## 4 Integrated Reflectance and Transmittance Imaging
### Quality Evaluation for Pickling Cucumbers and Pickles  
In 2015, pickling cucumber production in the U.S. was valued at nearly $173 million; the state of Michigan accounted for 32% of the total pickling cucumber tonnage [89]. Pickling cucumbers are primarily used for processing or pickling, and they are short and blocky, with thin skins and usually a color gradient from dark green at the stem end to light green at the blossom end [90]. Internal defects often occur in pickling cucumbers in the form of soft or watery carpel tissue, loose seeds and split or hollow center, due to physiological disorders and mechanical injuries [91], which downgrade the quality of processed products and cause economic loss for the pickling industry. Inspection and sorting of pickling cucumbers and pickles is needed for internal defects, together with external quality such as size, color and surface defects. The hyperspectral reflectance and transmittance imaging prototype was tested and evaluated for detection of internal defects and prediction of firmness and color of 'Journey' pickling cucumbers [28]. Mean and SD spectra were calculated from the spectra of all the pixels in the scan line, and used for model calibration. As shown in Figure 15, the reflectance spectra of normal and defective cucumbers, in the wavelength region of 500-675 nm, were strongly overlapped; while the transmittance of the defective cucumbers was consistently higher than that of normal cucumbers in the region of 675-1000 nm, suggesting the possibility of segregating defective cucumbers from normal ones by using the transmittance spectra. The difference in transmittance between normal and defective cucumbers is mainly attributed to the formation of water-soaked lesion, the tissue separation, or even the presence of hollow center in the defective cucumbers, which decrease the light scattering ability and thus allow more light to pass through. The use of the transmittance spectra combined with PLS discriminant analysis, achieved accuracies up to 99% for the detection of internal defects. The reflectance in the visible region, modelled by PLS, gave better predictions for skin color, with \(r^{2}\) up to 0.76 and 0.75 for skin chroma and hue, respectively. [PERSON] and [PERSON] [83] further investigated PCA for processing hyperspectral images for detection of internal defects in desalted whole pickles. The second principal component (PC) score images exhibited the strongest contrast between normal and defective pickles, which achieved an overall classification accuracy of 86% by a simple global thresholding method. The efficacy of the PC score images, however, remained to be validated through supervised classifiers. Identifying effective wavelengths is critical for implementation in a multispectral imaging mode, which greatly reduces the time for image acquisition and can also improves the detection performance. [PERSON] and [PERSON] [84] acquired hyperspectral reflectance (500-740 nm) and transmittance (740-1000 nm) images from normal and defective cucumbers and pickles (Figure 16). A branch and bound algorithm combined with the \(k\)-nearest neighbor (K-NN) classifier was utilized for wavelength selection. Two four-wavelength sets  
Figure 15: Typical reflectance (500–675 nm) and transmittance (675–1000 nm) spectra of normal and defective cucumbers. SD in the figure represents the standard deviation. Reproduced with permission from [28], Copyright Springer, 2008.  
of 745, 805, 965 and 985 nm, and 745, 765, 885 and 965 nm were obtained, both of which were located in the NIR region and achieved the best detection rates of 94.7% and 82.9% for cucumbers and pickles, respectively. The accuracies were comparable to those obtained by using the entire set of wavelengths combined with PLS discriminant analysis.