Click on “Download PDF” for the PDF version or on the title for the HTML version.

If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.

A non-destructive detection system for determination of multi-quality parameters of meat

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2016 ASABE Annual International Meeting  162461187.(doi:10.13031/aim.20162461187)
Authors:   Wenxiu Wang, Yankun Peng, Xiaochun Zheng, Fang Tian, Wensong Wei
Keywords:   Non-destructive detection system, VIS/NIR spectroscopy, Pork, Quality

Abstract. Visible/near infrared (VIS/NIR) spectroscopy has emerged as a non-destructive, environmental friendly, and rapid analytical technique in recent years, which is capable for on-line detection of quality attributes in the food processing industry. Based on the VIS/NIR spectroscopy, a non-destructive meat detection system was developed for simultaneous detection of meat quality attributes such as water content, cooking loss and tenderness. The hardware part of the system was consisted of the light source unit, spectral acquisition unit, control unit and computer. The software unit was programmed by C language based on VS2010 platform in Windows 7 software environment. Reflectance spectra of pork samples in 350~1100 nm and 1000~2500 nm based on the system were collected to test the performance of the system. Among them, 42samples were used for the models establishment for moisture content, cooking loss and tenderness, respectively. Savitzky-Golay (S-G) filter and standard normal variables transform (SNVT) were applied to eliminate the spectral noise and improve the signal to noise ratio. Then partial least square regression (PLSR) was used to correlate the spectra with reference water content, cooking loss and tenderness value based on full wave band spectral. The detection results from the experiment showed that for each parameter, the prediction models could give satisfactory results with high correlation coefficients and low rootmeansquare error. This work sufficiently demonstrated the proposed system could realize simultaneous detection of pork quality parameters including water content, cooking loss and tenderness with high accuracy.

(Download PDF)    (Export to EndNotes)