D.S. Jayas

Nanotechnology for the Food and Bioprocessing Industries

Several complex set of engineering and scientific challenges in the food and bioprocessing industries for manufacturing high quality and safe food through efficient and sustainable means can be solved through nanotechnology. Bacteria identification and food quality monitoring using biosensors; intelligent, active, and smart food packaging systems; and nanoencapsulation of bioactive food compounds are few examples of emerging applications of nanotechnology for the food industry. We review the background about the potential of nanotechnology, provide an overview of the current and future applications of nanotechnology relevant to food and bioprocessing industry, and identify the societal implications for successful implementation of nanotechnology.

Pulsed electric field processing of foods: a review.

Use of pulsed electric fields (PEFs) for inactivation of microorganisms is one of the more promising nonthermal processing methods. Inactivation of microorganisms exposed to high-voltage PEFs is related to the electromechanical instability of the cell membrane. Electric field strength and treatment time are the two most important factors involved in PEF processing. Encouraging results are reported at the laboratory level, but scaling up to the industrial level escalates the cost of the command charging power supply and of the high-speed electrical switch. In this paper, we critically review the results of earlier experimental studies on PEFs and we suggest the future work that is required in this field. Inactivation tests in viscous foods and in liquid food containing particulates must be conducted. A successful continuous PEF processing system for industrial applications has yet to be designed. The high initial cost of setting up the PEF processing system is the major obstacle confronting those who would encourage the systems industrial application. Innovative developments in high-voltage pulse technology will reduce the cost of pulse generation and will make PEF processing competitive with thermal-processing methods.

REVIEW OF THIN-LAYER DRYING AND WETTING EQUATIONS

ABSTRACT Thin-layer equations contribute to the understanding of the heat and mass transfer phenomena in agricultural products and computer simulations for designing new and improving existing commercial drying processes. Many different equations have been developed to represent thin-layer drying behaviour of the grains. Many thin-layer drying and rewetting equations are reviewed and discussed. Some suggestions for future coordinated research work arc given.

CLASSIFICATION OF CEREAL GRAINS USING MACHINE VISION: II. COLOR MODELS

A digital image analysis (DIA) algorithm was developed based on color features to classify individual kernels of Canada Western Red Spring (CWRS) wheat, Canada Western Amber Durum (CWAD) wheat, barley, oats, and rye. Eighteen color features (mean, variance, and range of red, green, and blue, and hue, saturation, and intensity) were used for the discriminant analysis. Grains from 15 growing regions (300 kernels per growing region) were used as the training data set and another five growing regions were used as the test data set. When the first 10 most significant color features were used in the color model and tested on an independent data set (the test data set where total number of kernels used was 10,500; for CWRS wheat, 300 kernels each were selected for three grades), the classification accuracies of CWRS wheat, CWAD wheat, barley, oats, and rye were 94.1, 92.3, 93.1, 95.2, and 92.5%, respectively. When the model was tested on the training data set (total number of kernels used was 31,500), the classification accuracies were 95.7, 94.4, 94.2, 97.6, and 92.5%, respectively, for CWRS wheat, CWAD wheat, barley, oats, and rye.

Classification of cereal grains using machine vision: IV. Combined morphology, color, and texture models.

Classification models by combining two or three feature sets (morphological, color, textural) were developed to classify individual kernels of Canada Western Red Spring (CWRS) wheat, Canada Western Amber Durum (CWAD) wheat, barley, oats, and rye. The mean accuracies (the average of the classification accuracies of the above mentioned cereal grains) of 98.6 and 99.3% were achieved when the morphology-texture model with the 15 most significant features was used to test on an independent data set (total number of kernels used was 10,500) and on the training data set (total number of kernels used was 31,500), respectively. When the morphology-color model (with the 15 most significant features) was tested on the independent data set and on the training data set, the mean accuracies were 99.4 and 99.6%, respectively. Similarly, using the texture-color model (with the 15 most significant features) the mean accuracies were 98.4 and 98.0% for the independent and the training data sets, respectively. The highest classification accuracies were achieved when the morphology-texture-color model was used. The mean accuracies using the 20 most significant features in the morphology-texture-color model were 99.7 and 99.8% when tested on the independent and the training data sets, respectively. The differences in mean accuracies were not significant when the models were tested with independent and training data sets.

