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Food Authentication, Tracing and Characterization: Novel Trends and Applications

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Analytical Chemistry".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 40760

Special Issue Editors


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Guest Editor
Department of Physical and Chemical Sciences, University of L’Aquila, Via Vetoio Coppito, 67100 L’Aquila, Italy
Interests: chromatographic analysis; application of chemometrics to data analysis and optimization; quantitative structure-property relationship methodology; food analysis; food traceability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Collesgues,

In recent years, a general awareness regarding the quality and the chemical-physical characteristics of the food used for human nutrition has increased in consumers. A keen interest has been developed regarding the harvesting areas and the cultivation methods of agro-foods, and concerning the procedures of breeding, processing and conserving muscle-foods. Often, the physicochemical peculiarities distinctive of the quality of an aliment are strictly linked to the know-how of a producer and to the pedoclimatic characteristics of specific geographical areas. Consequently, the possibility of developing analytical methods that allow one to guarantee the authenticity and the provenance of a product have become of great interest.

The present Special Issue is placed in this context, and it aims at providing a collection of modern chemical strategies developed with the intent of characterizing, authenticating or tracing food products. Consequently, submissions of research works focused on the use of chemical methods, possibly synergistically combined with chemometric approaches, to assess the quality and to inspect the geographical origin, the authenticity and the characteristics of high value-added foods, is highly encouraged. As long as they have the same context, reviews providing an overview of the latest trends in this area of interest are also very welcome.

Dr. Alessandra Biancolillo
Prof. Dr. Angelo Antonio D'Archivio
Guest Editors

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Keywords

  • Food characterization
  • Food quality check
  • Food traceability
  • Frauds detection
  • Trends in food analysis
  • Novel chemical tools in food analysis
  • Authentication
  • Multivariate data analysis
  • Classification

Published Papers (16 papers)

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Research

16 pages, 12957 KiB  
Article
Portable vs. Benchtop NIR-Sensor Technology for Classification and Quality Evaluation of Black Truffle
by Christoph Kappacher, Benedikt Trübenbacher, Klemens Losso, Matthias Rainer, Günther K. Bonn and Christian W. Huck
Molecules 2022, 27(3), 589; https://doi.org/10.3390/molecules27030589 - 18 Jan 2022
Cited by 9 | Viewed by 2159
Abstract
Truffles represent the best known and most expensive edible mushroom. Known as Ascomycetes, they belong to the genus Tuber and live in symbiosis with plant host roots. Due to their extraordinary taste and smell, truffles are sold worldwide for high prices of [...] Read more.
Truffles represent the best known and most expensive edible mushroom. Known as Ascomycetes, they belong to the genus Tuber and live in symbiosis with plant host roots. Due to their extraordinary taste and smell, truffles are sold worldwide for high prices of up to 3000–5000 euros per kilogram (Tuber magnatum PICO). Amongst black truffles, the species Tuber melanosporum VITTAD. is highly regarded for its organoleptic properties. Nonetheless, numerous different sorts of black truffle are offered at lower prices, including Tuber aestivum VITTAD., Tuber indicum and Tuber uncinatum, which represent the most frequently consumed types. Because truffles do not differ visually for inexperienced consumers, food fraud is likely to occur. In particular, for the highly prized Tuber melanosporum, which morphologically forms very similar fruiting bodies to those of Tuber indicum, there is a risk of fraud via imported truffles from Asia. In this study, 126 truffle samples belonging to the four mentioned species were investigated by four different NIR instruments, including three miniaturized devices—the Tellspec Enterprise Sensor, the VIAVI solutions MicroNIR 1700 and the Consumer Physics SCiO—working on different technical principles. Three different types of measurement techniques were applied for all instruments (outer shell, rotational device and fruiting body) in order to identify the best results for classification and quality assurance in a non-destructive manner. Results provided differentiation with an accuracy up to 100% for the expensive Tuber melanosporum from Tuber indicum. Classification between Tuber melanosporum, Tuber indicum, Tuber aestivum and Tuber uncinatum could also be achieved with success of 100%. In addition, quality monitoring including discrimination between fresh and frozen/thawed, and prediction of the approximate date of harvesting, was performed. Furthermore, feasibility studies according to the geographical origin of the truffle were attempted. The presented work compares the performance for prediction and quality monitoring of portable vs. benchtop NIR devices and applied measurement techniques in order to be able to present a suitable, accurate, fast, non-destructive and reliable method for consumers. Full article
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13 pages, 1935 KiB  
Article
Phytochemical Content and Antioxidant Activity of Malus domestica Borkh Peel Extracts
by Melnic Vasile, Andrea Bunea, Chira Romeo Ioan, Bunea Claudiu Ioan, Sonia Socaci and Mitre Viorel
Molecules 2021, 26(24), 7636; https://doi.org/10.3390/molecules26247636 - 16 Dec 2021
Cited by 9 | Viewed by 2675
Abstract
Apple is an important dietary source of carotenoids and phenolic compounds, and its regular consumption is associated with several health benefits. The aim of this study was to evaluate the phytochemical composition of fresh peels of four red-skinned (“Champion”, “Generos”, “Idared”, “Florina”) and [...] Read more.
