Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.8
5.2
Journals
Foods
Special Issues
Fast Non-destructive Detection Technology and Equipment for Food Quality and...
share announcement

Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety

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

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 38053

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Zhiming Guo

E-Mail Website
Guest Editor
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: nondestructive detection of food quality and safety; optical sensing and automation for food quality evaluation; advanced chemometrics methods
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhao Zhang

E-Mail Website
Guest Editor
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Interests: crop phenotype; smart orchard; intelligent agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Dong Hu

E-Mail Website
Guest Editor
College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou, China
Interests: food safety; machine vision; spectral imaging; artificial intelligence

Special Issue Information

Dear Colleagues,

Fast non-destructive detection technology and equipment for food quality and safety is a powerful technical support tool to ensure the development of food industry informatization and intelligence, with the advantages of fast speed, convenient operation, and easy online inspection. During the past two decades, those technologies have found numerous successful applications for food and agricultural product detection and processing. Owing to improvements in the manufacture of photoelectric sensor pieces and progress in artificial intelligence and software algorithms, fast nondestructive detection technologies provide more accurate, reliable, and stable solutions for food quality and safety detection and processing. They are closely integrated with the Internet of Things and intelligent manufacturing, promoting a new wave of innovation in intelligent manufacturing in the food industry. The application of new sensing technology and equipment in fast nondestructive detection of food has always been at the forefront of scientific and technological research. This issue aims to focus on the latest research progress of the application and jointly discuss the focus of development of this research direction.

Prof. Dr. Zhiming Guo
Prof. Dr. Zhao Zhang
Dr. Dong Hu
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

  • machine vision
  • near infrared spectroscopy
  • surface-enhanced raman spectroscopy 
  • spectral imaging 
  • gas sensor 
  • biosensor 
  • electrochemical sensor 
  • artificial intelligence 
  • deep learning 
  • food Internet of Things

Published Papers (23 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Editorial

Jump to: Research

6 pages, 1113 KiB  
Editorial
Fast Nondestructive Detection Technology and Equipment for Food Quality and Safety
by Zhiming Guo and Heera Jayan
Foods 2023, 12(20), 3744; https://doi.org/10.3390/foods12203744 - 12 Oct 2023
Cited by 1 | Viewed by 1134
Abstract
Fast nondestructive detection technology in food quality and safety evaluation is a powerful support tool that fosters informatization and intelligence in the food industry, characterized by its rapid processing, convenient operation, and seamless online inspection [...] Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Figure 1

Research

Jump to: Editorial

16 pages, 2342 KiB  
Article
Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning
by Yi-Ming Cao, Yan Zhang, Shuang-Ting Yu, Kai-Kuo Wang, Ying-Jie Chen, Zi-Ming Xu, Zi-Yao Ma, Hong-Lu Chen, Qi Wang, Ran Zhao, Xiao-Qing Sun and Jiong-Tang Li
Foods 2023, 12(17), 3154; https://doi.org/10.3390/foods12173154 - 22 Aug 2023
Cited by 2 | Viewed by 1045
Abstract
Hyperspectral imaging (HSI) has been applied to assess the texture profile analysis (TPA) of processed meat. However, whether the texture profiles of live fish muscle could be assessed using HSI has not been determined. In this study, we evaluated the texture profile of [...] Read more.
Hyperspectral imaging (HSI) has been applied to assess the texture profile analysis (TPA) of processed meat. However, whether the texture profiles of live fish muscle could be assessed using HSI has not been determined. In this study, we evaluated the texture profile of four muscle regions of live common carp by scanning the corresponding skin regions using HSI. We collected skin hyperspectral information from four regions of 387 scaled and live common carp. Eight texture indicators of the muscle corresponding to each skin region were measured. With the skin HSI of live common carp, six machine learning (ML) models were used to predict the muscle texture indicators. Backpropagation artificial neural network (BP-ANN), partial least-square regression (PLSR), and least-square support vector machine (LS-SVM) were identified as the optimal models for predicting the texture parameters of the dorsal (coefficients of determination for prediction (rp) ranged from 0.9191 to 0.9847, and the root-mean-square error for prediction ranged from 0.1070 to 0.3165), pectoral (rp ranged from 0.9033 to 0.9574, and RMSEP ranged from 0.2285 to 0.3930), abdominal (rp ranged from 0.9070 to 0.9776, and RMSEP ranged from 0.1649 to 0.3601), and gluteal (rp ranged from 0.8726 to 0.9768, and RMSEP ranged from 0.1804 to 0.3938) regions. The optimal ML models and skin HSI data were employed to generate visual prediction maps of TPA values in common carp muscles. These results demonstrated that skin HSI and the optimal models can be used to rapidly and accurately determine the texture qualities of different muscle regions in common carp. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Graphical abstract

