Browsing by Author "Gayo, Javier"

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    Software Analysis Techniques For Odor Analysis and Classification Using the Electronic Nose
    (2002-08-19) Gayo, Javier; Dr. Peter L. Mente, Committee Member; Dr. S. Andrew Hale, Committee Co-Chair; Dr. Susan M. Blanchard, Committee Co-Chair
    The objectives of this thesis were to compare methods of feature extraction and data classification used in electronic nose. The NC State electronic nose (e-nose) was used to discriminate between SkipJack tuna (Katsuwonus pelamis) samples cooked at three temperatures: raw, heated to 55°C, and heated to 85°C. The thirty-six samples were analyzed by the e-nose on three separate days. The data were combined into one large set and randomly divided into a training (60%) and a testing (40%) set. The samples were labeled according to cooking treatment. Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) were used for feature extraction. Extracted features from the training and testing sets were used to achieve a classification percentage using Least Squares (LS) and K-Nearest Neighbor (KNN). Data from a bell integral were used to train a feed-forward Artificial Neural Network (ANN) with a backpropagation algorithm. LDA proved to be a better method of feature extraction than PCA. ANN performance was not statistically different from LS, and performed better than KNN, with PCA as feature extraction. Both KNN and LS using LDA as feature extraction outperformed the ANN and the same methods using PCA.
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    Species Authenticity and Detection of Economic Adulteration of Atlantic Blue Crab Meat Using VIS/NIR Spectroscopy
    (2006-08-06) Gayo, Javier; Dr. S. M. Blanchard, Committee Co-Chair; Dr. S. A. Hale, Committee Co-Chair
    The application of Visible and Near-Infrared (VIS/NIR) spectroscopy to determine economic adulteration of crabmeat was determined. Crabmeat samples were adulterated in 10% increments according to weight. The adulterants chosen were surimi-based imitation crabmeat, due to its low cost and availability, and blue swimmer crabmeat, the most prevalent type of crabmeat imported into the United States. Several data pre-treatments and different chemometric analyses, Partial Least Squares (PLS), Principal Component Regression (PCR), and Multiple Linear Regression (MLR), were investigated to determine the predictive ability of VIS/NIR spectroscopy in detecting economic adulteration and species authenticity. In addition, wavelength variables selected by a genetic algorithm were evaluated to improve predictive ability of economic adulteration of crabmeat. Absorption spectra of adulterated samples were dominated by water overtones. Absorption decreased for increasing level of adulteration. PLS was favored over PCR due to its predictive ability and lower number of latent variables used in model development. The first derivative data pre-treatment generated the best results, though similar results were gathered with the untreated data. First derivative data, using data from a correlogram, generated the best PLS model to detect economic adulteration of crabmeat adulterated with surimi-based imitation crabmeat. Economic adulteration percentage was predicted within 2.5%. The second derivative data generated the highest errors and, thus, was not deemed an appropriate pre-treatment method. For samples adulterated with blue swimmer, the first derivative data also generated the best results utilizing the full spectrum with a Standard Error of Calibration (SEC) and Standard Error of Prediction (SEP) of 5.64. Using a partitioned spectrum, however, the second derivative data performed better (SEC and SEP of 4.91 and 5.17, respectively). Regardless of the data pre-treatment, VIS/NIR spectroscopy was able to detect species authenticity and economic adulteration to less than 6% for samples adulterated with blue swimmer crabmeat. Variable selection via genetic algorithm enabled MLR to detect economic adulteration, making this the best overall method due to its low SEC and SEP (4.21 and 3.98, respectively). All of these findings provide a baseline for the design and development of a fast, reliable, and accurate technology for on-line detection of economic adulteration of crabmeat.