Determination of senescent spotting in banana (Musa cavendish) using fractal texture Fourier image

Effect of Agave Fructans on Changes in Chemistry, Morphology and Composition in the Biomass Growth of Milk Kefir Grains

Prebiotic effects have been attributed to agave fructans through bacterial and yeast fermentations, but there are few reports on their use as raw materials of a carbon source. Kefir milk is a fermented drink with lactic acid bacteria and yeast that coexist in a symbiotic association. During fermentation, these microorganisms mainly consume lactose and produce a polymeric matrix called kefiran, which is an exopolysaccharide composed mainly of water-soluble glucogalactan, suitable for the development of bio-degradable films. Using the biomass of microorganisms and proteins together can be a sustainable and innovative source of biopolymers. In this investigation, the effects of lactose-free milk as a culture medium and the addition of other carbon sources (dextrose, fructose, galactose, lactose, inulin and fructans) in concentrations of 2, 4 and 6% w/w, coupled with initial parameters such as temperature (20, 25 and 30 °C), % of starter inoculum (2, 5 and 10% w/w) was evaluated. The method of response surface analysis was performed to determine the optimum biomass production conditions at the start of the experiment. The response surface method showed that a 2% inoculum and a temperature of 25 °C were the best parameters for fermentation. The addition of 6% w/w agave fructans in the culture medium favored the growth of biomass (75.94%) with respect to the lactose-free culture medium. An increase in fat (3.76%), ash (5.57%) and protein (7.12%) content was observed when adding agave fructans. There was an important change in the diversity of microorganisms with an absence of lactose. These compounds have the potential to be used as a carbon source in a medium culture to increase kefir granule biomass. There was an important change in the diversity of microorganisms with an absence of lactose, where the applied image digital analysis led to the identification of the morphological changes in the kefir granules through modification of the profile of such microorganisms.

Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review

The ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systematic Reviews approach, 1548 studies were extracted from journals and conferences, using different research databases, and 35 were included in the final review for key parameters. These studies suggest the dominance of banana fingers as input data, a sensor camera as the preferred capturing device, and appropriate features, such as color, that can provide better detection. Among six stages of ripeness, the studies employing the four mentioned stages performed better in terms of accuracy and coefficient of determination value. Among all the works for detecting ripeness stages prediction, convolutional neural networks were found to perform sufficiently well with large datasets, whereas conventional artificial neural networks and support vector machines attained better performance for sensor-related data. However, insufficient information on the dataset and capturing device, limited data availability, and exploitation of data augmentation techniques are limitations in existing studies. Thus, effectively addressing these shortcomings and close collaboration with experts to predict the ripeness stages should be pursued.

Comparative Evaluation of the Thermal, Structural, Chemical and Morphological Properties of Bagasse from the Leaf and Fruit of Bromelia hemisphaerica Lam. Delignified by Organosolv

Bromelia hemisphaerica Lam., a wild plant native to Mexico, has medicinal attributes and is mainly used for its hemisphericin content in foods. However, the residues of its leaves and fruits are underutilized, representing an area of opportunity for foods or materials. Lignocellulosic material from leaves and fruit bagasse was isolated using an organosolv treatment to separate their components (cellulose, hemicellulose and lignin) and to determine the influence after processing on the physicochemical, thermal and microstructural characteristics. The extracted fiber presented a cellulose content of 44% in the leaf and 33.5% in the fruit. The release of lignin after the organosolv process represented a greater amount of amorphous material in the leaf than in the fruit. By FTIR and X-ray diffraction (DRx), the change in the crystallinity of the cellulose was determined (from 18% higher in the leaf than the fruit before to 14% higher in the fruit after the organosolv process), with values similar to type I cellulose. The thermal properties showed a high order in the structure of the cellulose. Microscopy and digital analysis techniques showed the microstructural changes and the effectiveness of delignification during the process. It is concluded that the leaf fiber of B. hemisphaerica presents characteristics that make it useful as a potential ingredient for food product development and other uses.

