Nondestructive detection of apple defect combining optical fiber spectra with pattern recognition

In order to prove that the non-destructive detection of apple surface defect combining optical fiber spectroscopy with pattern recognition was effective, an optical fiber spectrum acquisition system was used to collect spectral data of apples with and without surface defect. Standard normal variation (SNV) and first derivative were used to preprocess the original spectral data. Principal component analysis (PCA) was used to reduce the dimension of the pre-processed spectral data to extract the characteristic spectra of apples with surface defect. By using k nearest neighbor (KNN) pattern recognition method and partial least squares discriminant analysis method, recognition model of apple defect was established. The results show that, the first eight principal components with cumulative contribution over 99% are selected as the characteristic spectral data of the sample set by using principal component analysis and the dimensionality reduction of spectral data is well realized. By using first order derivative+KNN recognition model for correction set and SNV+KNN recognition model for prediction concentration, the recognition rate of normal apples and defected apples is 96.0%. The feasibility of non-destructive detection of apple surface defect based on optical fiber spectroscopy and pattern recognition is verified.