## 5 Spatially-Resolved Spectroscopic Technique for Optical Property Measurement
### Algorithms and Instrumentation
#### 5.2.2 Instrument Development  
A bench-top prototype, named Optical Property Analyzer (OPA) (Figure 20), has been developed for optical property measurement by incorporating the inverse algorithms and optimal design parameters. The OPA has two light sources; one is used for optical property measurement and the other is the line light for general hyperspectral imaging applications. The following discussion is only focused on its optical property measurement functions. The hardware of the OPA, as shown in Figure 20a, consist of three major parts: imaging, illumination, and sample positioning. The imaging unit is composed of a high performance electron-multiplying CCD camera (Luca R604, Andor Technology, South Windsor, CT, USA), a line-scan imaging spectrograph (ImSpector V10E, Spectral Imaging Ltd., Oulu, Finland) covering the spectral range of 400-1000 nm, and a prime lens. The illumination unit uses a 20 W broadband QTH lamp as a light source, which is connected to a DC-regulated controller. The broadband light is delivered through an optical fiber that is coupled with a specially-designed focusing lens to generate a 1-mm diameter beam incident upon the sample at 15\({}^{\circ}\) from the vertical axis, with 1.5 mm offset distance between the incident point and the scanning line. The sample positioning unit includes a motorized horizontal stage, a manually adjustable vertical stage, and a sample holder for positioning samples to a specified position. During measurement, a sample is first moved to the specified height via the vertical stage, and it then starts to move horizontally, in synchronization with the imaging system. The software of the OPA is developed using Microsoft Visual C#, providing integrated functions for system control, image acquisition, real-time data processing and on-screen output display. Figure 21 shows the windows of parameter setup for image acquisition and optical property computation. The software offers two options of diffusion models, i.e., the single-layer Farrell model (Equation (14)) and single-layer Kienle model (Equation (15)), to calculate the spectra of \(\mu_{\text{a}}\) and \(\mu_{\text{s}}^{\prime}\) (The two-layer model, shown on the screen of Figure 21b, has not been activated). Prior to the computation of optical properties, several options for pre-processing raw spatially-resolved diffuse reflectance profiles (spatial profiles for short) are available in the software. Because of the symmetry of the spatial profiles with respect to the light incident point (Figure 22c), the spatial profiles from the left and right parts are usually averaged to enhance the SNR of the data. Furthermore, smoothing and correction for fruit geometry and non-uniform instrument responses can also be implemented. As shown in Figure 21b, once the parameters for pre-processing of spatial profiles, diffusion model, input and output options are all selected, the program will calculate the absorption and scattering spectra automatically. To improve the repeatability of measurements, the system would take 19 scans (the default setting) from each sample and the final optical property spectra are the average over the 19 measurements. Figure 21: (a) Schematic and (b) photo of the hyperspectral imaging-based optical property analyzer (OPA). Figure 20: (a) Schematic and (b) photo of the hyperspectral imaging-based optical property analyzer (OPA). [PERSON] and [PERSON] [104] evaluated the performance of the OPA in terms of accuracy, precision and sensitivity by using liquid model samples made up with different dyes and fat emulsion, whose optical properties were determined by three reference methods (i.e., transmittance, integrating sphere and empirical equation). The average estimated errors were 23% for \(\mu_{\mathrm{a}}\) over the spectral range of 530-850 nm and 7% for \(\mu_{\mathrm{s}}^{\prime}\) over the spectral range of 500-900 nm, when the OPA was evaluated against the transmittance and integrating sphere methods for \(\mu_{\mathrm{a}}\) and \(\mu_{\mathrm{s}}^{\prime}\), respectively.