Detction of the wooden breast defect
Application Development

Detction of the wooden breast defect

April 2018 from EVK Service • about 6 minutes to read

In-line analysis of critical control points (as defined by HACCP) and the selective removal of defects has become a costly problem to manage in the food processing industry. Through many years’ experience and many successful innovations in sensor-based sorting and analysis, EVK has developed, in conjunction with our partners, an effective detection and sorting solution for chicken wooden breasts.

Introduction

The concept behind HACCP is to provide a management tool for food safety assurance at all stages, from production to consumption [1]. This tool is now standard worldwide for maintaining and enforcing food safety standards.

1 Conduct a hazard analysis
2 Identify critical control points
3 Establish critical limits for each control point
4 Establish critical control point monitoring requirements
5 Establish corrective actions
6 Establish corrective actions
7 Establish record keeping procedures

Table 1: The seven steps of HACCP principles [2].

Detection of foreign material and dangerous foodborne diseases is part and parcel of food production in accordance with HACCP hygiene management’s seven steps (see in Table 1).

It is through a combination of spatially-resolved simultaneous analysis, of multiple analytes in real-time, and a powerful ejection steering unit in-camera, that EVK has managed to develop an ideal in-line solution for food production line management – that is in accordance with HACCP guidelines.

EVK’s HELIOS cameras, when combined further with the proprietary software package SQALAR are capable of solving many of these HACCP defined tasks through the in-camera capabilities of detection and ejection steering. HELIOS is therefore suited to the HACCP guidelines in many respects, for instance: Principle 3 is essentially covered, as is principle 6 (through data statistics), and principles 4 and 5 are certainly covered – these are the monitoring and sorting tasks that HELIOS excels at.

 

 

 

The problem

A practical example from the food industry, which is relevant to HACCP principles, is the wooden breast syndrome in chicken meat production. Wooden breast defect is a growth defect of chicken breasts which, although not dangerous, is undesirable for the consumer. This is a problem that costs billions of euro every year for the chicken production industry to fix. This is usually done by using manual sorting lines to remove the defective breast and thereafter extract lesser cuts, to recuperate some profit margins.

The problem
Fig 1: Wooden Breast defect in different forms in ascending order, from 0% (left) to 90% (right).

The solution

EVK developed a solution for an industrial partner, using quantitative hyperspectral analysis to increase sorting specificity, that sorts in real-time and at the production line. This application example illustrates how EVK collaborates with partners, to develop sensor-based solutions for them that are tailored to their particular food safety, and process optimization, challenges.

Sensor-based quality assurance removes the need for manual intervention, and in addition, performs real-time selective removal of objects that do not meet quality criteria.

The detection of this defect requires a more sophisticated analysis approach than that used for dealing with simple, one-to-one, correlations of a measurement value to an observable. Hence, detection using colour cameras (see Figure 1), or using NIR simple spectra, is insufficient for sorting good, from bad quality, chicken breasts. The reason is that many observable chemometric differences, between normal and wooden chicken breasts, do not necessarily correlate with the actual defect. Many of the differences are natural variations that are not related to the product quality. In fact, the wooden defect is hard to define and is therefore actually done subjectively, by experienced workers, using haptic touch.

The detection of this defect requires a more sophisticated analysis approach than that used for dealing with simple, one-to-one, correlations of a measurement value to an observable. Hence, detection using colour cameras (see Figure 1), or using NIR simple spectra, is insufficient for sorting good, from bad quality, chicken breasts. The reason is that many observable chemometric differences, between normal and wooden chicken breasts, do not necessarily correlate with the actual defect. Many of the differences are natural variations that are not related to the product quality. In fact, the wooden defect is hard to define and is therefore actually done subjectively, by experienced workers, using haptic touch.

The optical sensor system used is a HELIOS NIR G2 CLASS near-infrared hyperspectral camera, with halogen lighting fitted, to monitor a conveyor belt. Data analysis and creation of a chemometric model are performed with the EVK SQUALAR software packet.

Fig. 2: Mass test of ten good product fillets (or two rows) and ten wooden breast fillets (and two rows).

Relevant parameters, for a which a spectral feature correlation to wooden breast defect was found, include: Hardness, moisture, protein content, calcium concentrations and manual visual assessment. These were measured separately and used as reference inputs for creating a chemometric model. Hardness was measured using a Durometer, moisture using microwave dehydration and weighing, and calcium and protein content were measured by an external food laboratory. Manual assessment was carried out at the production line by experienced personnel

Only with these parameters is it possible to solve this classification and sorting problem, in an industrially relevant manner. The logical combination of defect correlated features is what delivers the reliable and predictable performance, that is necessary for removing chicken wooden breasts from the production line.

An illustrative example of such a logical combination is shown in figure 2. In this test, ten bad and ten good examples of chicken breasts were inspected. Two quantitative parameters are represented using the two colour channels: red and green. The resulting colour hue in the chemical image shows the relative dependence, between the two parameters; with a greenish hue showing the location of the defect.

The final model that solves the detection problem is more complicated. Parts of the modelling are shown in figure 3, with the algorithm results shown in false colours and wooden breast defects shown in percentages.

 

 

 

 

Fig 3: SQALAR software used for quantitative analysis. Raw input images are shown (left) with several reference spectra (middle, top) that are used in combination with nominal values from manual assessment (middle, bottom), to create an output false colour image, showing the defects in percent (right).

Conclusion

The example discussed here is a solution that uses HSI analysis for quality inspection. It is a problem that cannot be solved with a straightforward application of sensors, including hyperspectral spectral cameras. Instead, the wooden breast defect is only solvable through the wealth of application experience accrued by EVK, and the quantitative chemical imaging processing it developed [3]. Today, this application example is performing successfully on a production line of a European chicken meat processor.

Authors: Dr. Eduard Gilli, Dr. Matthias Kerschhaggl, Alexander Fetz

 

Presentation of EVK specialist Christoph Riemer on this topic

Quantitative Chemical Imaging (QCI) in Industry

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