Mid-infrared technique to forecast cooked puree properties from raw apples: A potential strategy towards sustainability and precision processing

• Published in: Food chemistry 2021-09, Vol.355 (2), p.129636-129636, Article 129636
• Main Authors: Lan, Weijie, Renard, Catherine M.G.C., Jaillais, Benoit, Buergy, Alexandra, Leca, Alexandre, Chen, Songchao, Bureau, Sylvie
• Format: Article
• Language: English
• Subjects: Biochemistry; Biochemistry, Molecular Biology; Chemical and Process Engineering; Cooking; Direct standardization; Discriminant analysis; Engineering Sciences; Food and Nutrition; Food engineering; Food Handling; Fruit - chemistry; Green Chemistry Technology; Least-Squares Analysis; Life Sciences; Malus - chemistry; Malus x domestica Borkh; Mid infrared spectroscopy; PLS models; Spectrophotometry, Infrared; Taste; Viscosity
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2021.129636

•MIRS discriminated purees cooked from different apples and processing conditions.•MIRS on purees gave robust predictions of soluble solids and acidity (RPD ≥ 3.1).•Spectra of purees could be calculated from spectra of homogenized raw apples.•The calculated spectra allowed acceptable predictions of puree viscosity (RPD ≥ 2.5). The potential of MIRS was investigated to: i) differentiate cooked purees issued from different apples and process conditions, and ii) predict the puree quality characteristics from the spectra of homogenized raw apples. Partial least squares (PLS) regression was tested both, on the real spectra of cooked purees and their reconstructed spectra calculated from the spectra of homogenized raw apples by direct standardization. The cooked purees were well-classified according to apple thinning practices and cold storage durations, and to different heating and grinding conditions. PLS models using the spectra of homogenized raw apples can anticipate the titratable acidity (the residual predictive deviation (RPD) = 2.9), soluble solid content (RPD = 2.8), particle averaged size (RPD = 2.6) and viscosity (RPD ≥ 2.5) of cooked purees. MIR technique can provide sustainable evaluations of puree quality, and even forecast texture and taste of purees based on the prior information of raw materials.