13 pages, 3055 KiB  
Article
SERS with Flexible β-CD@AuNP/PTFE Substrates for In Situ Detection and Identification of PAH Residues on Fruit and Vegetable Surfaces Combined with Lightweight Network
by Mengqing Qiu, Le Tang, Jinghong Wang, Qingshan Xu, Shouguo Zheng and Shizhuang Weng
Foods 2023, 12(16), 3096; https://doi.org/10.3390/foods12163096 - 17 Aug 2023
Cited by 1 | Viewed by 1174
Abstract
The detection of polycyclic aromatic hydrocarbons (PAHs) on fruit and vegetable surfaces is important for protecting human health and ensuring food safety. In this study, a method for the in situ detection and identification of PAH residues on fruit and vegetable surfaces was [...] Read more.
The detection of polycyclic aromatic hydrocarbons (PAHs) on fruit and vegetable surfaces is important for protecting human health and ensuring food safety. In this study, a method for the in situ detection and identification of PAH residues on fruit and vegetable surfaces was developed using surface-enhanced Raman spectroscopy (SERS) based on a flexible substrate and lightweight deep learning network. The flexible SERS substrate was fabricated by assembling β-cyclodextrin-modified gold nanoparticles (β-CD@AuNPs) on polytetrafluoroethylene (PTFE) film coated with perfluorinated liquid (β-CD@AuNP/PTFE). The concentrations of benzo(a)pyrene (BaP), naphthalene (Nap), and pyrene (Pyr) residues on fruit and vegetable surfaces could be detected at 0.25, 0.5, and 0.25 μg/cm2, respectively, and all the relative standard deviations (RSD) were less than 10%, indicating that the β-CD@AuNP/PTFE exhibited high sensitivity and stability. The lightweight network was then used to construct a classification model for identifying various PAH residues. ShuffleNet obtained the best results with accuracies of 100%, 96.61%, and 97.63% for the training, validation, and prediction datasets, respectively. The proposed method realised the in situ detection and identification of various PAH residues on fruit and vegetables with simplicity, celerity, and sensitivity, demonstrating great potential for the rapid, nondestructive analysis of surface contaminant residues in the food-safety field. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Graphical abstract

15 pages, 2871 KiB  
Article
Spoilage Monitoring and Early Warning for Apples in Storage Using Gas Sensors and Chemometrics
by Limei Yin, Heera Jayan, Jianrong Cai, Hesham R. El-Seedi, Zhiming Guo and Xiaobo Zou
Foods 2023, 12(15), 2968; https://doi.org/10.3390/foods12152968 - 06 Aug 2023
Cited by 1 | Viewed by 2227
Abstract
In the process of storage and cold chain logistics, apples are prone to physical bumps or microbial infection, which easily leads to spoilage in the micro-environment, resulting in widespread infection and serious post-harvest economic losses. Thus, development of methods for monitoring apple spoilage [...] Read more.
In the process of storage and cold chain logistics, apples are prone to physical bumps or microbial infection, which easily leads to spoilage in the micro-environment, resulting in widespread infection and serious post-harvest economic losses. Thus, development of methods for monitoring apple spoilage and providing early warning of spoilage has become the focus for post-harvest loss reduction. Thus, in this study, a spoilage monitoring and early warning system was developed by measuring volatile component production during apple spoilage combined with chemometric analysis. An apple spoilage monitoring prototype was designed to include a gas monitoring array capable of measuring volatile organic compounds, such as CO2, O2 and C2H4, integrated with the temperature and humidity sensor. The sensor information from a simulated apple warehouse was obtained by the prototype, and a multi-factor fusion early warning model of apple spoilage was established based on various modeling methods. Simulated annealing–partial least squares (SA-PLS) was the optimal model with the correlation coefficient of prediction set (Rp) and root mean square error of prediction (RMSEP) of 0.936 and 0.828, respectively. The real-time evaluation of the spoilage was successfully obtained by loading an optimal monitoring and warning model into the microcontroller. An apple remote monitoring and early warning platform was built to visualize the apple warehouse’s sensors data and spoilage level. The results demonstrated that the prototype based on characteristic gas sensor array could effectively monitor and warn apple spoilage. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Figure 1

12 pages, 2042 KiB  
Article
Raman Spectroscopic Study of Five Typical Plasticizers Based on DFT and HF Theoretical Calculation
by Tong Sun, Yitao Wang, Mingyue Li and Dong Hu
Foods 2023, 12(15), 2888; https://doi.org/10.3390/foods12152888 - 29 Jul 2023
Cited by 1 | Viewed by 778
Abstract
Phthalic acid esters (PAEs) are the most commonly used plasticizers, and long-term or high levels of exposure to PAEs have a huge potential risk to human health. In this study, the theories of Hartree–Fock (HF) and density functional theory (DFT) with different hybrid [...] Read more.
Phthalic acid esters (PAEs) are the most commonly used plasticizers, and long-term or high levels of exposure to PAEs have a huge potential risk to human health. In this study, the theories of Hartree–Fock (HF) and density functional theory (DFT) with different hybrid methods and basis sets were used to calculate the theoretical Raman spectra of five PAEs, and the comparison of calculated spectra between different theories, hybrid methods, and basis sets was conducted to determine the suitable theory with hybrid method and basis set for PAEs. Also, the Raman vibrations were assigned to the Raman peaks of PAEs according to the theoretical and experimental Raman spectra. The results indicate that DFT is more suitable for the theoretical study of PAEs than HF. In DFT, the hybrid method of B3LYP is more applicable to the theoretical study of PAEs than B3PW91, and the basis set of 6-311G(d, p) obtains the most consistent theoretical Raman spectra with the experimental spectra for PAEs. This study finds the optimal combination of the theoretical method and basis set for PAEs, and it will contribute to the establishment of the Raman fingerprint and the development of rapid detection for PAEs in the future. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Figure 1

