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The Application of Computer Vision in Food Analysis

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

Deadline for manuscript submissions: closed (20 December 2021) | Viewed by 14784

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Special Issue Editors

Dr. Francisco J. Rodríguez-Pulido

E-Mail Website
Guest Editor

Food Colour and Quality Lab., Área de Nutrición y Bromatología, Facultad de Farmacia, Universidad de Sevilla, 41012 Sevilla, Spain
Interests: use of imaging techniques for quality assessment in the agri-food industry

Dr. Wen-Hao Su

E-Mail Website
Co-Guest Editor

College of Engineering, China Agricultural University, Beijing 100083, China
Interests: smart urban agriculture; artificial intelligence; agricultural robotics; automated control; unmanned aerial vehicle; plant phenotyping; computer vision; crop plant signaling; machine (deep) learning; food processing and safety; fluorescence imaging; hyper/multispectral imaging; Vis/NIR/MIR imaging spectroscopy
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Special Issue Information

Dear Colleagues,

Computer Vision has revolutionized the way quality is assessed in food products. Using cameras, it is possible to estimate not only the physicochemical properties of food products but also their geometric distribution. RGB, hyperspectral, thermal, or even X-Ray imaging systems are increasingly used for these purposes. On the one hand, the constant reduction in the cost of equipment is leading more and more companies to turn to this type of inspection system. On the other hand, the development of new prediction methods based on Machine Learning is leading to a new upsurge among the research of groups working in this field.

For these reasons, MDPI wants to give researchers the opportunity to disseminate their research results by submitting their original and high-quality research articles, review works, and short communications to the Foods peer-reviewed Special Issue: “The Application of Computer Vision in Food Analysis”.

We look forward to receiving your contributions.

Dr. Francisco J. Rodríguez-Pulido
Guest Editor
Dr. Wen-Hao Su
Co-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

computer vision
food analysis
food processing
hyperspectral imaging
image analysis
machine learning
NIR
quality inspection
spectroscopy

Published Papers (5 papers)

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Research

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15 pages, 2367 KiB

Open AccessArticle

A Rapid Non-Destructive Hyperspectral Imaging Data Model for the Prediction of Pungent Constituents in Dried Ginger

by Nahidul Hoque Samrat, Joel B. Johnson, Simon White, Mani Naiker and Philip Brown

Foods 2022, 11(5), 649; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11050649 - 23 Feb 2022

Cited by 9 | Viewed by 2283

Abstract

Ginger is best known for its aromatic odour, spicy flavour and health-benefiting properties. Its flavour is derived primarily from two compound classes (gingerols and shogaols), with the overall quality of the product depending on the interaction between these compounds. Consequently, a robust method for determining the ratio of these compounds would be beneficial for quality control purposes. This study investigated the feasibility of using hyperspectral imaging to rapidly determine the ratio of 6-gingerol to 6-shogoal in dried ginger powder. Furthermore, the performance of several pre-processing methods and two multivariate models was explored. The best-performing models used partial least squares regression (PSLR) and least absolute shrinkage and selection operator (LASSO), using multiplicative scatter correction (MSC) and second derivative Savitzky–Golay (2D-SG) pre-processing. Using the full range of wavelengths (~400–1000 nm), the performance was similar for PLSR (R² ≥ 0.73, RMSE ≤ 0.29, and RPD ≥ 1.92) and LASSO models (R² ≥ 0.73, RMSE ≤ 0.29, and RPD ≥ 1.94). These results suggest that hyperspectral imaging combined with chemometric modelling may potentially be used as a rapid, non-destructive method for the prediction of gingerol-to-shogaol ratios in powdered ginger samples. Full article

(This article belongs to the Special Issue The Application of Computer Vision in Food Analysis)

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17 pages, 3720 KiB

Open AccessArticle

Research Progress in Imaging Technology for Assessing Quality in Wine Grapes and Seeds

by Francisco J. Rodríguez-Pulido, Ana Belén Mora-Garrido, María Lourdes González-Miret and Francisco J. Heredia

Foods 2022, 11(3), 254; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11030254 - 18 Jan 2022

Cited by 9 | Viewed by 1907

Abstract

The chemical composition of wine grapes changes qualitatively and quantitatively during the ripening process. In addition to the sugar content, which determines the alcohol content of the wine, it is necessary to consider the phenolic composition of the grape skins and seeds to obtain quality red wines. In this work, some imaging techniques have been used for the comprehensive characterisation of the chemical composition of red grapes (cv. Tempranillo and cv. Syrah) grown in a warm-climate region during two seasons. In addition, and for the first time, mathematical models trained with laboratory images have been extrapolated for using in field images, obtaining interesting results. Determination coefficients of 0.90 for sugars, 0.73 for total phenols, and 0.73 for individual anthocyanins in grape skins have been achieved with a portable hyperspectral camera between 400 and 1000 nm, and 0.83 for total and individual phenols in grape seeds with a desktop hyperspectral camera between 900 and 1700 nm. Full article

