"Ion Ionescu de la Brad" University of Agricultural Sciences and Veterinary Medicine, Faculty of Horticulture, Str. Aleea M. Sadoveanu, No. 3, 700490 Iasi, Romania.
Food Chem. 2014 Feb 1;144:80-6. doi: 10.1016/j.foodchem.2013.05.131. Epub 2013 Jun 6.
Evaluation of water status in Jonathan apples was performed for 20 days. Loss moisture content (LMC) was carried out through slow drying of wholes apples and the moisture content (MC) was carried out through oven drying and lyophilisation for apple samples (chunks, crushed and juice). We approached a non-destructive method to evaluate LMC and MC of apples using image processing and multilayer neural networks (NN) predictor. We proposed a new simple algorithm that selects the texture descriptors based on initial set heuristically chosen. Both structure and weights of NN are optimised by a genetic algorithm with variable length genotype that led to a high precision of the predictive model (R(2)=0.9534). In our opinion, the developing of this non-destructive method for the assessment of LMC and MC (and of other chemical parameters) seems to be very promising in online inspection of food quality.
对 Jonathan 苹果的水分状态进行了 20 天的评估。通过缓慢干燥整个苹果进行失水量（LMC）的测定，通过烘箱干燥和冻干进行苹果样品（块、粉碎和果汁）的水分含量（MC）的测定。我们采用图像处理和多层神经网络（NN）预测器，提出了一种非破坏性方法来评估苹果的 LMC 和 MC。我们提出了一种新的简单算法，该算法基于启发式选择的初始集选择纹理描述符。NN 的结构和权重由具有可变长度基因型的遗传算法进行优化，从而导致预测模型具有高精度（R(2)=0.9534）。我们认为，这种用于评估 LMC 和 MC（以及其他化学参数）的无损方法的开发，在食品质量的在线检测中似乎非常有前途。