12 pages, 2076 KiB  
Article
Development of a Sensitive SERS Method for Label-Free Detection of Hexavalent Chromium in Tea Using Carbimazole Redox Reaction
by Limei Yin, Heera Jayan, Jianrong Cai, Hesham R. El-Seedi, Zhiming Guo and Xiaobo Zou
Foods 2023, 12(14), 2673; https://doi.org/10.3390/foods12142673 - 11 Jul 2023
Cited by 5 | Viewed by 1050
Abstract
Tea plants absorb chromium-contaminated soil and water and accumulate in tea leaves. Hexavalent chromium (Cr6+) is a very toxic heavy metal; excessive intake of tea containing Cr6+ can cause serious harm to human health. A reliable and sensitive surface-enhanced Raman [...] Read more.
Tea plants absorb chromium-contaminated soil and water and accumulate in tea leaves. Hexavalent chromium (Cr6+) is a very toxic heavy metal; excessive intake of tea containing Cr6+ can cause serious harm to human health. A reliable and sensitive surface-enhanced Raman spectroscopy (SERS) method was developed using Au@Ag nanoparticles as an enhanced substrate for the determination of Cr6+ in tea. The Au@AgNPs coated with carbimazole showed a highly selective reaction to Cr6+ in tea samples through a redox reaction between Cr6+ and carbimazole. The Cr6+ in the contaminated tea sample reacted with methimazole—the hydrolysate of carbimazole—to form disulfide, which led to the decrease in the Raman intensity of the peak at 595 cm−1. The logarithm of the concentration of Cr6+ has a linear relationship with the Raman intensity at the characteristic peak and showed a limit of detection of 0.945 mg/kg for the tea sample. The carbimazole functionalized Au@AgNPs showed high selectivity in analyzing Cr6+ in tea samples, even in the presence of other metal ions. The SERS detection technique established in this study also showed comparable results with the standard ICP-MS method, indicating the applicability of the established technique in practical applications. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Figure 1

11 pages, 5150 KiB  
Article
Evaluation of 60Co Irradiation on Volatile Components of Turmeric (Curcumae Longae Rhizoma) Volatile Oil with GC–IMS
by Ye He, Lu Yin, Wei Zhou, Hongyan Wan, Chang Lei, Shunxiang Li and Dan Huang
Foods 2023, 12(13), 2489; https://doi.org/10.3390/foods12132489 - 26 Jun 2023
Cited by 4 | Viewed by 1049
Abstract
60Co irradiation is an efficient and rapid sterilization method. The aim of this work is to determine the changes in essential-oil composition under different irradiation intensities of 60Co and to select an appropriate irradiation dose with GC–IMS. Dosages of 0, 5, [...] Read more.
60Co irradiation is an efficient and rapid sterilization method. The aim of this work is to determine the changes in essential-oil composition under different irradiation intensities of 60Co and to select an appropriate irradiation dose with GC–IMS. Dosages of 0, 5, and 10 kGy of 60Co were used to analyze turmeric (Curcumae Longae Rhizoma) volatile oil after 60Co irradiation (named JH-1, JH-2, and JH-3). The odor fingerprints of volatile organic compounds in different turmeric volatile oil samples were constructed by headspace solid-phase microextraction and gas chromatography–ion mobility spectrometry (GC–IMS) after irradiation. The differences in odor fingerprints of volatile organic compounds (VOCs) were compared by principal component analysis (PCA). The results showed that 97 volatile components were detected in the volatile oil of Curcuma longa, and 64 components were identified by database retrieval. With the change in irradiation intensity, the volatile compounds in the three turmeric volatile oil samples were similar, but the peak intensity was significantly different, which was attributed to the change in compound composition and content caused by different irradiation doses. In addition, the principal component analysis showed that JH-2 and JH-3 were relatively correlated, while JH-1 and JH-3 were far from each other. In general, different doses of 60Co irradiation can affect the content of volatile substances in turmeric volatile oil. With the increase in irradiation dose, the peak area decreased, and so the irradiation dose of 5 kGy/min was better. It is shown that irradiation technology has good application prospects in the sterilization of foods with volatile components. However, we must pay attention to the changes in radiation dose and chemical composition. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Figure 1

10 pages, 953 KiB  
Article
Prediction of Anthocyanidins Content in Purple Chinese Cabbage Based on Visible/Near Infrared Spectroscopy
by Ya-Qin Wang, Guang-Min Liu, Li-Ping Hu, Xue-Zhi Zhao, De-Shuang Zhang and Hong-Ju He
Foods 2023, 12(9), 1922; https://doi.org/10.3390/foods12091922 - 08 May 2023
Cited by 4 | Viewed by 1254
Abstract
Purple Chinese cabbage (PCC) has become a new breeding trend due to its attractive color and high nutritional quality since it contains abundant anthocyanidins. With the aim of rapid evaluation of PCC anthocyanidins contents and screening of breeding materials, a fast quantitative detection [...] Read more.
Purple Chinese cabbage (PCC) has become a new breeding trend due to its attractive color and high nutritional quality since it contains abundant anthocyanidins. With the aim of rapid evaluation of PCC anthocyanidins contents and screening of breeding materials, a fast quantitative detection method for anthocyanidins in PCC was established using Near Infrared Spectroscopy (NIR). The PCC samples were scanned by NIR, and the spectral data combined with the chemometric results of anthocyanidins contents obtained by high-performance liquid chromatography were processed to establish the prediction models. The content of cyanidin varied from 93.5 mg/kg to 12,802.4 mg/kg in PCC, while the other anthocyanidins were much lower. The developed NIR prediction models on the basis of partial least square regression with the preprocessing of no-scattering mode and the first-order derivative showed the best prediction performance: for cyanidin, the external correlation coefficient (RSQ) and standard error of cross-validation (SECV) of the calibration set were 0.965 and 693.004, respectively; for total anthocyanidins, the RSQ and SECV of the calibration set were 0.966 and 685.994, respectively. The established models were effective, and this NIR method, with the advantages of timesaving and convenience, could be applied in purple vegetable breeding practice. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Figure 1

