Abstract
Acerola (Malpighia emarginata DC) is an exotic fruit that has a high agro-industrial potential. It is known to be rich in ascorbic acid, phenolic compounds, and carotenoid pigments. These nutrients make acerola one of the best sources of natural antioxidants, helping to prevent many conditions and delay aging. Acerola fruit is transformed into concentrate juice then powder to be incorporated into nutritional supplements. The natural ascorbic acid content of juice powders must be between 16 and 17%. Unfortunately, the origin of ascorbic acid in acerola-based products is not always natural. That is to say, some food manufacturers add synthetic ascorbic acid to reach the recommended values (16 to 17%), which can be considered as a falsification of the product. Since a decade, the control of the life cycle and the quality of foodstuffs is an increasingly important concern. In this context, EVEAR Extraction (French company) establishes a high level of traceability of its extracts by combining sourcing, extraction processes and laboratory controls throughout the production process. The determination of the composition of raw material and final products can be determined by spectrometric analysis and more precisely by Nuclear Magnetic Resonance (NMR) spectroscopy. However, spectral analysis remains a tedious and time-consuming task requiring an expert.
In this study, the feasibility of discriminating acerola-based product was investigated using 1H NMR spectroscopy in combination with a supervised classification procedure consisting of several steps: principal component analysis (PCA), a fast Fourier transform (FFT) and a neuronal network classification. A total of 6 classes (Colored Acerola powder, Acerola concentrate, Acerola powder, Ascorbic Acid, Acerola with added ascorbic acid, Other extract) were examined. Following the classical approaches, we opted for a convergent network using hidden layers and a divergent output. The results demonstrate that 1H NMR spectroscopy combined with ANN analysis is an effective tool for verifying the nature of Acerola samples.
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Combis, L. et al. (2023). NMR Spectroscopy and Learning-Based Classification of Acerola Samples. In: Noël, F., Nyffenegger, F., Rivest, L., Bouras, A. (eds) Product Lifecycle Management. PLM in Transition Times: The Place of Humans and Transformative Technologies. PLM 2022. IFIP Advances in Information and Communication Technology, vol 667. Springer, Cham. https://doi.org/10.1007/978-3-031-25182-5_32
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DOI: https://doi.org/10.1007/978-3-031-25182-5_32
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