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Advanced Analytical Strategies in Food Safety and Quality Monitoring

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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 October 2022) | Viewed by 29423

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Special Issue Editors

Prof. Dr. Xiaobo Zou

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Guest Editor

School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd, Zhenjiang 212013, China
Interests: rapid and nondestructive detection methods and equipment for food and agricultural products

Dr. Jiyong Shi

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Guest Editor

School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: electrochemical sensor; fluorescence sensor; hyperspectral imaging; chemometrics; nondestructive detection

Special Issue Information

Dear Colleagues,

Food safety and quality have received increasing attention because of the increased demand for healthy food. Nowadays, food safety and quality assurance are still being challenged. Some well-developed analytical methods have been widely used to determine food safety and quality, but they are generally high in cost or time-consuming. Hence, it is desirable to develop analytical strategies to monitor food safety and quality rapidly or even in real-time. To achieve this, many efforts have been made in optical and electrical/electrochemical methods. Most of these methods are based on sensors and indicators that are generally small in size with rapid response to targets. As foods are generally complex matrices, the selectivity, sensitivity, and stability of the sensors and indicators should be sufficiently effective. To date, with the development of advanced materials, such as composite materials and nanomaterials, these sensors and indicators have been endowed with superior performance. We believe that increasing novel and advanced analytical strategies will be put into practical application and therefore help to protect our health.

Prof. Dr. Xiaobo Zou
Dr. Jiyong Shi
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

NIR/IR
hyperspectral imaging
electronic nose
machine vision
electrochemistry
raman spectroscopy
fluorescence spectroscopy
intelligent packaging
nanomaterials

Published Papers (13 papers)

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Research

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13 pages, 1970 KiB

Open AccessArticle

Effects of Pulsed Pressure Curing on Beef Quality

by Chuang Li, Jiyong Shi, Xiaodong Zhai, Zhikun Yang, Xiaowei Huang, Zhihua Li, Yanxiao Li and Xiaobo Zou

Foods 2023, 12(3), 656; https://0-doi-org.brum.beds.ac.uk/10.3390/foods12030656 - 03 Feb 2023

Cited by 1 | Viewed by 1306

Abstract

The study was proposed to investigate the effects of pulsed pressure curing on the beef absorption of the curing solution, cooking loss, moisture content, centrifugal loss, salt content, sensory attributes, texture, microstructures and volatile compounds. Curing methods include the following four treatments: (1) control group 1—static curing (SC); (2) control group 2—vacuum curing (VC); (3) control group 3—pressurized curing (PC); and (4) treatment group—pulsed pressure curing (PPC). The acquired results revealed that pulsed pressure curing significantly boosts the curing efficiency and moisture content, decreases cooking loss in beef, brightens meat color, and enhances texture compared to static curing, vacuum curing, and pressurized curing. Additionally, centrifugal losses were not impaired, and sensory findings revealed that PPC significantly improved the saltiness of beef. TPA results showed that the springiness and cohesiveness of PPC were greatly increased, and hardness and chewiness were significantly reduced. Moreover, PPC significantly reduced the content of 1-octen-3-ol and 1-hexanol. Scanning electron microscopy (SEM) images documented that pulsed pressure curing can effectively increase the tenderness of beef. This study demonstrates that processed meat product efficiency and sensory attributes should be taken into account when selecting a curing technique, and the PPC technique has an advantage in both areas. Full article

(This article belongs to the Special Issue Advanced Analytical Strategies in Food Safety and Quality Monitoring)

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13 pages, 1549 KiB

Open AccessArticle

Application of Imaging and Artificial Intelligence for Quality Monitoring of Stored Black Currant (Ribes nigrum L.)

by Ewa Ropelewska

Foods 2022, 11(22), 3589; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11223589 - 11 Nov 2022

Cited by 11 | Viewed by 1435

Abstract

The objective of this study was to assess the influence of storage under different storage conditions on black currant quality in a non-destructive and inexpensive manner using image processing and artificial intelligence. Black currants were stored at a room temperature of 20 ± 1 °C and a temperature of 3 °C (refrigerator). The images of black currants directly after harvest and fruit stored for one and two weeks were obtained using a digital camera. Then, texture parameters were computed from the images converted to color channels R (red), G (green), B (blue), L (lightness component from black to white), a (green for negative and red for positive values), b (blue for negative and yellow for positive values), X (component with color information), Y (lightness), and Z (component with color information). Models for the classification of black currants were built using various machine learning algorithms based on selected textures for RGB, Lab, and XYZ color spaces. Models built using the IBk, multilayer perceptron, and multiclass classifier for textures from RGB color space, and the IBk algorithm for textures from Lab color space distinguished unstored black currants and samples stored in the room for one and two weeks with an average accuracy of 100%, and the kappa statistic and weighted averages of precision, recall, Matthews correlation coefficient (MCC), receiver operating characteristic (ROC) area, and precision–recall (PRC) area equal to 1.000. This indicated a very distinct change in the external structure of the fruit after the first week and more and more visible changes in quality with increasing storage time. A classification accuracy reaching 98.67% (multilayer perceptron, Lab color space) for the samples stored in the refrigerator may indicate smaller quality changes caused by storage at a low temperature. The approach combining image textures and artificial intelligence turned out to be promising to monitor the quality changes in black currants during storage. Full article

