Quantitative Detection of Acetamiprid in Black Tea Based on Ultraviolet Absorption Spectroscopy and Chemometrics

   To achieve nondestructive and accurate detection of acetamiprid (ACE) in black tea, a method based on ultraviolet (UV) absorption spectroscopy and chemometrics is proposed. First, the absorption spectra of 60 black tea samples containing varying ACE concentrations are acquired using a UV-visible spectrophotometer. Afterward, the effective variables are extracted by bootstrapping soft shrinkage (BOSS), the successive projections algorithm (SPA), and the competitive adaptive reweighted sampling (CARS) algorithm. Finally, the prediction models are developed using partial least-squares regression (PLSR) and gray wolf optimizer least-squares support vector regression (GWO-LSSVR). The results demonstrate that the CARS-GWO-LSSVR model performs best, with a determination coefficient (Rp2) and root mean square error for the prediction set (RMSEP) of 0.9991 and 0.0994 mg/L respectively. Furthermore, to assess the feasibility of the proposed method under interference from other pesticides, experiments are conducted using imidacloprid as an interferent. The results demonstrate that the method exhibits high-precision detection of ACE in black tea even in the presence of such interference. Thus, UV absorption spectroscopy combined with chemometrics can achieve nondestructive and accurate detection of ACE in black tea.

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