17 pages, 4754 KiB  
Article
Analysis of Light Penetration Depth in Apple Tissues by Depth-Resolved Spatial-Frequency Domain Imaging
by Tongtong Zhou, Dong Hu, Dekai Qiu, Shengqi Yu, Yuping Huang, Zhizhong Sun, Xiaolin Sun, Guoquan Zhou, Tong Sun and Hehuan Peng
Foods 2023, 12(9), 1783; https://doi.org/10.3390/foods12091783 - 25 Apr 2023
Cited by 2 | Viewed by 1174
Abstract
Spatial-frequency domain imaging (SFDI) has been developed as an emerging modality for detecting early-stage bruises of fruits, such as apples, due to its unique advantage of a depth-resolved imaging feature. This paper presents theoretical and experimental analyses to determine the light penetration depth [...] Read more.
Spatial-frequency domain imaging (SFDI) has been developed as an emerging modality for detecting early-stage bruises of fruits, such as apples, due to its unique advantage of a depth-resolved imaging feature. This paper presents theoretical and experimental analyses to determine the light penetration depth in apple tissues under spatially modulated illumination. Simulation and practical experiments were then carried out to explore the maximum light penetration depths in ‘Golden Delicious’ apples. Then, apple experiments for early-stage bruise detection using the estimated reduced scattering coefficient mapping were conducted to validate the results of light penetration depths. The results showed that the simulations produced comparable or a little larger light penetration depth in apple tissues (~2.2 mm) than the practical experiment (~1.8 mm or ~2.3 mm). Apple peel further decreased the light penetration depth due to the high absorption properties of pigment contents. Apple bruises located beneath the surface peel with the depth of about 0–1.2 mm could be effectively detected by the SFDI technique. This study, to our knowledge, made the first effort to investigate the light penetration depth in apple tissues by SFDI, which would provide useful information for enhanced detection of early-stage apple bruising by selecting the appropriate spatial frequency. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Figure 1

13 pages, 4794 KiB  
Article
High-Stability Bi-Layer Films Incorporated with Liposomes @Anthocyanin/Carrageenan/Agar for Shrimp Freshness Monitoring
by Junjun Zhang, Yan Yang, Jianing Zhang, Jiyong Shi, Li Liu, Xiaowei Huang, Wenjun Song, Zhihua Li, Xiaobo Zou and Megan Povey
Foods 2023, 12(4), 732; https://doi.org/10.3390/foods12040732 - 08 Feb 2023
Cited by 2 | Viewed by 1688
Abstract
High-stability bi-layer films were prepared by incorporating anthocyanin-loaded liposomes into carrageenan and agar (A-CBAL) for non-destructive shrimp freshness monitoring. The encapsulation efficiency of the anthocyanin-loaded liposomes increased from 36.06% to 46.99% with an increasing ratio of lecithin. The water vapor transmission (WVP) of [...] Read more.
High-stability bi-layer films were prepared by incorporating anthocyanin-loaded liposomes into carrageenan and agar (A-CBAL) for non-destructive shrimp freshness monitoring. The encapsulation efficiency of the anthocyanin-loaded liposomes increased from 36.06% to 46.99% with an increasing ratio of lecithin. The water vapor transmission (WVP) of the A-CBAL films, with a value of 2.32 × 10−7 g · m−1 · h−1 · pa−1, was lower than that of the film with free anthocyanins (A-CBA). The exudation rate of the A-CBA film reached 100% at pH 7 and pH 9 after 50 min, while the A-CBAL films slowed down to a value lower than 45%. The encapsulation of anthocyanins slightly decreased the ammonia sensitivity. Finally, the bi-layer films with liposomes successfully monitored shrimp freshness with visible color changes to the naked eye. These results indicated that films with anthocyanin-loaded liposomes have potential applications in high-humidity environments. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Figure 1

15 pages, 5661 KiB  
Article
An Improved YOLOv5 Model: Application to Mixed Impurities Detection for Walnut Kernels
by Lang Yu, Mengbo Qian, Qiang Chen, Fuxing Sun and Jiaxuan Pan
Foods 2023, 12(3), 624; https://doi.org/10.3390/foods12030624 - 01 Feb 2023
Cited by 9 | Viewed by 1939
Abstract
Impurity detection is an important link in the chain of food processing. Taking walnut kernels as an example, it is difficult to accurately detect impurities mixed in walnut kernels before the packaging process. In order to accurately identify the small impurities mixed in [...] Read more.
Impurity detection is an important link in the chain of food processing. Taking walnut kernels as an example, it is difficult to accurately detect impurities mixed in walnut kernels before the packaging process. In order to accurately identify the small impurities mixed in walnut kernels, this paper established an improved impurities detection model based on the original YOLOv5 network model. Initially, a small target detection layer was added in the neck part, to improve the detection ability for small impurities, such as broken shells. Secondly, the Tansformer-Encoder (Trans-E) module is proposed to replace some convolution blocks in the original network, which can better capture the global information of the image. Then, the Convolutional Block Attention Module (CBAM) was added to improve the sensitivity of the model to channel features, which make it easy to find the prediction region in dense objects. Finally, the GhostNet module is introduced to make the model lighter and improve the model detection rate. During the test stage, sample photos were randomly chosen to test the model’s efficacy using the training and test set, derived from the walnut database that was previously created. The mean average precision can measure the multi-category recognition accuracy of the model. The test results demonstrate that the mean average precision (mAP) of the improved YOLOv5 model reaches 88.9%, which is 6.7% higher than the average accuracy of the original YOLOv5 network, and is also higher than other detection networks. Moreover, the improved YOLOv5 model is significantly better than the original YOLOv5 network in identifying small impurities, and the detection rate is only reduced by 3.9%, which meets the demand of real-time detection of food impurities and provides a technical reference for the detection of small impurities in food. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Figure 1

