Rapid quantitative typing spectra model for distinguishing sweet and bitter apricot kernels

Amygdalin content in apricot kernels is an essential factor in the rapid and nondestructive identification of sweet or bitter apricot kernels through spectroscopy. Now, amygdalin content has been determined by high-performance liquid chromatography and near-infrared spectral database to construct a model so that the sweet or bitter apricot kernels could be identified and classified. Principal component analysis-K-nearest neighbor classification algorithm combined with multivariate scattering correction pretreatment method could distinguish sweet and bitter apricot kernels in the wavelength range of 1650-1740 nm with 98.3% accuracy and apricot kernel species with 96.3% recognition rate in the full wavelength spectrum. Furthermore, prediction of amygdalin content in bitter and sweet apricot kernels by partial least squares model was superior to that by back-propagation neural network model. This study provides a theoretical basis for quality identification of apricot kernel quality, as well as a method for nondestructive and rapid detection of sweet and bitter apricot kernels.

Automated summary

This study uses spectroscopy to rapidly identify sweet and bitter apricot kernels by determining the amygdalin content. A model has been established to classify and differentiate sweet and bitter apricot kernels. The combination of principal component analysis-K-nearest neighbor classification algorithm and multivariate scattering correction pretreatment method can accurately distinguish sweet and bitter apricot kernels in a specific wavelength range, as well as identify apricot kernel species in the full wavelength spectrum. Furthermore, the prediction of amygdalin content using the partial least squares model is superior to the back-propagation neural network model.