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

Texture analysis and artificial neural networks for identification of cereals-case study: wheat, barley and rape seeds

The scope of the research comprises an analysis and evaluation of samples of rape, barley and wheat seeds. The experiments were carried out using the author's original research object. The air flow velocities to transport seeds, were set at 15, 20 and 25 m s^-1. A database consisting of images was created, which allowed to determine 3 classes of kernels on the basis of 6 research variants, including their transportation way via pipe and the speed of sowing. The process of creating neural models was based on multilayer perceptron networks (MLPN) in Statistica (machine learning). It should be added that the use of MLPN also allowed identification of rape seeds, wheat seeds and barley seeds transported via pipe II at 20 m s^-1, for which the lowest RMS was 0.05 and the coefficient of classification accuracy was 0.94.

The Process of Pasting and Gelling Modified Potato Starch with LF-NMR

Currently, society expects convenience food, which is healthy, safe, and easy to prepare and eat in all conditions. On account of the increasing popularity of modified potato starch in food industry and its increasing scope of use, this study focused on improving the physical modification of native starch with temperature changes. As a result, it was found that the suggested method of starch modification with the use of microwave power of 150 W/h had an impact on the change in starch granules. The LF-NMR method determined the whole range of temperatures in which the creation of a starch polymer network occurs. Therefore, the applied LF-NMR technique is a highly promising, noninvasive physical method, which allows obtaining a better-quality structure of potato starch gels.

Development of an Artificial Neural Network Utilizing Particle Swarm Optimization for Modeling the Spray Drying of Coconut Milk

Spray drying techniques are one of the methods to preserve and extend the shelf-life of coconut milk. The objective of this research was to create a particle swarm optimization-enhanced artificial neural network (PSO-ANN) that could predict the coconut milk spray drying process. The parameters for PSO tuning were selected as the number of particles and acceleration constant, respectively, for both global and personal best using a 2k factorial design. The optimal PSO settings were recorded as global best, C1 = 4.0; personal best, C2 = 0; and number of particles = 100. When comparing different types of spray drying models, PSO-ANN had an MSE value of 0.077, GA-ANN had an MSE of 0.033, while ANN had an MSE of 0.082. Sensitivity analysis was conducted on all three models to evaluate the significance level of each parameter on the model, and it was discovered that inlet temperature had the most significant influence on the model performance. In conclusion, the PSO-ANN was found to be more effective than ANN but less effective than GA-ANN in predicting the quality of coconut milk powder.

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

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.

The Use of Image Analysis to Detect Seed Contamination-A Case Study of Triticale

Samples of triticale seeds of various qualities were assessed in the study. The seeds were obtained during experiments, reflecting the actual sowing conditions. The experiments were conducted on an original test facility designed by the authors of this study. The speed of the air (15, 20, 25 m/s) transporting seeds in the pneumatic conduit was adjusted to sowing. The resulting graphic database enabled the distinction of six classes of seeds according to their quality and sowing speed. The database was prepared to build training, validation and test sets. The neural model generation process was based on multi-layer perceptron networks (MLPN) and statistical (machine training). When the MLPN was used to identify contaminants in seeds sown at a speed of 15 m/s, the lowest RMS error of 0.052 was noted, whereas the classification correctness coefficient amounted to 0.99.

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

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.

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

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%.