The innovative approach to the assessment of differences in image textures between windfall apple samples dried using non-thermal and thermal techniques without and with ultrasound pretreatment

This study was aimed at comparing the differences in image textures of windfall apple samples dried using different non-thermal and thermal techniques without and with ultrasound pretreatment. Six classes of sliced windfall apple samples, such as freeze-dried (control), freeze-dried (ultrasound pretreatment), greenhouse-dried (control), greenhouse-dried (ultrasound pretreatment), open-sun-dried (control), and open-sun-dried (ultrasound pretreatment) were obtained. The models to distinguish apple samples were developed based on selected image textures using machine learning algorithms. In the first step of the analysis, all six classes were distinguished. Then, apple samples were classified in pairs without and with ultrasound pretreatment to determine the effect of pretreatment on apple structure. Additionally, models were built to distinguish three control samples from each other as well as separately to classify three samples with ultrasound pretreatment. In the case of the classification of all six apple samples, an overall accuracy reached 80.83% for a model built using a Logistic Model Tree algorithm from the group of Trees and there were no mixing cases between freeze-dried without and with ultrasound pretreatment and samples dried using other techniques. Samples freeze-dried without and with ultrasound pretreatment were distinguished from each other with an accuracy reaching 95% (Random Forest from Trees). Greenhouse-dried (control) and greenhouse-dried with ultrasound pretreatment apple samples were classified with an accuracy of up to 93.5% (Bayes Net from Bayes). Whereas an accuracy reached 95% (Bayes Net) for the classification of open-sun-dried (control) vs. open-sun-dried with ultrasound pretreatment apples. Apple samples dried using freeze drying, greenhouse drying and open-sun drying without pretreatment were distinguished with an overall accuracy of up to 83.67% (Random Forest), and samples dried by freeze drying, greenhouse drying and open-sun drying with ultrasound pretreatment – with an accuracy of up to 93.67% (Bayes Net). The developed models may be used in practice to distinguish apple samples dried using different techniques and samples dried with and without pretreatment.