Detection of blood spots in eggs by hyperspectral transmittance imaging

Blood spots are one of undesired inclusions in eggs,whose detection success is highly dependent on shell color.This research reports a method for detecting blood spots in light brown-shelled eggs on the basis of hyperspectral transmittance images.The normalized spectra of intact eggs and their shells were acquired.Five feature wavelengths of intact eggs selected by the successive projections algorithm and 3 absorption peak locations of eggshells were regarded as characteristic bands.The k-nearest neighbor(kNN)and support vector machine(SVM)algorithms were adopted to develop detection models.The latter achieved better performance.The overall classification accuracy increased to 100% by merging normalized spectra of intact eggs at 5 feature wavelengths with 3 absorption peaks of eggshells as input variables of SVM-based model.Moreover,a practical SVM-based model with 96.43% overall classification accuracy was established by replacing inputs with normalized spectra of intact eggs at characteristic bands.

Detection of apple firmness with a novel loudspeaker-based excitation device

Firmness is one of the important indices to evaluate the internal quality of fruit.In this study,a noncontact loudspeaker-based detection system was developed to evaluate apple firmness.The structural parameters of the excitation device were modified in the single-factor experiments,and the best combination of structural parameters was that the inner diameter of the gasket was 40 mm;the distance between fruit surface and loudspeaker was 95 mm.Besides,the proper posture style was that the apple was placed with its stem upward.After the modification of the Laser Doppler Vibrometer(LDV)method,the vibration response signals of 48 apples were measured to establish the firmness prediction model.The results showed that the better prediction performance of stiffness was obtained in multiple models.The Back Propagation Neural Network(BPNN)model had the best prediction performance by using parameters of elasticity index(EI),the peak value at the second resonance frequency f_(2)(A_(2)),and peak area S,with a correlation coefficient of prediction(r_(p))of 0.914;root mean square error of prediction(RMSEP)of 0.491 N/mm.Therefore,the proposed detection system is feasible to nondestructively detect apple firmness,which has the potential to be applied in online detection.