HYPERSPECTRAL REFLECTANCE AND FLUORESCENCE LINE-SCAN IMAGING FOR ONLINE QUALITY AND SAFETY INSPECTION OF APPLES

Authors: Kim, Moon; Chen, Yud; CHO, BYOUNG-KWAN - CHUNGNAM NAT UNIV, KOREA; Chao, Kuanglin - Kevin Chao; Lefcourt, Alan; Chan, Diane

Submitted to: Sensing and Instrumentation for Food Quality and Safety
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/14/2007
Publication Date: 7/25/2007
Citation: Kim, M.S., Chen, Y.R., Cho, B., Chao, K., Lefcourt, A.M., Chan, D.E. 2007. Hyperspectral reflectance and fluorescence line-scan imaging for online quality and safety inspection of apples. Sensing and Instrumentation for Food Quality and Safety. 1(3):151-159.

Interpretive Summary: A recently developed hyperspectral online line-scan system integrated with a commercial apple-sorting machine was evaluated to detect fecal contamination and defects on apples at a processing line speed of over three apples per second. Results showed that fluorescence imaging (using a two-band ratio) could achieve 100% detection of fecal spots on artificially contaminated apples with a 100% detection rate and no false positives regardless of the presence of defects. An NIR two-band reflectance ratio coupled with a simple classification method based on the mean intensity and homogeneity of the ratio achieved 99.5% apple defect classification accuracy with a false positive rate of only 2%. The most significant and important outcome of this investigation is that a line-scan inspection system can potentially provide the capability for current sorting mechanisms, such as by size and color, in addition to additional sorting for quality and safety attributes of food products. Presented online inspection system and methodologies are useful to food scientists, engineers, regulatory government agencies (FSIS and FDA), and food processing industries.

Technical Abstract: The Instrumentation and Sensing Laboratory has recently developed a rapid online line-scan imaging system capable of both hyperspectral Vis/NIR reflectance and fluorescence in the Vis with UV-A excitation. The hyperspectral online line-scan system integrated with a commercial apple-sorting machine was evaluated to inspect apples for fecal contamination and defects at a processing line speed of over three apples per second. Results showed that fluorescence imaging (using a two-band ratio) could achieve 100% detection of fecal spots on artificially contaminated apples with 100% detection rate and no false positives regardless of the presence of defects. An NIR two-band reflectance ratio coupled with a simple classification method based on the mean intensity and homogeneity of the ratio achieved a 99.5% apple defect classification accuracy with a false positive rate of only 2%. The presented NIR processing regime overcame the presence of stem/calyx on apples that typically has been a problematic source for false-positives in the detection of defects. The most significant and important outcome of this investigation is a line-scan inspection system can potentially provide the capability for current sorting mechanisms, such as by size and color, in addition to further sorting for quality and safety attributes of food products. We believe that this line-scan based online imaging system offers great potential as a value-added dynamic inspection system due to its capability for multi-tasking to meet a variety of inspection objectives. A multi-tasking inspection system that can meet current industry sorting needs with the added benefit of safety inspection without requiring significant modification of existing infrastructure or incurring significant costs may lead the apple industry to consider adopting voluntary measures to further enhance safe production and processing of fruits.