A Simple Digital Imaging Method for Dirt Detection on Eggshells

The objective of this research was to develop an off-line vision system to detect defective eggshells, i.e., with dirty eggshell, by employing a classification algorithm based on a few logical operations, allowing a further implementation in an on-line grading process. In particular, this work was focused to study the feasibility of identifying and differentiating dirt stains on brown eggshells caused by organic residuals, from natural stains, caused by deposits of pigments. Digital images were acquired from 384 clean and dirty brown eggshells by employing a CCD camera endowed with 15 monochromatic filters (440-940 nm). Each dirty eggshell presented only one kind of defect, i.e., blood stains, feathers and white, clear or dark faces, while clean eggshells did not present organic residuals or evidences of feather, but their external color was characterized by clear or dark natural stains. A MatLab® devoted code was developed in order to classify samples as clean or dirty. The program was constituted by three major steps: first, the research of an opportune combination of monochromatic images in order to isolate the eggshell from the background; second, the detection of the dirt stains; third, the classification of the images samples into the dirty or clean group. The proposed classification algorithm was able to correctly classify near 93% of the samples. The robustness of the proposed classification was observed applying an external validation to a second set of samples, obtaining similar percentage of correctly classified samples (92%)

Automatic Identification of Defects on Eggshell Through a Multispectral Vision System

The objective of this research was to develop an off-line artificial vision system to automatically detect defective eggshells, i.e., dirty or cracked eggshells, by employing multispectral images with the final purpose to adapt the system to an on-line grading machine. In particular, this work was focused to study the feasibility of identifying organic stains on brown eggshells (dirty eggshell), caused by blood, feathers, feces, etc., from natural stains, caused by deposits of pigments on the outer layer of clean eggshells. During the analysis a total of 384 eggs were evaluated (clean: 148, dirty: 236). Dirty samples were evaluated visually in order to classify them according to the kind of defect (blood, feathers, and white, clear or dark feces), and clean eggshells were classified on the basis of the colour of the natural stains (clear or dark). For each sample digital images were acquired by employing a Charged Coupled Device (CCD) camera endowed with 15 monochromatic filters (440-940 nm). A Matlab® function was developed in order to automate the process and analyze images, with the aim to classify samples as clean or dirty. The program was constituted by three major steps: first, the research of an opportune combination of monochromatic images in order to isolate the eggshell from the background; second, the detection of the dirt stains; third, the classification of the images samples into the dirty or clean group on the basis of geometric characteristics of the stains (area in pixel). The proposed classification algorithm was able to correctly classify near 98% of the samples with a very low processing time (0.05s). The robustness of the proposed classification was observed applying an external validation to a second set of samples (n = 178), obtaining similar percentage of correctly classified samples (97%)

Energy analysis of a pasta factory and application of cogeneration

The energy production factor has become increasingly important following the trend within energy markets toward ever greater margins for optimisation. In the light of this fact, energy analysis provides a useful tool for determining the consumption of individual process phases in order to devise comprehensive intervention strategies aimed at more rational energy usage. The application of cogeneration to food-production processes is likewise of great interest, due to the opportunity it affords for improving the overall energy performance of facilities. The present study is concerned with the application of the above technologies to a pasta factory, beginning with a thorough energy analysis of the existing installation to select the most appropriate type of cogeneration system, and the development of a project to accommodate its anticipated future expansion in years to come. The factory in question consists of a line for the production of short dried pasta shapes having a capacity of 2,000 kg/h. Production runs continuously from Monday to Friday, and the process requires both electrical and thermal energy (in the form of superheated water at 130\ub0C). Thermal analysis For the thermal analysis, mass and energy balances were determined by measuring the moisture and temperature of product exiting the individual loads within the drying process, and using the values for temperature and humidity of the machines recorded by the supervision system by means of capacitive/resistive sensors. The factory produces forty-seven different types of short dried pasta shapes, which can be roughly classified into two groups according to their specific volume: "short pasta" and "soup pasta". The experimental analysis was conducted on the above two groups as well as on "bowtie" and "protein-enriched corkscrew" pasta shapes which, due to their lower throughput and special recipes, are better able to highlight any differences in thermal demand. Next, the thermal demand for each of the above four types of pasta was determined and, based upon the contribution of each group to the total annual output of the factory, a weighted average of the thermal requirements of the individual process loads was computed, and found to be 288.7 kcal/kg. Electrical analysis For the electrical analysis, the various loads within the plant were identified and suitable reduction coefficients estimated, partly on the basis of experience and partly based on graphs obtained from tests with current probes on the electrical panels. These graphs indicated that during certain work cycles the load does not always absorb maximum power, and so these cycles were monitored to better understand the functioning of the individual machines. The results of the electrical analysis were used to estimate the contribution of each load to the total consumption, and hence identify the process phases that incurred the highest electrical expenditure. The average monthly consumption was found to be 123,793 kWh which, for a monthly pasta output of 665,448 kg, again estimated using a weighted average, gave an overall specific energy consumption of 0.186 kWh/kg. Application of cogeneration After determining the overall energy consumption of the pasta factory in its present state, the feasibility of installing a cogeneration plant was evaluated, with the pasta factory divided into 4 production lines having a total capacity of 7,000 kg/h, and assuming 7,000 working h/year. On the basis of the assumptions made and the results of a market survey, the system chosen was a natural gas-fuelled reciprocating Otto cycle engine with 1,105 kWe rated power, connected in parallel to the electricity supply grid, and an auxiliary boiler. The cogenerator was slightly undersized to ensure it would always operate at full load, for maximum efficiency. An economic analysis and profitability analysis were carried out to ascertain that the savings accrued over the life time of the system could reimburse the investment cost. The Pay-Back Period (PBP) was found to be 29 months, and the resultant Net Present Value (NPV) indicates that the project is viable, with the annual revenues sufficient to both pay back the interest and recover the initial outlay before the end of the useful life of the investment