Alex Martynenko

Computer-Vision System for Control of Drying Processes

Computer-vision system (CVS) for control of a drying process with a portable CCD camera with IEEE-1396 interface and configurable software LabView 7.0 and IMAQTM 6.1 was developed. An object area was continuously monitored through the CVS by extracting the green plane from the RGB color space followed by thresholding and pixel counting. An object color was continuously monitored through the CVS as color intensity in the hue-saturation-intensity (HSI) color space. The observability of a drying process was provided due to online image analysis and correlation of image attributes (area, color, texture) with physical parameters of drying (moisture, quality). A relationship between area shrinkage and moisture content was used for online estimation of actual moisture content. A relationship between color intensity and quality was used for online estimation of quality degradation. Experimental study of the CVS for ginseng drying showed advantages of computer-vision for online monitoring of important state variables, such as moisture content and material quality. Color measurements demonstrated high sensitivity of quality to drying conditions: drying at 50°C resulted in significant color changes and unacceptable quality degradation. The quality of roots in three-stage (38-50-38°C) drying process was compatible with recommended isothermal (38°C) drying due to significant (30–40%) reduction of drying time. This control strategy was used in a pilot batch dryer for temperature control with respect to quality. Testing of a pilot dryer with embedded CVS proved stability and robustness of control strategy, combined with high accuracy in the estimation of moisture content (8–14% of error with 95% confidence). The composite moisture measurements at the endpoint demonstrated uniform drying of root mixture to target moisture content 0.1 g/g (db) with minor variations between individual roots in the range of 0.07–0.12 g/g.

Computer Vision for Real-Time Measurements of Shrinkage and Color Changes in Blueberry Convective Drying

The effect of temperature on blueberry drying rate, shrinkage, and color changes was evaluated from drying experiments for both high bush (Vaccinium corymbosum L.) and wild (Vaccinium angustifolium) blueberries. Drying temperature significantly affected texture and color of both varieties. Temperatures above 55°C caused a significant color change (ΔE > 25) within 30 min of the beginning of drying, followed by a significant drop in density from 1.02 to 0.38 g/cm3. In contrast, drying at temperatures below 50°C resulted in nonsignificant color changes and an eventual density increase to 1.26 g/cm3. It follows that blueberry color could be used as an early stage indicator of quality degradation in the process of drying.

The System of Correlations Between Moisture, Shrinkage, Density, and Porosity

The system of four correlations, establishing relationship between moisture, shrinkage, density, and porosity in three-phase capillary-porous materials was introduced. It is based on the fundamental relationship between these variables and useful for real-time estimation of density and porosity from measurable moisture content and shrinkage.

Texture Changes During Drying of Apple Slices

The objective of this study was to investigate the textural changes of apple slices undergoing convective drying. Texture profile analysis (TPA) measurements at 20% compression clearly showed three periods in texture development: softening, uniform hardness, and hardening. In the period of softening, the initial hardness of 2,260 g exponentially decreased to 40–90 g, remained low in the moisture content range of 3.0–0.5 g/g, and rapidly increased below 0.5 g/g. Cohesiveness, springiness, resilience, and chewiness demonstrated similar three-phase behavior with the dependence on moisture content in the period of softening, constant values in the period of uniform hardness, and an inversion point below 0.5 g/g. In the period of hardening, the texture parameters were dependent on temperature.

Thermodynamics, transport phenomena, and electrochemistry of external field-assisted nonthermal food technologies

ABSTRACT Interest in the development and adoption of nonthermal technologies is burgeoning within the food and bioprocess industry, the associated research community, and among the consumers. This is evident from not only the success of some innovative nonthermal technologies at industrial scale, but also from the increasing number of publications dealing with these topics, a growing demand for foods processed by nonthermal technologies and use of natural ingredients. A notable feature of the nonthermal technologies such as cold plasma, electrohydrodynamic processing, pulsed electric fields, and ultrasound is the involvement of external fields, either electric or sound. Therefore, it merits to study the fundamentals of these technologies and the associated phenomenon with a unified approach. In this review, we revisit the fundamental physical and chemical phenomena governing the selected technologies, highlight similarities, and contrasts, describe few successful applications, and finally, identify the gaps in research.

