Scanning electron microscopy combined with image processing technique: Microstructure and texture analysis of legumes and vegetables for instant meal

Different methods for textural evaluation of freeze-dried candies during storage

The textural changes during storage of two freeze-dried candies developed from blackcurrant fruits, unflavored yogurt, and different alternative sweeteners, one sweetened with honey/isomalt (HI) and another sweetened with isomalt/stevia (IS), were analyzed using three different methods (instrumental, sensory, and image analysis). Fresh candies were in the supercooled state and presented different structural and textural characteristics (HI: compact and homogeneous, and IS: porous and crunchy), with Fmax values of 139 ± 14 and 174 ± 16 N for HI and IS, respectively. After storage, the instrumental analysis showed approximately 60% average drop in Fmax and W values, in agreement with the decrease observed by sensory analysis in hardness, fracturability, and crispness. Image analysis showed an increase in parameters related to the homogeneity and the uniformity/smoothness for HI. Pearson's correlation coefficients analysis showed that there was a good correlation between the three techniques used, suggesting that the joint use of these methods could be performed for a better understanding of complex food texture.

Freeze-Dried Cooked Chickpeas: Considering a Suitable Alternative to Prepare Tasty Reconstituted Dishes

The current trend in food consumption is toward convenience, i.e., fast food. The present work aims to study the potential of incorporating freeze-dried cooked chickpeas into a complex and traditional dish in Spanish gastronomy, such as Cocido, which has this legume as the main ingredient. Cocido is a two-course meal: a thin-noodle soup and a mix of chickpeas, several vegetables, and meat portions. The textural properties, sensory qualities, and rehydration kinetics of chickpeas of three Spanish varieties were investigated to select the most suitable cooking conditions to obtain freeze-dried chickpeas of easy rehydration whilst maintaining an adequate sensory quality for the preparation of the traditional dish. The sensory quality of various vegetables and meat portions, cooked under different conditions, was evaluated after freeze-drying and rehydration. It was possible to reproduce the sensory quality of the traditional dish after rehydration with water, heating to boiling in a microwave oven for 5 min, and resting for 10 min. Therefore, it is possible to commercialize complex dishes based on pulses and other cooked and freeze-dried ingredients as reconstituted meals with a wide nutrient profile. Nevertheless, additional research is required on the shelf life, together with other economic and marketing issues such as design of a proper packaging, that would allow consumption as a two-course meal.

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.

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.