Detection of the Degree of Natural Mildew of Camellia oleifera Fruit Using Visible/Near Infrared, Mid- and Short-Wave Near Infrared Spectroscopy

This study explored the feasibility of applying visible and near-infrared (400‒1000 nm), and mid- and short-wave near infrared (900‒1700 nm) spectroscopy for detecting Camellia oleifera fruit with different degrees of natural mildew. The near infrared spectra of the equatorial shady and sunny sides, and the junction surfaces of samples with different degrees of mildew were collected in two wavelength bands. The average spectra were analyzed by principal component analysis (PCA), revealing that samples with different mildew degrees could be clustered into different groups, and the first and second principal components (PC1 and PC2) were effective in distinguishing the samples in each category. The full-spectrum partial least squares-discriminant analysis (PLS-DA) model based on the original spectra performed better than of its counterpart based on the preprocessed spectra. In the selection of characteristic wavelengths, successive projections algorithm (SPA) was found to be superior to PC loadings in establishing the simplified models for both spectral ranges. The correct classification rate and kappa coefficient were 84.4% and 0.7667 for the prediction set, respectively. Based on the observation of confusion matrices for the prediction set, the specificities of the two optimal simplified models for the prediction of each degree of mildew were equivalent to each other and above 0.84. However, mid- and short-wave near infrared spectra in the wavelength range of 900–1700 nm provided slightly higher sensitivity (0.72) in classifying samples with moderate degree of mildew. Our study showed that near-infrared spectroscopy could be used to detect the degree of natural mildew of C. oleifera fruit, and visible and near-infrared spectroscopy showed comparable results to mid- and short-wave near infrared spectroscopy. Considering its lower cost, visible and near-infrared spectroscopy has a better application prospect for real-time detection.