This study was conducted to develop a simple, rapid, and accurate lateral flow immunoassay (LFIA) detection method for point-of-care diagnosis. The one-dot LFIA for aflatoxin B1 (AFB1) was based on the modified competitive binding format using competition between AFB1 and colloidal gold-AFB1-BSA conjugate for antibody binding sites in the test zone. A Smartphone-based reading system consisting of a Samsung Galaxy S2 Smartphone, a LFIA reader, and a Smartphone application for the image acquisition and data analysis. The detection limit of one-dot LFIA for AFB1 is 5 μg/kg. This method provided semi-quantitative analysis of AFB1 samples in the range of 5 to 1,000 μg/kg. Using combination of the one-dot LFIA and the Smartphone-based reading system, it is possible to conduct a more fast and accurate point-of-care diagnosis.

Whole-cell Systemic Evolution of Ligands by Exponential enrichment (SELEX) is the process by which aptamers specific to target cells are developed. Aptamers selected by whole-cell SELEX have high affinity and specificity for bacterial surface molecules and live bacterial targets. To identify DNA aptamers specific to Staphylococcus aureus, we applied our rapid whole-cell SELEX method to a single-stranded ssDNA library. To improve the specificity and selectivity of the aptamers, we designed, selected, and developed two categories of aptamers that were selected by two kinds of whole-cell SELEX, by mixing and combining FACS analysis and a counter-SELEX process. Using this approach, we have developed a biosensor system that employs a high affinity aptamer for detection of target bacteria. FAM-labeled aptamer sequences with high binding to S. aureus, as determined by fluorescence spectroscopic analysis, were identified, and aptamer A14, selected by the basic whole-cell SELEX using a once-off FACS analysis, and which had a high binding affinity and specificity, was chosen. The binding assay was evaluated using FACS analysis. Our study demonstrated the development of a set of whole-cell SELEX derived aptamers specific to S. aureus; this approach can be used in the identification of other bacteria.

A rapid and simple immuno-chromatographic assay was developed to detect aflatoxin B1 (AFB1). The assay was based on a modified competitive binding format using colloidal gold and polyclonal antibody (Pab) conjugates. The anti-AFB1 Pab was immobilized to a defined detection zone on a porous nitrocellulose membrane and colloidal gold particles were conjugated to AFB1-BSA which served as a detection reagent. The AFB1-containing sample was added to the membrane and allowed to move with AFB1-BSA-coated particles dried on the conjugation pad. The mixture was then passed along the porous membrane by capillary action past the Pab in the detection zone, which captured AFB1 or AFB1-BSA. AFB1 in the sample inhibits binding of AFB1-BSA conjugated gold particles to the Pab and prevents formation of a red color dot. In the absence of AFB1, AFB1-BSA conjugated gold particles bound to the Pab, give a red color within this detection zone. With this method, 10 μg/mL of AFB1 was detected in less than 10 min. The developed AFB1 assay also showed no cross reaction to Ochratoxin A (OTA).

In this study, we developed a viability evaluation method for pepper (Capsicum annuum L.) seeds based on hyperspectral reflectance imaging. The reflectance spectra of pepper seeds in the 400–700 nm range are collected from hyperspectral reflectance images obtained using blue, green, and red LED illumination. A partial least squares–discriminant analysis (PLS-DA) model is developed to classify viable and non-viable seeds. Four spectral ranges generated with four types of LEDs (blue, green, red, and RGB), which were pretreated using various methods, are investigated to develop the classification models. The optimal PLS-DA model based on the standard normal variate for RGB LED illumination (400–700 nm) yields discrimination accuracies of 96.7% and 99.4% for viable seeds and nonviable seeds, respectively. The use of images based on the PLS-DA model with the first-order derivative of a 31.5-nm gap for red LED illumination (600–700 nm) yields 100% discrimination accuracy for both viable and nonviable seeds. The results indicate that a hyperspectral imaging technique based on LED light can be potentially applied to high-quality pepper seed sorting.

Citrus fruits were sorted based on external qualities, such as size, weight, and color, and internal qualities, such as soluble solid content (SSC), acidity, and firmness. Visible and near-infrared (VNIR) hyperspectral imaging techniques were used as rapid and nondestructive techniques for determining the internal quality of fruits. The applicability of the VNIR hyperspectral imaging technique for predicting the SSC in citrus fruits was evaluated in this study. A VNIR hyperspectral imaging system with a wavelength range of 400–1000 nm and 100 W light source was used to acquire hyperspectral images from citrus fruits in two orientations (i.e., stem and calyx ends). The SSC prediction model was developed using partial least-squares regression (PLSR). Spectrum preprocessing, effective wavelength selection through competitive adaptive reweighted sampling (CARS), and outlier detection were used to improve the model performance. The performance of each model was evaluated using the coefficient of determination (R²) and root mean square error (RMSE). In the present study, the PLSR model was developed using only a citrus cultivar. The SSC prediction CARS-PLSR model with outliers removed exhibited R² and RMSE values of approximatively 0.75 and 0.56 °Brix, respectively. The results of this study are expected to be useful in similar fields such as agricultural and food post-harvest management, as well as in the development of an online system for determining the SSC of citrus fruits.