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Recent Developments in the Applications of Computer Vision Technology for...
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Recent Developments in the Applications of Computer Vision Technology for Food Quality

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

Deadline for manuscript submissions: closed (20 December 2020) | Viewed by 12387

Special Issue Editor

Dr. Yoshio Makino

E-Mail Website
Guest Editor
Graduate School of Agricultural and Life Science, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-Ku, Tokyo 113-8657, Japan
Interests: computational chemistry; food science; food chemistry; cheminformatics and computational chemistry; analytical chemistry; packaging; metabolomics; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Food quality is mainly evaluated on the basis of internal nutrient value and appearance (external quality). External quality, including color, odor,disease, shape, etc., is very important because it is the first aspect sensed by consumers.

Computer vision is an interdisciplinary scientific field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do.

Computer vision system is useful for objective and rapid inspection of commodities. In this Special Issue, research on computer vision technology for evaluating external qualities of foods including beverages, agricultural products after harvest, and processed food is desirable to include. Contents on statistical and mathematical modeling, so-called artificial intelligence (AI), for constructing automation systems implementing digital camera are also welcome.  

Papers focusing exclusively on agricultural products before harvesting will not be covered by this issue.

Assoc. Prof. Yoshio Makino
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

  • Color
  • External quality
  • Fresh food
  • Beverage
  • Light sensing
  • Mathematical model
  • Nondestructive analysis
  • Processed food
  • Statistical analysis
  • Visible light
  • Inspection
  • Diagnosis of adulteration

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Published Papers (3 papers)

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Research

12 pages, 1789 KiB  
Article
Intramuscular Fat Prediction Using Color and Image Analysis of Bísaro Pork Breed
by Alfredo Teixeira, Severiano R. Silva, Marianne Hasse, José M. H. Almeida and Luis Dias
Foods 2021, 10(1), 143; https://doi.org/10.3390/foods10010143 - 12 Jan 2021
Cited by 9 | Viewed by 5119
Abstract
This work presents an analytical methodology to predict meat juiciness (discriminant semi-quantitative analysis using groups of intervals of intramuscular fat) and intramuscular fat (regression analysis) in Longissimus thoracis et lumborum (LTL) muscle of Bísaro pigs using as independent variables the animal carcass weight [...] Read more.
This work presents an analytical methodology to predict meat juiciness (discriminant semi-quantitative analysis using groups of intervals of intramuscular fat) and intramuscular fat (regression analysis) in Longissimus thoracis et lumborum (LTL) muscle of Bísaro pigs using as independent variables the animal carcass weight and parameters from color and image analysis. These are non-invasive and non-destructive techniques which allow development of rapid, easy and inexpensive methodologies to evaluate pork meat quality in a slaughterhouse. The proposed predictive supervised multivariate models were non-linear. Discriminant mixture analysis to evaluate meat juiciness by classified samples into three groups—0.6 to 1.1%; 1.25 to 1.5%; and, greater than 1.5%. The obtained model allowed 100% of correct classifications (92% in cross-validation with seven-folds with five repetitions). Polynomial support vector machine regression to determine the intramuscular fat presented R2 and RMSE values of 0.88 and 0.12, respectively in cross-validation with seven-folds with five repetitions. This quantitative model (model’s polynomial kernel optimized to degree of three with a scale factor of 0.1 and a cost value of one) presented R2 and RSE values of 0.999 and 0.04, respectively. The overall predictive results demonstrated the relevance of photographic image and color measurements of the muscle to evaluate the intramuscular fat, rarther than the usual time-consuming and expensive chemical analysis. Full article
(This article belongs to the Special Issue Recent Developments in the Applications of Computer Vision Technology for Food Quality)
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14 pages, 2071 KiB  
Article
Digitization of Broccoli Freshness Integrating External Color and Mass Loss
by Yoshio Makino and Genki Amino
Foods 2020, 9(9), 1305; https://doi.org/10.3390/foods9091305 - 16 Sep 2020
Cited by 9 | Viewed by 3579
Abstract
Yellowing of green vegetables due to chlorophyll decomposition is a phenomenon indicating serious deterioration of freshness, and it is evaluated by measuring color space values. In contrast, mass reduction due to water loss is a deterioration of freshness observed in all horticultural crops. [...] Read more.
Yellowing of green vegetables due to chlorophyll decomposition is a phenomenon indicating serious deterioration of freshness, and it is evaluated by measuring color space values. In contrast, mass reduction due to water loss is a deterioration of freshness observed in all horticultural crops. Therefore, in this study, we propose a novel freshness evaluation index for green vegetables that combines the degree of greenness and mass loss. The green color retention rate was measured using a computer vision system, and the mass retention rate was measured by weighing. Linear discriminant analysis (LDA) was performed using both variables (greenness and mass) as covariates to obtain a single freshness evaluation value (first canonical variable). The correct classification of storage period length by LDA was 96%. Green color retention alone allowed for classification of storage durations between 0 day and 10 days, whereas LDA could classify storage durations between 0 day and 12 days. The novel freshness evaluation index proposed by this research, which integrates greenness and mass, has been shown to be more accurate than the conventional evaluation index that uses only greenness. Full article
(This article belongs to the Special Issue Recent Developments in the Applications of Computer Vision Technology for Food Quality)
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13 pages, 2292 KiB  
Article
Prediction of Degreening Velocity of Broccoli Buds Using Hyperspectral Camera Combined with Artificial Neural Networks
by Yoshio Makino and Yumi Kousaka
Foods 2020, 9(5), 558; https://doi.org/10.3390/foods9050558 - 2 May 2020
Cited by 7 | Viewed by 3036
Abstract
Developing a noninvasive technique to estimate the degreening (loss of green color) velocity of harvested broccoli was attempted. Loss of green color on a harvested broccoli head occurs heterogeneously. Therefore, hyperspectral imaging technique that stores spectral reflectance with spatial information was used in [...] Read more.
Developing a noninvasive technique to estimate the degreening (loss of green color) velocity of harvested broccoli was attempted. Loss of green color on a harvested broccoli head occurs heterogeneously. Therefore, hyperspectral imaging technique that stores spectral reflectance with spatial information was used in the present research. Using artificial neural networks (ANNs), we demonstrated that the reduction velocity of chlorophyll at a site on a broccoli head was related to the second derivative of spectral reflectance data at 15 wavelengths from 405 to 960 nm. The reduction velocity was predicted using the ANNs model with a correlative coefficient of 0.995 and a standard error of prediction of 5.37 × 10−5 mg·g−1·d−1. The estimated reduction velocity was effective for predicting the chlorophyll concentration of broccoli buds until 7 d of storage, which was established as the maximum time for maintaining marketability. This technique may be useful for nondestructive prediction of the shelf life of broccoli heads. Full article
(This article belongs to the Special Issue Recent Developments in the Applications of Computer Vision Technology for Food Quality)
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