Classification of cereal grains using machine vision: I. Morphology models.

An algorithm was developed based on morphological features to classify individual kernels of Canada Western Red Spring (CWRS) wheat, Canada Western Amber Durum (CWAD) wheat, barley, oats, and rye. Twenty-three morphological features were used for the discriminant analysis. Grains from 15 growing regions (300 kernels per growing region) across Western Canada were used as the training data set and from another five growing regions as the test data set. The classification accuracies of individual kernels using the 10 most significant features in the morphology model were 98.9, 93.7, 96.8, 99.9, and 81.6%, respectively for CWRS wheat, CWAD wheat, barley, oats, and rye when tested on an independent data set (i.e., the test data set where the total number of kernels used was 10 500; for CWRS wheat, 300 kernels each were selected for three grades). When the model was tested on the training data set (total number of kernels used was 31 500), the classification accuracies of CWRS wheat, CWAD wheat, barley, oats, and rye were 98.9, 91.6, 97.9, 100.0, and 91.6%, respectively.

Cereal grain and dockage identification using machine vision

Algorithms were written to extract a total of 230 features (51 morphological, 123 colour, and 56 textural) from the high-resolution images of kernels of five grain types [barley, Canada Western Amber Durum (CWAD) wheat, Canada Western Red Spring (CWRS) wheat, oats, and rye] and five broad categories of dockage constituents [broken wheat kernels, chaff, buckwheat, wheat spikelets (one to three wheat kernels inside husk), and canola (rapeseed with low erucic acid content in the oil and low glucosinolate content in the meal)]. Different feature models, viz. morphological, colour, texture, and a combination of the three, were tested for their classification performances using a neural network classifier. Kernels and dockage particles with well-defined characteristics (e.g. CWRS wheat, buckwheat, and canola) showed near-perfect classification whereas particles with irregular and undefined features (e.g. chaff and wheat spikelets) were classified with accuracies of around 90%. The similarities in shape and size of some of the particles of chaff and wheat spikelets with the kernels of barley and oats affected the classification accuracies of the latter, adversely.

Fungal Detection in Wheat Using Near-Infrared Hyperspectral Imaging

Different species of fungi infect grain in the field and storage facilities. Contamination by fungi in grain is detected and quantified by traditional methods, such as microbial incubation and microscopic detection, which are subjective, labor intensive, and time consuming. An accurate and timely detection technique for fungal growth in grain is needed to prevent grain from spoiling and to reduce quality loss. In this study, the potential of near-infrared hyperspectral imaging to detect fungal infection in wheat was investigated. Wheat kernels infected with storage fungi, namely Penicillium spp., Aspergillus glaucus, and Aspergillus niger, were scanned using a hyperspectral imaging system, and a total of 20 image slices at evenly spaced wavelengths between 1000 to 1600 nm were acquired to form a hypercube. A multivariate image analysis (MIA) technique based on principal component analysis (PCA) was used to reduce the dimensionality of the image hypercubes. Two-class and four-class classification models were developed by applying k-means clustering and discriminant (linear, quadratic, and Mahalanobis) analyses. Two-class discriminant classification models gave maximum classification accuracy of 100%, and on average 97.8% infected kernels were correctly classified by the linear discriminant classifier. The four-class linear discriminant classifier correctly classified more than 95% of the kernels infected with Penicillium and 91.7% healthy kernels. However, the discriminant classifiers misclassified the kernels infected with A. niger and A. glaucus.

Storage and drying of grain in Canada: low cost approaches

Most Canadian grain (>70% of harvests) is stored on the farm. High moisture content of grain at harvest rapidly leads to spoilage and occasionally the production of the mycotoxins sterigmatocystin, ochratoxin A, or citrinin. Near ambient drying systems that consist of an electrical fan at the base of a granary blowing air into a plenum beneath a perforated floor under stored grain is relatively economical. Heat produced by the fan can dry grain by 2% moisture content in 2 months at a cost for electricity of Can.

EVALUATION OF THE GAB EQUATION FOR THE ISOTHERMS OF AGRICULTURAL PRODUCTS

0.87/tonne. Hot air dryers are used on wet grain at harvest to rapidly lower 21% moisture content maize to 15% moisture content at a cost of Can.