|
|
|
| A Feasibility Study of Rapid Discrimination of Raw Meat and Adulterated Meat Based on Near-Infrared Spectroscopy and Artificial Neural Net Work Model |
| YANG Zhi-min;DING Wu |
| College of Food Science and Engineering, Northwest A & F University, Yangling 712100, China |
|
|
|
|
Abstract Feasibility of a nondestructive detection method based on near infrared reflectance (NIR) spectroscopy and
chemometrics was put forward for discriminating adulterated meat added with other materials and establishing the classifying
recognition model for adulterated meats. First, near-infrared combination of principal components and Fisher were set up to
discriminate raw meat and adulterated meat, and the value －0.657 for the weighted mean was set as the distinction threshold. The
result indicated that 2/20 samples was wrongly judged and the distinguishing rate was 90%. Then, near-infrared combined
principal components and MLP neural network were used to establish three layer neural network identification model for raw
meat and three types of adulterated meats, and the recognition rate was 94.2% in the model prediction set consisting of 52
samples. Accordingly, NIRS near-infrared method combined with chemometrics has the potential to detect adulteration in raw
meats and to recognize the adulteration categories.
|
|
|
|
|
|
|
|
|
|
|
2011, Vol. 25 
