Detection of pears with moldy core using online full-transmittance spectroscopy combined with supervised classifier comparison and variable optimization

Abstract

Moldy core is a serious disease that influences the quality of pears. There is no apparent difference between the diseased and sound fruit because this kind of disease mainly occurs in core of pears. In automatic detection and grading of pear quality, it is very desirable to detect pears with moldy core, especially at the early stage. This study used the visible and near infrared (Vis/NIR) full-transmittance spectroscopy, combined with wavelength selection algorithms and supervised classifiers, to discriminate 'Ya' pears with moldy core. The spectra of pears were collected using an online measurement system. Savitzky-Golay smoothing and standard normal variables (SGS-SNV) were used to preprocess the spectra. Four variable selection algorithms, including Monte Carlo-uninformative variable elimination (MC-UVE), bootstrapping soft shrinkage (BOSS), combination algorithm MC-UVE-SPA (successive projections algorithm), and combination algorithm BOSS-SPA, were used to extract the effective wavelengths. Four supervised classifiers, including support vector machine (SVM), least squares-support vector machine (LS-SVM), random forest (RF), and partial least square discriminant analysis (PLS-DA), were used for modeling. The two-class classification (i.e., sound and diseased) and the three-class classification (i.e., sound, slightly moldy, and severely moldy) models were established based on full wavelengths and selected wavelengths, respectively. The performance of all models was evaluated by considering some indicators such as 'Tolerability', ‘Stability’, ‘Accuracy’ and ‘Complexity’. It indicated that BOSS-SPA-PLS-DA and BOSS-SPA-LS-SVM were the optimal models for two-class and three-class classification with the overall accuracy of 99.76 % and 94.71 %, respectively. This study indicated that the online detection of 'Ya' pears with moldy core using the Vis/NIR full-transmittance spectra technology was feasible. Moreover, the proposed method has the potential to be used for online detection of early moldy core.