Artificial neural networks and electron microscopy to evaluate the quality of fruit and vegetable spray-dried powders. Case study: Strawberry powder

Przybył, Krzysztof; Gawałek, Jolanta; Koszela, Krzysztof; Wawrzyniak, Jolanta; Gierz, Łukasz

doi:10.1016/j.compag.2018.10.033

Cited by 49 publications

(51 citation statements)

References 48 publications

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“…The hidden layer was composed of four radial neurons with a radial PSP function and an exponential activation function. The network output consisted of one neuron with a linear PSP function and a linear (saturated) activation function, representing a two-state nominal variable [29,30]. The generated neural model was trained using optimization algorithms implemented in Statistica v. 10 package.…”

Section: Resultsmentioning

confidence: 99%

Neural Visual Detection of Grain Weevil (Sitophilus granarius L.)

et al. 2020

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A significant part of cereal production is intended for agri-food processing, which implies a necessity to search for and implement modern storage systems for this product. Stored grain is exposed to many unfavorable factors, particularly caryopsis macro-damage caused mainly by grain weevil (Sitophilus granarius L.). This triggers a substantial decrease in the value of the stored material, thus resulting in serious economic losses. Due to this fact, it is necessary to take steps to effectively detect this pest’s presence when grain is delivered to storage facilities. The purpose of this work was to identify the representative physical characteristics of wheat caryopsis affected by grain weevil. An automated visual system was developed to ease the detection of damaged kernels and adult weevils. In order to obtain the empirical data, a decision was made to take advance of SKCS 4100 (the Perten Single Kernel Characterization System). The measurements obtained were used to build the training sets necessary in the process of ANN (artificial neural network) learning with digital neural classifiers. Next, a set of identifying neural models was created and verified, and then the optimal topology was selected. The utilitarian goal of the research was to support the decision-making process taking place during grain storage.

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Section: Resultsmentioning

confidence: 99%

Neural Visual Detection of Grain Weevil (Sitophilus granarius L.)

et al. 2020

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“…Using them in intelligent information systems to perform analysis and recognition with sound or images simplifies production processes, the systematic monitoring of processes and the control of production process as well as the quality evaluation of finished goods. The research work that was done is one the examples of using ANN [2,34,35] which aims, among other things, at automating the process of controlling the quality condition of dried fruits with acoustic signals.…”

Section: Preparation Of Artificial Neural Networkmentioning

confidence: 99%

“…The customer expectations and information included in the EU Commission Regulation changed the criteria of the quality evaluation of products. The approval of products has influence on, among other things, the looks [1], taste, aroma and texture [2], safety and nutritional values of products [3,4]. It is not allowed to accept an inadequate quality of fresh fruits, which can be, among other things, immature, overripe, moldy or too dirty.…”

Section: Introductionmentioning

confidence: 99%

Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks

Przybył

Duda

Koszela

et al. 2020

Sensors

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In this paper, the authors used an acoustic wave acting as a disturbance (acoustic vibration), which travelled in all directions on the whole surface of a dried strawberry fruit in its specified area. The area of space in which the acoustic wave occurs is defined as the acoustic field. When the vibrating surface—for example, the surface of the belt—becomes the source, then one can observe the travelling of surface waves. For any shape of the surface of the dried strawberry fruit, the signal of travelling waves takes the form that is imposed by this irregular surface. The aim of this work was to research the effectiveness of recognizing the two trials in the process of convection drying on the basis of the acoustic signal backed up by neural networks. The input variables determined descriptors such as frequency (Hz) and the level of luminosity (dB). During the research, the degree of crispiness relative to the degree of maturity was compared. The results showed that the optimal neural model in respect of the lowest value of the root mean square turned out to be the Multi-Layer Perceptron network with the technique of dropping single fruits into water (data included in the learning data set Z2). The results confirm that the choice of method can have an influence on the effectives of recognizing dried strawberry fruits, and also this can be a basis for creating an effective and fast analysis tool which is capable of analyzing the degree of ripeness of fruits including their crispness in the industrial process of drying fruits.

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“…It also lowers the concentration of glucose in the blood, which is especially important for people who suffer from diabetes. Vitamins as well as minerals have a positive effect on organism, for example in cancer and anti-sclerotic prevention, they also have antibacterial properties (Gawałek et al 2017;Przybył et al 2018). It happens like that mainly because fruits and vegetables are characterized by low caloric value, almost zero fat presence and high water content.…”

Section: Introductionmentioning

confidence: 99%

“…Application of artificial neural networks (ANN) in issues related to classification and regression issues resulted in the discovery of quality changes concerning food products (Przybył et al 2018). The method is based on developing neural models, which is relatively faster and more efficient than traditional computing methods.…”

Section: Introductionmentioning

confidence: 99%

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

2020

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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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Artificial neural networks and electron microscopy to evaluate the quality of fruit and vegetable spray-dried powders. Case study: Strawberry powder

Cited by 49 publications

References 48 publications

Neural Visual Detection of Grain Weevil (Sitophilus granarius L.)

Neural Visual Detection of Grain Weevil (Sitophilus granarius L.)

Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks

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

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