## 4 Integrated Reflectance and Transmittance Imaging
### Quality Evaluation for Pickling Cucumbers and Pickles
The authors also observed that a spectral resolution between 20 and 40 nm was sufficient for defect detection of cucumber and pickles, which allowed using bandpass filters with a relatively large bandwidth for implementing these wavelengths in a multispectral imaging system. In another study using LEDs replacing broadband QTH lamps as illumination sources, [PERSON] et al. [87] acquired hyperspectral reflectance images (400-700 nm) by using two white LEDs, and transmittance images (700-1000 nm) by using four 830-nm and four 890-nm infrared LEDs, from 'Journey' pickling cucumbers at two conveyor speeds of 85 and 165 mm/s. Reflectance images below 700 nm carried surface or color features information, while transmittance images above 700 nm contained information about the internal features of the cucumbers (Figure 17). Minimum redundancy-maximum relevance (MRMR) was used to identify the best five two-band ratios in the spectral region of 700-950 nm. Discriminant analysis based on the Mahalanobis distance was used to develop classification models, and the two-band ratio of 887/837 nm gave the best accuracies of 95.1% and 94.2% at the conveyor speeds of 85 and 165 mm/s, respectively, for a two-class classification scheme. The classification performance, however, deteriorated when one more class was added, mainly due to the difficulty differentiating between slightly and severely defective classes. Pest/insect infestation can pose a serious quality and safety problem for the pickle processor, as many countries have zero tolerance for importing pest-infested pickled products. The infestation  
Figure 16: Example of normal (**top**) and defective (**bottom**) slices of fresh pickling cucumbers (**left**) and pickles (**right**). Reproduced with permission from [84], Copyright Elsevier, 2010. Figure 17: Hyperspectral images of normal and defective pickling cucumbers, where R and T represent reflectance and transmittance, respectively. Reproduced with permission from [87], Copyright Springer, 2014.  
is generally hidden in the product and cannot be easily detected from outside. [PERSON] and [PERSON] [86] reported on the detection of fruit fly infestation in pickling cucumbers (Figure 18) using hyperspectral reflectance and transmittance imaging. Three sensing modes, reflectance (450-740 nm), transmittance (740-1000 nm) and their combination were compared for differentiating infested cucumbers from normal ones by PLS discriminant analysis. The transmittance mode achieved the overall accuracies of 88%-93%, which were superior to the reflectance mode, while integration of the two sensing modes did not result in better detection accuracy compared to transmittance alone. Nevertheless, all the three sensing modes performed better than manual inspection. Chilling injury is a physiological disorder that occurs when fresh products are exposed to low or chilling temperatures during postharvest handling. Cucumbers are susceptible to chilling injury, which is characterized by surface lesions, pitting, internal discoloration and decay. [PERSON] et al. [88] used the hyperspectral imaging prototype for chilling injury detection of 'Excursion' pickling cucumbers. Affected cucumbers exhibited increased transmittance in the spectral region of 750-930 nm as chilling injury progressed, due to the occurrence of tissue collapse, translucent tissue and water-soaked spots. The authors explored different waveband selection methods and supervised classifiers, based on either spectral or image texture features, for chilling injury detection, and achieved the best overall detection accuracies of more than 90%.