Abstract

Background: The accurate enumeration and identification of probiotic strains are essential for product quality. The plate count (PC) gold standard enumerates viable, culturable cells but does not by itself resolve individual strains within multi-strain consortia, and molecular methods (qPCR, ddPCR) are costly and face recognised challenges in quantifying relative strain abundance. Fourier transform infrared (FTIR) spectroscopy is a promising phenotypic alternative. Methods: We developed an FTIR-based artificial neural network classifier to identify and quantify a four-strain probiotic blend comprising Lactobacillus acidophilus LA02, Lacticaseibacillus rhamnosus LR04, Limosilactobacillus fermentum LF08, and Bifidobacterium animalis subsp. lactis BS01 compared against selective plate counting and species-specific PCR. Results: The classifier correctly identified all 36 test colonies (100%; 95% Clopper–Pearson CI: 90.3–100%); descriptive cluster analysis indicated spectral distinctiveness (silhouette = 0.908; Davies–Bouldin = 0.127; cophenetic correlation = 0.955). Enumeration agreement with selective plate counting was assessed descriptively (Pearson r = 0.78, 95% CI [−0.72, 1.00], n = 4 strain means; mean difference −0.028 log10 CFU/mL; all strain-level differences < 0.1 log10 CFU/mL). PCR confirmed all FTIR classifications. Conclusions: This proof-of-concept study demonstrates the feasibility of coupling cultivation with spectroscopic identification in a hybrid PC + FTIR workflow for multi-strain probiotic quality control. Because the findings derive from a single blend preparation analysed in technical replicates, they characterise this dataset and require confirmation on independently prepared batches before the approach can be regarded as a validated method.

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