Abstract
A non-destructive and novel in situ acoustic sensor approach based on the sound absorption spectra was developed for identifying and classifying different seed types. The absorption coefficient spectra were determined by using the impedance tube measurement method. Subsequently, a multivariate statistical analysis, i.e., principal component analysis (PCA), was performed as a way to generate a classification of the seeds based on the soft independent modelling of class analogy (SIMCA) method. The results show that the sound absorption coefficient spectra of different seed types present characteristic patterns which are highly dependent on seed size and shape. In general, seed particle size and sphericity were inversely related with the absorption coefficient. PCA presented reliable grouping capabilities within the diverse seed types, since the 95% of the total spectral variance was described by the first two principal components. Furthermore, the SIMCA classification model based on the absorption spectra achieved optimal results as 100% of the evaluation samples were correctly classified. This study contains the initial structuring of an innovative method that will present new possibilities in agriculture and industry for classifying and determining physical properties of seeds and other materials.
Highlights
IntroductionSeeds are traditionally manually identified for binning, but this practice is tedious, labour-consuming and imprecise [2]
The aim of this study is to develop a novel non-destructive and in situ sensor approach based on sound absorption phenomena for identifying and classifying different seed types by means of multivariate statistical analysis
These patterns diverge significantly when they are compared among different seed types, but they are very similar within samples of the same seed type
Summary
Seeds are traditionally manually identified for binning, but this practice is tedious, labour-consuming and imprecise [2]. More accurate methods, such as polyacrylamide gel electrophoresis, high performance liquid chromatography, protein electrophoretic and molecular marker, have been used for seed varietal identification [3,4,5] they are destructive, time consuming and relatively costly techniques [6]. To facilitate the automation of the process, rapid, in situ and non-destructive identification of seed type and variety is required as a way to unload and direct automatically the seeds to the correct receiving bin within the seed handling facility
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