Apple is an important dietary source of carotenoids and phenolic compounds, and its regular consumption is associated with several health benefits. The aim of this study was to evaluate the phytochemical composition of fresh peels of four red-skinned (“Champion”, “Generos”, “Idared”, “Florina”) and two yellow-skinned (“Golden Delicious”, “Reinette Simirenko”) apple varieties. Antioxidant activity of apple peel extracts was determined by ferric reducing antioxidant power (FRAP) and ABTS radical scavenging capacity assays. Total carotenoid and polyphenolic contents were determined spectrophotometrically, while the profile of individual carotenoids and anthocyanins (in red-skinned varieties) was analyzed using high-performance liquid chromatography coupled to a photodiode array detector (HPLC-PDA). Carotenoid composition was specific for each variety, and total carotenoid content was slightly higher in yellow-skinned apple peels compared to red-skinned varieties. In contrast, total phenolic content was higher in the peels of red-skinned cultivars. Anthocyanin profile was predominated by cyanidin-3-O-galactoside. Antioxidant potential followed the trend of the total polyphenolic content, being highest in “Florina”, as measured by both FRAP and ABTS assays. Our results demonstrated apple peels have high phytochemical content with diverse compositions, and their regular consumption can be an excellent source of antioxidants. Full article
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15 pages, 4323 KiB  
Article
Origin Identification of Hungarian Honey Using Melissopalynology, Physicochemical Analysis, and Near Infrared Spectroscopy
by Zsanett Bodor, Zoltan Kovacs, Csilla Benedek, Géza Hitka and Hermann Behling
Molecules 2021, 26(23), 7274; https://doi.org/10.3390/molecules26237274 - 30 Nov 2021
Cited by 14 | Viewed by 3485
Abstract
The objective of the study was to check the authenticity of Hungarian honey using physicochemical analysis, near infrared spectroscopy, and melissopalynology. In the study, 87 samples from different botanical origins such as acacia, bastard indigo, rape, sunflower, linden, honeydew, milkweed, and sweet chestnut [...] Read more.
The objective of the study was to check the authenticity of Hungarian honey using physicochemical analysis, near infrared spectroscopy, and melissopalynology. In the study, 87 samples from different botanical origins such as acacia, bastard indigo, rape, sunflower, linden, honeydew, milkweed, and sweet chestnut were collected. The samples were analyzed by physicochemical methods (pH, electrical conductivity, and moisture), melissopalynology (300 pollen grains counted), and near infrared spectroscopy (NIRS:740–1700 nm). During the evaluation of the data PCA-LDA models were built for the classification of different botanical and geographical origins, using the methods separately, and in combination (low-level data fusion). PC number optimization and external validation were applied for all the models. Botanical origin classification models were >90% and >55% accurate in the case of the pollen and NIR methods. Improved results were obtained with the combination of the physicochemical, melissopalynology, and NIRS techniques, which provided >99% and >81% accuracy for botanical and geographical origin classification models, respectively. The combination of these methods could be a promising tool for origin identification of honey. Full article
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17 pages, 2033 KiB  
Article
Rapid Full-Cycle Technique to Control Adulteration of Meat Products: Integration of Accelerated Sample Preparation, Recombinase Polymerase Amplification, and Test-Strip Detection
by Aleksandr V. Ivanov, Demid S. Popravko, Irina V. Safenkova, Elena A. Zvereva, Boris B. Dzantiev and Anatoly V. Zherdev
Molecules 2021, 26(22), 6804; https://doi.org/10.3390/molecules26226804 - 11 Nov 2021
Cited by 8 | Viewed by 2957
Abstract
Verifying the authenticity of food products is essential due to the recent increase in counterfeit meat-containing food products. The existing methods of detection have a number of disadvantages. Therefore, simple, cheap, and sensitive methods for detecting various types of meat are required. In [...] Read more.