(This article belongs to the Special Issue Advanced Analytical Strategies in Food Safety and Quality Monitoring)

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13 pages, 2428 KiB

Open AccessArticle

Detection of the Inoculated Fermentation Process of Apo Pickle Based on a Colorimetric Sensor Array Method

by Mengyao Wang, Jiawei Liu, Lu Huang and Haiying Liu

Foods 2022, 11(22), 3577; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11223577 - 10 Nov 2022

Cited by 1 | Viewed by 1714

Abstract

Apo pickle is a traditional Chinese fermented vegetable. However, the traditional fermentation process of Apo pickle is slow, easy to ruin, and cannot be judged with regard to time. To improve fermentation, LP-165 (L. Plantarum), which has a high salt tolerance, acidification, and growth capacity, was chosen as the starter culture. Meanwhile, a colorimetric sensor array (CSA) sensitive to pickle volatile compounds was developed to differentiate Apo pickles at varying degrees of fermentation. The color components were extracted from each dye in the color change profiles and were analyzed using principal component analysis (PCA) and linear discriminant analysis (LDA). The fermentation process of the Apo pickle was classified into four phases by LDA. The accuracy of backward substitution verification was 99% and the accuracy of cross validation was 92.7%. Furthermore, the partial least squares regression (PLSR) showed that data from the CSA were correlated with pH total acid, lactic acid, and volatile acids of the Apo pickle. These results illustrate that the CSA reacts quickly to inoculated Apo pickle and could be used to detect fermentation. Full article

(This article belongs to the Special Issue Advanced Analytical Strategies in Food Safety and Quality Monitoring)

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24 pages, 8533 KiB

Open AccessArticle

Adulteration Detection of Edible Bird’s Nests Using Rapid Spectroscopic Techniques Coupled with Multi-Class Discriminant Analysis

by Jing Sheng Ng, Syahidah Akmal Muhammad, Chin Hong Yong, Ainolsyakira Mohd Rodhi, Baharudin Ibrahim, Mohd Noor Hidayat Adenan, Salmah Moosa, Zainon Othman, Nazaratul Ashifa Abdullah Salim, Zawiyah Sharif, Faridah Ismail, Simon D. Kelly and Andrew Cannavan

Foods 2022, 11(16), 2401; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11162401 - 10 Aug 2022

Cited by 3 | Viewed by 1560

Abstract

Edible bird’s nests (EBNs) are vulnerable to adulteration due to their huge demand for traditional medicine and high market price. Presently, there are pressing needs to explore field-deployable rapid screening techniques to detect adulteration of EBNs. The objective of this study is to explore the feasibility of using a handheld near-infrared (VIS/SW-NIR) spectroscopic device for the determination of EBN authenticity against the benchmark performance of a benchtop mid-infrared (MIR) spectrometer. Forty-nine authentic EBNs from the different states in Malaysia and 13 different adulterants (five types) were obtained and used to simulate the adulteration of EBNs at 1, 5 and 10% adulteration by mass (a total of 15 adulterated samples). The VIS/SW-NIR and MIR spectra collated were subsequently processed, modelled and classified using multi-class discriminant analysis. The VIS/SW-NIR results showed 100% correct classification for the collagen and nutrient agar classes in authenticity classification, while for the other classes, the lowest correct classification rate was 96.3%. For MIR analysis, only the karaya gum class had 100% correct classification whilst for the other four classes, the lowest rate of correct classification was at 94.4%. In conclusion, the combination of spectroscopic analysis with chemometrics can be a powerful screening tool to detect EBN adulteration. Full article

(This article belongs to the Special Issue Advanced Analytical Strategies in Food Safety and Quality Monitoring)

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11 pages, 14954 KiB

Open AccessCommunication

A Novel Gas Sensor for Detecting Pork Freshness Based on PANI/AgNWs/Silk

by Yahui Li, Yanxiao Li, Jiyong Shi, Zhihua Li, Xin Wang, Xuetao Hu, Yunyun Gong and Xiaobo Zou

