Infrared guided smart food formulation: an innovative spectral reconstruction strategy to develop anticipated and constant apple puree products
2024
Wang, Zhenjie | Bureau, Sylvie | Jaillais, Benoit | Renard, Catherine, M.G.C. | Chen, Xiao | Sun, Yali | Lv, Daizhu | Pan, Leiqing | Lan, Weijie | Nanjing Agricultural University (NAU) | Sécurité et Qualité des Produits d'Origine Végétale (SQPOV) ; Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | École nationale vétérinaire, agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS) | Statistique, Sensométrie et Chimiométrie (StatSC) ; École nationale vétérinaire, agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Département Aliments, produits biosourcés et déchets - INRAE (TRANSFORM) ; Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Sécurité et Qualité des Produits d'Origine Végétale (SQPOV) ; Institut National de la Recherche Agronomique (INRA)-Avignon Université (AU) | Chinese Academy of Tropical Agricultural Sciences (CATAS) | This work was supported by the 'Interfaces' project, an Agropolis Foundation Flashship project publicly funded through the ANR (French Research Agency) under the 'Investissements d'Avenir' program ( Labex Agro, coordinated by Agropolis Fondation), the National Natural Science Foundation of China (NSFC,32302204), and Research Startup Foundation (ANR-10-LABX-01-001) Nanjing Agricultural University (No. 804120).
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Afficher plus [+] Moins [-]anglais. An innovative chemometric method was developed to exploit visible and near-infrared (Vis-NIR) spectroscopy to guide food formulation to reach the anticipated and constant quality of final products. First, a total of 671 spectral variables related to the puree quality characteristics were identified by spectral variable selection methods. Second, the concentration profiles from multivariate curve resolution-alternative least squares (MCR-ALS) made it possible to reconstruct the identified spectral variables of formulated purees. Partial least square based on the reconstructed Vis-NIR spectral variables was evidenced to predict the final puree quality, such as a* values (RPD = 3.30), total sugars (RPD = 2.64), titratable acidity (RPD = 2.55) and malic acid (RPD = 2.67), based only on the spectral data of composed puree cultivars. These results open the possibility of controlling puree formulation: a multiparameter optimization of the color and taste of final puree products can be obtained using only the VisNIR spectral data of single-cultivar purees.
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