Evaluation of a Non‐destructive High‐voltage Technique for the Detection of Pinhole Leaks in Flexible and Semi‐rigid Packages for Foods

Yue, Song; Gera, Mohit; Jain, B.K.; Koontz, John L.

doi:10.1002/pts.2040

Cited by 8 publications

(5 citation statements)

References 16 publications

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“…Barnes et al (2012) used polarized light stress analysis and laser scatter imaging for seal inspection in polymer trays, yet up to now, the application was limited to transparent packaging materials [14]. Song et al (2014) presented a methodology called "high voltage leak detection", which can only be used for the inspection of non-conductive packaging concepts containing liquid-based food products in contact with the packaging material [15]. A novel type of ultrasound inspection consists of sending a "ping" of ultrasound to a pack and observing the echo.…”

Section: Related Workmentioning

confidence: 99%

Active Infrared Thermography for Seal Contamination Detection in Heat-Sealed Food Packaging

D'huys¹,

Saeys²,

Ketelaere³

2016

J. Imaging

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Packaging protects food products from environmental influences, assuring quality and safety throughout shelf life if properly performed. Packaging quality depends on the quality of the packaging material and of the closure or seal. A common problem possibly jeopardizing seal quality is the presence of seal contamination, which can cause a decreased seal strength, an increased packaging failure risk and leak formation. Therefore, early detection and removal of seal contaminated packages from the production chain is crucial. In this work, a pulsed-type active thermography method using the heated seal bars as an excitation source was studied for detecting seal contamination. Thermal image sequences of contaminated seals were recorded shortly after sealing. The detection performances of six thermal image processing methods, based on a single frame, a fit of the cooling profiles, thermal signal reconstruction, pulsed phase thermography, principal component thermography and a matched filter, were compared. High resolution digital images served as a reference to quantify seal contamination, and processed thermal images were mapped to these references. The lowest detection limit (equivalent diameter 0.60 mm) was obtained for the method based on a fit of the cooling profiles. Moreover, the detection performance of this method did not depend strongly on the time after sealing at which recording of the thermal images was started, making it a robust and generally applicable method.

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Section: Related Workmentioning

confidence: 99%

Active Infrared Thermography for Seal Contamination Detection in Heat-Sealed Food Packaging

D'huys¹,

Saeys²,

Ketelaere³

2016

J. Imaging

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“…Yoon S. Song et al studied the important role the key variables play in the detection of tiny pinhole leakages in flexible bags and semi-rigid cups using high-voltage technology. They applied 0.25-10 kV to the packaging, and the results showed that the HVLD technology could detect defects of less than 10 µm [21]. Moll et al studied and verified the use of high voltage to detect the defects of blow-fill-seal containers with an accuracy of 100% [22].…”

Section: Introductionmentioning

confidence: 99%

Nondestructive Detection of Microcracks in Poultry Eggs Based on the Electrical Characteristics Model

et al. 2022

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The eggshell is the major source of protection for the inside of poultry eggs from microbial contamination. Timely detection of cracked eggs is the key to improving the edible rate of fresh eggs, hatching rate of breeding eggs and the quality of egg products. Different from traditional detection based on acoustics and vision, this paper proposes a nondestructive method of detection for eggshell cracks based on the egg electrical characteristics model, which combines static and dynamic electrical characteristics and designs a multi-layer flexible electrode that can closely fit the eggshell surface and a rotating mechanism that takes into account different sizes of eggs. The current signals of intact eggs and cracked eggs were collected under 1500 V of DC voltage, and their time domain features (TFs), frequency domain features (FFs) and wavelet features (WFs) were extracted. Machine learning algorithms such as support vector machine (SVM), linear discriminant analysis (LDA), decision tree (DT) and random forest (RF) were used for classification. The relationship between various features and classification algorithms was studied, and the effectiveness of the proposed method was verified. Finally, the method is proven to be universal and generalizable through an experiment on duck eggshell microcrack detection. The experimental results show that the proposed method can realize the detection of eggshell microcracks of less than 3 μm well, and the random forest model combining the three features mentioned above is proven to be the best, with a detection accuracy of cracked eggs and intact eggs over 99%. This nondestructive method can be employed online for egg microcrack inspection in industrial applications.

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“…Recent sensor developments for quantifying the size and distribution of holes in the material along the production line have been suggested in the literature. The sizes of holes in the coated paperboard can be estimated by correlating the amount of current leaking between cathodes positioned on both sides of the paperboard, which acts as a capacitor [ 7 ]. This method is considered non-destructive, but the high voltage applied by the method can increase the initial size of the pores in the packaging.…”

Section: Introductionmentioning

confidence: 99%

Paperboard Coating Detection Based on Full-Stokes Imaging Polarimetry

Martelo

Lundgren

Andersson

2020

Sensors

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The manufacturing of high-quality extruded low-density polyethylene (PE) paperboard intended for the food packaging industry relies on manual, intrusive, and destructive off-line inspection by the process operators to assess the overall quality and functionality of the product. Defects such as cracks, pinholes, and local thickness variations in the coating can occur at any location in the reel, affecting the sealable property of the product. To detect these defects locally, imaging systems must discriminate between the substrate and the coating. We propose an active full-Stokes imaging polarimetry for the classification of the PE-coated paperboard and its substrate (before applying the PE coating) from industrially manufactured samples. The optical system is based on vertically polarized illumination and a novel full-Stokes imaging polarimetry camera system. From the various parameters obtained by polarimetry measurements, we propose implementing feature selection based on the distance correlation statistical method and, subsequently, the implementation of a support vector machine algorithm that uses a nonlinear Gaussian kernel function. Our implementation achieves 99.74% classification accuracy. An imaging polarimetry system with high spatial resolution and pixel-wise metrological characteristics to provide polarization information, capable of material classification, can be used for in-process control of manufacturing coated paperboard.

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Evaluation of a Non‐destructive High‐voltage Technique for the Detection of Pinhole Leaks in Flexible and Semi‐rigid Packages for Foods

Cited by 8 publications

References 16 publications

Active Infrared Thermography for Seal Contamination Detection in Heat-Sealed Food Packaging

Active Infrared Thermography for Seal Contamination Detection in Heat-Sealed Food Packaging

Nondestructive Detection of Microcracks in Poultry Eggs Based on the Electrical Characteristics Model

Paperboard Coating Detection Based on Full-Stokes Imaging Polarimetry

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