VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits

• N-way PLS-DA applied on features extracted from hyperspectral images is a powerful tool for citrus fruit discrimination.
• A double cross-validation strategy is used to validate the discriminant models.
• Permutation testing on VIP values is used to select the most discriminant spectral channels.
• Multispectral cameras capturing few wavelengths are able to discriminate infected citrus fruits.

In this work an N-way partial least squares regression discriminant analysis (NPLS-DA) methodology is developed to detect symptoms of disease caused by Penicillium digitatum in citrus fruits (green mould) using visible/near infrared (VIS/NIR) hyperspectral images. To build the discriminant model a set of oranges and mandarins was infected by the fungus and another set was infiltrated just with water for control purposes. A double cross-validation strategy is used to validate the discriminant models. Finally, permutation testing is used to select a few bands offering the best correct classification rates in the validation set. The discriminant models developed here can be potentially implemented in a fruit packinghouse to detect infected citrus fruits at their arrival from the field with affordable multispectral (3–5 channels) cameras installed in the packinglines.