Submit to Foods Review for Foods Propose a Special Issue

Journal Browser

► Journal Browser

Applications of Non-destructive Technologies for Food Quality and Safety

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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 December 2022) | Viewed by 15739

Share This Special Issue

Special Issue Editors

Prof. Dr. Leiqing Pan

E-Mail Website
Guest Editor

College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
Interests: non-destructive detection; spectral; imaging; food composition; food safety; identification; regression; modeling; chemometrics
Special Issues, Collections and Topics in MDPI journals

Dr. Qiang Liu

E-Mail Website
Guest Editor

College of Food Science and Engineering, Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing University of Finance and Economics, Nanjing 210023, China
Interests: interaction of light properties cover with the physiological metabolism and related data analysis in cereal; application of novel non-destructive detection technologies for food composition determination

Special Issue Information

Dear Colleagues,

The quality and safety of food have caught more and more attention for worldwide business. Food is known to have various macro and micro-nutrients, as well as can easily deteriorate during handling, transportation and processing. Novel and reliable paths to detect, identify, characterize, and monitor quality and safety issues in fields of food industries are of great interest.

Many are new non-destructive techniques allow the simultaneous evaluation of physical and chemical information that provides a solution for many issues faced in the field of food industry. Their application, based on the principle of imaging, optical, ultrasound, electrical, acoustic properties, etc., has proven useful for the evaluation of food quality and safety. Currently, many researchers have documented that the non-destructive techniques for application in food composition determination, geographical traceability, safety evaluation, authentication and traceability are reported. Due to the growing awareness of the importance of food safety, the future of using non-destructive techniques in the application for food quality and safety is brilliant. The primary purpose of this Special Issue is to create a collection of novel non-destructive techniques for the application in food quality and safety evaluation.

Prof. Dr. Leiqing Pan
Dr. Qiang Liu
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

non-destructive detection
food composition
food safety
identification
regression
modeling
chemometrics

Published Papers (6 papers)

Download All Papers

Research

Jump to: Review

14 pages, 2605 KiB

Open AccessArticle

Study on Black Spot Disease Detection and Pathogenic Process Visualization on Winter Jujubes Using Hyperspectral Imaging System

by Mengwei Jiang, Yiting Li, Jin Song, Zhenjie Wang, Li Zhang, Lijun Song, Bingyao Bai, Kang Tu, Weijie Lan and Leiqing Pan

Foods 2023, 12(3), 435; https://doi.org/10.3390/foods12030435 - 17 Jan 2023

Cited by 4 | Viewed by 1774

Abstract

In this work, the potential of a hyperspectral imaging (HSI) system for the detection of black spot disease on winter jujubes infected by Alternaria alternata during postharvest storage was investigated. The HSI images were acquired using two systems in the visible and near-infrared (Vis-NIR, 400–1000 nm) and short-wave infrared (SWIR, 1000–2000 nm) spectral regions. Meanwhile, the change of physical (peel color, weight loss) and chemical parameters (soluble solids content, chlorophyll) and the microstructure of winter jujubes during the pathogenic process were measured. The results showed the spectral reflectance of jujubes in both the Vis-NIR and SWIR wavelength ranges presented an overall downtrend during the infection. Partial least squares discriminant models (PLS-DA) based on the HSI spectra in Vis-NIR and SWIR regions of jujubes both gave satisfactory discrimination accuracy for the disease detection, with classification rates of over 92.31% and 91.03%, respectively. Principal component analysis (PCA) was carried out on the HSI images of jujubes to visualize their infected areas during the pathogenic process. The first principal component of the HSI spectra in the Vis-NIR region could highlight the diseased areas of the infected jujubes. Consequently, Vis-NIR HSI and NIR HSI techniques had the potential to detect the black spot disease on winter jujubes during the postharvest storage, and the Vis-NIR HSI spectral information could visualize the diseased areas of jujubes during the pathogenic process. Full article

(This article belongs to the Special Issue Applications of Non-destructive Technologies for Food Quality and Safety)

