Significant Effect of Sample Pretreatment on Ara h1 Extraction and Improved Sensitive SWCNT-Based Detection through Optimization

Abstract

Single-walled carbon nanotube (SWCNT)-based nanobiosensors have received increasing attention from food researchers as a future instrument of food safety due to their high sensitivity. However, the pretreatment process of the sample applying to SWCNT-based nanobiosensor is required to be more delicate compared to other analyses. In this study, the pretreatment process of Ara h1 protein from its retained complex food matrix was optimized using various buffer compounds and the pretreated allergenic Ara h1 obtained for the optimized process was detected by SWCNT-based nanobiosensor. In the pretreatment process, the buffer extraction method with tris buffer (Tris-HNO3, pH 8.4) was developed and used to extract native peanut allergens from foods. The extraction procedure for Ara h1 from peanut butter foods was performed by varying the temperature, extraction time, and additives (NaCl and skim milk powder). The results of these tests using our SWCNT-based biosensor were analyzed to evaluate the allergenic nature of the extracts. The peak level of Ara h1 extraction was achieved as 84.60 ± 7.50 ng/mL at 21 °C/60 min with the mixture of Tris-HNO3 and 1 M NaCl. In addition, other significant Ara h1 extractions were found to be 29.59 ± 2.57 at 21 °C/15 min and 27.74 ± 1.33 ng/mL at 60 °C/15 min. This study emphasizes the importance of adjusting the extraction time and temperature with respect to the target allergen and food matrix components. After the optimization of the sample pretreatment, the precision of SWCNT-based nanobiosensor by the resistance difference (ΔR) of the SWCNT-based biosensor via linear sweep voltammetry in a potentiostat was identified using the pretreated Ara h1 sample from the processed food compared with the indirect enzyme-linked immunosorbent assay (ELISA) results.