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Technological Advancements in Food Processing, Rapid Detection, Process Monitoring, and Quality Control

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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 January 2023) | Viewed by 22679

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Special Issue Editors

Dr. Amin Reza Rajabzadeh

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Guest Editor

W Booth School of Engineering Practice and Technology, McMaster University, Hamilton, ON, Canada
Interests: biosensors; bioprocess monitoring and control; bioseparation and purification; nanomaterials and nanozymes

Dr. Syed Rahin Ahmed

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Guest Editor

W Booth School of Engineering Practice and Technology, McMaster University, Hamilton, ON, Canada
Interests: nanotechnology; nanomaterials; biosensors; nanozymes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There are numerous drivers in the context of food quality and safety, paramount of which is the increasing demand for the development of novel technologies for food processing and rapid and non-destructive detection technologies for quality inspection and monitoring.

This special issue invites papers that explore the development and application of innovative processing technologies that improve food quality and novel analytical strategies and chemometric methods for food safety monitoring and quality inspection, as well as related modeling, control, optimization, and machine learning approaches. For example, in the context of food quality control and monitoring, nanomaterials have shown promising applications in biosensors to detect contaminants, allergens, and pathogens. The integration of nanobiomaterials in technologies, such as smartphone-based sensors and paper-based assays enables the development of portable and cost-effective platforms for daily inspection purposes.

In this special issue, the aim is to discuss emerging technologies for food processing and next-generation chemical- and nanobiotechnology-based sensors and analytical methods for food quality monitoring and inspections, including the drawbacks and limitations of present techniques and potential challenges for future technologies.

Dr. Amin Reza Rajabzadeh
Dr. Syed Rahin Ahmed
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

food processing
food quality
novel foods
food safety and inspection
food quality monitoring and control
sensors, biosensors, and nanobiosensors
rapid and non-destructive testing
analytical techniques
bionanotechnology-based platforms for food analysis
simulation, control, optimization, and machine learning

Published Papers (9 papers)

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Research

18 pages, 10162 KiB

Open AccessArticle

Multi-Barley Seed Detection Using iPhone Images and YOLOv5 Model

by Yaying Shi, Jiayi Li, Zeyun Yu, Yin Li, Yangpingqing Hu and Lushen Wu

Foods 2022, 11(21), 3531; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11213531 - 06 Nov 2022

Cited by 4 | Viewed by 2046

Abstract

As a raw material for beer, barley seeds play a critical role in producing beers with various flavors. Unexcepted mixed varieties of barley seeds make malt quality uncontrollable and can even destroy beer flavors. To ensure the quality and flavor of malts and beers, beer brewers will strictly check the appropriate varieties of barley seeds during the malting process. There are wide varieties of barley seeds with small sizes and similar features. Professionals can visually distinguish these varieties, which can be tedious and time-consuming and have high misjudgment rates. However, biological testing requires professional equipment, reagents, and laboratories, which are expensive. This study aims to build an automatic artificial intelligence detection method to achieve high performance in multi-barley seed datasets. There are nine varieties of barley seeds (CDC Copeland, AC Metcalfe, Hockett, Scarlett, Expedition, AAC Synergy, Celebration, Legacy, and Tradition). We captured images of these original barley seeds using an iPhone 11 Pro. This study used two mixed datasets, including a single-barley seed dataset and a multi-barley seed dataset, to improve the detection accuracy of multi-barley seeds. The multi-barley seed dataset had random amounts and varieties of barley seeds in each image. The single-barley seed dataset had one barley seed in each image. Data augmentation can reduce overfitting and maximize model performance and accuracy. Multi-variety barley seed recognition deploys an efficient data augmentation method to effectively expand the barley dataset. After adjusting the hyperparameters of the networks and analyzing and augmenting the datasets, the YOLOv5 series network was the most effective in training the two barley seed datasets and achieved the highest performance. The YOLOv5x6 network achieved the second highest performance. The mAP (mean Average Precision) of the trained YOLOv5x6 was 97.5%; precision was 98.4%; recall was 98.1%; the average speed of image detection reached 0.024 s. YOLOv5x6 only trained the multi-barley seed dataset; the trained performance was greater than that of the YOLOv5 series. The two datasets had 39.5% higher precision, 27.1% higher recall, and 40.1% higher mAP than when just using the original multi-barley seed dataset. The multi-barley seed detection results showed high performance, robustness, and speed. Therefore, malting and brewing industries can assess the original barley seed quality with the assistance of fast, intelligent, and detected multi-barley seed images. Full article

(This article belongs to the Special Issue Technological Advancements in Food Processing, Rapid Detection, Process Monitoring, and Quality Control)