Foods 2022, 11(15), 2372; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11152372 - 08 Aug 2022

Cited by 3 | Viewed by 1972

Abstract

A novel, operational, reliable, flexible gas sensor based on silk fibroin fibers (SFFs) as a substrate was proposed for detecting the freshness of pork. Silk is one of the earliest animal fibers utilized by humans, and SFFs exposed many biological micromolecules on the surface. Thus, the gas sensor was fabricated through polyaniline (PANI) and silver nanowires (AgNWs) and deposited on SFFs by in-suit polymerization. With trimethylamine (TMA) as a model gas, the sensing properties of the PANI/AgNWs/silk composites were examined at room temperature, and the linear correlativity was very prominent between these sensing measures and the TMA measures in the range of 3.33 μg/L–1200 μg/L. When the pork sample is detected by the sensor, it can be classified into fresh or stale pork with the total volatile basic nitrogen (TVB-N) as an index. The result indicated that the gas sensor was effective and showed great potential for applications to detect the freshness of pork. Full article

(This article belongs to the Special Issue Advanced Analytical Strategies in Food Safety and Quality Monitoring)

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12 pages, 2375 KiB

Open AccessArticle

Physicochemical Properties, Stability and Texture of Soybean-Oil-Body-Substituted Low-Fat Mayonnaise: Effects of Thickeners and Storage Temperatures

by Wan Wang, Chuanbing Hu, Hong Sun, Jiale Zhao, Cong Xu, Yue Ma, Jiage Ma, Lianzhou Jiang and Juncai Hou

Foods 2022, 11(15), 2201; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11152201 - 24 Jul 2022

Cited by 17 | Viewed by 3692

Abstract

With the increasing consumer demand for low-fat and low-cholesterol foods, low-fat mayonnaise prepared from soybean oil body (SOB) substitute for egg yolk has great consumption potential. However, based on previous studies, it was found that the stability and sensory properties of mayonnaise substituted with SOB were affected due to there being less lecithin and SOB containing more water. Therefore, this study investigated the effects of different ratios of xanthan gum, pectin and modified starch as stabilizers on the apparent viscosity, stability, texture and microstructure of SOB-substituted mayonnaise. It was found that the apparent viscosity and stability of SOB-substituted mayonnaise increased significantly when xanthan gum, pectin and modified starch were added in a ratio of 2:1:1. Meanwhile, the emulsified oil droplets of SOB-substituted mayonnaise were similar in size and uniformly dispersed in the emulsion system with different thickener formulations. In addition, the storage stability of SOB-substituted mayonnaise was explored. Compared with full egg yolk mayonnaise, SOB-substituted mayonnaise had better oxidative stability and bacteriostatic, which is important for the storage of mayonnaise. This study provided a theoretical basis for the food industry application of SOB. Meanwhile, this study provided new ideas for the development and storage of low-fat mayonnaise. Full article

(This article belongs to the Special Issue Advanced Analytical Strategies in Food Safety and Quality Monitoring)

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16 pages, 3306 KiB

Open AccessArticle

Soybean Oil Bodies as a Milk Fat Substitute Improves Quality, Antioxidant and Digestive Properties of Yogurt

by Nianxu Dou, Rongbo Sun, Chengcheng Su, Yue Ma, Xuewei Zhang, Mengguo Wu and Juncai Hou

Foods 2022, 11(14), 2088; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11142088 - 14 Jul 2022

Cited by 3 | Viewed by 2167

Abstract

In this experiment, the effect of replacing milk fat with soybean fat body (25%, 50%, 75%, 100%) on the quality, antioxidant capacity and in vitro digestive characteristics of yogurt was investigated while maintaining the total fat content of the yogurt unchanged. The results showed that increasing the substitution amount of soy fat body for milk fat had little effect on the pH and acidity of yogurt during the storage period, while the physicochemical properties, degree of protein gel network crosslinking, saturated fatty acid content, PV value and TBARS value of the yogurt significantly decreased (p < 0.05). Meanwhile, protein content, solids content, unsaturated fatty acid content, tocopherol content and water holding capacity significantly increased (p < 0.05). Flavor analysis revealed that yogurts with soybean oil bodies were significantly different when compared to those without soybean oil bodies (p < 0.05), and yogurt with 25% substitution had the highest sensory score. After in vitro digestion, the free fatty acid release, antioxidant capacity and protein digestibility of soybean oil body yogurt were significantly higher (p < 0.05). The SDS-PAGE results showed that the protein hydrolysis of the soybean oil body yogurt was faster. Therefore, the use of an appropriate amount of soybean oil bodies to replace milk fat is able to enhance the taste of yogurt and improve the quality of the yogurt. Full article

