Multispectral Detection of Fecal Contamination on Apples Based on Hyperspectral Imagery: Part I. Application of Visible and Near–infrared Reflectance Imaging

Fecal contamination of apples is an important food safety issue. To develop automated methods to detect such contamination, a recently developed hyperspectral imaging system with a range of 450 to 851 nm was used to examine reflectance images of experimentally contaminated apples. Fresh feces from dairy cows were applied simultaneously as a thick patch and as a thin, transparent (not readily visible to the human eye), smear to four cultivars of apples (Red Delicious, Gala, Fuji, and Golden Delicious). To address differences in coloration due to environmental growth conditions, apples were selected to represent the range of green to red colorations. Hyperspectral images of the apples and fecal contamination sites were evaluated using principal component analysis with the goal of identifying two to four wavelengths that could potentially be used in an on–line multispectral imaging system. Results indicate that contamination could be identified using either three wavelengths in the green, red, and NIR regions, or using two wavelengths at the extremes of the NIR region under investigation. The three wavelengths in the visible and near–infrared regions offer the advantage that the acquired images could also be used commercially for color sorting. However, detection using the two NIR wavelengths was found to be less sensitive to variations in apple coloration. For both sets of wavelengths, thick contamination could easily be detected using a simple threshold unique to each cultivar. In contrast, results suggest that more computationally complex analyses, such as combining threshold detection with morphological filtering, would be necessary to detect thin contamination spots using reflectance imaging techniques.