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14 pages, 1406 KiB

Open AccessArticle

Tenderness of PGI “Ternera de Navarra” Beef Samples Determined by FTIR-MIR Spectroscopy

by María José Beriain, María Lozano, Jesús Echeverría, María Teresa Murillo-Arbizu, Kizkitza Insausti and Miguel Beruete

Foods 2022, 11(21), 3426; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11213426 - 29 Oct 2022

Viewed by 1405

Abstract

Understanding meat quality attribute changes during ageing by using non-destructive techniques is an emergent pursuit in the agroindustry research field. Using beef certified samples from the protected geographical indication (PGI) “Ternera de Navarra”, the primary goal of this study was to use Fourier transform infrared spectroscopy on the middle infrared region (FTIR-MIR) as a tool for the examination of meat tenderness evolution throughout ageing. Samples of the longissimus dorsi muscle of twenty young bulls were aged for 4, 6, 11, or 18 days at 4 °C. Animal carcass classification and sample proximate analysis were performed to check sample homogeneity. Raw aged steaks were analyzed by FTIR-MIR spectroscopy (4000–400 cm⁻¹) to record the vibrational spectrum. Texture profile analysis was performed using a multiple compression test (compression rates of 20%, 80%, and 100%). Compression values were found to decrease notably between the fourth and sixth day of ageing for the three compression rates studied. This tendency continued until the 18th day for C20. For C80 and C100, there was not a clear change in the 11th and 18th days of the study. Regarding FTIR-MIR as a prediction method, it achieved an R² lower than 40%. Using principal component analysis (PCA) of the results, the whole spectrum fingerprint was used in the discrimination of the starting and final ageing days with correct maturing time classifications. Combining the PCA treatment together with the discriminant analysis of spectral data allowed us to differentiate the samples between the initial and the final ageing points, but it did not single out the intermediate points. Full article

(This article belongs to the Special Issue Technological Advancements in Food Processing, Rapid Detection, Process Monitoring, and Quality Control)

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17 pages, 8996 KiB

Open AccessArticle

Rapid Testing System for Rice Quality Control through Comprehensive Feature and Kernel-Type Detection

by Huma Zia, Hafiza Sundus Fatima, Muhammad Khurram, Imtiaz Ul Hassan and Mohammed Ghazal

Foods 2022, 11(18), 2723; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11182723 - 06 Sep 2022

Cited by 6 | Viewed by 3161

Abstract

The assessment of food quality is of significant importance as it allows control over important features, such as ensuring adherence to food standards, longer shelf life, and consistency and quality of taste. Rice is the predominant dietary source of half the world’s population, and Pakistan contributes around 80% of the rice trade worldwide and is among the top three of the largest exporters. Hitherto, the rice industry has depended on antiquated methods of rice quality assessment through manual inspection, which is time consuming and prone to errors. In this study, an efficient desktop-application-based rice quality evaluation system, ‘National Grain Tech’, based on computer vision and machine learning, is presented. The analysis is based on seven main features, including grain length, width, weight, yellowness, broken, chalky, and/or damaged kernels for six different types of rice: IRRI-6, PK386, 1121 white and Selah, Super kernel basmati brown, and white rice. The system was tested in rice factories for 3 months and demonstrated 99% accuracy in determining the size, weight, color, and chalkiness of rice kernels. An accuracy of 98.8% was achieved for the classification of damaged and undamaged kernels, 98% for determining broken kernels, and 100% for paddy kernels. The results are significant because the developed system improves the local rice quality testing capacity through a faster, more accurate, and less expensive mechanism in comparison to previous research studies, which only evaluated four features of the singular rice type, rather than the seven features achieved in this study for six rice types. Full article

(This article belongs to the Special Issue Technological Advancements in Food Processing, Rapid Detection, Process Monitoring, and Quality Control)

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11 pages, 2848 KiB

Open AccessArticle

Electrochemical Sensing of Vanillin Based on Fluorine-Doped Reduced Graphene Oxide Decorated with Gold Nanoparticles

by Venkatesh S. Manikandan, Emmanuel Boateng, Sharmila Durairaj and Aicheng Chen

Foods 2022, 11(10), 1448; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11101448 - 17 May 2022