Verifying the authenticity of food products is essential due to the recent increase in counterfeit meat-containing food products. The existing methods of detection have a number of disadvantages. Therefore, simple, cheap, and sensitive methods for detecting various types of meat are required. In this study, we propose a rapid full-cycle technique to control the chicken or pig adulteration of meat products, including 3 min of crude DNA extraction, 20 min of recombinase polymerase amplification (RPA) at 39 °C, and 10 min of lateral flow assay (LFA) detection. The cytochrome B gene was used in the developed RPA-based test for chicken and pig identification. The selected primers provided specific RPA without DNA nuclease and an additional oligonucleotide probe. As a result, RPA–LFA, based on designed fluorescein- and biotin-labeled primers, detected up to 0.2 pg total DNA per μL, which provided up to 0.001% w/w identification of the target meat component in the composite meat. The RPA–LFA of the chicken and pig meat identification was successfully applied to processed meat products and to meat after heating. The results were confirmed by real-time PCR. Ultimately, the developed analysis is specific and enables the detection of pork and chicken impurities with high accuracy in raw and processed meat mixtures. The proposed rapid full-cycle technique could be adopted for the authentication of other meat products. Full article
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13 pages, 7320 KiB  
Article
Geographical Discrimination of Bell Pepper (Capsicum annuum) Spices by (HS)-SPME/GC-MS Aroma Profiling and Chemometrics
by Samantha Reale, Alessandra Biancolillo, Chiara Gasparrini, Luciano Di Martino, Valter Di Cecco, Aurelio Manzi, Marco Di Santo and Angelo Antonio D’Archivio
Molecules 2021, 26(20), 6177; https://doi.org/10.3390/molecules26206177 - 13 Oct 2021
Cited by 9 | Viewed by 2898
Abstract
Dried and ground red pepper is a spice used as seasoning in various traditional dishes all over the world; nevertheless, the pedoclimatic conditions of the diverse cultivation areas provide different chemical characteristics, and, consequently, diverse organoleptic properties to this product. In the present [...] Read more.
Dried and ground red pepper is a spice used as seasoning in various traditional dishes all over the world; nevertheless, the pedoclimatic conditions of the diverse cultivation areas provide different chemical characteristics, and, consequently, diverse organoleptic properties to this product. In the present study, the volatile profiles of 96 samples of two different ground bell peppers harvested in diverse Italian geographical areas, Altino (Abruzzo) and Senise (Lucania), and a commercial sweet paprika, have been studied by means of headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS). The investigation of their volatile profile has led to the identification of 59 analytes. Eventually, a discriminant classifier, Partial Least Squares Discriminant Analysis (PLS-DA), was exploited to discriminate samples according to their geographical origin. The model provided very accurate results in external validation; in fact, it correctly classified all the 30 test samples, achieving 100% correct classification (on the validation set). Furthermore, in order to understand which volatiles contribute the most at differentiating the bell peppers from the different origins, a variable selection approach, Variable Importance in Projection (VIP), was used. This strategy led to the selection of sixteen diverse compounds which characterize the different bell pepper spices. Full article
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15 pages, 431 KiB  
Article
Statistical Analysis of Chemical Element Compositions in Food Science: Problems and Possibilities
by Matthias Templ and Barbara Templ
Molecules 2021, 26(19), 5752; https://doi.org/10.3390/molecules26195752 - 23 Sep 2021
Cited by 5 | Viewed by 2697
Abstract
In recent years, many analyses have been carried out to investigate the chemical components of food data. However, studies rarely consider the compositional pitfalls of such analyses. This is problematic as it may lead to arbitrary results when non-compositional statistical analysis is applied [...] Read more.