► Show Figures

Figure 1

16 pages, 4068 KiB

Open AccessArticle

Study on Rice Grain Mildewed Region Recognition Based on Microscopic Computer Vision and YOLO-v5 Model

by Ke Sun, Yu-Jie Zhang, Si-Yuan Tong, Meng-Di Tang and Chang-Bao Wang

Foods 2022, 11(24), 4031; https://doi.org/10.3390/foods11244031 - 14 Dec 2022

Cited by 6 | Viewed by 2556

Abstract

This study aims to develop a high-speed and nondestructive mildewed rice grain detection method. First, a set of microscopic images of rice grains contaminated by Aspergillus niger, Penicillium citrinum, and Aspergillus cinerea are acquired to serve as samples, and the mildewed regions are marked. Then, three YOLO-v5 models for identifying regions of rice grain with contamination of Aspergillus niger, Penicillium citrinum, and Aspergillus cinerea in microscopic images are established. Finally, the relationship between the proportion of mildewed regions and the total number of colonies is analyzed. The results show that the proposed YOLO-v5 models achieve accuracy levels of 89.26%, 91.15%, and 90.19% when detecting mildewed regions with contamination of Aspergillus niger, Penicillium citrinum, and Aspergillus cinerea in the microscopic images of the verification set. The proportion of the mildewed region area of rice grain with contamination of Aspergillus niger/Penicillium citrinum/Aspergillus cinerea is logarithmically correlated with the logarithm of the total number of colonies (TVC). The corresponding determination coefficients are 0.7466, 0.7587, and 0.8148, respectively. This study provides a reference for future research on high-speed mildewed rice grain detection methods based on MCV technology. Full article

(This article belongs to the Special Issue Applications of Non-destructive Technologies for Food Quality and Safety)

► Show Figures

Graphical abstract

14 pages, 1244 KiB

Open AccessArticle

Classification and Feature Extraction Using Supervised and Unsupervised Machine Learning Approach for Broiler Woody Breast Myopathy Detection

by Aftab Siddique, Charles B. Herron, Jaroslav Valenta, Laura J. Garner, Ashish Gupta, Jason T. Sawyer and Amit Morey

Foods 2022, 11(20), 3270; https://doi.org/10.3390/foods11203270 - 20 Oct 2022

Cited by 1 | Viewed by 1718

Abstract

Bioelectrical impedance analysis (BIA) was established to quantify diverse cellular characteristics. This technique has been widely used in various species, such as fish, poultry, and humans for compositional analysis. This technology was limited to offline quality assurance/detection of woody breast (WB); however, inline technology that can be retrofitted on the conveyor belt would be more helpful to processors. Freshly deboned (n = 80) chicken breast fillets were collected from a local processor and analyzed by hand-palpation for different WB severity levels. Data collected from both BIA setups were subjected to supervised and unsupervised learning algorithms. The modified BIA showed better detection ability for regular fillets than the probe BIA setup. In the plate BIA setup, fillets were 80.00% for normal, 66.67% for moderate (data for mild and moderate merged), and 85.00% for severe WB. However, hand-held BIA showed 77.78, 85.71, and 88.89% for normal, moderate, and severe WB, respectively. Plate BIA setup is more effective in detecting WB myopathies and could be installed without slowing the processing line. Breast fillet detection on the processing line can be significantly improved using a modified automated plate BIA. Full article

(This article belongs to the Special Issue Applications of Non-destructive Technologies for Food Quality and Safety)

► Show Figures

Figure 1

11 pages, 1477 KiB

Open AccessArticle

Consensual Regression of Soluble Solids Content in Peach by Near Infrared Spectrocopy

by Lei-Ming Yuan, Lifan You, Xiaofeng Yang, Xiaojing Chen, Guangzao Huang, Xi Chen, Wen Shi and Yiye Sun

