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A CHICKEN CARCASS INSPECTION SYSTEM USING VISIBLE/NEAR‐INFRARED REFLECTANCE: IN‐PLANT TRIALS 1
Author(s) -
CHEN YUDREN,
HRUSCHKA WILLIAM R.,
EARLY HOWARD
Publication year - 2000
Publication title -
journal of food process engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.507
H-Index - 45
eISSN - 1745-4530
pISSN - 0145-8876
DOI - 10.1111/j.1745-4530.2000.tb00505.x
Subject(s) - reflectivity , instrumentation (computer programming) , principal component analysis , line (geometry) , poultry meat , near infrared reflectance spectroscopy , infrared , remote sensing , environmental science , analytical chemistry (journal) , mathematics , materials science , near infrared spectroscopy , optics , food science , biology , computer science , chemistry , statistics , chromatography , physics , geology , geometry , operating system
On‐line trials of an industrial prototype visible/near‐infrared spectrophotometer system developed by the Instrumentation and Sensing Laboratory for inspecting poultry for diseased and defective carcasses were conducted during an 8‐day period in a slaughter plant in New Holland, Pennsylvania. Spectra (470–960 nm) of 1174 normal and 576 abnormal (diseased and/or defective) chicken carcasses were measured. The instrument measured the spectra of veterinarian‐selected carcasses as they passed on a processing line at a speed of 70 birds per minute. Classification models using principal component analysis as a data pretreatment for input into neural networks were able to classify the carcasses from the spectral data with a success rate of 95%. Data from 3 days can predict the subsequent two days’chickens with high accuracy. This accuracy was consistent with the results obtained previously in off‐line studies. Thus, the method shows promise for separation of diseased and defective carcasses from wholesome carcasses in a partially automated inspection system. Details of the models using various training regimens are discussed.