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Emerging Technologies in Food and Beverages Authentication

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Food Science and Technology".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 12839

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Special Issue Editors

Dr. Dana Alina Magdas

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Guest Editor

National Institute for Research and Development of Isotopic and Molecular Technologies, Donat Street, No. 67–103, RO, 400293 Cluj-Napoca, Romania
Interests: food and beverages authentication; traceability; metabolomics; chemometrics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Camelia Berghian-Grosan

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Guest Editor

National Institute for Research & Development of Isotopic & Molecular Technologies, Strada Donath 67-103, 400293 Cluj-Napoca, Romania
Interests: Raman spectroscopy; food analysis; machine learning; hybrid materials synthesis and characterization

Special Issue Information

Dear Colleagues,

During the last years, the open circulation of goods alongside the increased interest of consumers in food and beverage authenticity and traceability led to the development of new analytical approaches that can differentiate among distinct categories. Some examples of which include the following: different geographical or botanical origin, distinct agricultural regime (organic vs. conventional), production years. Usually, a reliable method in this regard will generate an important amount of data that must be efficiently processed to extract the maximum amount of information. Thus, the development of new analytical methods goes hand in hand with the development of new data processing strategies to enhance analytical power. In this context, chemometric methods, machine learning and artificial intelligence tools started to be employed in food authentication to develop sensitive prediction models for food and beverage control.

This Special Issue aims to explore the latest development in the use of chemometric methods, machine learning and artificial intelligence tools for food and beverage authentication and traceability.

Dr. Dana Alina Magdas
Dr. Camelia Berghian-Grosan
Guest Editors

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Keywords

Food
Beverages
Authentication
Traceability
Chemometric Methods
Machine learning
Artificial intelligence

Published Papers (7 papers)

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Research

13 pages, 2374 KiB

Open AccessArticle

Characterization and Differentiation of Wild and Cultivated Berries Based on Isotopic and Elemental Profiles

by Gabriela Cristea, Adriana Dehelean, Romulus Puscas, Florina-Dorina Covaciu, Ariana Raluca Hategan, Csilla Müller Molnár and Dana Alina Magdas

Appl. Sci. 2023, 13(5), 2980; https://doi.org/10.3390/app13052980 - 25 Feb 2023

Viewed by 1035

Abstract

The isotopic content (δ¹³C, δ²H, δ¹⁸O) and concentrations of 30 elements (Li, Na, Mg, P, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Rb, Sr, Ag, Cd, Ba, Pb, La, Ce, Pr, Nd, [...] Read more.

The isotopic content (δ¹³C, δ²H, δ¹⁸O) and concentrations of 30 elements (Li, Na, Mg, P, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Rb, Sr, Ag, Cd, Ba, Pb, La, Ce, Pr, Nd, Sm, Eu, Gd, and Tb) were determined in different wild and cultivated berries (raspberry, seaberry, blackberry, cranberry, and blueberry). Partial least squares discriminant analysis (PLS-DA) was applied in order to develop models for differentiating berries according to their botanical origin and growing system. δ¹³C, δ²H, δ¹⁸O, Li, Na, Mg, P, Ca, V, Mn, Co, Ni, Zn, As, Rb, Sr, Ba, and Eu were identified as significant elements for the differentiation of berry species, based on which an 85% PLS-DA model accuracy was obtained. Similarly, the PLS-DA model developed for the growing system differentiation correctly classified 94.4% of the cultivated berries and 77.2% of the wild ones, based on the main predictors: δ¹³C, δ¹⁸O, Li, Na, Ca, Cr, Mn, Ni, Rb, and Ba. The developed PLS-DA model for the discrimination of wild blueberries from cultivated ones showed excellent levels of sensitivity (100%), specificity (100%), and accuracy (100%). Full article

(This article belongs to the Special Issue Emerging Technologies in Food and Beverages Authentication)

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14 pages, 2015 KiB

Open AccessArticle

Assigning the Geographical Origin of Meat and Animal Rearing System Using Isotopic and Elemental Fingerprints

by Adriana Dehelean, Gabriela Cristea, Romulus Puscas, Ariana Raluca Hategan and Dana Alina Magdas

Appl. Sci. 2022, 12(23), 12391; https://doi.org/10.3390/app122312391 - 03 Dec 2022

