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Foods
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Analytical Methods in Detecting Food Fraud and Food Authenticity
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Analytical Methods in Detecting Food Fraud and Food Authenticity

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 18608

Special Issue Editors

Dr. Zhuohong Xie

E-Mail Website
Guest Editor
United States Pharmacopeial Convention, 12601 Twinbrook Parkway, Rockville, MD 20852, USA
Interests: food chemistry; standardization of chemical analysis of foods and dietary supplements; non-targeted method analysis; food fraud prevention and detection
Prof. Dr. Qinchun Rao

E-Mail Website
Guest Editor
College of Human Sciences, Florida State University, 120 Convocation Way, Tallahassee, FL 32306-1493, USA
Interests: food chemistry; food allergen; food quality; food immunochemistry; food physicochemistry; food safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The modern food industry is the result of globalization and the continued advancement of research and development. Ingredients are produced, transported, and processed in different parts of the world; the food products are then sold to consumers around the globe. This creates an extremely complicated supply chain.

The complex of the supply chain is vulnerable to disruption and imbalances, which creates opportunies for economically motivated adulteration. Economically motivated adulteration, also known as food fraud, is defined as “The fraudulent addition of nonauthentic substances or removal or replacement of authentic substances without the purchaser's knowledge for the economic gain of the seller.” (FCC Appendix XVIII). Food fraud is a crime that dates back thousands of years and has been frequently seen in all major food categories, including spices, honey, protein ingredients, animal products, fat, and oils. There are two main solutions to address this issue. One solution is the chain of custody, which creates transparency and tracibility but requires dedicated work in all levels of the supply chain. The other solution is advanced testing to spot known and unknown fraud. In recent years, technological advancements such as mass spectrometry, portable detection techniques, chemometrics, immunity-based detection, sample preparation and separation, etc., have provided us with many tools to detect food fraud. This encouraged us to assemble the latest studies into this Special Issue, entitled “Analytical Methods in Detecting Food Fraud and Food Authenticity”.

Dr. Zhuohong Xie
Prof. Dr. Qinchun Rao
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 fraud
  • food authenticity
  • analytical testing
  • targeted testing
  • non-targeted testing
  • profiling
  • fingerprinting
  • method validation

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Published Papers (6 papers)