20 pages, 6652 KiB  
Article
Detection Method for Tomato Leaf Mildew Based on Hyperspectral Fusion Terahertz Technology
by Xiaodong Zhang, Yafei Wang, Zhankun Zhou, Yixue Zhang and Xinzhong Wang
Foods 2023, 12(3), 535; https://doi.org/10.3390/foods12030535 - 25 Jan 2023
Cited by 6 | Viewed by 1625
Abstract
Leaf mildew is a common disease of tomato leaves. Its detection is an important means to reduce yield loss from the disease and improve tomato quality. In this study, a new method was developed for the multi-source detection of tomato leaf mildew by [...] Read more.
Leaf mildew is a common disease of tomato leaves. Its detection is an important means to reduce yield loss from the disease and improve tomato quality. In this study, a new method was developed for the multi-source detection of tomato leaf mildew by THz hyperspectral imaging through combining internal and external leaf features. First, multi-source information obtained from tomato leaves of different disease grades was extracted by near-infrared hyperspectral imaging and THz time-domain spectroscopy, while the influence of low-frequency noise was removed by the Savitzky Golay (SG) smoothing algorithm. A genetic algorithm (GA) was used to optimize the selection of the characteristic near-infrared hyperspectral band. Principal component analysis (PCA) was employed to optimize the THz characteristic absorption spectra and power spectrum dimensions. Recognition models were developed for different grades of tomato leaf mildew infestation by incorporating near-infrared hyperspectral imaging, THz absorbance, and power spectra using the backpropagation neural network (BPNN), and the models had recognition rates of 95%, 96.67%, and 95%, respectively. Based on the near-infrared hyperspectral features, THz time-domain spectrum features, and classification model, the probability density of the posterior distribution of tomato leaf health parameter variables was recalculated by a Bayesian network model. Finally, a fusion diagnosis and health evaluation model of tomato leaf mildew with hyperspectral fusion THz was established, and the recognition rate of tomato leaf mildew samples reached 97.12%, which improved the recognition accuracy by 0.45% when compared with the single detection method, thereby achieving the accurate detection of facility diseases. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Figure 1

21 pages, 6131 KiB  
Article
Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning
by Peng Xu, Wenbin Sun, Kang Xu, Yunpeng Zhang, Qian Tan, Yiren Qing and Ranbing Yang
Foods 2023, 12(1), 144; https://doi.org/10.3390/foods12010144 - 27 Dec 2022
Cited by 6 | Viewed by 1799
Abstract
Seed quality affects crop yield and the quality of agricultural products, and traditional identification methods are time-consuming, complex, and irreversibly destructive. This study aims to establish a fast, non-destructive, and effective approach for defect detection in maize seeds based on hyperspectral imaging (HSI) [...] Read more.
Seed quality affects crop yield and the quality of agricultural products, and traditional identification methods are time-consuming, complex, and irreversibly destructive. This study aims to establish a fast, non-destructive, and effective approach for defect detection in maize seeds based on hyperspectral imaging (HSI) technology combined with deep learning. Raw spectra collected from maize seeds (200 each healthy and worm-eaten) were pre-processed using detrending (DE) and multiple scattering correction (MSC) to highlight the spectral differences between samples. A convolutional neural network architecture (CNN-FES) based on a feature selection mechanism was proposed according to the importance of wavelength in the target classification task. The results show that the subset of 24 feature wavelengths selected by the proposed CNN-FES can capture important feature information in the spectral data more effectively than the conventional successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) algorithms. In addition, a convolutional neural network architecture (CNN-ATM) based on an attentional classification mechanism was designed for one-dimensional spectral data classification and compared with three commonly used machine learning methods, linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). The results show that the classification performance of the designed CNN-ATM on the full wavelength does not differ much from the above three methods, and the classification accuracy is above 90% on both the training and test sets. Meanwhile, the accuracy, sensitivity, and specificity of CNN-ATM based on feature wavelength modeling can reach up to 97.50%, 98.28%, and 96.77% at the highest, respectively. The study shows that hyperspectral imaging-based defect detection of maize seed is feasible and effective, and the proposed method has great potential for the processing and analysis of complex hyperspectral data. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Figure 1

15 pages, 1900 KiB  
Article
Evaluation of Rice Degree of Milling Based on Bayesian Optimization and Multi-Scale Residual Model
by Weidong Chen, Wanyu Li and Ying Wang
Foods 2022, 11(22), 3720; https://doi.org/10.3390/foods11223720 - 19 Nov 2022
Cited by 2 | Viewed by 1492
Abstract
Traditional machine learning-based methods for the detection of rice degree of milling (DOM) that are not comprehensive in feature extraction and have low recognition rates fail to meet the demand for fast, non-destructive, and accurate detection. This paper presents a digital image processing [...] Read more.
Traditional machine learning-based methods for the detection of rice degree of milling (DOM) that are not comprehensive in feature extraction and have low recognition rates fail to meet the demand for fast, non-destructive, and accurate detection. This paper presents a digital image processing technology combined with deep learning to implement the classification of DOM of rice. An improved multi-scale information fusion model of the InceptionResNet–Bayesian optimization algorithm (IRBOA) was constructed based on the Inception-v3 structure and residual network (ResNet) model. It enables to automatically extract more comprehensive features of rice and determine the DOM of rice. Additionally, the important hyperparameters in the model were tuned by the BOA to optimize the recognition rate of rice DOM. The results show the hyperparameters optimized using the BOA are those that would not be chosen in manual tuning. The classification precision of the IRBOA model reached 99.22%, 94.92%, and 96.55% for well-milled, reasonably well-milled, and substandard rice, respectively, with an average accuracy of no less than 96.90%. This model improved 7.41% over the traditional machine learning model and at least 1.35% over the fashionable CNN model with strong generalization performance. This method effectively completes rapid, non-destructive, and accurate intelligent detection of rice DOM, which can supply a reliable and accurate technical mean for rice processing enterprises to guide the rice processing process. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Figure 1