Comparative Analysis of Fermentation Conditions on the Increase of Biomass and Morphology of Milk Kefir Grains

Kefir grains represent a symbiotic association group of yeasts, lactic acid bacteria and acetic acid bacteria within an exopolysaccharide and protein matrix known as kefiran. The mechanism of growth of a biomass of kefir after successive fermentations and optimal conditions is not well understood yet. Biomass growth kinetics were determined to evaluate the effects of temperatures (10 °C to 40 °C) and different substrates, such as monosaccharides (fructose, galactose, glucose), disaccharides (lactose, saccharose) and polysaccharides (Agave angustifolia fructans) at 2%, in reconstituted nonfat milk powder at 10% (w/v) and inoculated with 2% of milk kefir grain (105 CFU/g), after determining the pH kinetics. The best conditions of temperature and substrates were 20 °C and fructans and galactose. An increase in cells, grain sizes and a change in the morphology of the granules with the best substrates were observed using environmental scanning electron microscopy, confocal laser scanning microscopy and Image Digital Analysis (IDA). Kefir grains with agave fructans as their carbon source showed the higher fractal dimension (2.380), related to a greater co-aggregation ability of LAB and yeasts, and increase the formation of exopolysaccharides and the size of the kefir grains, which opens new application possibilities for the use of branched fructans as a substrate for the fermentation of milk kefir grains for the enhancement of cellular biomasses and exopolysaccharide production, as well as IDA as a characterization tool.

Development of a system to measure color in fresh and microwave dried banana slices

The main focus of this study was to develop and calibrate a computerized image analysis system in order to measure the color of banana (Musa Cavendish) under microwave treatment. Bananas were cut into 2 mm slice thickness and dried at two different microwave power level; 540 W and 180 W. An algorithmic was developed which converted RGB color value from a color image into CIE L*a*b* values very well (Error_L* = 2.163%, Error_a* = 4.458%, Error_b* = 5.224%). Once the calibration is completed, it was applied to measure the color change in the banana slice during drying. The value of L* decreased from 89.01 to 71.17 and from 82.60 to 72.53 for both microwave treated samples suggesting browning is taking place during the drying operation. The value of a* increased from - 0.80 to 11.50 and from - 3.90 to 5.18 for 540 and 180 W microwave treated banana slices respectively suggesting tendency of redness increased. The same type of increment was observed for b* value. It changed from 36.46 to 60.51 and 34.02 to 72.82 for 540 and 180 W microwave treated banana slices, respectively. Artificial Neural Network (ANN) modeling was used for prediction of the developed CVS's values.

El análisis de imágenes como herramienta de monitoreo en la deshidratación de rodajas de banana

Resumen El objetivo de este trabajo fue evaluar los cambios morfométricos, fractales y de color durante la deshidratación de rodajas de banana mediante el uso de análisis de imágenes y la relación de estos parámetros con la cinética de secado. El secado convectivo se realizó entre 75 °C y 134 °C a una velocidad de aire de 0,65 m s-1. Los resultados mostraron que las imágenes tomadas proporcionaron información cuantitativa de los cambios morfométricos (reducción de área y circularidad, y aumentos del perímetro) por efecto de los tratamientos térmicos. El análisis de la textura fractal detectó cambios en la intensidad de los pixeles, indicando que las superficies resultaron más complejas e irregulares a medida que disminuyó el contenido de humedad del producto. La dimensión fractal de contorno (DFcont) aumentó con el tiempo de secado. En relación al color del producto seco, los parámetros a* y b* aumentaron, mientras que la Luminosidad (L*) disminuyó, lo cual está asociado al oscurecimiento de las rodajas e influenciado por el aumento de la temperatura. Las micrografías determinaron la presencia de poros que fueron aumentando en cantidad cuando incrementó la temperatura. Los resultados muestran que el análisis de imágenes puede ser empleado como herramienta de monitoreo debido a su versatilidad, bajo costo, rapidez de respuesta, además de no ser una técnica destructiva.

Assessment of banana fruit maturity by image processing technique

Maturity stage of fresh banana fruit is an important factor that affects the fruit quality during ripening and marketability after ripening. The ability to identify maturity of fresh banana fruit will be a great support for farmers to optimize harvesting phase which helps to avoid harvesting either under-matured or over-matured banana. This study attempted to use image processing technique to detect the maturity stage of fresh banana fruit by its color and size value of their images precisely. A total of 120 images comprising 40 images from each stage such as under-mature, mature and over-mature were used for developing algorithm and accuracy prediction. The mean color intensity from histogram; area, perimeter, major axis length and minor axis length from the size values, were extracted from the calibration images. Analysis of variance between each maturity stage on these features indicated that the mean color intensity and area features were more significant in predicting the maturity of banana fruit. Hence, two classifier algorithms namely, mean color intensity algorithm and area algorithm were developed and their accuracy on maturity detection was assessed. The mean color intensity algorithm showed 99.1 % accuracy in classifying the banana fruit maturity. The area algorithm classified the under-mature fruit at 85 % accuracy. Hence the maturity assessment technique proposed in this paper could be used commercially to develop a field based complete automatic detection system to take decision on the right time of harvest by the banana growers.

