Automatic Quality Control of CerealsImage Acquisition and Intelligent Image Analysis

The increasing demands on the quality and con-sumption safety of grain imply, for food economy, increased expenditures for product and process su-pervision. Within the framework of the control of goods received and quality of the processing indus-tries, various laboratory methods are applied at present to check the properties of the products and the observance of the standards. The control of the goods received decides whether the supplied goods will be accepted and allocated the status “healthy and customary” or whether the goods before stor-age in the silo have to be subjected to a special treatment in order to reduce impurities, moisture, etc. The evaluation of the quality features is espe-cially important in order to store together grain of special quality grades or to process with regard to the desired quality grade of the final product.

Permanent Control of Food and Feed as to the Mycotoxin Contamination

Mycotoxins are secondary metabolic products which are produced during the growth of mould fungi on grain. Grain contaminated by mycotoxins as food or feed can cause acute intoxications or chronic diseases of humans and animals. Regulations and quality standards as part of the consumer protection law pre-scribe a permanent control of food and feed as to the mycotoxin contamination. Mycotoxins are determined according to chromatographic and immunochemical methods, respectively. These methods re-

quire a high expenditure of personnel and time. Generally they are not per-formed in the laboratories of the respec-tive company but by special analytical service companies.

The visual assessment of grain (analy-sis of foreign matter) cannot be replaced completely by other laboratory methods (physico-chemical and microbiological methods). This kind of inspection re-quires qualified personnel who can as-sess correctly the condition of the grain. In connection with the visual assessment a number of problems impair signifi-cantly this kind of inspection. The quality assessment not always can be defined by

Visual Analyses for the Quality Assessment

Physical, chemical, and sensory analyses are performed as well as visual analyses for the quality assessment of grain. Ex-perienced specialists examine in random samples the condition of the kernels. Anomalies of the kernels and clear dis-colourations are indicative of a microbial infestation. Of special importance and frequency are weed moulds of the fusar-ium genus. These mould fungi are pro-ducing mycotoxins and in brewer’s malt they are connected with the gushing of beer.

Fig. 1: Image acquisition and image analysis Fig. 2: Categories of grain quality

Dr. Petra Perner

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a clear “yes/no” answer, but the grain characteristics have flowing grades. Fur-ther, the result is determined by the sub-jectivity of the inspector.

Therefore, a system according to which the formation, structure and colour of the grain can be determined automatically, and the occurrence and concentration of fusariospores can be assessed, should be useful. Anomalies of the grain and fusar-ium infestation can be determined visu-ally by a microscopic image acquisition unit. In the receiving inspection the char-acteristics are an important indication of an increased microbial load. By appropri-ate combination of characteristics they can serve as additional “marker for a po-tential mycotoxin load” of the respective grain portion. The advantage of such a system is the fact that the samples can be analysed independently without qualified personnel, that the results are independ-ent of subjective factors and thus repro-ducible. By archiving the digital images an additional documentation for the qual-ity proof of the respective grain batch is possible within the control and supervi-sion conception of the company. Supervi-sion functions of the control of goods re-ceipt and quality can be automated, quantified and standardised. This will re-sult in savings of time, personnel and costs of the analyses.

For the application of the system deci-sion support for the kind and intensity of the cleaning regime to be applied and the storage of the received grain will be avail-able to the user. Mixes with healthy batches and resulting quality losses can be eliminated at simultaneous objective doc-umentation of the analytical results (QS systems, retraceability, right of recourse).

A System Was Developed that Can Carry out the Following Tasks:

automatic assessment of kernel sam-ples without human assistance,evaluation of the quality of kernel samples at the site,

quantitative determination of the per-centage of kernels of a certain quality grade with computer display,beside the possible visual assessment, information on the microbiological impurities of the kernels, in correla-tion with hygiene relevant parameters (such as the mycotoxin content) in or-der to perform the necessary treat-ment of the grain more targeted and tailored to the batch.objective and reproducible assess-ment of the sample and a reproduci-ble quality certificate for the sample.

The System Consists of:

handling and image recording unit for the digital image of the upper and un-derside of a kernel of the sample with light of different wave lengths in order to make visible also structures below the husk of the kernel,unit for taking and preparing the microbiological impurities of the ker-nel for digital image recording.

For the subsequent analysis of the digital images a multistage image analysis algo-rithm was developed which is analysing the surface of the kernel as well as the microbial impurities taken. For the anal-ysis of the surface of the kernel the fol-lowing conditions have to be considered:

Batches and atmospheric influences were eliminated so that the result of the analysis was not influenced (nor-malisation step).For the subsequent analysis the ker-nels are separated from the back-ground and singled (segmentation step).Discrimination relevant characteristics are extracted from the single kernels (characteristics extraction). A novel set of characteristics for the description of kernel anomalies was the result.On the basis of these characteristics the kernels are classified into one of 20 categories (decision step).

For the analysis of the microbiological impurities the following tasks were real-ised:

reliable detection of fungal spores in the impurities mix visible in the digital image,extraction of discrimination relevant characteristics for the description of the potential varieties of fungal spores (characteristic extraction),development of a classificator for the classification of the varieties and quantities of the fungal spores on the basis of these characteristics,on the basis of the result of the classi-ficator (variety and quantity of the fungal spores) a correlation with the mycotoxin load of the sample is estab-lished.

The system can determine the portion of grains of different categories such as for e.g.:

broken grain,shrivelled grain, shrunken grain,other grains, grains of contrasting va-rieties,kernels with discoloured germs,insect-damaged grain,sprouted grain, sprouts,grain dockage.

Finally, the system can present the per-centage of the kernels of different cate-gories in a report or on the display. Thus an objective and always reproducible documentation of the quality of the grain supplied is possible.

Contact:Dr. Petra PernerInstitute of Computer Vision and Applied Computer SciencesLeipzig, GermanyTel.: +49 341 8612 273Fax: +49 341 8612 275pperner@ibai-institut.dewww.ibai-institut.dewww.biomedvision.de

Fig. 3: Characteristics Fig. 4: Classification

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