(This article belongs to the Special Issue Advanced Analytical Strategies in Food Safety and Quality Monitoring)

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15 pages, 5149 KiB

Open AccessArticle

Easy-to-Use Visual Sensing System for Milk Freshness, Sensitized with Acidity-Responsive N-Doped Carbon Quantum Dots

by Xuetao Hu, Xinai Zhang, Yanxiao Li, Jiyong Shi, Xiaowei Huang, Zhihua Li, Junjun Zhang, Wenting Li, Yiwei Xu and Xiaobo Zou

Foods 2022, 11(13), 1855; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11131855 - 23 Jun 2022

Cited by 2 | Viewed by 2067

Abstract

This study established a flexible and eye-readable sensing system for the easy-to-use, visual detection of milk freshness, using acidity-responsive N-doped carbon quantum dots (N-CQDs). N-CQDs, rich in amino groups and with characteristic acidity sensitivity, exhibited high relative quantum yields of 25.2% and an optimal emission wavelength of 567 nm. The N-CQDs fluorescence quenching upon the dissociated hydrogen ions (H⁺) in milk and their reacting with the amino groups produced an excellent linear relation (R² = 0.996) between the fluorescence intensity and the milk acidity, which indicated that the fluorescence of the N-CQDs was highly correlated with milk freshness. Furthermore, a fluorescence sensor was designed by depositing the N-CQDs on filter-papers and starch-gel films, to provide eye-readable signals under UV light. A fluorescence colorimetric card was developed, based on the decrease in fluorescence brightness as freshness deteriorated. With the advantages of high sensitivity and eye readability, the proposed sensor could detect spoiled milk in advance and without any preprocessing steps, offering a promising method of assessing food safety. Full article

(This article belongs to the Special Issue Advanced Analytical Strategies in Food Safety and Quality Monitoring)

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9 pages, 2121 KiB

Open AccessCommunication

Detection of Cherry Quality Using YOLOV5 Model Based on Flood Filling Algorithm

by Wei Han, Fei Jiang and Zhiyuan Zhu

Foods 2022, 11(8), 1127; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11081127 - 14 Apr 2022

Cited by 13 | Viewed by 2571

Abstract

Presently, the quality of cherries in the market is uneven, because human senses are used to distinguish cherry quality, which consumes a lot of time and energy and does not achieve good results in terms of accuracy. If the internal quality indices, such as the PH value, sugar–acid ratio, and vitamin C content, of cherries are extracted using chemical methods, the detection speed will decrease. With the development of artificial intelligence (AI), image processing by AI algorithms has attracted broad attention. The YOLOv5 model in the YOLO series has many advantages, such as high detection accuracy, fast speed, small size, and so on, and has been used in face recognition, image recognition and other fields. However, owing to the influence of seasonal weather, the environment and other factors, the dataset used in the training model decreases the accuracy of image recognition. To improve the accuracy, a large amount of data must be used for model training, but this will decrease the model training speed. Because it is impossible to use all data in training, there will inevitably be recognition errors in the detection process. In this study, the cherry images in a dataset were extracted by the flooding filling algorithm. The extracted cherry images were used as a new dataset for training and recognition, and the results were compared to those obtained with non-extracted images. The dataset generated by the flooding filling algorithm was used for model training. After 20 training epochs, the accuracy rate reached 99.6%. Without using the algorithm to extract images, the accuracy rate was only 78.6% after 300 training epochs. Full article

(This article belongs to the Special Issue Advanced Analytical Strategies in Food Safety and Quality Monitoring)

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24 pages, 9350 KiB

Open AccessEditor’s ChoiceArticle

A Voting-Based Ensemble Deep Learning Method Focused on Multi-Step Prediction of Food Safety Risk Levels: Applications in Hazard Analysis of Heavy Metals in Grain Processing Products

by Zuzheng Wang, Zhixiang Wu, Minke Zou, Xin Wen, Zheng Wang, Yuanzhang Li and Qingchuan Zhang

Foods 2022, 11(6), 823; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11060823 - 13 Mar 2022

