ReviewJournal of food science and technology2023
Application of artificial neural network for the quality-based classification of spray-dried rhubarb juice powders.
Review in Journal of food science and technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Effects of drying processes on the chemical and physical properties of safflower: Towards a multidimensional quality evaluation model.PloS one · 2026Article
- Development and Prediction of a Non-Destructive Quality Index (Qi) for Stored Date Fruits Using VIS-NIR Spectroscopy and Artificial Neural Networks.Foods (Basel, Switzerland) · 2025Article
- Texture analysis and artificial neural networks for identification of cereals-case study: wheat, barley and rape seeds.Scientific reports · 2022Article
- Article
- Development of an Artificial Neural Network Utilizing Particle Swarm Optimization for Modeling the Spray Drying of Coconut Milk.Foods (Basel, Switzerland) · 2021Article
- Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders.Sensors (Basel, Switzerland) · 2021Article
- The Use of Image Analysis to Detect Seed Contamination-A Case Study of Triticale.Sensors (Basel, Switzerland) · 2020Article
- Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed.Sensors (Basel, Switzerland) · 2020Article
- Tapioca Dextrin as an Alternative Carrier in the Spray Drying of Fruit Juices-A Case Study of Chokeberry Powder.Foods (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of the study was to develop a neural model enabling classification of fruit spray dried powders, on the basis of graphic data acquired from a bitmap received in the process of spray drying. The neural model was developed with multi-layer perceptron topology. Input variables were expressed in 46 image descriptors based on RGB, YCbCr, HSV (B) and HSL models. Sensitivity analysis of input variables and principal component analysis determined the significance level of each attribute. The optimal model with the lowest error value root mean square, at the level of 0.04 contained 46 neurons in the input layer, 11 neurons in the hidden layer, 10 neurons in the output layer. The results allowed to show that dyeing force (color features) had influence on effective differentiation of the research material consisting of spray-dried powders of rhubarb juice with various dried juice content levels: 30, 40 and 50% as well as high ("H") and low ("L") level of saccharification a chosen carrier (potato maltodextrin).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.