Cited by 6 | Viewed by 1745

Abstract

In this study, the stable isotope, and elemental fingerprints of 120 meat samples were determined. The Partial Last Squares-Discriminant Analysis (PLS-DA) method was applied to build classification models for chicken and pork meat samples according to the geographical origin (different Romanian regions) and the animal growing system (animals coming from yard rearing systems versus animals coming from industrial farms). The accuracy of the geographical origin differentiation model was 93.8% for chicken and 71.8% for pork meat. The principal discrimination markers for this classification were: B, Na, K, V, As, Se, Rb, Nb, Cd, Sn, δ¹³C, δ²H, and δ¹⁸O (for chicken meat) and B, Na, Mg, K, Ca, V, Cr, Fe, Ni, Cu, Zn, As, Rb, Sr, Nb, Mo, Sn, Sb, Ba, Pb, δ¹³C, δ²H, and δ¹⁸O (for pork meat). The PLS-DA models were able to differentiate the meat samples according to the animal rearing system with 100% accuracy (for pork meat) and 98% accuracy (for chicken meat), based on the main predictors: B, K, V, Cr, Mn, Fe, Cu, Zn, Se, Rb, Nb, Sn, δ¹³C, and δ²H (for chicken meat) and Se, Rb, Nb, Sb, Ba, Pb, and δ¹³C (for pork meat). Full article

(This article belongs to the Special Issue Emerging Technologies in Food and Beverages Authentication)

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10 pages, 260 KiB

Open AccessArticle

Machine Learning Algorithms in Corroboration with Isotope and Elemental Profile—An Efficient Tool for Honey Geographical Origin Assessment

by Ariana Raluca Hategan, Dana Alina Magdas, Romulus Puscas, Adriana Dehelean, Gabriela Cristea and Bianca Simionescu

Appl. Sci. 2022, 12(21), 10894; https://doi.org/10.3390/app122110894 - 27 Oct 2022

Cited by 2 | Viewed by 941

Abstract

The application of artificial intelligence for the development of recognition models for food and beverages differentiation has benefited from increasing attention in recent years. For this scope, different machine learning (ML) algorithms were used in order to find the most suitable model for a certain purpose. In the present work, three ML algorithms, namely artificial neural networks (ANN), support vector machines (SVM) and k-nearest neighbors (KNN), were applied for constructing honey geographical classification models, and their performance was assessed and compared. A preprocessing step consisting of either a component reduction method or a supervised feature selection technique was applied prior to model development. The most efficient geographical differentiation models were obtained based on ANN, when a subset of features corresponding to the markers having the highest discrimination potential was used as input data. Therefore, when the samples aimed to be classified at an intercountry level, an accuracy of 95% was achieved; namely, 99% of the Romanian samples and 73% of the ones originating from other countries were correctly predicted. Promising results were also obtained for the intracountry honey discrimination; namely, the model built for classifying the Transylvanian samples from the ones produced in other Romanian regions had an 85% accuracy. Full article

(This article belongs to the Special Issue Emerging Technologies in Food and Beverages Authentication)

9 pages, 1091 KiB

Open AccessArticle

Botanical Origin Assessment of Honey Based on ATR-IR Spectroscopy: A Comparison between the Efficiency of Supervised Statistical Methods and Artificial Intelligence

by Maria David, Ariana Raluca Hategan, Dana Alina Magdas, Camelia Berghian-Grosan and Bianca Simionescu

Appl. Sci. 2022, 12(19), 9645; https://doi.org/10.3390/app12199645 - 26 Sep 2022

Cited by 5 | Viewed by 1449

Abstract

Food authenticity control represents a constant concern nowadays, and against this background, new means of food fraud detection are developed by research and control laboratories. Among the most accessible analytical methods in this regard, attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy proved to be an effective tool, being rapid, cost-effective, and not requiring solvent use. However, the generated experimental data need to be further processed in an efficient manner in order to be able to accurately assess the authenticity of a certain product. The temptation to pass some more available honey varieties as rarer ones might exist and in order to detect these types of miss labeling, we proposed in this study the development of new recognition models based on supervised chemometric models and artificial intelligence. In this way a comparison between the models’ capabilities constructed based on the association between ATR-IR spectroscopy with partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM), respectively, was performed. The most efficient models for the individual botanical differentiation were developed by applying SVM on the significant spectral markers, determined through a supervised method. Full article

(This article belongs to the Special Issue Emerging Technologies in Food and Beverages Authentication)

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10 pages, 1283 KiB

Open AccessArticle

Evaluation of Mushrooms Based on FT-IR Fingerprint and Chemometrics

by Ioana Feher, Cornelia Veronica Floare-Avram, Florina-Dorina Covaciu, Olivian Marincas, Romulus Puscas, Dana Alina Magdas and Costel Sârbu

