A new methodology combining microscopy observation with Artificial Neural Networks for the study of starch gelatinization

Measuring the rogue wave pattern triggered from Gaussian perturbations by deep learning

Weak Gaussian perturbations on a plane wave background could trigger lots of rogue waves (RWs), due to modulational instability. Numerical simulations showed that these RWs seemed to have similar unit structure. However, to the best of our knowledge, there are no relative results to prove that these RWs have the similar patterns for different perturbations, partly due to that it is hard to measure the RW pattern automatically. In this work, we address these problems from the perspective of computer vision via using deep neural networks. We propose a rogue wave detection network (RWD-Net) model to automatically and accurately detect RWs in the images, which directly indicates they have the similar computer vision patterns. For this purpose, we herein meanwhile have designed and release the corresponding dataset, termed as rogue wave dataset-10K (RWD-10K), which has 10191 RW images with bounding box annotations for each RW unit. In our detection experiments, we get 99.29% average precision on the test splits of the proposed dataset. Finally, we derive our metric, termed as the density of RW units, to characterize the evolution of Gaussian perturbations and obtain the statistical results on them.

Quantitative study of starch swelling capacity during gelatinization with an efficient automatic segmentation methodology

A novel image segmentation methodology combined with optical microscopy observation was developed for qualifying starch swelling. Starch granules in the micrograph were successfully segmented based on high-precision edges extraction achieved by Canny edge detection together with mathematical morphology operation. Granules were automatically identified by computer vision and characterized by giving quantifiable area of these granules. The evolved swelling process could be generally divided into two phases. During the first phase, starch granules were only swollen up by 2.56 %, which is hard to be identified by conventional naked eye. During the following narrow temperature interval (60-66 ℃), these starch granules were detected to swell up significantly by 9.08 %. Through the granule area variable, swelling capacity was high-throughput characterized, which allows for the whole evaluation to be completed within a couple of minutes. The proposed methodology showed a high accuracy and potential as a novel technique for characterizing gelatinization.

Ultrasound-assisted acid hydrolysis of cassava (Manihot esculenta) bagasse: Kinetics, acoustic field and structural effects

Improving the actual acid hydrolysis of cassava bagasse (CB) with the assistance of high-intensity ultrasound (US) was aimed in comparison with mechanical agitation (AG). The kinetics of reducing and total sugar release were mathematically modeled. The acoustic field characterization and apparent viscosity of the suspensions were correlated. Moreover, microscopic analyses (visible, fluorescence and polarized light) were carried out to identify changes produced by the treatments. Both AG and US-treatments showed themselves to be effective at hydrolyzing CB. However, US-experiments reached equilibrium in the reducing sugar release process earlier and obtained slightly higher values of total sugars released. The Naik model fitted the experimental data with good accuracy. A greater loss in the birefringence of the starch granules and the degradation of lignocellulosic matter was also observed in US-assisted hydrolysis. The actual acoustic power applied was reduced after hydrolysis, probably due to the increase in the apparent viscosity of the resulting suspensions.

STUDY OF THE EFFECTIVE VISCOSITY OF GELATINIIZED STARCH DISPERSIONS, BASED ON PHYSICALLY MODIFIED STARCHES, DEPENDING ON TECHNOLOGICAL FACTORS

The aim of the study is to investigate rheological properties of gelatinized starch dispersions, based on physically modified starches, depending on technological factors. Realization of the research aim allows to get products (sauces, creams, fillers for confectionary products and so on), using physically modified starches, able to realize products with given structural-mechanical parameters of quality and safety; and also to provide the rational use of raw material resources, to decrease the labor capacity of the technological process of making culinary products. There were analyzed modern development tendencies of technologies of physically modified starches and their use in food products technologies. Generalization of literary data became a base for using these starches in food products technologies, where the first turn attention is paid to the colloid stability of food systems. Studies of the thermal stability of gelatinized starch dispersions determined that most stable in the cycle “heating-cooling-repeated heating” are gelatinized starch dispersions, based on physically modified starch “Prima”, which effective viscosity doesn’t essentially decrease after repeated heating. In gelatinized starch dispersions, based on physically modified starch «Endura» and «Indulge», repeated heating is also accompanied by the inessential viscosity decrease. Gelatinized starch dispersions, based on corn amylopectin starch, are not thermostable, and their effective viscosity essentially decreases at repeated heating. There are established regularities of the mechanical effect on structural-mechanical properties of gelatinized starch dispersions. It has been determined, that gelatinized starch dispersions, based on physically modified starches «Prima», «Endura» and «Indulge», demonstrate stable characteristics, as opposite to native starches at the mechanical effect. The prospects of further studies in this direction are to investigate an influence of technological factors (change of рН medium, influence of enzymes, pectin substances, mineral salts) on structural-mechanical properties of gelatinized starch dispersions, based on physically modified starches.

Prediction of Pasting Properties of Dough from Mixolab Measurements Using Artificial Neuronal Networks

An artificial neuronal network (ANN) system was conducted to predict the Mixolab parameters which described the wheat flour starch-amylase part (torques C3, C4, C5, and the difference between C3-C4and C5-C4, respectively) from physicochemical properties (wet gluten, gluten deformation index, Falling number, moisture content, water absorption) of 10 different refined wheat flourssupplemented bydifferent levels of fungal α-amylase addition. All Mixolab parameters analyzed and the Falling number values were reduced with the increased level of α-amylase addition. The ANN results accurately predicted the Mixolab parameters based on wheat flours physicochemical properties and α-amylase addition. ANN analyses showed that moisture content was the most sensitive parameter in influencing Mixolab maximum torque C3 and the difference between torques C3 and C4, while wet gluten was the most sensitive parameter in influencing minimum torque C4 and the difference between torques C5 and C4, and α-amylase level was the most sensitive parameter in predicting maximum torque C5. It is obvious that the Falling number of all the Mixolab characteristics best predicted the difference between torques C3 and C4.

A new characterization methodology for starch gelatinization

A gelatinization degree control system, with a combination of Artificial Neural Networks (ANNs) and computer vision, was successfully developed. An intelligent measurement framework was purposely designed to achieve a precise investigation on phase transition and morphology change of starch in real time, as well as a process control during gelatinization. Base on a variation of birefringence number, the degree of gelatinization (DG) control system provided a direct and fast methodology without subjective uncertainty in studying starch gelatinization. In the course, the whole system was a cascade structure with the hot-stage temperature chosen as the inner-loop parameter, thus the granule morphology and birefringence at different DG could be easily observed and compared in real time, and the relative transition temperature was simultaneously calculated.