Improving the detection accuracy of the dual SERS aptasensor system with uncontrollable SERS "hot spot" using machine learning tools.

BACKGROUND: Simultaneous detection of food contaminants is crucial in addressing the collective health hazards arising from the presence of multiple contaminants. However, traditional multi-competitive surface-enhanced Raman scattering (SERS) aptasensors face difficulties in achieving simultaneous accurate detection of multiple target substances due to the uncontrollable SERS "hot spots". In this study, using chloramphenicol (CAP) and estradiol (E2) as two target substances, we introduced a novel approach that combines machine learning methods with a dual SERS aptasensor, enabling simultaneous high-sensitivity and accurate detection of both target substances. RESULTS: The strategy effectively minimizes the interference from characteristic Raman peaks commonly encountered in traditional multi-competitive SERS aptasensors. For this sensing system, the Au@4-MBA@Ag nanoparticles modified with sulfhydryl (SH)-CAP aptamer and Au@DTNB@Ag NPs modified with sulfhydryl (SH)-E2 aptamer were used as signal probes. Additionally, Fe3O4@Au nanoflowers integrated with SH-CAP aptamer complementary DNA and SH-E2 aptamer complementary DNA were used as capture probes, respectively. When compared to linear regression random forest, and support vector regression (SVR) models, the proposed artificial neural network (ANN) model exhibited superior precision, demonstrating R2 values of 0.963, 0.976, 0.991, and 0.970 for the training set, test set, validation set, and entire dataset, respectively. Validation with ten spectral groups reported an average error of 244 µg L-1. SIGNIFICANCE: The essence of our study lies in its capacity to address a persistent challenge encountered by traditional multiple competitive SERS aptasensors - the interference generated by uncontrollable SERS "hot spots" that hinders simultaneous quantification. The accuracy of the predictive model for simultaneous detection of two target substances was significantly improved using machine learning tools. This innovative technique offers promising avenues for the accurate and high-sensitive simultaneous detection of multiple food and environmental contaminants.

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives.

In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low-level, mid-level, and high-level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra-to-spectra, spectra-to-machine vision, spectra-to-electronic nose, and spectra-to-nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real-world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone-based multi-sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.