The present study describes a novel strategy that integrates refined Fourier transform infrared (FTIR) spectral analysis with a vector-subspace angle approach to rapidly and precisely quantify sweeteners in food products, thus overcoming the limitations of traditional analytical methods (e.g., cumbersome sample pretreatment and low discrimination rates for structurally similar compounds). Employing the sucralose reference spectrum as a case study, spectral point stacking was systematically compared with Savitzky-Golay (S-G) and simulated hyperbolic convolution (SH) algorithms. This approach revealed the superior performance of the SH algorithm in spectral smoothing, noise reduction, signal-to-noise ratio enhancement, and derivative spectral resolution. Consequently, spectral stacking combined with SH second-order derivatization was established as the optimal preprocessing protocol, enabling the construction of a feature-augmented reference spectral library encompassing 15 common sweeteners. The vector-subspace angle method was adopted as the similarity metric, with a discriminative threshold of θ<0.1 established for single-sweetener qualitative analysis. This framework was further extended with a stepwise subtraction algorithm to deconvolute multicomponent systems and evaluate the quality of commercial sucralose samples. Individual sweeteners were identified with 100% accuracy, high concordance between resolved components in compounded systems and their actual formulations, and with a strong alignment of commercial sample quality assessments by high-performance liquid chromatography (HPLC). As no elaborate sample preparation is required, this novel assessment method exhibits both expediency and precision, offering robust technical support for on-site sweetener screening and regulatory oversight in food safety.