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Applications of Non-destructive Optical Techniques for Quality and Authentication of...
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Applications of Non-destructive Optical Techniques for Quality and Authentication of Vegetal Crops

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 6975

Special Issue Editors

Dr. Maria Luisa Amodio

E-Mail Website
Guest Editor
Department of The Science of Agriculture, Food, Natural Resource and Engineering (DAFNE), Università degli studi di Foggia, Foggia, Italy
Interests: postharvest technology; quality; shelf-life; hyperspectral imaging; NIR
Dr. Sandra Munera

E-Mail Website
Guest Editor
Centro de Agroingeniería, Instituto Valenciano de Investigaciones Agrarias (IVIA), Valencia, Spain
Interests: food quality; non-destructive techniques; hyperspectral imaging; chemometrics

Special Issue Information

Dear Colleagues,

Nowadays, there are many studies and several commercial applications of NIR and VIS-NIR for the quality assessment of vegetal crops, mainly defect identification and content prediction of  the  main constituents (namely sugars and acids for fruit and vegetables, proteins for cereals and oil content for seeds). Some studies have investigated the appropriateness of these techniques for the prediction of minor constituents such as vitamins and antioxidants, whereas there has been increased interest in their application beyond the conventional concept of quality. Quality perception has evolved substantially over the past few decades. In a shift away from traditional attributes, other important aspects are now gaining relevance, such as the nutritional value of fresh horticultural products and the environmental impact of the production method. There is evidence of the potentiality of NIR and hyperspectral imaging (VIS-NIR) for the discrimination of crops, based on pre-harvest factors including genotype, origin and growing inputs. Within the theme of quality determination, for this Special Issue, we welcome papers assessing any aspect of product authentication, fraud identification and crop composition, particularly if related to phytonutrients or to marginal crops. All this information, in fact, would increase the product value if delivered to the final consumer. 

Dr. Maria Luisa Amodio
Dr. Sandra Munera
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

  • NIR
  • hyperspectral imaging
  • quality
  • authentication
  • discrimination
  • modeling

Published Papers (2 papers)

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Research

11 pages, 2319 KiB  
Article
Detection of Invisible Damages in ‘Rojo Brillante’ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics
by Sandra Munera, Alejandro Rodríguez-Ortega, Nuria Aleixos, Sergio Cubero, Juan Gómez-Sanchis and José Blasco
Foods 2021, 10(9), 2170; https://0-doi-org.brum.beds.ac.uk/10.3390/foods10092170 - 13 Sep 2021
Cited by 17 | Viewed by 2743
Abstract
The main cause of flesh browning in ‘Rojo Brillante’ persimmon fruit is mechanical damage caused during harvesting and packing. Innovation and research on nondestructive techniques to detect this phenomenon in the packing lines are necessary because this type of alteration is often only [...] Read more.
The main cause of flesh browning in ‘Rojo Brillante’ persimmon fruit is mechanical damage caused during harvesting and packing. Innovation and research on nondestructive techniques to detect this phenomenon in the packing lines are necessary because this type of alteration is often only seen when the final consumer peels the fruit. In this work, we have studied the application of hyperspectral imaging in the range of 450–1040 nm to detect mechanical damage without any external symptoms. The fruit was damaged in a controlled manner. Later, images were acquired before and at 0, 1, 2 and 3 days after damage induction. First, the spectral data captured from the images were analysed through an algorithm based on principal component analysis (PCA). The aim was to automatically separate intact and damaged fruit, and to detect the damage in the PC images when present. With this algorithm, 90.0% of intact fruit and 90.8% of damaged fruit were correctly detected. A model based on partial least squares—discriminant analysis (PLS-DA), was later calibrated using the mean spectrum of the pixels detected as damaged, to determine the moment when the fruit was damaged. The model differentiated fruit corresponding correctly to 0, 1, 2 and 3 days after damage induction, achieving a total accuracy of 99.4%. Full article
(This article belongs to the Special Issue Applications of Non-destructive Optical Techniques for Quality and Authentication of Vegetal Crops)
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14 pages, 2325 KiB  
Article
Comparison Performance of Visible-NIR and Near-Infrared Hyperspectral Imaging for Prediction of Nutritional Quality of Goji Berry (Lycium barbarum L.)
by Danial Fatchurrahman, Mojtaba Nosrati, Maria Luisa Amodio, Muhammad Mudassir Arif Chaudhry, Maria Lucia Valeria de Chiara, Leonarda Mastrandrea and Giancarlo Colelli
Foods 2021, 10(7), 1676; https://0-doi-org.brum.beds.ac.uk/10.3390/foods10071676 - 20 Jul 2021
Cited by 18 | Viewed by 3304
Abstract
The potential of hyperspectral imaging for the prediction of the internal composition of goji berries was investigated. The prediction performances of models obtained in the Visible-Near Infrared (VIS-NIR) (400–1000 nm) and in the Near Infrared (NIR) (900–1700 nm) regions were compared. Analyzed constituents [...] Read more.
The potential of hyperspectral imaging for the prediction of the internal composition of goji berries was investigated. The prediction performances of models obtained in the Visible-Near Infrared (VIS-NIR) (400–1000 nm) and in the Near Infrared (NIR) (900–1700 nm) regions were compared. Analyzed constituents included Vitamin C, total antioxidant, phenols, anthocyanin, soluble solids content (SSC), and total acidity (TA). For vitamin C and AA, partial least square regression (PLSR) combined with different data pretreatments and wavelength selection resulted in a satisfactory prediction in the NIR region obtaining the R2pred value of 0.91. As for phenols, SSC, and TA, a better performance was obtained in the VIS-NIR region yielding the R2pred values of 0.62, 0.94, and 0.84, respectively. However, the prediction of total antioxidant and anthocyanin content did not give satisfactory results. Conclusively, hyperspectral imaging can be a useful tool for the prediction of the main constituents of the goji berry (Lycium barbarum L.). Full article
(This article belongs to the Special Issue Applications of Non-destructive Optical Techniques for Quality and Authentication of Vegetal Crops)
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