Porosity, Bulk Density, and Volume Reduction During Drying: Review of Measurement Methods and Coefficient Determinations

Several experimental methods for measuring porosity, bulk density, and volume reduction during drying of foodstuffs are available. These methods include, among others, geometric dimension, volume displacement, mercury porosimeter, micro-CT, and NMR. However, data on their accuracy, sensitivity, and appropriateness are scarce. This article reviews these experimental methods, areas of applications, and limits. In addition, the concept of porosity, bulk density, and volume reduction and their evolution as a function of moisture content during drying are presented. In this study, values of initial porosity (ϵ0) and density ratio (β) of some food products are summarized. It has been found that ϵ0 is highly dependent on the type of food products, while β ranges from 1.1 to 1.6. The possibility of calculating solid density based on food compositions has also been validated. The inter-predictions between porosity, bulk density, and volume density have been made mathematically evident.

Energy Aspects in Electrohydrodynamic Drying

Acknowledging favorable quality of products obtained through electrohydrodynamic (EHD) drying, there is a lack of knowledge about energy aspects of this promising, yet not commercialized, technology. This article is a critical review of EHD research, which may be crucial for future studies and industrial applications. In particular, effects of electrodes configuration and operating parameters on drying kinetics, energy consumption, and energy efficiency of EHD as compared to conventional drying are examined. Some engineering considerations for using EHD in industrial dryers are also discussed.

Non-Isothermal Drying of Medicinal Plants

An intelligent system for non-isothermal drying of medicinal plants, based on machine vision, sensor fusion, and neural network, was developed. Air temperature, velocity, and humidity, along with material size and moisture content were inputs to the neural model for diffusivity. Temperature, time, mass, volume, and color were inputs to the neural model for quality. Isothermal low-temperature drying of ginseng root and blueberry showed extremely low effective diffusivity (0.2–0.75)*10−10 m2/s. In contrast, non-isothermal drying demonstrated a potential to increase diffusivity and prevent quality losses. Testing of the intelligent drying system showed reduced drying time from 240 to 60 hours for ginseng, and from 110 to 30 hours for blueberry with desired product quality.

True, Particle, and Bulk Density of Shrinkable Biomaterials: Evaluation from Drying Experiments

A method of evaluation of bulk, particle, and true density of shrinkable biomaterials from drying experiments was developed. Density was calculated from instantaneous shrinkage and mass measurements, using the correlation between dimensionless values of density ρ, shrinkage ξ, and moisture content X. Bulk density evolution during drying was found to be dependent on temperature and material shrinkability. For example, the bulk density of wild blueberry increased from 1.03 to 1.26 g/cm3 at 40°C and decreased from 1.03 to 0.38 g/cm3 at 60°C. Particle density increased with moisture removal. True density was evaluated in the range 1.59–1.61 g/cm3 for wild blueberry and 1.45–1.56 g/cm3 for ginseng root.

Intelligent Computation of Moisture Content in Shrinkable Biomaterials

A technique of intelligent computation of moisture content in shrinkable biomaterials from multiple predictors was developed. All measurable predictors were structured in three sets: biomaterial properties (volume, density, porosity, diffusivity); drying conditions (time, air temperature, humidity, velocity, pressure); and drying technologies. Two typical drying models were considered: time-dependent (thermodynamical) and time-independent (relational). The relationship between predictors and moisture content was established on the basis of multi-factorial linear regression (MLR) and neural networks (NN). Accuracy of statistical approximation was strongly dependent on drying model and chosen set of predictors. Time-independent models demonstrated better accuracy (MSE = 0.214) than time-dependent models (MSE = 0.254). Redundant predictors did not affect the accuracy and generalization ability of statistical models. Results of NN training and testing showed superior accuracy with respect to statistical models. NN worked perfectly well for any combination of non-correlated predictors, improving accuracy to MSE = 0.01. Elimination of redundant predictors further improved accuracy and generalization ability of NN models. The performance of both models was tested for drying of ginseng roots in the range of temperatures from 38 to 50°C, sizes from 10 to 32 mm, and relative humidity from 12 to 40%. Due to the high accuracy and computational efficiency, NN can be used as online estimator of moisture content in drying process.