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Foods
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Rapid and Non-destructive Determination of Food Quality Control
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Rapid and Non-destructive Determination of Food Quality Control

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 10657

Special Issue Editor

Dr. Ricardo Páscoa

E-Mail Website
Guest Editor
REQUIMTE/LAQV, Department of Chemical Sciences, Faculty of Pharmacy of University of Porto, R. Jorge Viterbo Ferreira n°. 228, 4050-313 Porto, Portugal
Interests: development of rapid, easy, cost-effective; multiparametric analytical methods capable of being applied in-situ; vibrational spectroscopy; chemometrics; quality control of agro-food products; valorization of agro-food residues
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The quality control of food products is a constant concern of producers, consumers and legal authorities. In the case of producers, it is essential to have a tool that ensures the final quality of the product in an easy, rapid, reliable and non-destructive way. It is very difficult for producers to build a trustable brand and very easy to lose all the acquired trust. For consumers, it is important to trust that the products purchased will not have any detrimental effect on their health and that the product quality is guaranteed. Finally, legal authorities must monitor the quality of the products available to consumers to ensure their safety, in terms of their physicochemical compositions and origin, in addition to verifying possible adulterations. Therefore, it is necessary to develop analytical techniques capable of fulfilling all the proposed objectives in order to provide a higher security and protection of the food products regarding not only economic impact but also public health. If these techniques are also cost-effective, environmentally friendly and capable of being applied in situ, it would be very innovative and geared towards the needs of our society.

Dr. Ricardo Páscoa
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. 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

  • quality control
  • food
  • rapid
  • non-destructive
  • analytical methods

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Published Papers (4 papers)