Foods 2022, 11(8), 1095; https://doi.org/10.3390/foods11081095 - 11 Apr 2022

Cited by 8 | Viewed by 1529

Abstract

In order to reduce the uncertainty of the genetic algorithm (GA) in optimizing the near-infrared spectral calibration model and avoid the loss of spectral information of the unselected variables, a strategy of fusing consensus models is proposed to measure the soluble solids content (SSC) in peaches. A total of 266 peach samples were collected at four arrivals, and their interactance spectra were scanned by an integrated analyzer prototype, and then an internal index of SSC was destructively measured by the standard refractometry method. The near-infrared spectra were pre-processed with mean centering and were selected successively with a genetic algorithm (GA) to construct the consensus model, which was integrated with two member models with optimized weightings. One was the conventional partial least square (PLS) optimized with GA selected variables (PLS_GA), and the other one was the derived PLS developed with residual variables after GA selections (PLS_RV). The performance of PLS_RV models showed some useful spectral information related to peaches’ SSC and someone performed close to the full-spectral-based PLS model. Among these 10 runs, consensus models obtained a lower root mean squared errors of prediction (RMSEP), with an average of 1.106% and standard deviation (SD) of 0.0068, and performed better than that of the optimized PLS_GA models, which achieved a RMSEP of average 1.116% with SD of 0.0097. It can be concluded that the application of fusion strategy can reduce the fluctuation uncertainty of a model optimized by genetic algorithm, fulfill the utilization of the spectral information amount, and realize the rapid detection of the internal quality of the peach. Full article

(This article belongs to the Special Issue Applications of Non-destructive Technologies for Food Quality and Safety)

► Show Figures

Figure 1

Review

Jump to: Research

22 pages, 1107 KiB

Open AccessReview

Implications of Blending Pulse and Wheat Flours on Rheology and Quality Characteristics of Baked Goods: A Review

by Sunday J. Olakanmi, Digvir S. Jayas and Jitendra Paliwal

Foods 2022, 11(20), 3287; https://doi.org/10.3390/foods11203287 - 20 Oct 2022

Cited by 8 | Viewed by 2943

Abstract

Bread is one of the most widely consumed foods in all regions of the world. Wheat flour being its principal ingredient is a cereal crop low in protein. The protein content of a whole grain of wheat is about 12–15% and is deficit in some essential amino acids, for example, lysine. Conversely, the protein and fibre contents of legume crops are between 20 and 35% and 15 and 35%, respectively, depending on the type and cultivar of the legume. The importance of protein-rich diets for the growth and development of body organs and tissues as well as the overall functionality of the body is significant. Thus, in the last two decades, there has been a greater interest in the studies on the utilization of legumes in bread production and how the incorporation impacts the quality characteristics of the bread and the breadmaking process. The addition of plant-based protein flours has been shown to produce an improved quality characteristic, especially the nutritional quality aspect of bread. The objective of this review is to synthesize and critically investigate the body of research on the impact of adding legume flours on the rheological attributes of dough and the quality and baking characteristics of bread. Full article

(This article belongs to the Special Issue Applications of Non-destructive Technologies for Food Quality and Safety)

► Show Figures

Figure 1

20 pages, 2584 KiB

Open AccessReview

Emerging Approach for Fish Freshness Evaluation: Principle, Application and Challenges

by Zhepeng Zhang, Ying Sun, Shangyuan Sang, Lingling Jia and Changrong Ou

Foods 2022, 11(13), 1897; https://doi.org/10.3390/foods11131897 - 26 Jun 2022

Cited by 13 | Viewed by 4492

Abstract

Affected by micro-organisms and endogenous enzymes, fish are highly perishable during storage, processing and transportation. Efficient evaluation of fish freshness to ensure consumer safety and reduce raw material losses has received an increasing amount of attention. Several of the conventional freshness assessment techniques have plenty of shortcomings, such as being destructive, time-consuming and laborious. Recently, various sensors and spectroscopic techniques have shown great potential due to rapid analysis, low sample preparation and cost-effectiveness, and some methods are especially non-destructive and suitable for online or large-scale operations. Non-destructive techniques typically respond to characteristic substances produced by fish during spoilage without destroying the sample. In this review, we summarize, in detail, the principles and applications of emerging approaches for assessing fish freshness including visual indicators derived from intelligent packaging, active sensors, nuclear magnetic resonance (NMR) and optical spectroscopic techniques. Recent developments in emerging technologies have demonstrated their advantages in detecting fish freshness, but some challenges remain in popularization, optimizing sensor selectivity and sensitivity, and the development of algorithms and chemometrics in spectroscopic techniques. Full article

(This article belongs to the Special Issue Applications of Non-destructive Technologies for Food Quality and Safety)

► Show Figures