16 pages, 3399 KiB  
Article
A Method for Capture and Detection of Crop Airborne Disease Spores Based on Microfluidic Chips and Micro Raman Spectroscopy
by Xiaodong Zhang, Fei Bian, Yafei Wang, Lian Hu, Ning Yang and Hanping Mao
Foods 2022, 11(21), 3462; https://doi.org/10.3390/foods11213462 - 01 Nov 2022
Cited by 4 | Viewed by 1734
Abstract
Airborne crop diseases cause great losses to agricultural production and can affect people’s physical health. Timely monitoring of the situation of airborne disease spores and effective prevention and control measures are particularly important. In this study, a two-stage separation and enrichment microfluidic chip [...] Read more.
Airborne crop diseases cause great losses to agricultural production and can affect people’s physical health. Timely monitoring of the situation of airborne disease spores and effective prevention and control measures are particularly important. In this study, a two-stage separation and enrichment microfluidic chip with arcuate pretreatment channel was designed for the separation and enrichment of crop disease spores, which was combined with micro Raman for Raman fingerprinting of disease conidia and quasi identification. The chip was mainly composed of arc preprocessing and two separated enriched structures, and the designed chip was numerically simulated using COMSOL multiphysics5.5, with the best enrichment effect at W2/W1 = 1.6 and W4/W3 = 1.1. The spectra were preprocessed with standard normal variables (SNVs) to improve the signal-to-noise ratio, which was baseline corrected using an iterative polynomial fitting method to further improve spectral features. Raman spectra were dimensionally reduced using principal component analysis (PCA) and stability competitive adaptive weighting (SCARS), support vector machine (SVM) and back-propagation artificial neural network (BPANN) were employed to identify fungal spore species, and the best discrimination effect was achieved using the SCARS-SVM model with 94.31% discrimination accuracy. Thus, the microfluidic-chip- and micro-Raman-based methods for spore capture and identification of crop diseases have the potential to be precise, convenient, and low-cost methods for fungal spore detection. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Figure 1

21 pages, 4064 KiB  
Article
Real-Time Monitoring of the Quality Changes in Shrimp (Penaeus vannamei) with Hyperspectral Imaging Technology during Hot Air Drying
by Wenya Xu, Fan Zhang, Jiarong Wang, Qianyun Ma, Jianfeng Sun, Yiwei Tang, Jie Wang and Wenxiu Wang
Foods 2022, 11(20), 3179; https://doi.org/10.3390/foods11203179 - 12 Oct 2022
Cited by 5 | Viewed by 1791
Abstract
Hot air drying is the most common processing method to extend shrimp’s shelf life. Real-time monitoring of moisture content, color, and texture during the drying process is important to ensure product quality. In this study, hyperspectral imaging technology was employed to acquire images [...] Read more.
Hot air drying is the most common processing method to extend shrimp’s shelf life. Real-time monitoring of moisture content, color, and texture during the drying process is important to ensure product quality. In this study, hyperspectral imaging technology was employed to acquire images of 104 shrimp samples at different drying levels. The water distribution and migration were monitored by low field magnetic resonance and the correlation between water distribution and other quality indicators were determined by Pearson correlation analysis. Then, spectra were extracted and competitive adaptive reweighting sampling was used to optimize characteristic variables. The grey-scale co-occurrence matrix and color moments were used to extract the textural and color information from the images. Subsequently, partial least squares regression and least squares support vector machine (LSSVM) models were established based on full-band spectra, characteristic spectra, image information, and fused information. For moisture, the LSSVM model based on full-band spectra performed the best, with residual predictive deviation (RPD) of 2.814. For L*, a*, b*, hardness, and elasticity, the optimal models were established by LSSVM based on fused information, with RPD of 3.292, 2.753, 3.211, 2.807, and 2.842. The study provided an in situ and real-time alternative to monitor quality changes of dried shrimps. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Figure 1

17 pages, 3866 KiB  
Article
Real-Time Grading of Defect Apples Using Semantic Segmentation Combination with a Pruned YOLO V4 Network
by Xiaoting Liang, Xueying Jia, Wenqian Huang, Xin He, Lianjie Li, Shuxiang Fan, Jiangbo Li, Chunjiang Zhao and Chi Zhang
Foods 2022, 11(19), 3150; https://doi.org/10.3390/foods11193150 - 10 Oct 2022
Cited by 19 | Viewed by 3106
Abstract
At present, the apple grading system usually conveys apples by a belt or rollers. This usually leads to low hardness or expensive fruits being bruised, resulting in economic losses. In order to realize real-time detection and classification of high-quality apples, separate fruit trays [...] Read more.
At present, the apple grading system usually conveys apples by a belt or rollers. This usually leads to low hardness or expensive fruits being bruised, resulting in economic losses. In order to realize real-time detection and classification of high-quality apples, separate fruit trays were designed to convey apples and used to prevent apples from being bruised during image acquisition. A semantic segmentation method based on the BiSeNet V2 deep learning network was proposed to segment the defective parts of defective apples. BiSeNet V2 for apple defect detection obtained a slightly better result in MPA with a value of 99.66%, which was 0.14 and 0.19 percentage points higher than DAnet and Unet, respectively. A model pruning method was used to optimize the structure of the YOLO V4 network. The detection accuracy of defect regions in apple images was further improved by the pruned YOLO V4 network. Then, a surface mapping method between the defect area in apple images and the actual defect area was proposed to accurately calculate the defect area. Finally, apples on separate fruit trays were sorted according to the number and area of defects in the apple images. The experimental results showed that the average accuracy of apple classification was 92.42%, and the F1 score was 94.31. In commercial separate fruit tray grading and sorting machines, it has great application potential. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Figure 1