The spatial distribution of β-carotene impregnated in apple slices determined using image and fractal analysis

Changes in the concentration profiles of β-carotene caused by diffusion through parenchymatic dried apple tissue were characterized by image and fractal analysis. Apple slices were dried by convection, and then impregnated with an aqueous β-carotene solution. Scanning electron microscopy images of dried apple slices were captured and the fractal dimension (FD) values of the textures of the images were obtained (FDSEM). It was observed that the microstructure of the foodstuff being impregnated have an important effect on the impregnation phenomenon, generating irregular concentration profiles of β-carotene, which are numerically described by the fractal dimension FDPROFILES and are related to the diffusion process during impregnation in dried edible tissue.

Evaluation of agave fiber delignification by means of microscopy techniques and image analysis

Recently, the use of different types of natural fibers to produce paper and textiles from agave plants has been proposed. Agave atrovirens can be a good source of cellulose and lignin; nevertheless, the microstructural changes that happen during delignification have scarcely been studied. The aim of this work was to study the microstructural changes that occur during the delignification of agave fibers by means of microscopy techniques and image analysis. The fibers of A. atrovirens were obtained from leaves using convective drying, milling, and sieving. Fibers were processed using the Acetosolv pulping method at different concentrations of acetic acid; increasing acid concentration promoted higher levels of delignification, structural damage, and the breakdown of fiber clumps. Delignification followed by spectrometric analysis and microstructural studies were carried out by light, confocal laser scanning and scanning electron microscopy and showed that the delignification process follows three stages: initial, bulk, and residual. Microscopy techniques and image analysis were efficient tools for microstructural characterization during delignification of agave fibers, allowing quantitative evaluation of the process and the development of linear prediction models. The data obtained integrated numerical and microstructural information that could be valuable for the study of pulping of lignocellulosic materials.

Review: computer vision applied to the inspection and quality control of fruits and vegetables

This is a review of the current existing literature concerning the inspection of fruits and vegetables with the application of computer vision, where the techniques most used to estimate various properties related to quality are analyzed. The objectives of the typical applications of such systems include the classification, quality estimation according to the internal and external characteristics, supervision of fruit processes during storage or the evaluation of experimental treatments. In general, computer vision systems do not only replace manual inspection, but can also improve their skills. In conclusion, computer vision systems are powerful tools for the automatic inspection of fruits and vegetables. In addition, the development of such systems adapted to the food industry is fundamental to achieve competitive advantages.

Changes on image texture features of breakfast flakes cereals during water absorption

Normally breakfast cereal flakes are consumed by pouring them into a bowl and covering them with fresh or cold milk. During this process the liquid uptake causes changes in the surface and internal matrix of breakfast cereals that influence texture and integrity. Some breakfast cereal as flakes have a translucent structure that could provide information about the solid matrix and air cells and how they change during liquid absorption. The objective of the study was to assess the image texture changes of corn flakes and frosted flakes during water absorption at 5, 15 and 25 °C, employing 11 image feature textures extracted from grey-level co-occurrence matrix and grey-level run length matrix (at three directions) and to relate the fractal dimension (FD) of images with rupture force (RF) reduction during soaking of both flakes at 5 °C. The most relevant result from principal component analysis calculated with a matrix of 54 (soaking times) × 22 (texture features), shows that it was possible to distinguish an isolated group consisting of different soaking times at the same water temperature in each breakfast cereal flakes evaluated, corroborating that superficial liquid imbibition is important during the liquid absorption process when flakes are soaked. Furthermore, standardized FD could be related to RF in the period when samples tend to search for an equilibrium state.

Fractal descriptors in the Fourier domain applied to color texture analysis

The present work proposes the development of a novel method to provide descriptors for colored texture images. The method consists of two steps. First, we apply a linear transform in the color space of the image aiming at highlighting spatial structuring relations among the color of pixels. Second, we apply a multiscale approach to the calculus of fractal dimension based on Fourier transform. From this multiscale operation, we extract the descriptors that are used to discriminate the texture represented in digital images. The accuracy of the method is verified in the classification of two color texture datasets, by comparing the performance of the proposed technique to other classical and state-of-the-art methods for color texture analysis. The results showed an advantage of almost 3% of the proposed technique over the second best approach.