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| Title | Rapid testing in the food industry: the potential of Fourier transform near-infrared (FT-NIR) spectroscopy and spatially offset Raman spectroscopy (SORS) to detect raw material defects in hazelnuts (Corylus avellana L.) | ||||
| Date | 2024-06-17 | Attachment | , , , , , , , , | ||
Rapid testing in the food industry: the potential of Fourier transform near-infrared (FT-NIR) spectroscopy and spatially offset Raman spectroscopy (SORS) to detect raw material defects in hazelnuts (Corylus avellana L.)Loesel, H; Shakiba, N; Bachmann, R; Wenck, S; Le Tan, P; Creydt, M; Seifert, S; Hackl, T; Fischer, M Food Analytical Methods, 2024, Volume 17, pp. 486-497. The detection of raw material defects, e.g., due to incorrect or excessively long storage, is an important issue in incoming goods inspections in the food industry. Fast and easy-to-use analytical methods for evaluating the usability of raw materials are particularly important. In this study, the applicability of Fourier transform near-infrared (FT-NIR) spectroscopy and spatially offset Raman spectroscopy (SORS) for the detection of raw material defects was evaluated. For this purpose, six hazelnut batches stored at different temperatures, humidity levels, and storage times were used as examples in this pilot study. Classification models of samples before and after the different physical treatments show that the resulting changes can be detected by FT-NIR spectroscopy and SORS at elevated temperature and humidity. When one of the storage parameters is increased, FT-NIR spectroscopy is also useful for detecting differences between sample groups. In contrast, SORS cannot distinguish between pre- and post-stored samples when only one of the storage parameters is increased, making SORS unsuitable for incoming inspection of nuts. FT-NIR spectroscopy analysis is also a fast application, because freeze-drying of the sample material prior to analysis is not required as the results before and after freeze-drying are comparable. Combining the FT-NIR spectroscopy and SORS data in a low-level data fusion improved the classification models for samples stored at low storage temperatures, suggesting that the two methods provide complementary information. In summary, analyzing nuts with FT-NIR spectroscopy and SORS, as shown for hazelnuts, has the potential to identify abnormal samples during incoming goods inspections. |
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