In recent years, many analyses have been carried out to investigate the chemical components of food data. However, studies rarely consider the compositional pitfalls of such analyses. This is problematic as it may lead to arbitrary results when non-compositional statistical analysis is applied to compositional datasets. In this study, compositional data analysis (CoDa), which is widely used in other research fields, is compared with classical statistical analysis to demonstrate how the results vary depending on the approach and to show the best possible statistical analysis. For example, honey and saffron are highly susceptible to adulteration and imitation, so the determination of their chemical elements requires the best possible statistical analysis. Our study demonstrated how principle component analysis (PCA) and classification results are influenced by the pre-processing steps conducted on the raw data, and the replacement strategies for missing values and non-detects. Furthermore, it demonstrated the differences in results when compositional and non-compositional methods were applied. Our results suggested that the outcome of the log-ratio analysis provided better separation between the pure and adulterated data and allowed for easier interpretability of the results and a higher accuracy of classification. Similarly, it showed that classification with artificial neural networks (ANNs) works poorly if the CoDa pre-processing steps are left out. From these results, we advise the application of CoDa methods for analyses of the chemical elements of food and for the characterization and authentication of food products. Full article
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23 pages, 3073 KiB  
Article
Metabolomic Profiling of Fresh Goji (Lycium barbarum L.) Berries from Two Cultivars Grown in Central Italy: A Multi-Methodological Approach
by Mattia Spano, Alessandro Maccelli, Giacomo Di Matteo, Cinzia Ingallina, Mariangela Biava, Maria Elisa Crestoni, Jean-Xavier Bardaud, Anna Maria Giusti, Alessia Mariano, Anna Scotto D’Abusco, Anatoly P. Sobolev, Alba Lasalvia, Simonetta Fornarini and Luisa Mannina
Molecules 2021, 26(17), 5412; https://doi.org/10.3390/molecules26175412 - 06 Sep 2021
Cited by 13 | Viewed by 2849
Abstract
The metabolite profile of fresh Goji berries from two cultivars, namely Big Lifeberry (BL) and Sweet Lifeberry (SL), grown in the Lazio region (Central Italy) and harvested at two different periods, August and October, corresponding at the beginning and the end of the [...] Read more.
The metabolite profile of fresh Goji berries from two cultivars, namely Big Lifeberry (BL) and Sweet Lifeberry (SL), grown in the Lazio region (Central Italy) and harvested at two different periods, August and October, corresponding at the beginning and the end of the maturation, was characterized by means of nuclear magnetic resonance (NMR) and electrospray ionization Fourier transform ion cyclotron resonance (ESI FT-ICR MS) methodologies. Several classes of compounds such as sugars, amino acids, organic acids, fatty acids, polyphenols, and terpenes were identified and quantified in hydroalcoholic and organic Bligh-Dyer extracts. Sweet Lifeberry extracts were characterized by a higher content of sucrose with respect to the Big Lifeberry ones and high levels of amino acids (glycine, betaine, proline) were observed in SL berries harvested in October. Spectrophotometric analysis of chlorophylls and total carotenoids was also carried out, showing a decrease of carotenoids during the time. These results can be useful not only to valorize local products but also to suggest the best harvesting period to obtain a product with a chemical composition suitable for specific industrial use. Finally, preliminary studies regarding both the chemical characterization of Goji leaves generally considered a waste product, and the biological activity of Big Lifeberry berries extracts was also investigated. Goji leaves showed a chemical profile rich in healthy compounds (polyphenols, flavonoids, etc.) confirming their promising use in the supplements/nutraceutical/cosmetic field. MG63 cells treated with Big Lifeberry berries extracts showed a decrease of iNOS, COX-2, IL-6, and IL-8 expression indicating their significant biological activity. Full article
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16 pages, 15063 KiB  
Article
Green Multi-Platform Solution for the Quantification of Levodopa Enantiomeric Excess in Solid-State Mixtures for Pharmacological Formulations
by Alessandra Biancolillo, Stefano Battistoni, Regina Presutto and Federico Marini
Molecules 2021, 26(16), 4944; https://doi.org/10.3390/molecules26164944 - 15 Aug 2021
Viewed by 2055
Abstract
The aim of the present work was to develop a green multi-platform methodology for the quantification of l-DOPA in solid-state mixtures by means of MIR and NIR spectroscopy. In order to achieve this goal, 33 mixtures of racemic and pure l-DOPA [...] Read more.