(This article belongs to the Special Issue The Application of Computer Vision in Food Analysis)

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16 pages, 4912 KiB

Open AccessArticle

Spatial Frequency Domain Imaging System Calibration, Correction and Application for Pear Surface Damage Detection

by Yifeng Luo, Xu Jiang and Xiaping Fu

Foods 2021, 10(9), 2151; https://0-doi-org.brum.beds.ac.uk/10.3390/foods10092151 - 11 Sep 2021

Cited by 9 | Viewed by 2117

Abstract

Spatial frequency domain imaging (SFDI) is a non-contact wide-field optical imaging technique for optical property detection. This study aimed to establish an SFDI system and investigate the effects of system calibration, error analysis and correction on the measurement of optical properties. Optical parameter characteristic measurements of normal pears with three different damage types were performed using the calibrated system. The obtained absorption coefficient μ_a and the reduced scattering coefficient μ’_s were used for discriminating pears with different surface damage using a linear discriminant analysis model. The results showed that at 527 nm and 675 nm, the pears’ quadruple classification (normal, bruised, scratched and abraded) accuracy using the SFDI technique was 92.5% and 83.8%, respectively, which has an advantage compared with the conventional planar light classification results of 82.5% and 77.5%. The three-way classification (normal, minor damage and serious damage) SFDI technique was as high as 100% and 98.8% at 527 nm and 675 nm, respectively, while the classification accuracy of conventional planar light was 93.8% and 93.8%, respectively. The results of this study indicated that SFDI has the potential to detect different damage types in fruit and that the SFDI technique has a promising future in agricultural product quality inspection in further research. Full article

(This article belongs to the Special Issue The Application of Computer Vision in Food Analysis)

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13 pages, 1180 KiB

Open AccessArticle

Assessment of Sensory and Texture Profiles of Grape Seeds at Real Maturity Stages Using Image Analysis

by María Jesús Cejudo-Bastante, Francisco J. Rodríguez-Pulido, Francisco J. Heredia and M. Lourdes González-Miret

Foods 2021, 10(5), 1098; https://0-doi-org.brum.beds.ac.uk/10.3390/foods10051098 - 15 May 2021

Cited by 6 | Viewed by 1970

Abstract

The usefulness of digital image analysis in estimating sensory attributes of grape seeds in relation to maturation level was evaluated for the first time. Seeds from Syrah grapes harvested throughout the ripening period were grouped according to maturity using the DigiEye^® system. The discriminant ability, homogeneity, repeatability, and uniformity of a sensory panel were assessed after training on grape seeds. The aim was to evaluate the use of digital image techniques in order to accurately establish the maturity level of grape seeds, based on sensory and textural features. All sensory attributes (color, hardness, cracking, vegetal, bitterness and astringency) showed significant (p < 0.05) correlations with the chemical maturity stage. Color and vegetal (sensory attributes), together with deformation energy (instrumental texture parameter) (De), allowed for the classification of the seeds into four real maturity stages, hence their usefulness as grape seed ripening indicators. Significant (p < 0.05) and high-correlation factors were also found between instrumental and sensory attributes. Therefore, digital analysis can be a useful tool to better define the maturity stage in the vineyard, and to dispose of grape seeds with well-defined sensory profiles for specific oenological applications. This could help to determine the optimal harvest date to manage winemaking, in order to produce high quality wines in warm climates. Full article

(This article belongs to the Special Issue The Application of Computer Vision in Food Analysis)

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Review

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17 pages, 968 KiB

Open AccessFeature PaperReview

Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality

by Wen-Hao Su and Huidan Xue

Foods 2021, 10(9), 2146; https://0-doi-org.brum.beds.ac.uk/10.3390/foods10092146 - 10 Sep 2021

Cited by 24 | Viewed by 4324

Abstract

Imaging spectroscopy has emerged as a reliable analytical method for effectively characterizing and quantifying quality attributes of agricultural products. By providing spectral information relevant to food quality properties, imaging spectroscopy has been demonstrated to be a potential method for rapid and non-destructive classification, authentication, and prediction of quality parameters of various categories of tubers, including potato and sweet potato. The imaging technique has demonstrated great capacities for gaining rapid information about tuber physical properties (such as texture, water binding capacity, and specific gravity), chemical components (such as protein, starch, and total anthocyanin), varietal authentication, and defect aspects. This paper emphasizes how recent developments in spectral imaging with machine learning have enhanced overall capabilities to evaluate tubers. The machine learning algorithms coupled with feature variable identification approaches have obtained acceptable results. This review briefly introduces imaging spectroscopy and machine learning, then provides examples and discussions of these techniques in tuber quality determinations, and presents the challenges and future prospects of the technology. This review will be of great significance to the study of tubers using spectral imaging technology. Full article

(This article belongs to the Special Issue The Application of Computer Vision in Food Analysis)

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