Novel strategy for food safety risk management and communication: Risk identification for benzoic acid residues in pickled vegetables

Abstract

Abstract Benzoic acid (BA) is widely used as an antimicrobial preservative to prolong the shelf‐life of pickled vegetables. A method for rapidly determining the BA content in forty pickled vegetable samples was developed by coupling ultrasonic extraction with surface‐enhanced Raman scattering (SERS) and an adaptive iteratively reweighted penalized least‐squares (AirPLS) algorithm. The results obtained with this method were compared and correlated with those from high‐performance liquid chromatography measurements. Amplification of the Raman scattering via the SERS effect was induced by gold nanoparticles (AuNPs) when BA was irradiated with a 785 nm laser. The AirPLS algorithm was used to reduce the background interference signal, which was also amplified. The amplified Raman scattering effect of BA in the pickled vegetables displayed a positive and significant correlation with the HPLC concentration of BA, with high reproducibility. For HPLC determination of the concentration of BA in the range of 0–820 ppm, the BA monomer's intensity of the 944–1,005 cm−1 and 1,366–1,373 cm−1 peaks, and BA dimer's intensity of the 1,025 cm−1 and 1,465–1,482 cm−1 peaks in the SERS spectrum were respectively converted to the Z‐ratio BA monomer and Z‐ratio BA dimer standard scores by Z‐Score conversion. The sum's (Z‐ratio BA monomer + Z‐ratio BA dimer) sensitivity was 100%, and specificity was 90.9% by receiver operating characteristic curve. This study found that a Raman spectroscopy‐based monitoring method can be one of the fastest screening inspection options that can complete an analysis within a short period of time and produce reliable results. This approach is particularly cost‐effective, which makes it suitable for the initial screening of raw materials and provides an effective management strategy easy to communicate with food safety officials.

Keywords

• benzoic acid
• Raman
• risk management
• spectral analysis