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Research

13 pages, 1291 KiB  
Article
Rapid Determination of Nutmeg Shell Content in Ground Nutmeg Using FT-NIR Spectroscopy and Machine Learning
by Alissa Drees, Bernadette Bockmayr, Michael Bockmayr and Markus Fischer
Foods 2023, 12(15), 2939; https://doi.org/10.3390/foods12152939 - 2 Aug 2023
Cited by 2 | Viewed by 2406
Abstract
Nutmeg is a popular spice often used in ground form, which makes it highly susceptible to food fraud. Therefore, the aim of the present study was to detect adulteration of ground nutmeg with nutmeg shell via Fourier transform near-infrared (FT-NIR) spectroscopy. For this [...] Read more.
Nutmeg is a popular spice often used in ground form, which makes it highly susceptible to food fraud. Therefore, the aim of the present study was to detect adulteration of ground nutmeg with nutmeg shell via Fourier transform near-infrared (FT-NIR) spectroscopy. For this purpose, 36 authentic nutmeg samples and 10 nutmeg shell samples were analyzed pure and in mixtures with up to 50% shell content. The spectra plot as well as a principal component analysis showed a clear separation trend as a function of shell content. A support vector machine regression used for shell content prediction achieved an R2 of 0.944 in the range of 0–10%. The limit of detection of the prediction model was estimated to be 1.5% nutmeg shell. Based on random sub-sampling, the likelihood was found to be 2% that a pure nutmeg sample is predicted with a nutmeg shell content of >1%. The results confirm the suitability of FT-NIR spectroscopy for rapid detection and quantitation of the shell content in ground nutmeg. Full article
(This article belongs to the Special Issue Analytical Methods in Detecting Food Fraud and Food Authenticity)
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15 pages, 6747 KiB  
Article
Validation of a Real-Time PCR Assay for Identification of Fresh and Processed Carica papaya Botanical Material: Using Synthetic DNA to Supplement Specificity Evaluation
by Rajesh Patel, Adam C. Faller, Tiffany Nguyen, Zheng Quan, Corey Eminger, Swetha Kaul, Ted Collins, Yanjun Zhang, Peter Chang, Gary Swanson and Zhengfei Lu
Foods 2023, 12(3), 530; https://doi.org/10.3390/foods12030530 - 25 Jan 2023
Cited by 2 | Viewed by 2712
Abstract
Several commercially important botanicals have a lack of diagnostic testing options that can quickly and unambiguously identify materials of different matrices. Real-time PCR can be a useful, orthogonal approach to identification for its exceptional specificity and sensitivity. Carica papaya L. is a species [...] Read more.
Several commercially important botanicals have a lack of diagnostic testing options that can quickly and unambiguously identify materials of different matrices. Real-time PCR can be a useful, orthogonal approach to identification for its exceptional specificity and sensitivity. Carica papaya L. is a species with a lack of available identification methods, and one which features two distinct commercially relevant matrices: fresh fruit and powdered fruit extract. In this study, we demonstrate the successful design and validation of a real-time PCR assay for detection of papaya DNA extracted from the two matrices. We also propose a technique that can be used during exclusivity panel construction, when genuine botanical samples are not available for certain species: substitution with synthetic DNA. We demonstrate the use of this material to complete a comprehensive specificity evaluation and confidently determine suitable Ct cutoff values. Further, we demonstrate how ddPCR can be used to determine the copy number of the target sequence in a set amount of genomic DNA, to which synthetic DNA samples can be corrected, and how it can verify specificity of the primers and probe. Through the presentation of successful assay validation for papaya detection, this work serves as a guideline for how to approach specificity evaluation when non-target botanical samples are difficult to obtain and otherwise may not have been included in the exclusivity panel. Full article
(This article belongs to the Special Issue Analytical Methods in Detecting Food Fraud and Food Authenticity)
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16 pages, 3288 KiB  
Article
Research on the Authenticity of Mutton Based on Machine Vision Technology
by Chunjuan Zhang, Dequan Zhang, Yuanyuan Su, Xiaochun Zheng, Shaobo Li and Li Chen
Foods 2022, 11(22), 3732; https://doi.org/10.3390/foods11223732 - 21 Nov 2022
Cited by 10 | Viewed by 2488
Abstract
To realize the real-time automatic identification of adulterated minced mutton, a convolutional neural network (CNN) image recognition model of adulterated minced mutton was constructed. Images of mutton, duck, pork and chicken meat pieces, as well as prepared mutton adulterated with different proportions of [...] Read more.
To realize the real-time automatic identification of adulterated minced mutton, a convolutional neural network (CNN) image recognition model of adulterated minced mutton was constructed. Images of mutton, duck, pork and chicken meat pieces, as well as prepared mutton adulterated with different proportions of duck, pork and chicken meat samples, were acquired by the laboratory’s self-built image acquisition system. Among all images were 960 images of different animal species and 1200 images of minced mutton adulterated with duck, pork and chicken. Additionally, 300 images of pure mutton and mutton adulterated with duck, pork and chicken were reacquired again for external validation. This study compared and analyzed the modeling effectiveness of six CNN models, AlexNet, GoogLeNet, ResNet-18, DarkNet-19, SqueezeNet and VGG-16, for different livestock and poultry meat pieces and adulterated mutton shape feature recognition. The results show that ResNet-18, GoogLeNet and DarkNet-19 models have the best learning effect and can identify different livestock and poultry meat pieces and adulterated minced mutton images more accurately, and the training accuracy of all three models reached more than 94%, among which the external validation accuracy of the optimal three models for adulterated minced mutton images reached more than 70%. Image learning based on a deep convolutional neural network (DCNN) model can identify different livestock meat pieces and adulterated mutton, providing technical support for the rapid and nondestructive identification of mutton authenticity. Full article
(This article belongs to the Special Issue Analytical Methods in Detecting Food Fraud and Food Authenticity)
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13 pages, 2683 KiB  
Article