12 pages, 2035 KiB  
Article
Detection of Soluble Solids Content in Different Cultivated Fresh Jujubes Based on Variable Optimization and Model Update
by Haixia Sun, Shujuan Zhang, Rui Ren, Jianxin Xue and Huamin Zhao
Foods 2022, 11(16), 2522; https://doi.org/10.3390/foods11162522 - 20 Aug 2022
Cited by 4 | Viewed by 1441
Abstract
To solve the failure problem of the visible/near infrared (VIS/NIR) spectroscopy model, soluble solids content (SSC) detection for fresh jujubes cultivated in different modes was carried out based on the method of variable optimization and model update. Iteratively retained informative variables (IRIV) and [...] Read more.
To solve the failure problem of the visible/near infrared (VIS/NIR) spectroscopy model, soluble solids content (SSC) detection for fresh jujubes cultivated in different modes was carried out based on the method of variable optimization and model update. Iteratively retained informative variables (IRIV) and successive projections algorithm (SPA) algorithms were used to extract characteristic wavelengths, and least square support vector machine (LS-SVM) was used to establish detection models. Compared with IRIV, IRIV-SPA achieved better performance. Combined with the offset properties of the wavelength, repeated wavelengths were removed, and wavelength recombination was carried out to create a new combination of variables. Using these fused wavelengths, the model was recalibrated based on the Euclidean distance between samples. The LS-SVM detection model of SSC was established using the update method of wavelength fusion-Euclidean distance. Good prediction results were achieved using the proposed model. The determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of the test set on SSC of fresh jujubes cultivated in the open field were 0.82, 1.49%, and 2.18, respectively. The R2, RMSE, and RPD of the test set on SSC of fresh jujubes cultivated in the rain shelter were 0.81, 1.44%, and 2.17, respectively. This study realized the SSC detection of fresh jujubes with different cultivation and provided a method for the establishment of a robust VIS/NIR detection model for fruit quality, effectively addressing the industry need for identifying jujubes grown in the open field. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Figure 1

12 pages, 2816 KiB  
Article
Intelligent Evaluation of Stone Cell Content of Korla Fragrant Pears by Vis/NIR Reflection Spectroscopy
by Tongzhao Wang, Yixiao Zhang, Yuanyuan Liu, Zhijuan Zhang and Tongbin Yan
Foods 2022, 11(16), 2391; https://doi.org/10.3390/foods11162391 - 09 Aug 2022
Cited by 6 | Viewed by 1640
Abstract
Stone cells are a distinctive characteristic of pears and their formation negatively affects the quality of the fruit. To evaluate the stone cell content (SCC) of Korla fragrant pears, we developed a Vis/NIR spectroscopy system that allowed for the adjustment of the illuminating [...] Read more.
Stone cells are a distinctive characteristic of pears and their formation negatively affects the quality of the fruit. To evaluate the stone cell content (SCC) of Korla fragrant pears, we developed a Vis/NIR spectroscopy system that allowed for the adjustment of the illuminating angle. The successive projective algorithm (SPA) and the Monte Carlo uninformative variable elimination (MCUVE) based on the sampling algorithm were used to select characteristic wavelengths. The particle swarm optimization (PSO) algorithm was used to optimize the combination of penalty factor C and kernel function parameter g. Support vector regression (SVR) was used to construct the evaluation model of the SCC. The SCC of the calibration set ranged from 0.240% to 0.657% and that of the validation set ranged from 0.315% to 0.652%. The SPA and MCUVE were used to optimize 57 and 83 characteristic wavelengths, respectively. The combinations of C and g were (6.2561, 0.2643) and (2.5133, 0.1128), respectively, when different characteristic wavelengths were used as inputs of SVR, indicating that the first combination had good generalization ability. The correlation coefficients of the SPA-SVR model after pre-processing the standardized normal variate (SNV) for both sets were 0.966 and 0.951, respectively. These results show that the SNV-SPA-SVR model satisfied the requirements of intelligent evaluation of SCC in Korla fragrant pears. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Figure 1

8 pages, 990 KiB  
Article
Predicting Mandarin Fruit Acceptability: From High-Field to Benchtop NMR Spectroscopy
by Ignacio Migues, Fernando Rivas, Guillermo Moyna, Simon D. Kelly and Horacio Heinzen
Foods 2022, 11(16), 2384; https://doi.org/10.3390/foods11162384 - 09 Aug 2022
Cited by 3 | Viewed by 1434
Abstract
Recent advances in nuclear magnetic resonance (NMR) have led to the development of low-field benchtop NMR systems with improved sensitivity and resolution suitable for use in research and quality-control laboratories. Compared to their high-resolution counterparts, their lower purchase and running costs make them [...] Read more.
Recent advances in nuclear magnetic resonance (NMR) have led to the development of low-field benchtop NMR systems with improved sensitivity and resolution suitable for use in research and quality-control laboratories. Compared to their high-resolution counterparts, their lower purchase and running costs make them a good alternative for routine use. In this article, we show the adaptation of a method for predicting the consumer acceptability of mandarins, originally reported using a high-field 400 MHz NMR spectrometer, to benchtop 60 MHz NMR systems. Our findings reveal that both instruments yield comparable results regarding sugar and citric acid levels, leading to the development of virtually identical predictive linear models. However, the lower cost of benchtop NMR systems would allow cultivators to implement this chemometric-based method as an additional tool for the selection of new cultivars. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Graphical abstract

