Ropelewska Ewa, Lewandowski Mariusz
Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland.
Department of Horticultural Crop Breeding, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland.
Foods. 2024 Nov 25;13(23):3784. doi: 10.3390/foods13233784.
The target of breeding red-fleshed apples is to increase their potential health benefits related to red flesh coloration and consumer acceptance. The objective of this study was to determine the usefulness of four clones (90, 120, 156, and 158) of red-fleshed apples for freeze-drying compared to the cultivar 'Trinity'. Red-fleshed apples were dried in the form of slices using a laboratory freeze-dryer. The changes in color features and image texture parameters after drying and the sensory quality of freeze-dried samples were assessed. Trends of increase in the value of the parameter and decrease in the and parameters after freeze-drying were observed. Furthermore, freeze-drying caused statistically significant changes in analyzed image textures named XHMean, RHMean, SHMean, VHMean, LHMean, and UHMean. Machine learning models developed based on the color parameters , , and distinguished raw and freeze-dried red-fleshed apples with an average accuracy of 84% for clone 90 up to 99.0% for clone 156, and models based on twenty selected image textures exhibited an accuracy of 98.5% for clone 156 to 100% for clones 90 and 158 and the cultivar 'Trinity'. The very attractive external appearance, medium-intense fruity smell, crunchiness, and intense fruity taste of all the apple slices were revealed. The innovative aspect of this study included the comparison of the drying behavior and sensory quality of the new clones and a standard cultivar of red-fleshed apples. Moreover, innovative methods and results were used to determine the effect of freeze-drying on red-fleshed apple quality, considering novel models involving thousands of image textures and machine learning algorithms.
培育红肉苹果的目标是增加其与红肉颜色相关的潜在健康益处以及消费者接受度。本研究的目的是确定四个红肉苹果克隆株（90、120、156和158）与‘三一’品种相比用于冷冻干燥的适用性。红肉苹果以切片形式使用实验室冷冻干燥机进行干燥。评估了干燥后颜色特征和图像纹理参数的变化以及冷冻干燥样品的感官质量。观察到冷冻干燥后参数值增加以及参数和参数值降低的趋势。此外，冷冻干燥导致在分析的名为XHMean、RHMean、SHMean、VHMean、LHMean和UHMean的图像纹理中出现统计学上的显著变化。基于颜色参数、和开发的机器学习模型区分生的和冷冻干燥的红肉苹果，克隆株90的平均准确率为84%，克隆株156高达99.0%，基于二十个选定图像纹理的模型对克隆株156的准确率为98.5%，对克隆株90和158以及‘三一’品种的准确率为100%。所有苹果切片都呈现出非常吸引人的外观、中等强度的果香、脆度和浓郁的果味。本研究的创新之处包括比较新克隆株与红肉苹果标准品种的干燥行为和感官质量。此外，考虑到涉及数千个图像纹理和机器学习算法的新模型，采用创新方法和结果来确定冷冻干燥对红肉苹果品质的影响。