Assessing the Levels of Robusta and Arabica in Roasted Ground Coffee Using NIR Hyperspectral Imaging and FTIR Spectroscopy
by Woranitta Sahachairungrueng, Chanyanuch Meechan, Nutchaya Veerachat, Anthony Keith Thompson and Sontisuk Teerachaichayut
Foods 2022, 11(19), 3122; https://doi.org/10.3390/foods11193122 - 7 Oct 2022
Cited by 20 | Viewed by 3666
Abstract
It has been reported that some brands of roasted ground coffee, whose ingredients are labeled as 100% Arabica coffee, may also contain the cheaper Robusta coffee. Thus, the objective of this research was to test whether near-infrared spectroscopy hyperspectral imaging (NIR-HSI) or Fourier [...] Read more.
It has been reported that some brands of roasted ground coffee, whose ingredients are labeled as 100% Arabica coffee, may also contain the cheaper Robusta coffee. Thus, the objective of this research was to test whether near-infrared spectroscopy hyperspectral imaging (NIR-HSI) or Fourier transform infrared spectroscopy (FTIRs) could be used to test whether samples of coffee were pure Arabica or whether they contained Robusta, and if so, what were the levels of Robusta they contained. Qualitative models of both the NIR-HSI and FTIRs techniques were established with support vector machine classification (SVMC). Results showed that the highest levels of accuracy in the prediction set were 98.04 and 97.06%, respectively. Quantitative models of both techniques for predicting the concentration of Robusta in the samples of Arabica with Robusta were established using support vector machine regression (SVMR), which gave the highest levels of accuracy in the prediction set with a coefficient of determination for prediction (Rp2) of 0.964 and 0.956 and root mean square error of prediction (RMSEP) of 5.47 and 6.07%, respectively. It was therefore concluded that the results showed that both techniques (NIR-HSI and FTIRs) have the potential for use in the inspection of roasted ground coffee to classify and determine the respective levels of Arabica and Robusta within the mixture. Full article
(This article belongs to the Special Issue Analytical Methods in Detecting Food Fraud and Food Authenticity)
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22 pages, 745 KiB  
Article
Amino Acid Fingerprinting of Authentic Nonfat Dry Milk and Skim Milk Powder and Effects of Spiking with Selected Potential Adulterants
by Sneh D. Bhandari, Tiffany Gallegos-Peretz, Thomas Wheat, Gregory Jaudzems, Natalia Kouznetsova, Katya Petrova, Dimple Shah, Daniel Hengst, Erika Vacha, Weiying Lu, Jeffrey C. Moore, Pierre Metra and Zhuohong Xie
Foods 2022, 11(18), 2868; https://doi.org/10.3390/foods11182868 - 16 Sep 2022
Cited by 5 | Viewed by 2743
Abstract
A collaborative study was undertaken in which five international laboratories participated to determine amino acid fingerprints in 39 authentic nonfat dry milk (NFDM)/skim milk powder (SMP) samples. A rapid method of amino acid analysis involving microwave-assisted hydrolysis followed by ultra-high performance liquid chromatography-ultraviolet [...] Read more.
A collaborative study was undertaken in which five international laboratories participated to determine amino acid fingerprints in 39 authentic nonfat dry milk (NFDM)/skim milk powder (SMP) samples. A rapid method of amino acid analysis involving microwave-assisted hydrolysis followed by ultra-high performance liquid chromatography-ultraviolet detection (UHPLC-UV) was used for quantitation of amino acids and to calculate their distribution. The performance of this rapid method of analysis was evaluated and was used to determine the amino acid fingerprint of authentic milk powders. The distribution of different amino acids and their predictable upper and lower tolerance limits in authentic NFDM/SMP samples were established as a reference. Amino acid fingerprints of NFDM/SMP were compared with selected proteins and nitrogen rich compounds (proteins from pea, soy, rice, wheat, whey, and fish gelatin) which can be potential economically motivated adulterants (EMA). The amino acid fingerprints of NFDM/SMP were found to be affected by spiking with pea, soy, rice, whey, fish gelatin and arginine among the investigated adulterants but not by wheat protein and melamine. The study results establish an amino acid fingerprint of authentic NFDM/SMP and demonstrate the utility of this method as a tool in verifying the authenticity of milk powders and detecting their adulteration. Full article
(This article belongs to the Special Issue Analytical Methods in Detecting Food Fraud and Food Authenticity)
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8 pages, 1030 KiB  
Communication
A dPCR Method for Quantitative Authentication of Wild Lingonberry (Vaccinium vitis-idaea) versus Cultivated American Cranberry (V. macrocarpon)
by Katja Karppinen, Anna Avetisyan, Anne Linn Hykkerud and Laura Jaakola
Foods 2022, 11(10), 1476; https://doi.org/10.3390/foods11101476 - 19 May 2022
Cited by 9 | Viewed by 2558
Abstract
Berries of the genus Vaccinium are highly valued health-beneficial superfoods, which are commonly subjected to adulteration and mixed with each other, or with other common berry species. A quantitative DNA-based method utilizing a chip-based digital polymerase chain reaction (dPCR) technique was developed for [...] Read more.
Berries of the genus Vaccinium are highly valued health-beneficial superfoods, which are commonly subjected to adulteration and mixed with each other, or with other common berry species. A quantitative DNA-based method utilizing a chip-based digital polymerase chain reaction (dPCR) technique was developed for identifying and quantifying wild lingonberry (V. vitis-idaea) and cultivated American cranberry (V. macrocarpon). The dPCR method with species-specific primers for mini-barcoding was designed based on the indel regions found in the trnI-CAU–trnL-CAA locus in the chloroplast genome. The designed primers were able to amplify only target species, enabling to distinguish the two closely related species with good sensitivity. Our results illustrated the ability of the method to identify lingonberry and American cranberry DNA using PCR without the need for probes or further sequencing. The dPCR method could also quantify the DNA copy number in mixed samples. Based on this study, the method provides a basis for a simple, fast, and sensitive quantitative authentication analysis of lingonberry and American cranberry by dPCR. Moreover, it can also provide a platform for authentication analyses of other plant species as well by utilizing the indel regions of chloroplast genomes. Full article
(This article belongs to the Special Issue Analytical Methods in Detecting Food Fraud and Food Authenticity)
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