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Novel Spectroscopy Technologies in Food: Towards On-Site (Value-Chain/Industry) Applications
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Novel Spectroscopy Technologies in Food: Towards On-Site (Value-Chain/Industry) Applications

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Engineering and Technology".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 6450

Special Issue Editors

Prof. Margarita Ruiz-Altisent

E-Mail Website
Guest Editor
LPF-TAGRALIA, Departamento de Ingeniería Agroforestal, E.T.S.I. Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, CEI-Moncloa. Avda. Complutense s/n, Madrid 28040, Spain
Interests: engineering of food and agriculture; physical properties of food materials; food processing; computer vision; plant biology; food quality; evaluation of international research projects
Dr. Belén Diezma

E-Mail Website
Guest Editor
Laboratory of Physical Properties-Advanced Technologies in Agrifood (LPF_Tagralia), ETSIAAB, Universidad Politécnica de Madrid, Av. Puerta de Hierro, 2–4, 28040 Madrid, Spain
Interests: post-harvest sensing technologies (on-line and at-line); spectroscopic properties, hyperspectral and multispectral imaging systems; implementation of sensors; electronics and ICT in agricultural machinery; precision farming; (computer sensing and monitoring of animal production, etc.)

Special Issue Information

For many years, food spectroscopic methods and applications have been the subject of numerous research studies, but the corresponding actual innovations are still not significant in the food industry. Many techniques are ready, many promising procedures are published, but not many have been made suitable for their introduction in the industry. In this Special Issue, we expect original and “breaking” papers, dealing with near-to-industry applications, and based on various possible technological solutions, such as:

Spectroscopy and hyperspectral imaging VIS/NIR (=VNIR)—SWIR, LWIR, MWIR; fluorescence spectroscopy; NMR and MRI on-line; X-ray on-line; LIBS = laser induced breakdown spectroscopy; Tera–Hertz spectroscopy and imaging; advanced applications and methods in chemometrics; big data applications, and any other which is relevant to this description.

The applications may deal with foods of plant origin (fruits, greens, cereals, oils, powders, bakery, etc.), animal products (meat, dairy, eggs, fish, etc.), including the detection of any features or components with beneficial or detrimental effects affecting quality, including contaminants, microbials, pesticides, antibiotics and mycotoxins. The aim is to include high-quality contributions which enhance and demonstrate the development of instrumentation, based on the above listed, or any other spectroscopic technologies, and which show potential for their use in the industry.

Prof. Margarita Ruiz-Altisent
Dr. Belén Diezma
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

  • VIS/NIR
  • MWIR
  • fluorescence
  • NMR
  • X-ray
  • hyper-spectral imaging
  • LIBS
  • plant and animal foods
  • contaminants
  • quantitative analysis

Published Papers (3 papers)