Cited by 13 | Viewed by 2377

Abstract

4-hydroxy-3-methoxybenzaldehyde (vanillin) is a biophenol compound that is relatively abundant in the world’s most popular flavoring ingredient, natural vanilla. As a powerful antioxidant chemical with beneficial antimicrobial properties, vanillin is not only used as a flavoring agent in food, beverages, perfumery, and pharmaceutical products, it may also be employed as a food-preserving agent, and to fight against yeast and molds. The widespread use of vanilla in major industries warrants the need to develop simple and cost-effective strategies for the quantitative determination of its major component, vanillin. Herein, we explore the applications of a selective and sensitive electrochemical sensor (Au electrodeposited on a fluorine-doped reduced-graphene-oxide-modified glassy-carbon electrode (Au/F-rGO/GCE)) for the detection of vanillin. The electrochemical performance and analytical capabilities of this novel electrochemical sensor were investigated using electrochemical techniques including cyclic voltammetry and differential pulse voltammetry. The excellent sensitivity, selectivity, and reproducibility of the proposed electrochemical sensor may be attributed to the high conductivity and surface area of the formed nanocomposite. The high performance of the sensor developed in the present study was further demonstrated with real-sample analysis. Full article

(This article belongs to the Special Issue Technological Advancements in Food Processing, Rapid Detection, Process Monitoring, and Quality Control)

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10 pages, 3979 KiB

Open AccessArticle

Positively Charged Gold Quantum Dots: An Nanozymatic “Off-On” Sensor for Thiocyanate Detection

by Syed Rahin Ahmed, Masoomeh Sherazee, Seshasai Srinivasan and Amin Reza Rajabzadeh

Foods 2022, 11(9), 1189; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11091189 - 19 Apr 2022

Cited by 15 | Viewed by 2152

Abstract

The concentration of thiocyanate (SCN⁻) in bodily fluids is a good indicator of potential and severe health issues such as nasal bleeding, goiters, vertigo, unconsciousness, several inflammatory diseases, and cystic fibrosis. Herein, a visual SCN⁻ sensing method has been developed using the enzyme-like nature of positively charged gold quantum dots (Au QDs) mixed with 3,3′,5,5′-tetramethylbenzidine (TMB) and hydrogen peroxide (H₂O₂₎. This research also reports a new method of synthesizing positively charged Au QDs directly from gold nanoparticles through a hydrothermal process. Microscopic imaging has showed that the Au QDs were 3–5 nm in size, and the emission wavelength was at 438 nm. Au QDs did not display any enzyme-like nature while mixed up with TMB and H₂O₂. However, the nanozymatic activity of Au QDs appeared when SCN⁻ was included, leading to a very low detection limit (LOD) of 8 nM and 99–105% recovery in complex media. The steady-state kinetic reaction of Au QDs showed that Au QDs had a lower Michaelis–Menten constant (Km) toward H₂O₂ and TMB, which indicates that the Au QDs had a higher affinity for H₂O₂ and TMB than horseradish peroxidase (HRP). A mechanism study has revealed that the scavenging ability of hydroxyl (•OH) radicals by the SCN⁻ group plays an important role in enhancing the sensitivity in this study. The proposed nanozymatic “Off–On” SCN⁻ sensor was also successfully validated in commercial milk samples. Full article

(This article belongs to the Special Issue Technological Advancements in Food Processing, Rapid Detection, Process Monitoring, and Quality Control)

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20 pages, 439 KiB

Open AccessArticle

Content of Health-Promoting Fatty Acids in Commercial Sheep, Cow and Goat Cheeses

by Arkadiusz Szterk, Karol Ofiara, Bartosz Strus, Ilkhom Abdullaev, Karolina Ferenc, Maria Sady, Sylwia Flis and Zdzisław Gajewski

Foods 2022, 11(8), 1116; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11081116 - 13 Apr 2022

Cited by 9 | Viewed by 2520

Abstract

The study aimed to examine samples of different market original sheep cow and goat cheeses, in respect of the content and profile of FA with special emphasis on health-promoting FA. The content of fatty acids in the examined cheeses was highly differentiated and depended on the sort and type of cheese. The content of fatty acid groups in milk fat varied within the limits: SFA, 55.2–67.2%; SCSFA, 10.9–23.4%; BCFA, 1.6–2.9%; MUFA, 15.2–23.4%; PUFA, 1.9–4.3%; trans-MUFA, 1.8–6.0%; and CLA, 1.0–3.1%. From among the examined cheeses, the seasonal sheep cheeses (Oscypek) and mountain cow cheeses were characterized by the highest content of health-promoting fatty acids. The content of health-promoting fatty acids in the fat fraction of these cheeses was CLA 2.1–3.1%, trans-MUFA 3.5–6%, BCFA 2.7–2.9%, and SCSFA 12–18%. Full article

(This article belongs to the Special Issue Technological Advancements in Food Processing, Rapid Detection, Process Monitoring, and Quality Control)

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16 pages, 3765 KiB

Open AccessArticle

A New Perspective to Tribocharging: Could Tribocharging Lead to the Development of a Non-Destructive Approach for Process Monitoring and Quality Control of Powders?

by Hadi Mehrtash, Dinara Konakbayeva, Solmaz Tabtabaei, Seshasai Srinivasan and Amin Reza Rajabzadeh

