The excitation and emission spectra formulated 3D contours, from which isopotential trajectory was selected for the direct detection of urinary δ-aminolevulinic acid, using derivative matrix isopotential synchronous fluorescence spectrometry.

Rapid analysis of components in complex matrices has always been a major challenge in constructing sensing methods, especially concerning time and cost. The detection of pesticide residues is an important task in food safety monitoring, which needs efficient methods. Here, we constructed a machine learning-assisted synchronous fluorescence sensing approach for the rapid and simultaneous quantitative detection of two important benzimidazole pesticides, thiabendazole (TBZ) and fuberidazole (FBZ), in red wine. First, fluorescence spectra data were collected using a second derivative constant-energy synchronous fluorescence sensor. Next, we established a prediction model through the machine learning approach. With this approach, the recovery rate of TBZ and FBZ detection of pesticide residues in red wine was 101% ± 5% and 101% ± 15%, respectively, without resorting complicated pretreatment procedures. This work provides a new way for the combination of machine learning and fluorescence techniques to solve the complexity in multi-component analysis in practical applications.

Early and sensitive detection of δ-aminolevulinic acid (δ-ALA) and porphobilinogen (PBG) is the cornerstone of diagnosis and effective treatment for acute porphyria. However, at present, the quantifying strategies demand multiple solvent extraction steps or chromatographic approaches to separate δ-ALA and PBG prior to quantification. These methods are both time-consuming and laborious. Otherwise, in conventional spectrofluorimetry, the overlapping spectra of the two analytes cause false diagnosis. To overcome this challenge, we present a two-step approach based on derivative matrix-isopotential synchronous fluorescence spectrometry (DMISFS) and the Hantzsch reaction, realizing the simple and simultaneous detection of δ-ALA and PBG in urine samples. The first step is chemical derivatization of the analytes by Hantzsch reaction. The second step is the determination of the target analytes by combining MISFS and the first derivative technique. The proposed approach accomplishes following advantages: 1) The MISFS technique improves the spectral resolution and resolves severe spectral overlap of the analytes, alleviating tedious and complicated pre-separation processes; 2) First derivative technique removes the background interference of δ-ALA on PBG and vice versa, ensuring high sensitivity; 3) Both the analytes can be determined simultaneously via single scanning, enabling rapid detection. The obtained detection limits for δ-ALA and PBG were 0.04 μmol L−1 and 0.3 μmol L−1, respectively. Within-run precisions (intra and inter-day CVs) for both the analytes were <5%. Further, this study would serve to enhance the availability of early and reliable quantitative diagnosis for acute porphyria in both scientific and clinical laboratories.