INRAE, Avignon University, UMR408 Sécurité et Qualité des Produits d'Origine Végétale, F-84000 Avignon, France.
INRAE, Avignon University, UMR408 Sécurité et Qualité des Produits d'Origine Végétale, F-84000 Avignon, France; INRAE, TRANSFORM Division, F-44000 Nantes, France.
Food Chem. 2021 Sep 1;355:129636. doi: 10.1016/j.foodchem.2021.129636. Epub 2021 Mar 20.
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.
研究了 MIRS 的潜力，以：i）区分来自不同苹果和加工条件的烹饪纯品，ii）根据均质生苹果的光谱预测纯品的质量特性。偏最小二乘（PLS）回归在烹饪纯品的实际光谱及其通过直接标准化从均质生苹果光谱计算的重建光谱上均进行了测试。根据苹果削皮方法和冷藏时间，以及不同的加热和研磨条件，烹饪纯品得到了很好的分类。使用均质生苹果光谱的 PLS 模型可以预测可滴定酸度（剩余预测偏差（RPD）= 2.9）、可溶性固形物含量（RPD = 2.8）、颗粒平均粒径（RPD = 2.6）和粘度（RPD ≥ 2.5）。MIR 技术可以提供可持续的纯品质量评估，甚至可以根据原材料的先验信息预测纯品的质地和口感。
