Separating Septicemic and Normal Chicken Livers by Visible/Near–infrared Spectroscopy and Back–propagation Neural Networks

Abstract

The visible/near-infrared spectra of 300 chicken livers were analyzed to explore the feasibility of using spectroscopy to separate septicemic livers from normal livers. Three strategies involving offset, second difference, and functional link methods were applied to preprocess the spectra, while principal component analysis (PCA) was utilized to reduce the input data dimensions. PCA scores were fed into a feed-forward back-propagation neural network for classification. The results showed no obvious difference in classification accuracy between offset and non-offset data when no other preprocessing method was applied. The full 400-2498 nm wavelength region produced better results than the 400-700 nm, 400-1098 nm, and 1102-2498 nm sub-regions when more than 30 PCA scores were used. In general, the classification accuracy was improved by increasing the number of scores of input data, but too many scores diminished performance. The functional link test showed that using functional-link spectra selected at every third point with 60 scores achieved the same classification accuracy as that obtained when using all the data points with 90 scores. The best classification model used offset correction followed by second difference (g = 31) and 60 scores. It achieved a classification accuracy of 98% for normal and 94% for septicemic livers.

Opinion of the Scientific Panel on biological hazards (BIOHAZ) on Revision of Meat Inspection for Beef raised in Integrated Production Systems

“…A number of instrumental techniques (computerised image analysis; machine vision) have been used to differentiate normal and abnormal appearance of carcasses/organs in post-mortem inspection of poultry (Watkins et al, 2000;Chao et al, 2002;Hsieh et al, 2002;Park and Chen, 2000;Van Hoof and Ectors, 2002). In pigs, instrumental/ automated methods have been used primarily for assessment of some meat quality-related parameters (e.g.…”

Estimation of Nitrogen Content by Spectral Responses of Cabbage Seedlings Using Artificial Neural Network Approach

“…The training scheme of ANN used the calibration sample set divided from all samples as the main objects to decrease error of the model and the remaining samples were as the validating objects to confirm the situation of over-fitting. Besides, the Savebest strategy (Hsieh et al, 2002) of ANN was also adopted. Therefore, the same sample sets of calibration and validation with SMLR were analyzed to determine the proper number of nodes in hidden layers and to confirm the ability of ANN for converging the error of nitrogen content prediction.…”

“…Recently, Hsieh et al, (2002) had developed an ANN classification model for carcass (94% accuracy), but a large number of wavelengths was required. With fewer parameters, Mutanga and Skidmore (2004) compared the ANN and SMLR to map the grass nitrogen concentration in an African savanna rangeland.…”

Combining multispectral reflectance and fluorescence imaging for identifying bruises and stem-end/calyx regions on Golden Delicious apples

“…However, it is a very helpful laboratory-based setup for selecting the efficient wavebands for a machine vision system, or the so-called multispectral imaging system, which can work as fast as required in practice. In the food quality assessment, the hyperspectral and multispectral imaging technique has been used for the inspection of poultry carcasses [11][12][13], defects detection or quality determination on apples and tomatoes [14][15][16][17][18]. In most of the hyperspectral imaging attempts for defects detection on apples, the samples were oriented manually to provide the most contrast between normal and abnormal areas.…”