Application of artificial neural network for the quality-based classification of spray-dried rhubarb juice powders

Przybył, Krzysztof; Gawałek, Jolanta; Koszela, Krzysztof

doi:10.1007/s13197-020-04537-9

Cited by 23 publications

(15 citation statements)

References 38 publications

(36 reference statements)

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“…In the research, electron microscopy was carried out (SEM) [ 22 ], which is currently gaining popularity in terms of recognizing the morphological structure of food. The authors noticed that the morphological structure of fruit powder particles allows an efficient evaluation of their quality [ 4 , 7 , 12 , 23 ]. Determining texture parameters was based on a well-known matrix of co-occurrence of gray bands (GLCM) isolated from scanning images [ 7 , 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders

Przybył

Koszela

Adamski

et al. 2021

Sensors

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In the paper, an attempt was made to use methods of artificial neural networks (ANN) and Fourier transform infrared spectroscopy (FTIR) to identify raspberry powders that are different from each other in terms of the amount and the type of polysaccharide. Spectra in the absorbance function (FTIR) were prepared as well as training sets, taking into account the structure of microparticles acquired from microscopic images with Scanning Electron Microscopy (SEM). In addition to the above, Multi-Layer Perceptron Networks (MLPNs) with a set of texture descriptors (machine learning) and Convolution Neural Network (CNN) with bitmap (deep learning) were devised, which is an innovative attitude to solving this issue. The aim of the paper was to create MLPN and CNN neural models, which are characterized by a high efficiency of classification. It translates into recognizing microparticles (obtaining their homogeneity) of raspberry powders on the basis of the texture of the image pixel.

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Section: Introductionmentioning

confidence: 99%

Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders

Przybył

Koszela

Adamski

et al. 2021

Sensors

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“…The growing consumption of instant foods around the world causes an increasing demand for dried products. Consumers expect products that enable quick and easy preparation of meals and their storage [1]. An increasing demand for natural and health-promoting food is the second most important trend in the food industry [2].…”

Section: Introductionmentioning

confidence: 99%

Tapioca Dextrin as an Alternative Carrier in the Spray Drying of Fruit Juices—A Case Study of Chokeberry Powder

Gawałek

Domian

2020

Foods

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This paper analyses the semi-industrial process of spray drying chokeberry juice with carbohydrate polymers used as a carrier. Tapioca dextrin (Dx) was proposed and tested as an alternative carrier and it was compared with maltodextrin carriers (MDx), which are the most common in industrial practice. The influence of selected process parameters (carrier type and content, inlet air temperature, atomiser speed) on the characteristics of dried chokeberry powder was investigated. The size and microstructure of the powder particles, the bulk and apparent density, porosity, flowability, yield and bioactive properties were analysed. In comparison with MDx, the Dx carrier improved the handling properties, yield and bioactive properties. An increase in the Dx carrier content improved the phenolic content, antioxidant capacity, flowability and resulted in greater yield of the powder. An increase in the drying temperature increased the size of particles and improved powder flowability but it also caused a greater loss of the phenolic content and antioxidant capacity. The rotary atomizer speed had the most significant effect on the bioactive properties of obtained powders, which increased along with its growth. The following conditions were the most favourable for chokeberry juice with tapioca dextrin (Dx) as the carrier: inlet air temperature, 160 °C; rotary atomizer speed, 15,000 rpm; and Dx carrier content, 60%.

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“…Currently, one of the most modern techniques of image processing [ 19 , 20 ] and image classification [ 21 , 22 ] in decision-making processes is the use of artificial intelligence methods [ 23 , 24 ]. In recent years, artificial neural networks have become so popular and effective that they started to be used in various problematic areas, among other things, in the optimization of food processes [ 25 ]. On account of the complexity of neural network processes supporting, among other things, visualization techniques [ 26 ], a proper formulation of decision-making problems is required, hence, it is possible to solve given problems.…”

Section: Introductionmentioning

confidence: 99%

Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed

Przybył

Wawrzyniak

Koszela

et al. 2020

Sensors

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This paper endeavors to evaluate rapeseed samples obtained in the process of storage experiments with different humidity (12% and 16% seed moisture content) and temperature conditions (25 and 30 °C). The samples were characterized by different levels of contamination with filamentous fungi. In order to acquire graphic data, the analysis of the morphological structure of rapeseeds was carried out with the use of microscopy. The acquired database was prepared in order to build up training, validation, and test sets. The process of generating a neural model was based on Convolutional Neural Networks (CNN), Multi-Layer Perceptron Networks (MLPN), and Radial Basis Function Networks (RBFN). The classifiers that were compared were devised on the basis of the environments Tensorflow (deep learning) and Statistica (machine learning). As a result, it was possible to achieve the lowest classification error of 14% for the test set, 18% classification error for MLPN, and 21% classification error for RBFN, in the process of recognizing mold in rapeseed with the use of CNN.

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Application of artificial neural network for the quality-based classification of spray-dried rhubarb juice powders

Cited by 23 publications

References 38 publications

Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders

Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders

Tapioca Dextrin as an Alternative Carrier in the Spray Drying of Fruit Juices—A Case Study of Chokeberry Powder

Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed

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