The aim of the present work was to develop a green multi-platform methodology for the quantification of l-DOPA in solid-state mixtures by means of MIR and NIR spectroscopy. In order to achieve this goal, 33 mixtures of racemic and pure l-DOPA were prepared and analyzed. Once spectra were collected, partial least squares (PLS) was exploited to individually model the two different data blocks. Additionally, three different multi-block approaches (mid-level data fusion, sequential and orthogonalized partial least squares, and sequential and orthogonalized covariance selection) were used in order to simultaneously handle data from the different platforms. The outcome of the chemometric analysis highlighted the quantification of the enantiomeric excess of l-DOPA in enantiomeric mixtures in the solid state, which was possible by coupling NIR and PLS, and, to a lesser extent, by using MIR. The multi-platform approach provided a higher accuracy than the individual block analysis, indicating that the association of MIR and NIR spectral data, especially by means of SO-PLS, represents a valid solution for the quantification of the l-DOPA excess in enantiomeric mixtures. Full article
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23 pages, 16433 KiB  
Article
Nuclear Magnetic Resonance Metabolomics with Double Pulsed-Field-Gradient Echo and Automatized Solvent Suppression Spectroscopy for Multivariate Data Matrix Applied in Novel Wine and Juice Discriminant Analysis
by José Enrique Herbert-Pucheta, José Daniel Lozada-Ramírez, Ana E. Ortega-Regules, Luis Ricardo Hernández and Cecilia Anaya de Parrodi
Molecules 2021, 26(14), 4146; https://doi.org/10.3390/molecules26144146 - 07 Jul 2021
Cited by 7 | Viewed by 3557
Abstract
The quality of foods has led researchers to use various analytical methods to determine the amounts of principal food constituents; some of them are the NMR techniques with a multivariate statistical analysis (NMR-MSA). The present work introduces a set of NMR-MSA novelties. First, [...] Read more.
The quality of foods has led researchers to use various analytical methods to determine the amounts of principal food constituents; some of them are the NMR techniques with a multivariate statistical analysis (NMR-MSA). The present work introduces a set of NMR-MSA novelties. First, the use of a double pulsed-field-gradient echo (DPFGE) experiment with a refocusing band-selective uniform response pure-phase selective pulse for the selective excitation of a 5–10-ppm range of wine samples reveals novel broad 1H resonances. Second, an NMR-MSA foodomics approach to discriminate between wine samples produced from the same Cabernet Sauvignon variety fermented with different yeast strains proposed for large-scale alcohol reductions. Third a comparative study between a nonsupervised Principal Component Analysis (PCA), supervised standard partial (PLS-DA), and sparse (sPLS-DA) least squares discriminant analysis, as well as orthogonal projections to a latent structures discriminant analysis (OPLS-DA), for obtaining holistic fingerprints. The MSA discriminated between different Cabernet Sauvignon fermentation schemes and juice varieties (apple, apricot, and orange) or juice authentications (puree, nectar, concentrated, and commercial juice fruit drinks). The new pulse sequence DPFGE demonstrated an enhanced sensitivity in the aromatic zone of wine samples, allowing a better application of different unsupervised and supervised multivariate statistical analysis approaches. Full article
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14 pages, 2996 KiB  
Article
Characterization of Basil Volatile Fraction and Study of Its Agronomic Variation by ASCA
by Alessandro D’Alessandro, Daniele Ballestrieri, Lorenzo Strani, Marina Cocchi and Caterina Durante
Molecules 2021, 26(13), 3842; https://doi.org/10.3390/molecules26133842 - 24 Jun 2021
Cited by 5 | Viewed by 2111
Abstract
Basil is a plant known worldwide for its culinary and health attributes. It counts more than a hundred and fifty species and many more chemo-types due to its easy cross-breeds. Each species and each chemo-type have a typical aroma pattern and selecting the [...] Read more.
