Surface-enhanced Raman scattering (SERS) spectroscopy has significant applications in various fields such as food safety, environmental monitoring, and life sciences. In recent years, there has been growing interest in the quantitative detection of substance concentrations using SERS spectroscopy. SERS fiber probes, which offer outstanding practical value, enable in situ and on-site detection of substances in complex environments, making them highly suitable for practical quantitative measurements. However, because the reuse of fiber probes is challenging owing to contamination by the substances being measured, it is essential to prepare a large number of fiber probes that exhibit good interchangeability in batches. This allows the establishment of a statistical quantitative relationship between the spectral amplitude and substance concentration through large-sample spectral detection. This statistical quantitative relationship can thereafter be used to determine the concentrations of unknown samples. However, the interchangeability differences of fiber probes prepared from similar and different batches and the elimination or compensation for interchangeability degradation during batch preparation for quantitative detection have not yet been studied. In this study, we employ an electrostatic adsorption self-assembly method to prepare tapered SERS fiber probes. By assimilating, statistically averaging, and fitting large-sample spectral data measured from different batches of fiber probes, we obtain high-precision quantitative curves and achieve quantitative detection of thiram samples.
A batch of bare tapered fibers is fixed onto a specially designed disc. Subsequently, a monolayer of uniformly distributed gold nanospheres is grown on the surface of these fibers at the same density under the optimized electrostatic adsorption self-assembly conditions. Subsequently, a batch of tapered SERS fiber probes with excellent interchangeability is obtained. Tapered SERS fiber probes are prepared by repeating the same preparation process under identical conditions. During the testing stage, fiber probes from the same batch, which exhibit good interchangeability, are initially used to individually test a series of thiram standard solutions with varying concentrations. The spectral data obtained from these single tests are thereafter fitted to establish a quantitative relationship between the spectral amplitude and the sample concentration for that particular batch. Subsequently, spectral data obtained from single tests using different batch probes are fitted to obtain a quantitative relationship for each batch. Based on these relationships, spectral calibration factors are calculated to account for variations across different batches. Ultimately, the spectral data measured by the probes from different batches are assimilated into a single batch using calibration factors such that large-sample spectral data can be collected. Spectral data are statistically averaged and fitted to obtain a high-precision quantitative curve. The quantitative detection capability of this curve is assessed using recovery testing experiments.
The results reveal that fiber probes from the same batch exhibit good interchangeability, with the relative standard deviation (RSD) of the spectral amplitude measured for the same sample concentration being less than 8%. Fiber probes from different batches exhibit greater variability owing to inherent factors in the chemical growth process, with an RSD of 15% for the spectral amplitude measured for the same sample concentration (Fig.3). The quantitative relationship between the spectral amplitude measured by probes from the same batch and concentration is investigated, and the results indicate that for all batches of fiber probes measured, the spectral amplitudes measured by probes from a single batch follow a Langmuir function relationship with thiram concentration, but the quantitative relationships obtained are different for each batch (Fig.4). Using the spectral calibration factors obtained from the quantitative curves of single tests from each batch, ten batches of spectral data are successfully assimilated to the same batch level, and a high-precision quantitative relationship curve is obtained through statistical averaging and data fitting of the assimilated large-sample spectral data with a fitting degree of up to 0.999 (Fig.5). The quantitative curves obtained after assimilating the spectral data to the 1st, 4th, and 8th batches individually exhibit excellent quantitative detection capabilities, and the recovery rates for thiram-spiked samples at concentrations of 8×10-7 mol/L and 8×10-8 mol/L fall within the range of 90%?110% (Table 2).
In this study, we investigate the quantitative SERS detection performance of tapered fiber probes prepared in batches using an electrostatic adsorption self-assembly method. Under consistent preparation and detection conditions, different fiber probes from the same batch exhibit excellent interchangeability, with an RSD of the SERS spectral amplitude of less than 8% for thiram samples at the same concentration. To address the issue of the reduced interchangeability of fiber probes from different batches, which does not meet the demands for the number of probes needed in practical quantitative detection applications, we propose and demonstrate a method to assimilate the spectral data measured by probes from different batches to those of probes from a single batch. By statistically averaging and fitting the assimilated large-sample spectral data, we obtain a calibration curve for the SERS quantitative detection of thiram samples in the concentration range of 2×10-8?10-6 mol/L. Using this calibration curve, the recovery rates for tests on spiked thiram samples at concentrations of 8×10-7 mol/L and 8×10-8 mol/L reach 90%?110%. The proposed method for the batch preparation of tapered SERS fiber probes, the assimilation method of spectral data from probes prepared in different batches, and the scheme for obtaining high-precision quantitative detection curves through statistical averaging of large-sample spectral data are expected to provide references for practical SERS quantitative detection applications.
