A liquid crystal tunable filter based shortwave infrared spectral imaging system: Design and integration

Abstract

This paper presents the methodology to design and integrate a liquid crystal tunable filter (LCTF) based shortwave infrared (SWIR) spectral imaging system. The system consisted of an LCTF-based SWIR spectral imager, an illumination unit, a frame grabber, and a computer with the data acquisition software. The spectral imager included an InGaAs camera (320 × 256 pixels), an SWIR lens (50 mm, F/1.4), and an LCTF (20 mm aperture). Four multifaceted reflector halogen lamps (35 W, 12 VDC) were used to build the illumination unit. The system was integrated by a LabVIEW program for data acquisition. It can capture hyperspectral or multispectral images of the test object in the spectral range of 900–1700 nm. The system was validated by differentiating sugar from wheat flour, and water from 95% ethanol. The results showed that the system can distinguish these materials in both spectral and spatial domains. This SWIR spectral imaging system could be a potential useful tool for nondestructive inspection of food quality and safety.

Introduction

Spectral imaging (SI) is a rapidly growing area for nondestructive inspections of food safety and quality. It often refers to hyperspectral imaging (HSI), which acquires images at hundreds of contiguous spectral bands. Spectral imaging that uses a small number of discrete spectral bands (<10) is often called multispectral imaging (MSI) (Lu and Park, 2008). In the last decade, SI has gained recognition for food safety and quality inspections due to its nondestructive inspection capabilities. It captures images of a test object at a number of narrow bandpass wavelengths. As a result, spectral images contain a wealth of spatial and spectral information of the test object, which can be used to investigate the intrinsic physical/chemical properties of the test object (Lu and Chen, 1998). The sensing capabilities of SI have been demonstrated by numerous applications reported in a broad range of food and agricultural products such as poultry (Park et al., 2010), beef (Naganathan et al., 2008), wheat (Singh et al., 2010), apple (ElMasry et al., 2008, Kim et al., 2008), citrus (Qin et al., 2009), and cucumber (Ariana and Lu, 2009).

Two technologies are commonly used by SI systems for food quality and safety inspection (Gowen et al., 2007): (i) imaging spectrograph based technology which disperses light with wavelengths across the CCD detector using a prism-grating-prism element resulting in a 3-D image cube obtained by line scanning; and (ii) electronically tunable filter (ETF) based technology which selects wavelength bands, takes spatial 2-D images one wavelength at a time, and then reconstructs the data into a 3-D image cube. Liquid crystal tunable filters (LCTF) and acousto-optical tunable filters (AOTF) are the two most commonly used ETFs. The LCTF-based HSI systems are relatively slower than the systems using AOTFs, but the former have better imaging performance since the LCTFs have larger apertures, relatively wider field of view, and lower wavefront distortions than the AOTFs (Evans et al., 1998).

In hyperspectral imaging, the line-scan technique has been predominantly used in food safety and quality inspection due to its high speed and high spectral resolution. In addition, compared to the ETF-based HSI technique, the line-scan technique is easier to integrate with the current online inspection systems using conveyor belts. Nevertheless, the ETF-based HSI system has its own advantages over the line-scan system. First, an ETF-based hyperspectral imaging system is a natural extension of a multispectral imaging system, which provides versatility to the system to be used for either HSI or MSI applications. Second, an ETF-based HSI system has an area of field of view (FOV), whereas the line-scan HSI systems can only see one line of the test object at a time (Kerekes and Schott, 2007). Third, the ETF-based HSI systems can select spectral bands randomly in addition to continuously (Gat, 2000). Thus, the ETF-based HSI systems have the advantage in instantaneous imaging applications requiring selective spectral information. Fourth, the parameter setting of an ETF-based SI system, such as the exposure time of the camera, is often dynamic and adjustable over each spectral band in a scan, while a line-scan system often has to keep its parameter setting constant during scanning. Another important advantage of the ETF-based HSI systems is that they do not rely on moving mechanical devices such as a linear conveyer module (Sun, 2010). Therefore, they are more compact, can be easily integrated with other applications, and have higher potential for field application.

Developing an ETF-based spectral imaging system requires many special considerations on system design, integration, and calibration. For instance, the low optical throughput of the LCTF requires special considerations with the selection of the lens and the design of the lighting. Gat (2000) summarized the fundamental principles and considerations of integrating an spectral imaging system using an ETF, with an example of integrating an LCTF-based HSI system in the range of 400–1000 nm. However, the paper did not discuss detailed methodologies for designing and calibrating the ETF-based spectral imaging system. Gebhart et al. (2007) quantitatively characterized their LCTF-based fluorescence and diffuse reflectance imaging system (400–720 nm) for detecting brain tumor demarcation. But methods and processes for designing and calibrating the system were less discussed in the article.

In food safety and quality inspection, generally, the ETF-based HSI systems have not been fully studied and used. In early applications, Evans et al. (1998) demonstrated an LCTF-based spectral imaging system (650–1050 nm) for studying plant health. Archibald et al. (1999) used a similar system to differentiate color class of wheat kernels. Later, Cogdill et al. (2004) reported an LCTF-based HSI system for predicting the concentrations of maize kernels’ constituents by using the hyperspectral transmittance imaging. Gomez-Sanchis et al. (2008) used an LCTF-based HSI system to detect the rottenness in mandarins in the spectral range of 460–1020 nm. Recently, a number of LCTF-based HSI applications in shortwave infrared (SWIR) range were reported. Williams et al. (2009) evaluated the hardness of the maize kernels by using both an LCTF-based (960–1662 nm) and a line-scan (1000–2498 nm) HSI systems. Similar LCTF-based SWIR spectral imaging systems (960–1700 nm) were applied to differentiate Canadian wheat classes (Mahesh et al., 2008) and to identify insect-damaged wheat kernels (Singh et al., 2009). However, most of the reported studies focused on presenting their applications, whereas the design and integration of their systems were seldom discussed.

This article demonstrates the methodology of designing an LCTF-based spectral imaging system in the spectral range of 900–1700 nm. The specific objectives were to: (1) present the methodology used in the design and integration of an LCTF-based SWIR spectral imaging system; (2) validate the system by differentiating four different food materials.