## 1 Introduction  
Tomatoes are the most important fruit grown worldwide with approximately 170.8 million tons produced in 2014, which are widely consumed in either fresh or processed form [1]. They are considered healthy because they contain high levels of lycopene, which is a natural antioxidant, as well as \(\beta\)-carotene, vitamin C, and vitamin E [2]. Selecting quality tomatoes based on external appearances such as size, shape, color, lack of defects and decay, while monitoring internal qualities such as sweetness, acidity, moisture content (MC), and nutritional content, are important for the quality and safety of finished products [3; 4]. Most instrumental techniques used to measure these properties are destructive, involve a considerable amount of manual work, and provide information in just a limited section of fruit. Therefore, it is necessary to develop a nondestructive measurement technique to obtain information on these quality attributes from whole samples, which will be very useful for producers, processors and distributors to ascertain the quality of tomatoes.  
Hyperspectral imaging (HSI), a new advanced technique, provides both spectra and spatial information for samples by integrating the principles of spectroscopic and imaging technologies in a system [5; 6; 7; 8]. The hyperspectral image is a three-dimensional (3-D) cube, normally called a hypercube, in which the spectral information is provided for each pixel in the image. HSI can also provide more detailed or complete information, including internal structure characteristics, morphological information, and chemical composition of the sample, compared with a single-machine vision technology or spectroscopy analysis technology [9]. Now defined, the HSI technique has been applied to the nondestructive measurement of agricultural products such as determination of MC [4; 10; 11; 12; 13; 14], pH estimation [10; 15; 16; 17; 18], soluble solid content (SSC) prediction, [10; 11; 16; 18; 19; 20; 21] and so many other applications.  
A previous study by [PERSON] et al. [21] developed a nondestructive method to evaluate chemical contents such as total soluble solids, total chlorophyll, carotenoid and ascorbic acid content in intact bell peppers by using hyperspectral imaging in the visible and near-infrared (550-850 nm) region. In addition, [PERSON] et al. [4] showed an image processing-based algorithm to evaluate the potential of HSI combined with artificial neural networks to spatial mapping of MC in tomato fruits in the spectral range of 400-1000 nm. Another study by [PERSON] et al. [10] demonstrated that HSI in the visible and near-infrared (400-1000 nm) regions was tested for nondestructive determination of MC, SSC, and acidity (expressed as pH) in intact strawberries using the partial least squares (PLS) analysis, a multivariate calibration technique. However, to the best of our knowledge, up to now, there is no report on determining chemical components (MC, pH and SSC) of intact tomatoes using the hyperspectral imaging technique.  
Therefore, the overall objective of this study was to develop the HSI technique using PLS regression analysis for rapid and nondestructive measurement of MC, pH, and SSC in intact tomatoes. The specific objectives of this study were to acquire the hyperspectral images of intact tomatoes; secondly, to recognize the important region of interests (ROIs) and extract the corresponding spectral data within the acquired hyperspectral images; and, finally, to establish the quantitative analysis model using PLS regression and create chemical images by applying regression coefficients resulting from the best PLS regression model.