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Applications of Hyperspectral Imaging for the Chemometric Assessment of Food...
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Applications of Hyperspectral Imaging for the Chemometric Assessment of Food Composition

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

Deadline for manuscript submissions: closed (20 March 2021) | Viewed by 13592

Special Issue Editor

Dr. Nicola Caporaso

E-Mail Website
Guest Editor
Department of Agricultural Sciences, University of Naples Federico II, 80055 Portici, Italy
Interests: food quality; raw material characterisation; food aroma; rapid nondestructive food analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hyperspectral imaging is a relatively novel technology in the food sector. It merges the benefits of computer vision with spectroscopy. When near-infrared (NIR) spectroscopy is applied, it provides the advantages of getting chemical information from a sample, allowing building calibration or classification models based on biochemical properties of food samples. Its application can also allow for the studying of the spatial distribution of a compound of property of importance for food quality. 

Hyperspectral imaging has been applied at the research level for a wide range of food products, from meat, to dairy products, fruits and vegetables, and grains. Due to the huge amount of data and the redundancy of some information, challenges still exist regarding the implementation of this technology at the food industry level, in a holistic approach taking into consideration food chemistry and biochemistry, sensor development, hardware, and chemometrics. 

You are therefore invited to submit your research papers on any aspect related to applications of multi- or hyperspectral imaging in the field of food science and technology, and industrial applications are particularly welcome. 

The final deadline for submitting manuscripts is 10 December 2020. All manuscripts will be peer-reviewed, and manuscripts received before the deadline will be immediately processed.

Dr. Nicola Caporaso
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

  • Hyperspectral imaging
  • NIR spectroscopy
  • Non-destructive assessment
  • Food quality and composition
  • Industrial applications.

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Published Papers (3 papers)

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Research

10 pages, 1632 KiB  
Article
Artificial Intelligence Empowered Multispectral Vision Based System for Non-Contact Monitoring of Large Yellow Croaker (Larimichthys crocea) Fillets
by Shengnan Wang, Avik Kumar Das, Jie Pang and Peng Liang
Foods 2021, 10(6), 1161; https://doi.org/10.3390/foods10061161 - 21 May 2021
Cited by 10 | Viewed by 2726
Abstract
A non-contact method was proposed to monitor the freshness (based on TVB-N and TBA values) of large yellow croaker fillets (Larimichthys crocea) by using a visible and near-infrared hyperspectral imaging system (400–1000 nm). In this work, the quantitative calibration models were [...] Read more.
A non-contact method was proposed to monitor the freshness (based on TVB-N and TBA values) of large yellow croaker fillets (Larimichthys crocea) by using a visible and near-infrared hyperspectral imaging system (400–1000 nm). In this work, the quantitative calibration models were built by using feed-forward neural networks (FNN) and partial least squares regression (PLSR). In addition, it was established that using a regression coefficient on the data can be further compressed by selecting optimal wavelengths (35 for TVB-N and 18 for TBA). The results validated that FNN has higher prediction accuracies than PLSR for both cases using full and selected reflectance spectra. Moreover, our FNN based model has showcased excellent performance even with selected reflectance spectra with rp = 0.978, R2p = 0.981, and RMSEP = 2.292 for TVB-N, and rp = 0.957, R2p = 0.916, and RMSEP = 0.341 for TBA, respectively. This optimal FNN model was then utilized for pixel-wise visualization maps of TVB-N and TBA contents in fillets. Full article
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for the Chemometric Assessment of Food Composition)
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18 pages, 6014 KiB  
Article
Determination of Drying Patterns of Radish Slabs under Different Drying Methods Using Hyperspectral Imaging Coupled with Multivariate Analysis
by Dongyoung Lee, Santosh Lohumi, Byoung-Kwan Cho, Seung Hyun Lee and Hyunmo Jung
Foods 2020, 9(4), 484; https://doi.org/10.3390/foods9040484 - 12 Apr 2020
Cited by 15 | Viewed by 4815
Abstract
Drying kinetics and the moisture distribution map of radish slabs under different drying methods (hot-air drying (HAD), microwave drying (MD), and hot-air and microwave combination drying (HMCD)) were determined and visualized by hyperspectral image (HSI) processing coupled with a partial least square regression [...] Read more.
Drying kinetics and the moisture distribution map of radish slabs under different drying methods (hot-air drying (HAD), microwave drying (MD), and hot-air and microwave combination drying (HMCD)) were determined and visualized by hyperspectral image (HSI) processing coupled with a partial least square regression (PLSR)-variable importance in projection (VIP) model, respectively. Page model was the most suitable in describing the experimental moisture loss data of radish slabs regardless of the drying method. Dielectric properties (DP, ε ) of radish slices decreased with the decrease in moisture content (MC) during MD, and the penetration depth of microwaves in radish was between 0.81 and 1.15 cm. The PLSR-VIP model developed with 38 optimal variables could result in the high prediction accuracies for both the calibration ( R c a l 2 = 0.967 and RMSEC = 4.32 % ) and validation ( R v a l 2 = 0.962 and RMSEC = 4.45 % ). In visualized drying patterns, the radish slabs dried by HAD had a higher moisture content at the center than at the edges; however, the samples dried by MD contained higher moisture content at the edges. The nearly uniform drying pattern of radish slabs under HMCD was observed in hyperspectral images. Drying uniformity of radish slabs could be improved by the combination drying method, which significantly reduces drying time. Full article
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for the Chemometric Assessment of Food Composition)
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16 pages, 7311 KiB  
Article
Rapid Screen of the Color and Water Content of Fresh-Cut Potato Tuber Slices Using Hyperspectral Imaging Coupled with Multivariate Analysis
by Qinlin Xiao, Xiulin Bai and Yong He
Foods 2020, 9(1), 94; https://doi.org/10.3390/foods9010094 - 16 Jan 2020
Cited by 42 | Viewed by 5201
Abstract
Color index and water content are important indicators for evaluating the quality of fresh-cut potato tuber slices. In this study, hyperspectral imaging combined with multivariate analysis was used to detect the color parameters (L*, a*, b*, Browning index (BI), [...] Read more.
Color index and water content are important indicators for evaluating the quality of fresh-cut potato tuber slices. In this study, hyperspectral imaging combined with multivariate analysis was used to detect the color parameters (L*, a*, b*, Browning index (BI), L*/b*) and water content of fresh-cut potato tuber slices. The successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were used to extract characteristic wavelengths, partial least squares (PLS) and least squares support vector machine (LS-SVM) were utilized to establish regression models. For color prediction, R2c, R2p and RPD of all the LSSVM models established for the five color indicators L*, a*, b*, BI, L*/b* were exceeding 0.90, 0.84 and 2.1, respectively. For water content prediction, R2c, R2p, and RPD of the LSSVM models were over 0.80, 0.77 and 1.9, respectively. LS-SVM model based on full spectra was used to reappear the spatial distribution of color and water content in fresh-cut potato tuber slices by pseudo-color imaging since it performed best in most cases. The results illustrated that hyperspectral imaging could be an effective method for color and water content prediction, which could provide solid theoretical basis for subsequent grading and processing of fresh-cut potato tuber slices. Full article
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for the Chemometric Assessment of Food Composition)
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