HighlightsIn the Emission Head (EH) configuration differences in apple bruise severity were well captured.A good representation of new samples variability, in calibration, ensured robust quantitative PLS-DA models.EH mode with PLS-DA is an attractive spectroscopic option for inline apple sorting based on bruise damage. Abstract. Bruise damage in apples is very common and undesirable because it hinders consumer satisfaction and greatly contributes to food loss. Fast detection of bruise damage in fruit using spectroscopic systems is still problematic, especially in terms of quantitative and objective assessments of mechanical damage and standardization of bruise measurement method, among other issues. Non-destructive techniques among which is near infrared (NIR) spectroscopy are under development as a potential solution carrier for such issues. A study of bruise damage was conducted on three apple cultivars using Fourier transform (FT) near infrared spectroscopy in two configurations (‘emission head’ of Bruker’s Matrix-F and ‘integrating sphere’ of Bruker’s multipurpose analyzer, MPA). The emission head (EH) allows for contactless large sample (100 mm) exposure that simulates on-line applications, while the MPA (sample size: 22 mm) is commonly used for in-laboratory analysis of inhomogeneous material such as fruit. Bruise damages were mechanically induced in apples, bruise sizes measured physically and destructively. Partial least squares discriminant analysis (PLS-DA) was used to determine the differences captured by the scanning spectrometers in apple fruit tissues. Discriminant analysis revealed that in both sample acquisition modes, distinction between bruised and non-bruised apple fruit tissue was achieved with high (from 78% to 93%) accuracy of classification (ACcl) based solely on spectral data. The classification accuracy improved when individual cultivars were considered and ranged from 94% to 96%. Classification models were tested for robustness and showed that both cultivar and bruise severity had influence on classification models’ performance. The results showed ability of the emission head configuration in detecting bruises and differentiating between severity of bruises in apple fruit, thus making it a good candidate for use in rapid detection and quantitative assessment of bruising in apple on sorting lines. Possibilities for improving the classification model performance and ensuring their robustness for the EH were suggested. Keywords: Apple bruise, Discriminant analysis, Model performance, Model threshold, NIR spectroscopy.
Bruise damage is a very commonly occurring defect in apple fruit which facilitates disease occurrence and spread, leads to fruit deterioration and can greatly contribute to postharvest loss. The detection of bruises at their earliest stage of development can be advantageous for screening purposes. An experiment to induce soft bruises in Golden Delicious apples was conducted by applying impact energy at different levels, which allowed to investigate the detectability of bruises at their latent stage. The existence of bruises that were rather invisible to the naked eye and to a digital camera was proven by reconstruction of hyperspectral images of bruised apples, based on effective wavelengths and data dimensionality reduced hyperspectrograms. Machine learning classifiers, namely ensemble subspace discriminant (ESD), k-nearest neighbors (KNN), support vector machine (SVM) and linear discriminant analysis (LDA) were used to build models for detecting bruises at their latent stage, to study the influence of time after bruise occurrence on detection performance and to model quantitative aspects of bruises (severity), spanning from latent to visible bruises. Over all classifiers, detection models had a higher performance than quantitative ones. Given its highest speed in prediction and high classification performance, SVM was rated most recommendable for detection tasks. However, ESD models had the highest classification accuracy in quantitative (>85%) models and were found to be relatively better suited for such a multiple category classification problem than the rest.
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