract
The fast differentiation and classification of herb samples are complicated processes due to the presence of many various chemical compounds. Traditionally, separation techniques have been employed for the identification and quantification of compounds present in different plant matrices, but they are tedious, time-consuming [...] Read more.
The fast differentiation and classification of herb samples are complicated processes due to the presence of many various chemical compounds. Traditionally, separation techniques have been employed for the identification and quantification of compounds present in different plant matrices, but they are tedious, time-consuming and destructive. Thus, a non-targeted approach would be specifically advantageous for this purpose. In the present study, spectroscopy in the visible and near-infrared range and pattern recognition techniques, including the principal component analysis (PCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), super k-nearest neighbor (SKNN) and support vector machine (SVM) techniques, were applied to develop classification models that enabled the discrimination of various commercial dried herbs, including mint, linden, nettle, sage and chamomile. The classification error rates in the validation data were below 10% for all the classification methods, except for SKNN. The results obtained confirm that spectroscopy and pattern recognition methods constitute a good non-destructive tool for the rapid identification of herb species that can be used in routine quality control by the pharmaceutical industry, as well as herbal suppliers, to avoid mislabeling. Full article
(This article belongs to the Special Issue Non-destructive Techniques for Sustainable Food Quality Evaluation)
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20 pages, 15738 KiB  
Article
U-Net-Based Foreign Object Detection Method Using Effective Image Acquisition System: A Case of Almond and Green Onion Flake Food Process
by Guk-Jin Son, Dong-Hoon Kwak, Mi-Kyung Park, Young-Duk Kim and Hee-Chul Jung
Sustainability 2021, 13(24), 13834; https://0-doi-org.brum.beds.ac.uk/10.3390/su132413834 - 14 Dec 2021
Cited by 5 | Viewed by 3515
Abstract
Supervised deep learning-based foreign object detection algorithms are tedious, costly, and time-consuming because they usually require a large number of training datasets and annotations. These disadvantages make them frequently unsuitable for food quality evaluation and food manufacturing processes. However, the deep learning-based foreign [...] Read more.
Supervised deep learning-based foreign object detection algorithms are tedious, costly, and time-consuming because they usually require a large number of training datasets and annotations. These disadvantages make them frequently unsuitable for food quality evaluation and food manufacturing processes. However, the deep learning-based foreign object detection algorithm is an effective method to overcome the disadvantages of conventional foreign object detection methods mainly used in food inspection. For example, color sorter machines cannot detect foreign objects with a color similar to food, and the performance is easily degraded by changes in illuminance. Therefore, to detect foreign objects, we use a deep learning-based foreign object detection algorithm (model). In this paper, we present a synthetic method to efficiently acquire a training dataset of deep learning that can be used for food quality evaluation and food manufacturing processes. Moreover, we perform data augmentation using color jitter on a synthetic dataset and show that this approach significantly improves the illumination invariance features of the model trained on synthetic datasets. The F1-score of the model that trained the synthetic dataset of almonds at 360 lux illumination intensity achieved a performance of 0.82, similar to the F1-score of the model that trained the real dataset. Moreover, the F1-score of the model trained with the real dataset combined with the synthetic dataset achieved better performance than the model trained with the real dataset in the change of illumination. In addition, compared with the traditional method of using color sorter machines to detect foreign objects, the model trained on the synthetic dataset has obvious advantages in accuracy and efficiency. These results indicate that the synthetic dataset not only competes with the real dataset, but they also complement each other. Full article
(This article belongs to the Special Issue Non-destructive Techniques for Sustainable Food Quality Evaluation)
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11 pages, 916 KiB  
Article
A Method of Evaluating Apple Juice Adulteration with Sucrose Based on Its Electrical Properties and RCC Model
by Joanna Katarzyna Banach and Ryszard Żywica
Sustainability 2021, 13(12), 6716; https://0-doi-org.brum.beds.ac.uk/10.3390/su13126716 - 13 Jun 2021
Cited by 3 | Viewed by 2403
Abstract
This study aimed to identify possibilities of controlling basic quality attributes (total soluble solids, organic acids, density, pH) and assessing the adulteration of natural dissociating solids with sucrose in apple juice produced from Malus domestica Borkh, var. Cortland, Idared, and Lobo (family Rosaceae [...] Read more.
This study aimed to identify possibilities of controlling basic quality attributes (total soluble solids, organic acids, density, pH) and assessing the adulteration of natural dissociating solids with sucrose in apple juice produced from Malus domestica Borkh, var. Cortland, Idared, and Lobo (family Rosaceae Juss), using electrical parameters (conductivity Z, Y; capacity Cp, Cs) and the RCC equivalent electrical model. The feasibility of employing electrical parameters was established based on correlations between selected quality attributes of apple juices varying in sucrose contents in the extract TSSConc (0%, 15%, 20%, 25%, 30%) and their electrical parameters measured in a frequency range of 100 Hz to 100 kHz. The significant (p ≤ 0.01) correlations obtained between the selected physicochemical parameters of juice (TSSConc, OA) and electrical properties point to the feasibility of using them as an alternative quality assessment method to the reference methods (refractometric or potentiometric titration) used by the external supervising bodies. The electrical parameters (including Z100Hz and Y100Hz) measured in the RCC model can, in the future, aid the design of a simple tool for the quantitative determination of apple juice adulteration with sucrose. They also encourage further research of this electrical method as an alternative to traditional analytical methods for evaluating the authenticity or adulteration of commercial fruit juices with sucrose or other sweetening agents. Full article
(This article belongs to the Special Issue Non-destructive Techniques for Sustainable Food Quality Evaluation)
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