Basil is a plant known worldwide for its culinary and health attributes. It counts more than a hundred and fifty species and many more chemo-types due to its easy cross-breeds. Each species and each chemo-type have a typical aroma pattern and selecting the proper one is crucial for the food industry. Twelve basil varieties have been studied over three years (2018–2020), as have four different cuts. To characterize the aroma profile, nine typical basil flavour molecules have been selected using a gas chromatography–mass spectrometry coupled with an olfactometer (GC–MS/O). The concentrations of the nine selected molecules were measured by an ultra-fast CG e-nose and Principal Component Analysis (PCA) was applied to detect possible differences among the samples. The PCA results highlighted differences between harvesting years, mainly for 2018, whereas no observable clusters were found concerning varieties and cuts, probably due to the combined effects of the investigated factors. For this reason, the ANOVA Simultaneous Component Analysis (ASCA) methodology was applied on a balanced a posteriori designed dataset. All the considered factors and interactions were statistically significant (p < 0.05) in explaining differences between the basil aroma profiles, with more relevant effects of variety and year. Full article
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11 pages, 1058 KiB  
Article
Detection of Frozen–Thawed Duck Fatty Liver by MALDI-TOF Mass Spectrometry: A Chemometrics Study
by Laurent Aubry, Thierry Sayd, Claude Ferreira, Christophe Chambon, Annie Vénien, Sylvie Blinet, Marie Bourin, Angélique Travel, Maeva Halgrain, Véronique Santé-Lhoutellier and Laetitia Théron
Molecules 2021, 26(12), 3508; https://doi.org/10.3390/molecules26123508 - 09 Jun 2021
Cited by 3 | Viewed by 2042
Abstract
The marketing of poultry livers is only authorized as fresh, frozen, or deep-frozen. The higher consumer demand for these products for a short period of time may lead to the marketing of frozen–thawed poultry livers: this constitutes fraud. The aim of this study [...] Read more.
The marketing of poultry livers is only authorized as fresh, frozen, or deep-frozen. The higher consumer demand for these products for a short period of time may lead to the marketing of frozen–thawed poultry livers: this constitutes fraud. The aim of this study was to design a method for distinguishing frozen–thawed livers from fresh livers. For this, the spectral fingerprint of liver proteins was acquired using Matrix-Assisted Laser Dissociation Ionization-Time-Of-Flight mass spectrometry. The spectra were analyzed using the chemometrics approach. First, principal component analysis studied the expected variability of commercial conditions before and after freezing–thawing. Then, the discriminant power of spectral fingerprint of liver proteins was assessed using supervised model generation. The combined approach of mass spectrometry and chemometrics successfully described the evolution of protein profile during storage time, before and after freezing-thawing, and successfully discriminated the fresh and frozen–thawed livers. These results are promising in terms of fraud detection, providing an opportunity for implementation of a reference method for agencies to fight fraud. Full article
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15 pages, 3899 KiB  
Article
1H-NMR Profiling Shows as Specific Constituents Strongly Affect the International EVOO Blends Characteristics: The Case of the Italian Oil
by Francesca Calò, Chiara Roberta Girelli, Federica Angilè, Laura Del Coco, Lucia Mazzi, Daniele Barbini and Francesco Paolo Fanizzi
Molecules 2021, 26(8), 2233; https://doi.org/10.3390/molecules26082233 - 13 Apr 2021
Cited by 5 | Viewed by 1800
Abstract
Considering the growing number of extra virgin olive oil (EVOO) producers in the world, knowing the influence of olive oils with different geographical origins on the characteristics of the final blend becomes an interesting goal. The present work is focused on commercial organic [...] Read more.
