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Editorial

Successful Applications of NIR Spectroscopy and NIR Imaging in the Food Processing Chain

by
Giacomo Squeo
1,* and
José Manuel Amigo
2,3
1
Department of Soil Plant and Food Sciences, University of Bari Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy
2
Department of Analytical Chemistry, University of the Basque Country UPV/EHU, P.O. Box 644, 48080 Bilbao, Spain
3
IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
*
Author to whom correspondence should be addressed.
Submission received: 7 August 2023 / Accepted: 11 August 2023 / Published: 13 August 2023
Forty years ago, Near InfraRed (NIR) was considered a sleeping technique among the spectroscopic ones. Thanks to the technological advances suffered in recent decades, we can say that NIR is now a consolidated technique, and with rapidly increasing applications. After attracting more attention inside the laboratories, NIR Spectroscopy (NIRS) moved out of them, being used in companies (in-line and on-line process control), various fields (airborne devices and handheld devices), and even space (satellites). Many factors have contributed to this success story, whose end is, we believe, still far from being written. Several of the most important are cited here: the economic accessibility of powerful devices, the technical evolution of the instrumentation, and the acceptance of the use of chemometrics, whose impact in the field on NIRS is absolutely needed due to the chemical and physical features of NIR radiation. Other factors, such as the miniaturization of the instruments and the association with imaging techniques, which occurred with the launch of the 4th industrial revolution and the development of the Industry 4.0 paradigm, have been pivotal.
The present Special Issue was developed within this framework with the aim of collecting scientific articles showing the potential of NIRS, coupled or not with imaging techniques, for new, diverse, and innovative real industrial applications. The impressive flexibility of NIRS is shown in the collected articles, which span from the in-line estimation of fat marbling in whole beef striploin by NIR hyperspectral imaging [1] to the in-line application of NIRS for quality monitoring in a large-scale cheese production plant [2]; from the discrimination of normal vs. dark, firm, and dry (DFD) beef meat and the prediction of quality traits [3] to the amino acid profiling of quality protein maize [4]; from the tracking of sugar content distribution of white strawberry by NIR hyperspectral imaging [5] to the estimation of black root mould infection in apples [6], and the detection of the “Dangshan” physiological disease of pears [7].
As the readers will appreciate, interesting applications on both animal and vegetal products are reported, and in-line or post-harvest scenarios are considered. Consequently, various multivariate data analysis approaches were used to treat the different data. In conclusion, we are confident that the contributions collected in the Special Issue “Successful Applications of NIR Spectroscopy and NIR Imaging in the Food Processing Chain” will provide interesting insights to those interested in using and developing analytical and process solutions based on NIRS.

Author Contributions

G.S. and J.M.A. contributed equally to the writing and editing of the editorial note. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Wold, J.P.; Solberg, L.E.; Gaarder, M.Ø.; Carlehøg, M.; Sanden, K.W.; Rødbotten, R. In-Line Estimation of Fat Marbling in Whole Beef Striploins (Longissimus lumborum) by NIR Hyperspectral Imaging. A Closer Look at the Role of Myoglobin. Foods 2022, 11, 1219. [Google Scholar] [CrossRef] [PubMed]
  2. Solberg, L.E.; Wold, J.P.; Dankel, K.; Øyaas, J.; Måge, I. In-Line Near-Infrared Spectroscopy Gives Rapid and Precise Assessment of Product Quality and Reveals Unknown Sources of Variation—A Case Study from Commercial Cheese Production. Foods 2023, 12, 1026. [Google Scholar] [CrossRef] [PubMed]
  3. Tejerina, D.; Oliván, M.; García-Torres, S.; Franco, D.; Sierra, V. Use of Near-Infrared Spectroscopy to Discriminate DFD Beef and Predict Meat Quality Traits in Autochthonous Breeds. Foods 2022, 11, 3274. [Google Scholar] [CrossRef] [PubMed]
  4. Alamu, E.O.; Menkir, A.; Adesokan, M.; Fawole, S.; Maziya-Dixon, B. Near-Infrared Reflectance Spectrophotometry (NIRS) Application in the Amino Acid Profiling of Quality Protein Maize (QPM). Foods 2022, 11, 2779. [Google Scholar] [CrossRef] [PubMed]
  5. Seki, H.; Ma, T.; Murakami, H.; Tsuchikawa, S.; Inagaki, T. Visualization of Sugar Content Distribution of White Strawberry by Near-Infrared Hyperspectral Imaging. Foods 2023, 12, 931. [Google Scholar] [CrossRef]
  6. Sha, W.; Hu, K.; Weng, S. Statistic and Network Features of RGB and Hyperspectral Imaging for Determination of Black Root Mold Infection in Apples. Foods 2023, 12, 1608. [Google Scholar] [CrossRef] [PubMed]
  7. Chen, Y.; Liu, L.; Rao, Y.; Zhang, X.; Zhang, W.; Jin, X. Identifying the “Dangshan” Physiological Disease of Pear Woolliness Response via Feature-Level Fusion of Near-Infrared Spectroscopy and Visual RGB Image. Foods 2023, 12, 1178. [Google Scholar] [CrossRef] [PubMed]
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MDPI and ACS Style

Squeo, G.; Amigo, J.M. Successful Applications of NIR Spectroscopy and NIR Imaging in the Food Processing Chain. Foods 2023, 12, 3041. https://0-doi-org.brum.beds.ac.uk/10.3390/foods12163041

AMA Style

Squeo G, Amigo JM. Successful Applications of NIR Spectroscopy and NIR Imaging in the Food Processing Chain. Foods. 2023; 12(16):3041. https://0-doi-org.brum.beds.ac.uk/10.3390/foods12163041

Chicago/Turabian Style

Squeo, Giacomo, and José Manuel Amigo. 2023. "Successful Applications of NIR Spectroscopy and NIR Imaging in the Food Processing Chain" Foods 12, no. 16: 3041. https://0-doi-org.brum.beds.ac.uk/10.3390/foods12163041

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