1. Introduction
Lateral flow immunoassay, also known as immunochromatographic (ICG) assay, has been reported on a great deal for its several benefits—high sensitivity, ease of operation, low budget, etc. [
1,
2,
3,
4]. It utilizes antigen and antibody properties for the rapid detection of an analyte. Among the diverse labels of the antibody, colloidal gold particles are widely used [
2,
5,
6,
7,
8]. Accordingly, colloidal gold-based ICG assay has been demonstrated to be potentially useful for medical diagnosis and detection of food safety, pathogen, drugs, environment, etc. [
5,
8,
9,
10,
11,
12,
13,
14]. Therefore, more and more research focuses on the applications of colloidal gold-based ICG assay from qualitative or semi–qualitative detection with the naked eye for precise quantitative detection.
Based on the operating principles and the concerned hardware of the ICG assay detection system, they can be categorized into two groups, which are image processing detection systems and photoelectric detection systems, respectively. Image processing detection systems use an image capture unit (camera or image scanner) to obtain an image of the whole test strip and performs the specific image processing algorithm to achieve the detection results. For example, Chia-Hsien et al. presents an optical inspection system based on the Taguchi method, which can achieve better linearity and decrease the standard deviation [
15]. In photoelectric detection systems, a moving unit driven by a driving motor is used to scan the test strip and a photodiode is employed for photoelectric conversion. The scans can be performed very rapidly and it gets a 1-D signal along the scanning axis which results in a lower computational burden. In this context, several studies have been reported on photoelectric detection systems for ICG assays. For example, Gu et al. developed a portable fluorescence reader for the determination of C-reactive protein, which has a good sensitivity of 0.1 mg/L and linear dynamic range extended to 400 mg/L [
16]. Yan et al. reports an ICG assay-based biosensor for rapid quantitative detection of
Yersinia pestis [
17]. Obviously, the existing research mainly focus on increasing the dynamic detection range or application fields. However, calibration of the detection system, which has a great influence on the detection accuracy, has not been addressed properly. Therefore, this work develops a printed calibration strip for the calibration of an ICG assay-based photoelectric detection system.
Optical density (OD) value indicates the amount of light absorbed by a solution of organic molecules on the test strip measured by a spectrophotometer, which can be used to estimate the concentration of the colloidal gold particles on the test strip. Therefore, this work was based on analyzing features of the test strip by performing an image processing algorithm, which seeks the relation of the OD value and characteristic quantity of the test strip image. According to the obtained color information of hue (H), saturation (S), and value (V) of the test strips, the calibration strip is printed. Further, a photoelectric detection system tests the printed calibration strip for verification. The general steps of this work are described as follows: firstly, an image of ICG assay test strip is captured by an image acquisition device, followed by noise reduction using mean and median filters. Then, a fuzzy c-means (FCM) clustering algorithm and maximin-distance algorithm are proposed for image processing in the HSV color space, which extracts a test line of the strip image. In addition, experiments with different HCG solutions and different detection times are conducted to find the best characteristic quantity. By analyzing the linear coefficient, an average value of H at 14 min is chosen as the characteristic quantity for the calibration test strip and the empirical formula between H and OD values is obtained. Therefore, H is predicted by a number of selected OD values and S and V are calculated. Then, H, S, and V are transferred to the RGB color space and a high-resolution printer is used to print the RGB image of the test strip on cellulose nitrate membranes. Finally, verification of these printed calibration strips is performed by analyzing the linear correlation between OD and the spectral reflectance of the printed calibration strips.
The rest of this paper is organized as follows:
Section 2 introduces the quantitative detection system followed by methodology in
Section 3.
Section 4 presents the experimental results and discussions. Finally, the conclusions are drawn in
Section 5.
5. Conclusions
This work develops a calibration strip for immunochromatographic (ICG) assay photoelectric detection systems. An image of the test strip is captured by an image acquisition device. Mean and median filters are used to reduce noise in the acquired image. Without insufficient or over segmentation, the proposed FCM algorithm and maximin-distance algorithm has a good performance on extraction of the test line. Further, experiments with different HCG solution and different detection times are conducted to find the best characteristic quantity, which indicates that the average value of H at 14 min has the best linear coefficient with a concentration of HCG (95.24%) and OD value (98.12%). Therefore, the empirical formula between H and OD (optical density) values is established, by which H, S, and V are calculated. Additionally, H, S, and V values are transferred to the RGB color space and a high-resolution printer is used to print the RGB images on cellulose nitrate membranes. Finally, OD and the spectral reflectance of the printed calibration strips are analyzed with a good linear correlation (R2 = 98.78%), which indicates that the developed printed calibration strip is effective for the calibration of the ICG assay detection system.