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Research

16 pages, 4395 KiB  
Article
Lipid Profile Quantification and Species Discrimination of Pine Seeds through NIR Spectroscopy: A Feasibility Study
by Mariem Khouja, Ricardo N. M. J. Páscoa, Diana Melo, Anabela S. G. Costa, M. Antónia Nunes, Abdelhamid Khaldi, Chokri Messaoud, M. Beatriz P. P. Oliveira and Rita C. Alves
Foods 2022, 11(23), 3939; https://doi.org/10.3390/foods11233939 - 6 Dec 2022
Cited by 2 | Viewed by 1629
Abstract
Pine seeds are known for their richness in lipid compounds and other healthy substances. However, the reference procedures that are commonly applied for their analysis are quite laborious, time-consuming, and expensive. Therefore, it is important to develop rapid, accurate, multi-parametric, cost-effective and, essentially, [...] Read more.
Pine seeds are known for their richness in lipid compounds and other healthy substances. However, the reference procedures that are commonly applied for their analysis are quite laborious, time-consuming, and expensive. Therefore, it is important to develop rapid, accurate, multi-parametric, cost-effective and, essentially, environmentally friendly analytical techniques that are easily implemented at an industrial scale. The viability of using near-infrared (NIR) spectroscopy to analyse the seed lipid content and profile of three different pine species (Pinus halepensis, Pinus brutia and Pinus pinaster) was investigated. Moreover, species discrimination using NIR was also attempted. Different chemometric models, namely partial least squares (PLS) regression, for lipid analysis, and partial least square—discriminant analysis (PLS-DA), for pine species discrimination, were applied. In relation to the discrimination of pine seed species, a total of 90.5% of correct classification rates were obtained. Regarding the quantification models, most of the compounds assessed yielded determination coefficients (R2P) higher than 0.80. The best PLS models were obtained for total fat, vitamin E, saturated and monounsaturated fatty acids, C20:2, C20:1n9, C20, C18:2n6c, C18:1n9c, C18 and C16:1. Globally, the obtained results demonstrated that NIR spectroscopy is a suitable analytical technique for lipid analysis and species discrimination of pine seeds. Full article
(This article belongs to the Special Issue Rapid and Non-destructive Determination of Food Quality Control)
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8 pages, 1104 KiB  
Article
A Rapid Detection of Whole Wheat Gluten Quality by a Novel Chemometric Technique—GlutoPeak
by Secil Turksoy and Demet Onar
Foods 2022, 11(13), 1927; https://doi.org/10.3390/foods11131927 - 28 Jun 2022
Cited by 4 | Viewed by 2175
Abstract
The study aims to accurately detect gluten quality of whole wheat flour without a refining process by measuring gluten aggregation properties with a novel and non-destructive chemometric technique called GlutoPeak, coupled with principal component analyses (PCA) and hierarchical cluster analyses (HCA). For this [...] Read more.
The study aims to accurately detect gluten quality of whole wheat flour without a refining process by measuring gluten aggregation properties with a novel and non-destructive chemometric technique called GlutoPeak, coupled with principal component analyses (PCA) and hierarchical cluster analyses (HCA). For this purpose, whole wheat flour samples from 125 common bread wheat cultivars were analyzed for protein content (PC), wet gluten content (WGC), and Zeleny sedimentation value (SV). The correlations of GlutoPeak indices (peak maximum time, PMT; maximum torque, MT; torque 15 s before MT, AM; torque 15 s after MT) with other conventional wheat quality parameters were evaluated. Results indicated that MT had high correlations with WGC (r = 0.627, p < 0.05) and PC (r = 0.589, p < 0.05) while PC (r = 0.511, p < 0.05) and WGC (r = 0.566, p < 0.05) values had moderate correlations with the GlutoPeak PM index. Considering the effect of regions, the MT and PM GlutoPeak indices are powerful parameters to discriminate whole wheat flour samples by their gluten strengths. In conclusion, the GlutoPeak test can be a powerful and reliable tool for prediction of refined and unrefined wheat quality without being time-consuming. Full article
(This article belongs to the Special Issue Rapid and Non-destructive Determination of Food Quality Control)
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16 pages, 2222 KiB  
Article
Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies
by Aimi Aznan, Claudia Gonzalez Viejo, Alexis Pang and Sigfredo Fuentes
Foods 2022, 11(9), 1181; https://doi.org/10.3390/foods11091181 - 19 Apr 2022
Cited by 8 | Viewed by 3314
Abstract
Aroma and other physicochemical parameters are important attributes influencing consumer perception and acceptance of rice. However, current methods using multiple instruments and laboratory analysis make these assessments costly and time-consuming. Therefore, this study aimed to assess rice quality traits of 17 commercial rice [...] Read more.
Aroma and other physicochemical parameters are important attributes influencing consumer perception and acceptance of rice. However, current methods using multiple instruments and laboratory analysis make these assessments costly and time-consuming. Therefore, this study aimed to assess rice quality traits of 17 commercial rice types using a low-cost electronic nose and portable near-infrared spectrometer coupled with machine learning (ML). Specifically, artificial neural networks (ANN) were used to classify the type of rice and predict rice quality traits (aromas, color, texture, and pH of cooked rice) as targets. The ML models developed showed that the chemometrics obtained from both sensor technologies successfully classified the rice (Model 1: 98.7%; Model 2: 98.6%) and predicted the peak area of aromas obtained by gas chromatography-mass spectroscopy found in raw (Model 3: R = 0.95; Model 6: R = 0.95) and cooked rice (Model 4: R = 0.98; Model 7: R = 0.96). Furthermore, a high R = 0.98 was obtained for Model 5 to estimate the color, texture, and pH of cooked rice. The proposed method is rapid, low-cost, reliable, and may help the rice industry increase high-quality rice production and accelerate the adoption of digital technologies and artificial intelligence to support the rice value chain. Full article
(This article belongs to the Special Issue Rapid and Non-destructive Determination of Food Quality Control)
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15 pages, 3187 KiB  
Article
Quantitation of Water Addition in Octopus Using Time Domain Reflectometry (TDR): Development of a Rapid and Non-Destructive Food Analysis Method
by Bárbara Teixeira, Helena Vieira, Sandra Martins and Rogério Mendes
Foods 2022, 11(6), 791; https://doi.org/10.3390/foods11060791 - 9 Mar 2022
Cited by 3 | Viewed by 2783
Abstract
A rapid and non-destructive method based in time domain reflectometry analysis (TDR), which detects and quantifies the water content in the muscle, was developed for the control of abusive water addition to octopus. Common octopus samples were immersed in freshwater for different periods [...] Read more.
A rapid and non-destructive method based in time domain reflectometry analysis (TDR), which detects and quantifies the water content in the muscle, was developed for the control of abusive water addition to octopus. Common octopus samples were immersed in freshwater for different periods (0.5–32 h) to give a wide range of moisture contents, representing different commercial conditions. Control and water-added octopus were analyzed with a TDR sensor, and data correlated with moisture content were used for calibration and method validation. A maximum limit of moisture content of 85.2 g/100 g in octopus is proposed for conformity assessment, unless the label indicates that water (>5%) was added. Calibration results showed that TDR analysis can discriminate control and water-added octopus, especially for octopus immersed for longer periods (32 h). In addition, moisture content can be quantified in octopus using only TDR analysis (between 80 and 90 g/100 g; RMSE = 1.1%). TDR data and correlation with moisture content show that this non-destructive methodology can be used by the industry and quality control inspections for assessment of octopus quality and to verify compliance with legislation, promoting fair trade practices, and further contributing to a sustainable use of resources. Full article
(This article belongs to the Special Issue Rapid and Non-destructive Determination of Food Quality Control)
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