Considering the growing number of extra virgin olive oil (EVOO) producers in the world, knowing the influence of olive oils with different geographical origins on the characteristics of the final blend becomes an interesting goal. The present work is focused on commercial organic EVOO blends obtained by mixing multiple oils from different geographical origins. These blends have been studied by 1H-NMR spectroscopy supported by multivariate statistical analysis. Specific characteristics of commercial organic EVOO blends originated by mixing oils from Italy, Tunisia, Portugal, Spain, and Greece were found to be associated with the increasing content of the Italian component. A linear progression of the metabolic profile defined characteristics for the analysed samples—up to a plateau level—was found in relation to the content of the main constituent of the Italian oil, the monocultivar Coratina. The Italian constituent percentage appears to be correlated with the fatty acids (oleic) and the polyphenols (tyrosol, hydroxytyrosol, and derivatives) content as major and minor components respectively. These results, which highlight important economic aspects, also show the utility of 1H-NMR associated with chemometric analysis as a powerful tool in this field. Mixing oils of different national origins, to obtain blends with specific characteristics, could be profitably controlled by this methodology. Full article
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13 pages, 2301 KiB  
Article
Using Bioelements Isotope Ratios and Fatty Acid Composition to Deduce Beef Origin and Zebu Feeding Regime in Cameroon
by Matteo Perini, Mohamadou Bawe Nfor, Federica Camin, Silvia Pianezze and Edi Piasentier
Molecules 2021, 26(8), 2155; https://doi.org/10.3390/molecules26082155 - 08 Apr 2021
Cited by 5 | Viewed by 1879
Abstract
The purpose of this study was to address the lack of knowledge regarding the stable isotopic composition of beef from zebu cattle reared in tropical Africa. Sixty beef carcasses belonging to the most common zebu breeds (Goudali, white Fulani, and red Mbororo) were [...] Read more.
The purpose of this study was to address the lack of knowledge regarding the stable isotopic composition of beef from zebu cattle reared in tropical Africa. Sixty beef carcasses belonging to the most common zebu breeds (Goudali, white Fulani, and red Mbororo) were selected and classified according to their subcutaneous fat color (white, cream or yellow). The stable isotope ratios of five bioelements—H, O, C, N, and S—in muscle fractions and the fatty acids composition were analyzed. Zebu meat from Cameroon shows peculiar δ13C values, related to the almost exclusive intake of grazed tropical grasses with photosynthetic cycle C4. It also shows δ2H and δ18O values higher than those reported in other areas of the world and correlated with the isotopic composition of animal drinking water. The white subcutaneous fat (“white type”) zebu showed higher δ2H and lower δ13C than the “yellow type”, that is correlated with a higher content of polyunsaturated fatty acid (PUFA) and a lower amount of saturated fatty acid (SFA) and monounsaturated fatty acid (MUFA). Multielement analysis seems to provide promising results for tracing the regional origin of Cameroon beef and some aspects of the livestock system, such as the nutritional status of the animals. Full article
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10 pages, 1899 KiB  
Article
Release Kinetics Studies of Early-Stage Volatile Secondary Oxidation Products of Rapeseed Oil Emitted during the Deep-Frying Process
by Tomasz Majchrzak and Andrzej Wasik
Molecules 2021, 26(4), 1006; https://doi.org/10.3390/molecules26041006 - 14 Feb 2021
Cited by 5 | Viewed by 1764
Abstract
The research concerns the use of proton transfer reaction mass spectrometer to track real-time emissions of volatile secondary oxidation products released from rapeseed oil as a result of deep-frying of potato cubes. Therefore, it was possible to observe a sudden increase of volatile [...] Read more.