10 pages, 1156 KiB  
Article
Calibration of Near Infrared Spectroscopy of Apples with Different Fruit Sizes to Improve Soluble Solids Content Model Performance
by Xiaogang Jiang, Mingwang Zhu, Jinliang Yao, Yuxiang Zhang and Yande Liu
Foods 2022, 11(13), 1923; https://doi.org/10.3390/foods11131923 - 28 Jun 2022
Cited by 10 | Viewed by 1896
Abstract
The transmission spectrum of apples is affected by the fruit’s size, which leads to poor prediction performance of the soluble solids content (SSC) models built for their different apple sizes. In this paper, three sets of near infrared (NIR) spectra of apples with [...] Read more.
The transmission spectrum of apples is affected by the fruit’s size, which leads to poor prediction performance of the soluble solids content (SSC) models built for their different apple sizes. In this paper, three sets of near infrared (NIR) spectra of apples with various apple diameters were collected by applying NIR spectroscopy detection equipment to compare the spectra differences among various apple diameter groups. The NIR spectra of apples were corrected by studying the extinction rates within different apples. The corrected spectra were used to develop a partial least squares prediction model for their soluble solids content. Compared with the prediction model of the soluble solids content of apples without size correction, the Rp of PLSR improved from 0.769 to 0.869 and RMSEP declined from 0.990 to 0.721 in the small fruit diameter group; the Rp of PLSR improved from 0.787 to 0.932 and RMSEP declined from 0.878 to 0.531 in the large fruit diameter group. The proposed apple spectra correction method is effective and can be used to reduce the influence of sample diameter on NIR spectra. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Figure 1

12 pages, 21281 KiB  
Article
Assembled Reduced Graphene Oxide/Tungsten Diselenide/Pd Heterojunction with Matching Energy Bands for Quick Banana Ripeness Detection
by Xian Li, Chengcheng Xu, Xiaosong Du, Zhen Wang, Wenjun Huang, Jie Sun, Yang Wang and Zhemin Li
Foods 2022, 11(13), 1879; https://doi.org/10.3390/foods11131879 - 24 Jun 2022
Cited by 2 | Viewed by 1625
Abstract
The monitoring of ethylene is of great importance to fruit and vegetable quality, yet routine techniques rely on manual and complex operation. Herein, a chemiresistive ethylene sensor based on reduced graphene oxide (rGO)/tungsten diselenide (WSe2)/Pd heterojunctions was designed for room-temperature (RT) [...] Read more.
The monitoring of ethylene is of great importance to fruit and vegetable quality, yet routine techniques rely on manual and complex operation. Herein, a chemiresistive ethylene sensor based on reduced graphene oxide (rGO)/tungsten diselenide (WSe2)/Pd heterojunctions was designed for room-temperature (RT) ethylene detection. The sensor exhibited high sensitivity and quick p-type response/recovery (33/13 s) to 10–100 ppm ethylene at RT, and full reversibility and excellent selectivity to ethylene were also achieved. Such excellent ethylene sensing behaviors could be attributed to the synergistic effects of ethylene adsorption abilities derived from the negative adsorption energy and the promoted electron transfer across the WSe2/Pd and rGO/WSe2 interfaces through band energy alignment. Furthermore, its application feasibility to banana ripeness detection was verified by comparison with routine technique through simulation experiments. This work provides a feasible methodology toward designing and fabricating RT ethylene sensors, and may greatly push forward the development of modernized intelligent agriculture. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Figure 1

21 pages, 5853 KiB  
Article
Identification of Maize with Different Moldy Levels Based on Catalase Activity and Data Fusion of Hyperspectral Images
by Wenchao Wang, Wenqian Huang, Huishan Yu and Xi Tian
Foods 2022, 11(12), 1727; https://doi.org/10.3390/foods11121727 - 13 Jun 2022
Cited by 7 | Viewed by 1792
Abstract
Maize is susceptible to mold infection during growth and storage due to its large embryo and high moisture content. Therefore, it is essential to distinguish the moldy sample from healthy groups to prevent the spread of mold and avoid huger economic losses. Catalase [...] Read more.
Maize is susceptible to mold infection during growth and storage due to its large embryo and high moisture content. Therefore, it is essential to distinguish the moldy sample from healthy groups to prevent the spread of mold and avoid huger economic losses. Catalase is a metabolite in the growth of microorganisms; hence, all maize samples were accurately divided into four moldy grades (health, mild, moderate, and severe levels) by determining their catalase activity. The visible and shortwave near-infrared (Vis-SWNIR) and longwave near-infrared (LWNIR) hyperspectral images were investigated to jointly identify the moldy levels of maize. Spectra and texture information of each maize sample were extracted and used to build the classification models of maize with different moldy levels in pixel-level fusion and feature-level fusion. The result showed that the feature-level fusion of spectral and texture within Vis-SWNIR and LWNIR regions achieved the best results, overall prediction accuracy reached 95.00% for each moldy level, all healthy maize was correctly classified, and none of the moldy samples were misclassified as healthy level. This study illustrated that two hyperspectral image systems, with complementary spectral ranges, combined with feature selection and data fusion strategies, could be used synergistically to improve the classification accuracy of maize with different moldy levels. Full article
(This article belongs to the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-23
Back to TopTop