Infrared guided smart food formulation: an innovative spectral reconstruction strategy to develop anticipated and constant apple puree products

Zhenjie Wang; Sylvie Bureau; Benoit Jaillais; Catherine M. G. C. Renard; Xiao Chen; Yali Sun; Daizhu Lv; Leiqing Pan; Weijie Lan

Infrared guided smart food formulation: an innovative spectral reconstruction strategy to develop anticipated and constant apple puree products

2024

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 Vis-NIR spectral data of single-cultivar purees.

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