The research concerns the use of proton transfer reaction mass spectrometer to track real-time emissions of volatile secondary oxidation products released from rapeseed oil as a result of deep-frying of potato cubes. Therefore, it was possible to observe a sudden increase of volatile organic compound (VOC) emissions caused by immersion of the food, accompanied by a sudden release of steam from a potato cube and a decrease of the oil temperature by more than 20 °C. It was possible to identify and monitor the emission of major secondary oxidation products such as saturated and unsaturated aldehydes, namely acrolein, pentanal, 2-hexenal, hexanal, 2-nonenal and 2-decenal. Each of them has an individual release characteristic. Moreover, the impact of different initial frying temperatures on release kinetics was investigated. Subsequently, it was possible to approximate the cumulative emission by a second-degree polynomial (R2 ≥ 0.994). Using the proposed solution made it possible for the first time to observe the impact of the immersion of food in vegetable oil on the early emission of thermal degradation products oil. Full article
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16 pages, 1415 KiB  
Article
Structural Changes in Milled Wood Lignin (MWL) of Chinese Quince (Chaenomeles sinensis) Fruit Subjected to Subcritical Water Treatment
by Wen-Yue Wang, Zhao Qin, Hua-Min Liu, Xue-De Wang, Jing-Hao Gao and Guang-Yong Qin
Molecules 2021, 26(2), 398; https://doi.org/10.3390/molecules26020398 - 13 Jan 2021
Cited by 14 | Viewed by 2286
Abstract
Subcritical water treatment has received considerable attention due to its cost effectiveness and environmentally friendly properties. In this investigation, Chinese quince fruits were submitted to subcritical water treatment (130, 150, and 170 °C), and the influence of treatments on the structure of milled [...] Read more.
Subcritical water treatment has received considerable attention due to its cost effectiveness and environmentally friendly properties. In this investigation, Chinese quince fruits were submitted to subcritical water treatment (130, 150, and 170 °C), and the influence of treatments on the structure of milled wood lignin (MWL) was evaluated. Structural properties of these lignin samples (UL, L130, L150, and L170) were investigated by high-performance anion exchange chromatography (HPAEC), FT-IR, gel permeation chromatography (GPC), TGA, pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS), 2D-Heteronculear Single Quantum Coherence (HSQC) -NMR, and 31P-NMR. The carbohydrate analysis showed that xylose in the samples increased significantly with higher temperature, and according to molecular weight and thermal analysis, the MWLs of the pretreated residues have higher thermal stability with increased molecular weight. The spectra of 2D-NMR and 31P-NMR demonstrated that the chemical linkages in the MWLs were mainly β-O-4′ ether bonds, β-5′ and β-β′, and the units were principally G- S- H- type with small amounts of ferulic acids; these results are consistent with the results of Py-GC/MS analysis. It is believed that understanding the structural changes in MWL caused by subcritical water treatment will contribute to understanding the mechanism of subcritical water extraction, which in turn will provide a theoretical basis for developing the technology of subcritical water extraction. Full article
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15 pages, 2382 KiB  
Article
Authentication and Provenance of Walnut Combining Fourier Transform Mid-Infrared Spectroscopy with Machine Learning Algorithms
by Hongyan Zhu and Jun-Li Xu
Molecules 2020, 25(21), 4987; https://doi.org/10.3390/molecules25214987 - 28 Oct 2020
Cited by 5 | Viewed by 2007
Abstract
Different varieties and geographical origins of walnut usually lead to different nutritional values, contributing to a big difference in the final price. The conventional analytical techniques have some unavoidable limitations, e.g., chemical analysis is usually time-expensive and labor-intensive. Therefore, this work aims to [...] Read more.
Different varieties and geographical origins of walnut usually lead to different nutritional values, contributing to a big difference in the final price. The conventional analytical techniques have some unavoidable limitations, e.g., chemical analysis is usually time-expensive and labor-intensive. Therefore, this work aims to apply Fourier transform mid-infrared spectroscopy coupled with machine learning algorithms for the rapid and accurate classification of walnut species that originated from ten varieties produced from four provinces. Three types of models were developed by using five machine learning classifiers to (1) differentiate four geographical origins; (2) identify varieties produced from the same origin; and (3) classify all 10 varieties from four origins. Prior to modeling, the wavelet transform algorithm was used to smooth and denoise the spectrum. The results showed that the identification of varieties under the same origin performed the best (i.e., accuracy = 100% for some origins), followed by the classification of four different origins (i.e., accuracy = 96.97%), while the discrimination of all 10 varieties is the least desirable (i.e., accuracy = 87.88%). Our results implicated that using the full spectral range of 700–4350 cm−1 is inferior to using the subsets of the optimal spectral variables for some classifiers. Additionally, it is demonstrated that back propagation neural network (BPNN) delivered the best model performance, while random forests (RF) produced the worst outcome. Hence, this work showed that the authentication and provenance of walnut can be realized effectively based on Fourier transform mid-infrared spectroscopy combined with machine learning algorithms. Full article
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