## 1 Introduction  
Food products are evaluated or inspected for their appearance attributes, such as color, size or shape and absence of surface defects, and internal properties and characteristics like defects and eating quality that is defined by texture and flavor attributes [1; 2]. Traditionally, manual and/or destructive methods are commonly used for quality assessment of food products, and they are only limited to a small number of samples because the tests are destructive, slow, and costly or labor intensive. Food quality constitutes multiple attributes or characteristics, some of which are not well defined or can only be measured using empirical or destructive methods, such as the Magness-Taylor test for firmness measurement of horticultural products. While technologies for inspecting, sorting and grading surface characteristics of fruits and vegetables are now being widely used commercially, non-destructive evaluation of internal quality, such as texture and flavor, still presents great technical challenges. Today, consumers have a wide range of choices for food products, and they are demanding higher quality, more consistent and safer products at a reasonable price. Hence the agricultural and food industries are being faced with more challenging tasks of inspecting food products to meet the ever-increasing consumer demand, while maintaining or enhancing profitability in the face of intensive market competition. Rapid, objective inspection, sorting and grading of each product item is a critical step for ensuring superior, consistent quality of the final products. The past two decades have seen significant progress in the development of non-destructive sensing technologies for automated quality evaluation and safety inspection of agricultural and food products. Among the most successful technologies are near-infrared spectroscopy (NIRS) and machine vision [3,4]. In NIRS, the near-infrared (NIR) light covering the wavelength range of about 750-2500 nm is incident on the food product in the form of diffuse reflectance, transmittance or interactance, and the acquired spectra are then quantitatively related to the chemical and physical properties of the product. NIRS is easy to use, fast and non-destructive (or requiring minimal sample preparation), and hence has been widely used for property characterization, composition analysis, and quality and safety evaluation of food and agricultural products. However, NIRS is point measurement and is incapable of, or inadequate for, evaluating the spatial features of the object being detected. Moreover, NIR measurements are considered empirical (or black-box measurements) and indirect, because they only provide aggregate amount of information on the light reflected from or transmitted through the sample and the spectral measurements are dependent on the specific setting or configuration of the lighting source and detection probe. Hence more recently, researchers in the food and agriculture field have explored or developed new spectroscopic measurement techniques that are grounded on the well-established physical principles (light scattering and absorption) and optical theories (e.g., radiation transfer or diffusion theory) [5]. Although still in an early developmental stage, these new techniques, including spatially-resolved, spatial frequency domain, time-resolved and frequency domain, have the potential for providing more effective quality evaluation of horticultural and food products. Machine vision is conventionally referred to as a collection of the imaging methods that use broadband visible (VIS, 400-700 nm) light in monochromatic or color mode, and it simulates human vision to perform inspection tasks. Compared to NIRS, machine vision is capable of inspecting, grading and sorting horticultural and food products based on their spatial surface features such as shape, size and color [6,7], and the presence of surface defects [8,9]. The technology is now being widely used for quality inspection, sorting and grading of horticultural and food products.