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Research

14 pages, 1831 KiB  
Article
Multiblock Analysis Applied to Fluorescence and Absorbance Spectra to Estimate Total Polyphenol Content in Extra Virgin Olive Oil
by Natalia Hernández-Sánchez, Lourdes Lleó, Belén Diezma, Eva Cristina Correa, Blanca Sastre and Jean-Michel Roger
Foods 2021, 10(11), 2556; https://doi.org/10.3390/foods10112556 - 23 Oct 2021
Cited by 4 | Viewed by 1516
Abstract
A fast and easy methodology to estimate total polyphenol content in extra virgin olive oil was developed by applying the chemometric multiblock method sequential and orthogonalized partial least squares (SO-PLS) in order to combine front-face emission fluorescence spectra (270 nm excitation wavelength) and [...] Read more.
A fast and easy methodology to estimate total polyphenol content in extra virgin olive oil was developed by applying the chemometric multiblock method sequential and orthogonalized partial least squares (SO-PLS) in order to combine front-face emission fluorescence spectra (270 nm excitation wavelength) and absorbance spectra. The hypothesis of this work stated that inner-filter effects in fluorescence spectra that would reduce the estimation performance of a single block model could be overcome by incorporating the absorbance spectral information of the compounds causing them. Different spectral preprocessing algorithms were applied. Double cross-validation with 50 iterations was implemented to improve the robustness of the obtained results. The PLSR model on the single block of fluorescence raw spectra achieved an RMSEP of 177.11 mg·kg−1 as the median value, and the complexity of the model was high, as the median value of latent variables (LVs) was eight. Multiblock SO-PLS models with pretreated fluorescence and absorbance spectra provided better performance, although artefacts could be introduced by transformation. The combination of fluorescence and absorbance raw data decreased the RMSEP median to 134.45 mg·kg−1. Moreover, the complexity of the model was greatly reduced, which contributed to an increase in robustness. The median value of LVs was three for fluorescence data and only one for absorbance data. Validation of the methodology could be addressed by further work considering a higher number of samples and a detailed composition of polyphenols. Full article
(This article belongs to the Special Issue Novel Spectroscopy Technologies in Food: Towards On-Site (Value-Chain/Industry) Applications)
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17 pages, 2616 KiB  
Article
Application of Absorption and Scattering Properties Obtained through Image Pre-Classification Method Using a Laser Backscattering Imaging System to Detect Kiwifruit Chilling Injury
by Zhuo Yang, Mo Li, Andrew R. East and Manuela Zude-Sasse
Foods 2021, 10(7), 1446; https://doi.org/10.3390/foods10071446 - 22 Jun 2021
Cited by 8 | Viewed by 2342
Abstract
Kiwifruit chilling injury (CI) damage occurs after long-term exposure to low temperature. A non-destructive approach to detect CI injury was tested in the present study, using a laser backscattering image (LBI) technique calibrated with 56 liquid phantoms for providing absorption coefficient (µa [...] Read more.
Kiwifruit chilling injury (CI) damage occurs after long-term exposure to low temperature. A non-destructive approach to detect CI injury was tested in the present study, using a laser backscattering image (LBI) technique calibrated with 56 liquid phantoms for providing absorption coefficient (µa) and reduced scattering coefficient (µs’). Calibration of LBI resulted in a true-positive (TP) classification of 91.5% and 65.6% of predicted µs’ and µa, respectively. The optical properties of ‘SunGold™’and ‘Hayward’ kiwifruit were analysed at 520 nm with a two-step protocol capturing pre-classification according to the LBI parameters used in the calibration and estimation with the Farrell equation. Severely injured kiwifruit showed white corky tissue and water soaking, reduced soluble solids content and firmness measured destructively. Non-destructive classification results for ‘SunGold™’ showed a high percentage of TP for severe CI of 92% and 75% using LBI parameters directly and predicted µa and µs’ after pre-classification, respectively. The classification accuracy for severe CI ‘Hayward’ kiwifruit with LBI parameter was low (58%) and with µa and µs’ decreased further (35%), which was assumed to be due to interference caused by the long trichomes on the fruit surface. Full article
(This article belongs to the Special Issue Novel Spectroscopy Technologies in Food: Towards On-Site (Value-Chain/Industry) Applications)
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11 pages, 2449 KiB  
Article
A Model Based on Clusters of Similar Color and NIR to Estimate Oil Content of Single Olives
by Claudio Fredes, Constantino Valero, Belén Diezma, Marco Mora, José Naranjo-Torres, Manuel Wilson and Gabriel Delgadillo
Foods 2021, 10(3), 609; https://doi.org/10.3390/foods10030609 - 13 Mar 2021
Cited by 3 | Viewed by 1887
Abstract
Lipid extraction using the traditional, destructive Soxhlet method is not able to measure oil content (OC) on a single olive. As the color and near infrared spectrum are key parameters to build an oil estimation model (EM), this study grouped olives with similar [...] Read more.
Lipid extraction using the traditional, destructive Soxhlet method is not able to measure oil content (OC) on a single olive. As the color and near infrared spectrum are key parameters to build an oil estimation model (EM), this study grouped olives with similar color and NIR for building EM of oil content obtained by Soxhlet from a cluster of similar olives. The objective was to estimate OC of individual olives, based on clusters of similar color and NIR in two seasons. This study was performed with Arbequina olives in 2016 and 2017. The descriptor of the cluster consisted of the three color channels of c1c2c3 color model plus 11 reflectance points between 1710 and 1735 nm of each olive, normalized with the Z-score index. Clusters of similar color and NIR spectrum were formed with the k-means++ algorithm, leaving a sufficient number of olives to perform the Soxhlet analysis of OC, as reference value of EM. The training of EM was based on Support Vector Machine. The test was performed with Leave One-Out Cross Validation in different training-testing combinations. The best EM predicted the OC with 6 and 13% deviation with respect to the real value when one season was tested with itself and with another season, respectively. The use of clustering in EM is discussed. Full article
(This article belongs to the Special Issue Novel Spectroscopy Technologies in Food: Towards On-Site (Value-Chain/Industry) Applications)
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