## 6 Concluding Remarks  
With an ever-growing demand for consistent and high quality food, there is an increasing need for continued R&D of new and more effective sensing technologies for food quality assessment. Over the past decade, our group has developed several innovative hyperspectral imaging-based sensing techniques or platforms, including spectral scattering imaging, integrated reflectance and transmittance imaging, and spatially-resolved spectroscopy. While the three platforms share some common features in hardware, they also provide their unique capabilities for different applications in property and quality evaluation of horticultural and food products. Spectral scattering imaging, including both multispectral and hyperspectral, provides an effective means for assessing internal quality of fruit. This technique is particularly suited for firmness prediction because it enhances the measurement of scattering or structure-related properties. Scattering imaging operated in multispectral mode is relatively simple and easy to implement, and the laser-based multispectral scattering imaging prototype has shown potential for online sorting and grading of apples for firmness. Our integrated hyperspectral reflectance and transmittance imaging system can operate in single reflectance and transmittance modes or in the integrated mode. The integrated sensing mode provides unique capabilities for simultaneous inspection of external quality characters (i.e., size, color and blemishes) and internal defects, and hence can be advantageous in food quality inspection. The hyperspectral imaging-based spatially-resolved spectroscopic technique offers a new approach to optical characterization of food and biological tissues over a broad spectral region. The technique is faster and simpler to implement than time-resolved and frequency domain techniques, and it is also advantageous compared to the existing optic fiber-based spatially-resolved configurations, in terms of speed, ease of use and spatial resolution in data acquisition. The prototype OPA, developed based on hyperspectral imaging technique, provides a valuable tool for property and quality evaluation of horticultural and food products. While these techniques have been demonstrated in laboratory for their performance and utilities for horticultural and food products, further research, however, is needed to transform these techniques for practical use. Spectral scattering imaging relies on effective extraction of meaningful scattering and/or spectral features and an appropriate calibration model for accurate and reliable quality predictions. Further research is needed to explore new image processing methods to obtain superior results. To implement the technique for online sorting and grading of fruit, a fruit orientation device must be developed so that each fruit can be oriented properly for scattering imaging. Improvements in scattering imaging for SSC prediction should be made through better selection of wavelengths and development of a more effective calibration model. Moreover, sensor fusion should also be considered by integrating scattering technique with other proven techniques such as NIRS, for more robust and accurate evaluation of multiple quality attributes including firmness and SSC. While hyperspectral scattering imaging and integrated reflectance and transmittance imaging offer a new, promising approach to food quality evaluation, they are still facing the cost and speed issues in practical adoption. One possible solution is to implement the techniques in multispectral mode with the use of a few optimum wavelengths. Moreover, further improvements in both hardware including illumination, conveying and detection units, and software involving image analysis and model calibration algorithms, are also critical for achieving desired inspection speed and accuracy. The prototype OPA is designed for laboratory use, and much work is needed for fast, real-timemeasurement of horticultural and food products.