Browsing by Subject "Food kernel inspection"

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Open Access
Identification of damaged wheat kernels and cracked-shell hazelnuts with impact acoustics time-frequency patterns
(American Society of Agricultural and Biological Engineers, 2008) Ince, N. F.; Onaran, I.; Pearson, T.; Tewfik, A. H.; Çetin, A. Enis; Kalkan, H.; Yardimci, Y.
A new adaptive time-frequency (t-f) analysis and classification procedure is applied to impact acoustic signals for detecting hazelnuts with cracked shells and three types of damaged wheat kernels. Kernels were dropped onto a steel plate, and the resulting impact acoustic signals were recorded with a PC-based data acquisition system. These signals were segmented with a flexible local discriminant bases (F-LDB) procedure in the time-frequency plane to extract discriminative patterns between damaged and undamaged food kernels. The F-LDB procedure requires no prior knowledge of the relevant time or frequency indices of the impact acoustics signals for classification. The method automatically finds all crucial time-frequency indices from the training data by combining local cosine packet analysis and a frequency axis clustering approach, which supports individual time and frequency band adaptation. Discriminant features are extracted from the adaptively segmented acoustic signal, sorted according to a Fisher class separability criterion, post-processed by principal component analysis, and fed to a linear discriminant classifier. Experimental results establish the superior performance of the proposed approach when compared to prior techniques reported in the literature or used in the field. The new approach separated damaged wheat kernels (IDK, pupal, and scab) from undamaged wheat kernels with 96%, 82%, and 94% accuracy, respectively. It also separated cracked-shell hazelnuts from those with undamaged shells with 97.1% accuracy. The adaptation capability of the algorithm to the time-frequency patterns of signals makes it a universal method for food kernel inspection that can resist the impact acoustic variability between different kernel and damage types.
Open Access
Subset selection with structured dictionaries in classification
(EURASIP, 2007) İnce, N. F.; Göksu, F.; Tewfik, A. H.; Onaran, İbrahim; Çetin, A. Enis
This paper describes a new approach for the selection of discriminant time-frequency features for classification. Unlike previous approaches that use the individual discrimination power of expansion coefficients, the proposed approach selects a subset of features by implementing a classifier directed pruning of an initial redundant set of candidate features. The candidate features are calculated from a structured redundant time-frequency analysis of the signal, such as an undecimated wavelet transform. We show that the proposed approach has a performance that is as good as or better than traditional classification approaches while using a much smaller number of features. In particular, we provide experimental results to demonstrate the superior performance of the algorithm in the area of impact acoustic classification for food kernel inspection. The proposed algorithm achieved 91.8% and 98.5% classification accuracies in separating open shell from closed shell pistachio nuts and discriminating between empty and full hazelnuts respectively. Traditional methods used in this area resulted in 82% and 97% classification accuracies respectively.