Abstract

Rapid and reliable inspection of food is essential to ensure food safety, particularly in mass production and processing environments. Many studies have focused on spectral imaging for poultry inspection; however, no research has explored the use of multispectral fluorescence imaging (MFI) for on-line poultry inspection. In this study, the feasibility of MFI for on-line detection of fecal matter from the ceca, colon, duodenum, and small intestine of poultry carcasses was investigated for the first time. A multispectral line-scan fluorescence imaging system was integrated with a commercial poultry conveying system, and the images of chicken carcasses with fecal contaminants were scanned at processing line speeds of one, three, and five birds per second. To develop an optimal detection and classification algorithm to distinguish upper and lower feces-contaminated parts from skin, the principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were first performed using the spectral data of the selected regions, and then applied in spatial domain to visualize the feces-contaminated area based on binary images. Our results demonstrated that for the spectral data analysis, both the PCA and PLS-DA can distinguish the high and low feces-contaminated area from normal skin; however, the PCA analysis based on selected band ratio images (F630 nm/F600 nm) exhibited better visualization and discrimination of feces-contaminated area, compared with the PLS-DA-based developed chemical images. A color image analysis using histogram equalization, sharpening, median filter, and threshold value (1) demonstrated 78% accuracy. Thus, the MFI system can be developed utilizing the two band ratios for on-line implementation for the effective detection of fecal contamination on chicken carcasses.

Highlights

  • It is necessary to supplant the current manual inspection of fecal contamination on the surface of chicken carcasses with autonomous fecal contamination inspection system because human inspectionSensors 2019, 19, 3483; doi:10.3390/s19163483 www.mdpi.com/journal/sensorsSensors 2019, 19, 3483 has limitations when it comes to detecting diluted fecal contaminations

  • The resultant principal components (PCs) images were enhanced with post image processing methods, such as histogram equalization, median filter,to and sharpening

  • A fecal contaminant detection technique for poultry carcasses using online multispectral fluorescence images based on multivariate analysis technique and image processing algorithms was investigated

Read more

Summary

Introduction

It is necessary to supplant the current manual inspection of fecal contamination on the surface of chicken carcasses with autonomous fecal contamination inspection system because human inspectionSensors 2019, 19, 3483; doi:10.3390/s19163483 www.mdpi.com/journal/sensorsSensors 2019, 19, 3483 has limitations when it comes to detecting diluted fecal contaminations. Machine vision techniques based on image processing algorithms are used for classifying and sorting agricultural products via surface inspection [1,2,3,4,5,6]. These techniques are useful for detecting foreign matter on the surface of agricultural products based on visible wavelengths; the internal quality and the molecular analysis of such substances cannot be assessed based on visible wavelengths. Spectroscopic methods such as near-infrared spectroscopy (NIRS) provide rapid evaluation of the internal qualities of agricultural products based on the vibrational motions of organic molecules. Spectroscopic techniques have been applied for the evaluation of the internal quality of agricultural products such as species discrimination [7,8,9,10], nutrient analysis [11,12,13], and internal defect detection [3,14,15,16,17,18,19]

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call