Cited by 12 | Viewed by 2814

Abstract

Grain processing products constitute an essential component of the human diet and are among the main sources of heavy metal intake. Therefore, a systematic assessment of risk factors and early-warning systems are vital to control heavy metal hazards in grain processing products. In this study, we established a risk assessment model to systematically analyze heavy metal hazards and combined the model with the K-means++ algorithm to perform risk level classification. We then employed deep learning models to conduct a multi-step prediction of risk levels, providing an early warning of food safety risks. By introducing a voting-ensemble technique, the accuracy of the prediction model was improved. The results indicated that the proposed model was superior to other models, exhibiting the overall accuracy of 90.47% in the 7-day prediction and thus satisfying the basic requirement of the food supervision department. This study provides a novel early-warning model for the systematic assessment of the risk level and further allows the development of targeted regulatory strategies to improve supervision efficiency. Full article

(This article belongs to the Special Issue Advanced Analytical Strategies in Food Safety and Quality Monitoring)

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13 pages, 2417 KiB

Open AccessArticle

Raman Spectroscopy and Improved Inception Network for Determination of FHB-Infected Wheat Kernels

by Mengqing Qiu, Shouguo Zheng, Le Tang, Xujin Hu, Qingshan Xu, Ling Zheng and Shizhuang Weng

Foods 2022, 11(4), 578; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11040578 - 17 Feb 2022

Cited by 11 | Viewed by 1857

Abstract

Detection of infected kernels is important for Fusarium head blight (FHB) prevention and product quality assurance in wheat. In this study, Raman spectroscopy (RS) and deep learning networks were used for the determination of FHB-infected wheat kernels. First, the RS spectra of healthy, mild, and severe infection kernels were measured and spectral changes and band attribution were analyzed. Then, the Inception network was improved by residual and channel attention modules to develop the recognition models of FHB infection. The Inception–attention network produced the best determination with accuracies in training set, validation set, and prediction set of 97.13%, 91.49%, and 93.62%, among all models. The average feature map of the channel clarified the important information in feature extraction, itself required to clarify the decision-making strategy. Overall, RS and the Inception–attention network provide a noninvasive, rapid, and accurate determination of FHB-infected wheat kernels and are expected to be applied to other pathogens or diseases in various crops. Full article

(This article belongs to the Special Issue Advanced Analytical Strategies in Food Safety and Quality Monitoring)

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Review

Jump to: Research

22 pages, 2292 KiB

Open AccessReview

A Review of Advanced Methods for the Quantitative Analysis of Single Component Oil in Edible Oil Blends

by Xihui Bian, Yao Wang, Shuaishuai Wang, Joel B. Johnson, Hao Sun, Yugao Guo and Xiaoyao Tan

Foods 2022, 11(16), 2436; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11162436 - 13 Aug 2022

Cited by 6 | Viewed by 2611

Abstract

Edible oil blends are composed of two or more edible oils in varying proportions, which can ensure nutritional balance compared to oils comprising a single component oil. In view of their economical and nutritional benefits, quantitative analysis of the component oils in edible oil blends is necessary to ensure the rights and interests of consumers and maintain fairness in the edible oil market. Chemometrics combined with modern analytical instruments has become a main analytical technology for the quantitative analysis of edible oil blends. This review summarizes the different oil blend design methods, instrumental techniques and chemometric methods for conducting single component oil quantification in edible oil blends. The aim is to classify and compare the existing analytical techniques to highlight suitable and promising determination methods in this field. Full article

(This article belongs to the Special Issue Advanced Analytical Strategies in Food Safety and Quality Monitoring)

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19 pages, 1668 KiB

Open AccessReview

Application of Laser-Induced Breakdown Spectroscopy and Chemometrics for the Quality Evaluation of Foods with Medicinal Properties: A Review

by Muhammad Hilal Kabir, Mahamed Lamine Guindo, Rongqin Chen, Alireza Sanaeifar and Fei Liu

Foods 2022, 11(14), 2051; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11142051 - 11 Jul 2022

Cited by 6 | Viewed by 2611

Abstract

Laser-induced Breakdown Spectroscopy (LIBS) is becoming an increasingly popular analytical technique for characterizing and identifying various products; its multi-element analysis, fast response, remote sensing, and sample preparation is minimal or nonexistent, and low running costs can significantly accelerate the analysis of foods with medicinal properties (FMPs). A comprehensive overview of recent advances in LIBS is presented, along with its future trends, viewpoints, and challenges. Besides reviewing its applications in both FMPs, it is intended to provide a concise description of the use of LIBS and chemometrics for the detection of FMPs, rather than a detailed description of the fundamentals of the technique, which others have already discussed. Finally, LIBS, like conventional approaches, has some limitations. However, it is a promising technique that may be employed as a routine analysis technique for FMPs when utilized effectively. Full article

(This article belongs to the Special Issue Advanced Analytical Strategies in Food Safety and Quality Monitoring)

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