Foods 2022, 11(5), 693; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11050693 - 26 Feb 2022

Cited by 7 | Viewed by 2136

Abstract

This study explores a new perspective on triboelectrification that could potentially lead to the development of a non-destructive approach for the rapid characterization of powders. Sieved yellow pea powders at various particle sizes and protein contents were used as a model system for the experimental charge measurements of the triboelectrified powders. A tribocharging model based on the prominent condenser model was combined with a Eulerian–Lagrangian computational fluid dynamics (CFD) model to simulate particle tribocharging in particle-laden flows. Further, an artificial neural network model was developed to predict particle–wall collision numbers based on a database obtained through CFD simulations. The tribocharging and CFD models were coupled with the experimental tribocharging data to estimate the contact potential difference of powders, which is a function of contact surfaces’ work functions and depends on the chemical composition of powders. The experimentally measured charge-to-mass ratios were linearly related to the calculated contact potential differences for samples with different protein contents, indicating a potential approach for the chemical characterization of powders. Full article

(This article belongs to the Special Issue Technological Advancements in Food Processing, Rapid Detection, Process Monitoring, and Quality Control)

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17 pages, 1470 KiB

Open AccessArticle

A Three-Stage Solidification Model for Food Particles

by Seshasai Srinivasan

Foods 2022, 11(1), 46; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11010046 - 24 Dec 2021

Cited by 2 | Viewed by 2290

Abstract

A three-stage solidification model for food droplets has been implemented in a computational fluid dynamics code. It comprises of an initial cooling stage that is based on the principles of convective heat transfer. This is followed by the solidification period that is initiated once the droplet cools to a phase change temperature. Finally, when the droplet is completely solidified, the tempering phase begins where the droplet cools to the temperature of the ambient air. The model has been validated with respect to the experimental data for cocoa butter. Additional simulations were made in which the crystallization behavior of the cocoa butter droplets in relation to the droplet size, ambient air temperature and the relative drop-gas velocity was investigated. It was found that the crystallization time is exponentially related to the droplet size. Further, it increased with the ambient temperature, but decreased with the relative drop-gas velocity. Overall, the results suggest operating at the extreme values of the process parameters, requiring high amount of energy, to minimize the crystallization time. It was concluded that there is a need for optimizing the operating conditions of the powder production process to minimize the energy requirement of the system while maintaining a reasonable crystallization time. Full article

(This article belongs to the Special Issue Technological Advancements in Food Processing, Rapid Detection, Process Monitoring, and Quality Control)

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18 pages, 3626 KiB

Open AccessArticle

The Combined Effects of Precision-Controlled Temperature and Relative Humidity on Artificial Ripening and Quality of Date Fruit

by Maged Mohammed, Abdelkader Sallam, Nashi Alqahtani and Muhammad Munir

Foods 2021, 10(11), 2636; https://0-doi-org.brum.beds.ac.uk/10.3390/foods10112636 - 30 Oct 2021

Cited by 15 | Viewed by 2838

Abstract

Due to climatic variation, in-situ date palm fruit ripening is significantly delayed, and some fruits (Biser) cannot become ripe naturally on the tree. Because of that issue, the vast quantity of produce is mere wasted. Few traditional methods are adopted to ripe these unripe fruits through open sun drying or solar tunnel dehydration techniques. However, these methods have minimal use due to ambient temperature and relative humidity (RH) instability. Therefore, the present study was designed to find a precise combination of temperature and RH to artificially ripe the unripe Biser fruits under controlled environment chambers. For that purpose, eighteen automated artificial ripening systems were developed. The Biser fruits (cv. Khalas) were placed immediately after harvesting in the treatment chambers of the systems with three set-point temperatures (45, 50, and 55 °C) and six set-point RH (30, 35, 40, 45, 50, and 55%) until ripening. The optimal treatment combination for artificial ripening of Biser fruits was 50 °C and 50% RH. This combination provided good fruit size, color, firmness, total soluble solids (TSS), pH, and sugars content. As a result, there was a reduction in fruit weight loss and had optimum fruit ripening time. On the other hand, low temperature and RH delayed the ripening process, deteriorated fruit quality, and caused more weight loss. Although the combination of the highest temperature and RH (55 °C and 55%) reduced ripening time, the fruits have higher weight loss and negative quality. Therefore, the artificial ripening of unripe date palm Biser fruits can be achieved using 50 °C temperature and 50% RH combination. These findings can be applied in the field using solar energy systems on a commercial scale to reduce the postharvest loss of date palm fruits. Full article

(This article belongs to the Special Issue Technological Advancements in Food Processing, Rapid Detection, Process Monitoring, and Quality Control)

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