&lt;title&gt;Hyperspectral analysis of fecal contamination: a case study of poultry&lt;/title&gt;

Heitschmidt, Jerry W.; Lanoue, M.; Mao, Chengye; May, George

doi:10.1117/12.335772

Cited by 9 publications

(6 citation statements)

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“…Hyperspectral imaging technologies are also being utilized in medical (Harvey, Lawlor, McNaught, & Fletcher-Holmes, 2002;Levenson, Wachman, Niu, & Farkas, 1998) biological, agricultural, and industrial areas (Heitschmidt, Lanoue, Mao, & May, 1998;Lu & Chen, 1998;Lu, Chen, Park, & Choi, 1999;Willoughby, Folkman, & Figueroa, 1996). Furthermore, this great potential hyperspectral imaging technology has been applied for food quality and safety (Hahn, 2002;Lu & Chen, 1998;Lu et al, 1999).…”

Section: Introductionmentioning

confidence: 96%

Performance of hyperspectral imaging system for poultry surface fecal contaminant detection

Park

Lawrence

Windham

et al. 2006

Journal of Food Engineering

117

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Section: Introductionmentioning

confidence: 96%

Performance of hyperspectral imaging system for poultry surface fecal contaminant detection

Park

Lawrence

Windham

et al. 2006

Journal of Food Engineering

117

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“…Kim et al (2001) established a laboratory−based hyperspectral imaging system combining the features of imaging and visible/near−infrared (Vis/NIR) spectroscopy to simultaneously acquire spectral and spatial information for various food commodities, and Kim et al (2002aKim et al ( , 2002b used hyperspectral imaging methods for fecal contamination detection on apples. Heitschmidt (1998) used hyperspectral analysis for fecal contamination of poultry products, and Chen et al (1998), Park et al (2002), and Windham et al (2001) showed methods of using hyperspectral imaging for detecting contamination of chicken carcasses. Lu and Chen (1998) and Lu et al (1999) found that only two or three essential spectral bands were required for on−line imaging applications to extract unwholesome conditions in food products.…”

mentioning

confidence: 99%

A Novel Integrated Pca and FLD Method on Hyperspectral Image Feature Extraction for Cucumber Chilling Damage Inspection

Cheng¹,

Chen²,

Tao³

et al. 2004

Transactions of the ASAE

143

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High−resolution hyperspectral imaging (HSI) provides an abundance of spectral data for feature analysis in image processing. Usually, the amount of information contained in hyperspectral images is excessive and redundant, and data mining for waveband selection is needed. In applications such as fruit and vegetable defect inspections, effective spectral combination and data fusing methods are required in order to select a few optimal wavelengths without losing the crucial information in the original hyperspectral data. In this article, we present a novel method that combines principal component analysis (PCA) and Fisher's linear discriminant (FLD) method to show that the hybrid PCA−FLD method maximizes the representation and classification effects on the extracted new feature bands. The method is applied to the detection of chilling injury on cucumbers. Based on tests on different types of samples, results show that this new integrated PCA−FLD method outperforms the PCA and FLD methods when they are used separately for classifications. This method adds a new tool for the multivariate analysis of hyperspectral images and can be extended to other hyperspectral imaging applications for fruit and vegetable safety and quality inspections.

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“…In the agricultural side, hyperspectral imaging technologies were applied for grass characterization, sugar contents in melon, apple bruise detection, wheat scab detection, and poultry fecal detection (Heitschmidt et al, 1998;Schut et al, 2002;Tsuta et al, 2002;Lu et al, 1999;Delwiche and Kim, 2000;Kim et al, 2001;Mehl et al, 2001;Windham et al, 2002Windham et al, , 2003bLawrence et al, 2002Lawrence et al, , 2003Park et al, 2003aPark et al, , 2003b.…”

mentioning

confidence: 99%

Detection of Cecal Contaminants in Visceral Cavity of Broiler Carcasses Using Hyperspectral Imaging

Park¹,

Lawrence²,

Windham³

et al. 2005

Applied Engineering in Agriculture

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Detecting fecal contaminants in the visceral cavity of the broiler is difficult but extremely important for poultry safety inspection. A hyperspectral imaging system was used for detecting internal cecal contaminants on broiler carcass halves. Two 565-and 517-nm wavelength images were selected from 512 calibrated hypercube image data. Image processing algorithms including band ratio, threshold, and median filtering were used to identify fecal contaminants from the internal cavity. The accuracy of detection algorithms to identify cecal contaminants varied with fecal threshold values and median filter as well. The imaging system identified cecal contaminants with 92.5% detection accuracy but also incorrectly identified 123 carcass features that were not considered as contaminants (false positives) and missed 15 actual contaminants when a fecal threshold value of 1.05 was employed. The higher accuracy (96.9%) and lower missed contaminants were obtained when a different fecal threshold value was used. However, in this case, false positives markedly increased.

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<title>Hyperspectral analysis of fecal contamination: a case study of poultry</title>

Cited by 9 publications

References 0 publications

Performance of hyperspectral imaging system for poultry surface fecal contaminant detection

Performance of hyperspectral imaging system for poultry surface fecal contaminant detection

A Novel Integrated Pca and FLD Method on Hyperspectral Image Feature Extraction for Cucumber Chilling Damage Inspection

Detection of Cecal Contaminants in Visceral Cavity of Broiler Carcasses Using Hyperspectral Imaging

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