Detection of Fecal Contamination on Cantaloupes Using Hyperspectral Fluorescence Imagery

Vargas, A.; Kim, Moon S.; Tao, Yang; Lefcourt, Alan M.; Chen, Yud-Ren; Luo, Yaguang; Song, Yoonseok; Buchanan, Robert L.

doi:10.1111/j.1365-2621.2005.tb11517.x

Cited by 88 publications

(44 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, reflectance-based quality control systems are becoming increasingly more versatile, robust and more affordable. Pesticide and contaminant (i.e., fecal) residues and surface defects on vegetables and fruits [1][2][3][4] and meat [5] are of particular interest due to recent concerns about outbreaks of food borne illnesses [3]. Thus, among farmers, crop consultants, extension services, food processors, food distributors, and agricultural inspection entities there is a need for real-time tools to detect and quantify pesticides and other surface residues on food and feed materials.…”

Section: Introductionmentioning

confidence: 99%

“…Standard analytical approaches used in analysis of reflectance data include (see [9] for review): discriminant analysis [10,11,26], principal component analysis [3,21,[26][27][28], multi-regression approaches, like partial least square (PLS) [12,29,30], use of spectral band ratios (indices) [3,[31][32][33], decision trees [34], artificial neural networks [17,28,34], and support vector machines [8,35]. One common denominator in all of these analytical approaches is that they do not incorporate-or take advantage of -the spatial information available in a HI data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Spatial Structure Analysis of Hyperspectral Imaging Data and Fourier Transformed Infrared Analysis to Determine Bioactivity of Surface Pesticide Treatment

et al. 2010

View full text Add to dashboard Cite

Many food products are subjected to quality control analyses for detection of surface residue/contaminants, and there is a trend of requiring more and more documentation and reporting by farmers regarding their use of pesticides. Recent outbreaks of food borne illnesses have been a major contributor to this trend. With a growing need for food safety measures and -smart applications‖ of insecticides, it is important to develop methods for rapid and accurate assessments of surface residues on food and feed items. As a model system, we investigated detection of a miticide applied to maize leaves and its miticidal bioactivity over time, and we compared two types of reflectance data: fourier transformed infrared (FTIR) data and hyperspectral imaging (HI) data. The miticide (bifenazate) was applied at a commercial field rate to maize leaves in the field, with or without application of a surfactant, and with or without application of a simulated -rain event‖. In addition, we collected FTIR and HI from untreated control leaves (total of five treatments). Maize leaf data were collected at seven time intervals from 0 to 48 hours after application. FTIR data were analyzed using conventional analysis of variance of miticide-specific vibration peaks. Two unique FTIR vibration peaks were associated with miticide application (1,700 cm −1 and 763 cm −1 ). The integrated intensities of these two OPEN ACCESSRemote Sensing 2010, 2 909 peaks, miticide application, surfactant, rain event, time between miticide application, and rain event were used as explanatory variables in a linear multi-regression fit to spider mite mortality. The same linear multi-regression approach was applied to variogram parameters derived from HI data in five selected spectral bands (664, 683, 706, 740, and 747 nm). For each spectral band, we conducted a spatial structure analysis, and the three standard variogram parameters (-sill‖, -range‖, and -nugget‖) were examined as possible -indicators‖ of miticide bioactivity. We demonstrated that both FTIR peaks and standard variogram parameters could be used to accurately predict spider mite mortality, but linear multi-regression fits based on standard variogram parameters had the highest accuracy and were successfully validated with independent data. Based on experimental manipulation of HI data, the use of spatial structure analysis in classification of HI data was discussed.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Spatial Structure Analysis of Hyperspectral Imaging Data and Fourier Transformed Infrared Analysis to Determine Bioactivity of Surface Pesticide Treatment

et al. 2010

View full text Add to dashboard Cite

show abstract

“…In 2005, Kim's research group probed the feasibility of hyperspectral fluorescence images in detecting fecal spots on cantaloupes that were artificially contaminated with bovine feces at varying concentrations (Vargas et al, 2005). To improve the detection algorithms, they presented several image processing tactics (such as single-band and two-band ratio images) and found the potential of the PCA processing of hyperspectral images in the detection of fecal contaminated spots (a minimum of 16-μg/mL dry fecal matter) on cantaloupes with minimal false positives.…”

