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Editorial

Advances in Spectrometric Techniques in Food Analysis and Authentication

Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation, St. Lucia Campus, The University of Queensland, Brisbane, QLD 4072, Australia
Foods 2023, 12(3), 438; https://doi.org/10.3390/foods12030438
Submission received: 10 January 2023 / Accepted: 14 January 2023 / Published: 17 January 2023
The demand from the food industry and consumers for analytical tools that can assure the quality (e.g., composition) and origin of foods (e.g., authenticity, fraud, provenance) in both the supply and value chains has increased over the past decades. Although there have been advances and improvements in analytical instrumentation and techniques that have excellent diagnostic capabilities, most of the existing routine methods of analysis are considered time-consuming and expensive. These issues have encouraged developments in the application of a wide range of spectrometric techniques, involving, among others, the utilization of vibrational spectroscopy combined with data analytics (e.g., chemometrics).
This Special Issue, “Advances in Spectrometric Techniques in Food Analysis and Authentication”, has compiled novel and recent applications of spectrometry-based techniques, including NIR, MIR, NMR, as well as other analytical techniques (e.g., ICP-MS and GC-MS) combined with chemometrics methods, to target issues associated with food analytics and authentication along the food supply and value chains (e.g., fraud, provenance, traceability).
In this Special Issue, Gajek and collaborators [1,2] have shown how the combination of chemometrics with ICP-MS data can be used to authenticate whisky samples. Chavez-Angel and collaborators have also described how vibrational spectroscopy (e.g., Raman and infrared) and thermal analysis can be combined to authenticate extra virgin olive oil [3]. The classification of coffee samples was also reported by combining both NIR and MIR spectroscopy with chemometric methods [4]. The utilization of spectral fingerprints was evaluated as a potential tool to screen the adulteration of traditional and Bourbon barrel-aged maple syrups [5]. The use of portable NIR instrumentation was also reported to discriminate and characterize individual goat muscles [6] and to differentiate meat species using a similarity index [7]. The use of a liquid–liquid microextraction method to enhance the flavor of seafood using GC-MS analysis [8], the analysis of saliva using MIR spectroscopy obtained from a sensory study [9], and the determination of the saponification value of fats and oils using 1H-NMR were also reported [10] in this Special Issue.
Overall, these applications have highlighted the importance of combining rapid analytical methods with chemometrics to improve our knowledge and understanding about foods.

Funding

This research received no external funding.

Data Availability Statement

Not applicable.

Conflicts of Interest

The author declares no conflict of interest.

References

  1. Gajek, M.; Pawlaczyk, A.; Maćkiewicz, E.; Albińska, J.; Wysocki, P.; Jóźwik, K.; Szynkowska-Jóźwik, M.I. Assessment of the Authenticity of Whisky Samples Based on the Multi-Elemental and Multivariate Analysis. Foods 2022, 11, 2810. [Google Scholar] [CrossRef] [PubMed]
  2. Gajek, M.; Pawlaczyk, A.; Jóźwik, K.; Szynkowska-Jóźwik, M.I. The Elemental Fingerprints of Different Types of Whisky as Determined by ICP-OES and ICP-MS Techniques in Relation to Their Type, Age, and Origin. Foods 2022, 11, 1616. [Google Scholar] [CrossRef] [PubMed]
  3. Chavez-Angel, E.; Puertas, B.; Kreuzer, M.; Soliva Fortuny, R.; Ng, R.C.; Castro-Alvarez, A.; Sotomayor Torres, C.M. Spectroscopic and Thermal Characterization of Extra Virgin Olive Oil Adulterated with Edible Oils. Foods 2022, 11, 1304. [Google Scholar] [CrossRef] [PubMed]
  4. Belchior, V.; Botelho, B.G.; Franca, A.S. Comparison of Spectroscopy-Based Methods and Chemometrics to Confirm Classification of Specialty Coffees. Foods 2022, 11, 1655. [Google Scholar] [CrossRef] [PubMed]
  5. Zhu, K.; Aykas, D.P.; Rodriguez-Saona, L.E. Pattern Recognition Approach for the Screening of Potential Adulteration of Traditional and Bourbon Barrel-Aged Maple Syrups by Spectral Fingerprinting and Classical Methods. Foods 2022, 11, 2211. [Google Scholar] [CrossRef] [PubMed]
  6. Hoffman, L.C.; Ingle, P.; Khole, A.H.; Zhang, S.; Yang, Z.; Beya, M.; Bureš, D.; Cozzolino, D. Characterisation and Identification of Individual Intact Goat Muscle Samples (Capra sp.) Using a Portable Near-Infrared Spectrometer and Chemometrics. Foods 2022, 11, 2894. [Google Scholar] [CrossRef] [PubMed]
  7. Cozzolino, D.; Bureš, D.; Hoffman, L.C. Evaluating the Use of a Similarity Index (SI) Combined with near Infrared (NIR) Spectroscopy as Method in Meat Species Authenticity. Foods 2023, 12, 182. [Google Scholar] [CrossRef] [PubMed]
  8. Luo, X.; Wang, X.; Du, M.; Xu, X. Dispersive Liquid-Liquid Microextraction Followed by HS-SPME for the Determination of Flavor Enhancers in Seafood Using GC-MS. Foods 2022, 11, 1507. [Google Scholar] [CrossRef] [PubMed]
  9. Ni, D.; Smyth, H.E.; Gidley, M.J.; Cozzolino, D. Predicting Satiety from the Analysis of Human Saliva Using Mid-Infrared Spectroscopy Combined with Chemometrics. Foods 2022, 11, 711. [Google Scholar] [CrossRef] [PubMed]
  10. Ivanova, M.; Hanganu, A.; Dumitriu, R.; Tociu, M.; Ivanov, G.; Stavarache, C.; Popescu, L.; Ghendov-Mosanu, A.; Sturza, R.; Deleanu, C.; et al. Saponification Value of Fats and Oils as Determined from 1H-NMR Data: The Case of Dairy Fats. Foods 2022, 11, 1466. [Google Scholar] [CrossRef] [PubMed]
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MDPI and ACS Style

Cozzolino, D. Advances in Spectrometric Techniques in Food Analysis and Authentication. Foods 2023, 12, 438. https://doi.org/10.3390/foods12030438

AMA Style

Cozzolino D. Advances in Spectrometric Techniques in Food Analysis and Authentication. Foods. 2023; 12(3):438. https://doi.org/10.3390/foods12030438

Chicago/Turabian Style

Cozzolino, Daniel. 2023. "Advances in Spectrometric Techniques in Food Analysis and Authentication" Foods 12, no. 3: 438. https://doi.org/10.3390/foods12030438

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