Appl. Sci. 2021, 11(20), 9577; https://doi.org/10.3390/app11209577 - 14 Oct 2021

Viewed by 1533

Abstract

Edible mushrooms have been recognized as a highly nutritional food for a long time, thanks to their specific flavor and texture, as well as their therapeutic effects. This study proposes a new, simple approach based on FT-IR analysis, followed by statistical methods, in order to differentiate three wild mushroom species from Romanian spontaneous flora, namely, Armillaria mellea, Boletus edulis, and Cantharellus cibarius. The preliminary data treatment consisted of data set reduction with principal component analysis (PCA), which provided scores for the next methods. Linear discriminant analysis (LDA) managed to classify 100% of the three species, and the cross-validation step of the method returned 97.4% of correctly classified samples. Only one A. mellea sample overlapped on the B. edulis group. When kNN was used in the same manner as LDA, the overall percent of correctly classified samples from the training step was 86.21%, while for the holdout set, the percent rose to 94.74%. The lower values obtained for the training set were due to one C. cibarius sample, two B. edulis, and five A. mellea, which were placed to other species. In any case, for the holdout sample set, only one sample from B. edulis was misclassified. The fuzzy c-means clustering (FCM) analysis successfully classified the investigated mushroom samples according to their species, meaning that, in every partition, the predominant species had the biggest DOMs, while samples belonging to other species had lower DOMs. Full article

(This article belongs to the Special Issue Emerging Technologies in Food and Beverages Authentication)

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11 pages, 1280 KiB

Open AccessFeature PaperArticle

Opportunities and Constraints in Applying Artificial Neural Networks (ANNs) in Food Authentication. Honey—A Case Study

by Ariana Raluca Hategan, Romulus Puscas, Gabriela Cristea, Adriana Dehelean, Francois Guyon, Arthur Jozsef Molnar, Valentin Mirel and Dana Alina Magdas

Appl. Sci. 2021, 11(15), 6723; https://doi.org/10.3390/app11156723 - 22 Jul 2021

Cited by 11 | Viewed by 1830

Abstract

The present work aims to test the potential of the application of Artificial Neural Networks (ANNs) for food authentication. For this purpose, honey was chosen as the working matrix. The samples were originated from two countries: Romania (50) and France (53), having as floral origins: acacia, linden, honeydew, colza, galium verum, coriander, sunflower, thyme, raspberry, lavender and chestnut. The ANNs were built on the isotope and elemental content of the investigated honey samples. This approach conducted to the development of a prediction model for geographical recognition with an accuracy of 96%. Alongside this work, distinct models were developed and tested, with the aim of identifying the most suitable configurations for this application. In this regard, improvements have been continuously performed; the most important of them consisted in overcoming the unwanted phenomenon of over-fitting, observed for the training data set. This was achieved by identifying appropriate values for the number of iterations over the training data and for the size and number of the hidden layers and by introducing of a dropout layer in the configuration of the neural structure. As a conclusion, ANNs can be successfully applied in food authenticity control, but with a degree of caution with respect to the “over optimization” of the correct classification percentage for the training sample set, which can lead to an over-fitted model. Full article

(This article belongs to the Special Issue Emerging Technologies in Food and Beverages Authentication)

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20 pages, 9050 KiB

Open AccessArticle

Edible Oils Differentiation Based on the Determination of Fatty Acids Profile and Raman Spectroscopy—A Case Study

by Florina-Dorina Covaciu, Camelia Berghian-Grosan, Ioana Feher and Dana Alina Magdas

Appl. Sci. 2020, 10(23), 8347; https://doi.org/10.3390/app10238347 - 24 Nov 2020

Cited by 14 | Viewed by 3229

Abstract

This study proposes a comparison between two analytical techniques for edible oil classification, namely gas-chromatography equipped with a flame ionization detector (GC-FID), which is an acknowledged technique for fatty acid analysis, and Raman spectroscopy, as a real time noninvasive technique. Due to the complexity of the investigated matrix, we used both methods in connection with chemometrics processing for a quick and valuable evaluation of oils. In addition to this, the possible adulteration of investigated oil varieties (sesame, hemp, walnut, linseed, sea buckthorn) with sunflower oil was also tested. In order to extract the meaningful information from the experimental data set, a supervised chemometric technique, namely linear discriminant analysis (LDA), was applied. Moreover, for possible adulteration detection, an artificial neural network (ANN) was also employed. Based on the results provided by ANN, it was possible to detect the mixture between sea buckthorn and sunflower oil. Full article

(This article belongs to the Special Issue Emerging Technologies in Food and Beverages Authentication)

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