Section: Fecal Contaminant Detection On Cantaloupesmentioning

confidence: 99%

Principal Component Analysis in the Development of Optical and Imaging Spectroscopic Inspections for Agricultural / Food Safety and Quality

Liu¹

2012

Principal Component Analysis - Engineering Applications

View full text Add to dashboard Cite

“…Hyperspectral imaging technology has been used widely in detection of food defects (Kong et al, 2004), assessment of food quality (Ariana and Renfu, 2010;Fontaine et al, 2002;Lu and Peng, 2006;Martens and Martens, 1986;Okamoto and Lee, 2009), classification of near-isogenic crop genotypes (Nansen et al, 2008), and in detection of surface contaminations (Lefcout and Kim, 2006;Mehl et al, 2004;Park et al, 2006;Vargas et al, 2005). Comprehensive reviews of the use of imaging technology in detection of traits in food products have been published (Gowen et al, 2007;Wang and Paliwal, 2007).…”

Section: Introductionmentioning

confidence: 99%

Use of variogram analysis to classify field peas with and without internal defects caused by weevil infestation

Nansen

Zhang

Aryamanesh

et al. 2014

Journal of Food Engineering

View full text Add to dashboard Cite

In this study, we acquired 72 (training data) and 30 (independent validation) high-spatial resolution (7 by 7 pixels per mm 2 ) hyperspectral imaging data [240 spectral bands from 392 to 889 nm (spectral resolution = 2.1 nm)] from samples of field peas (Pisum sativum) with and without pea weevil (Bruchus pisorum) infestation. The reflectance data were analyzed with linear discriminant analysis (LDA) or either reflectance values only or of a combination of reflectance values and variogram parameters (derived from variogram analysis) from a single spectral band (782 nm). All examined classification models were assessed based on sensitivity (ability to positively detect infestation), specificity (ability to positively detect noninfestation), and accurate classification of 30 samples of independent validation data. Highest classification performance was obtained with a combination of reflectance values in two spectral bands (641 nm and 868 nm) and variogram parameters derived from 782 nm. This classification model was associated with a sensitivity of 94.7% and a specificity of 100%. In addition, all 30 independent validation data were accurately classified (100%). For comparison, traditional linear discriminant analyses of 108,351 reflectance profiles from individual pixels in the 72 samples or average reflectance profiles from the 72 samples classified the validation data with 84.0% and 83.3% accuracy, respectively. We are unaware of any reflectance-based classification system, which -based on reflectance data acquired in only three channels (spectral bands) -can provide this level of sensitivity and specificity and classification of independent validation data of internal defects in food products. Accurate and reliable classification of food objects based reflectance values in only a few spectral bands would likely imply low computer processing requirements and rapid data analysis. Thus, we believe that the current classification method may be useful for quality control systems of a wide range of food products.

show abstract

Detection of Fecal Contamination on Cantaloupes Using Hyperspectral Fluorescence Imagery

Abstract: A A M. M. M. M. M. V , , AND tion of fecal contamination on cantaloupes.

Cited by 88 publications

References 8 publications

Using Spatial Structure Analysis of Hyperspectral Imaging Data and Fourier Transformed Infrared Analysis to Determine Bioactivity of Surface Pesticide Treatment

Using Spatial Structure Analysis of Hyperspectral Imaging Data and Fourier Transformed Infrared Analysis to Determine Bioactivity of Surface Pesticide Treatment

Principal Component Analysis in the Development of Optical and Imaging Spectroscopic Inspections for Agricultural / Food Safety and Quality

Use of variogram analysis to classify field peas with and without internal defects caused by weevil infestation

Contact Info

Product

Resources

About