Chemical Characterization of Wine Vinegars Belonging to the Vinagre de Montilla-Moriles Protected Designation of Origin, Using Near-Infrared Spectroscopy

Sánchez, María‐Teresa; Márquez, Rafael J. Astorga; Torres, Irina; Haba, María-José De la; Pérez‐Marín, Dolores; López, María-Isabel

doi:10.1007/s12161-019-01697-z

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Several studies have reported the use of NIR spectroscopy with cuvettes to determine vinegar quality (Table 1) [11][12][13][14]. The NIR analysis of vinegar samples has also been achieved using a liquid cell coupled to a reflector cover [15][16][17] and vials [18][19][20]. In previous studies, vinegar samples have been collected from packages, bottles, or fermentation systems and placed into sample cells (cuvettes, a liquid cell with a reflector cover, and vials) for NIR analysis.…”

Section: Introductionmentioning

confidence: 99%

Assessing Fermentation Broth Quality of Pineapple Vinegar Production with a Near-Infrared Fiber-Optic Probe Coupled with Stability Competitive Adaptive Reweighted Sampling

Kasemsumran,

Boondaeng,

Jungtheerapanich

et al. 2023

Molecules

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In this study, the performance of a near-infrared (NIR) fiber-optic probe coupled with stability competitive adaptive reweighted sampling (SCARS) was investigated for the analysis of acetic acid, ethanol, total soluble solids, caffeic acid, gallic acid, and tannic acid in the broth of pineapple vinegar during fermentation. The NIR spectra of the broth samples in the region of 11,536–3956 cm−1 were collected during vinegar fermentation promoted by Acetobacter aceti. This continuous biological process led to changes in the concentrations of all analytes studied. SCARS provided optimized and stabilized NIR spectral variables for the construction of a partial least squares (PLS) model for each analyte using a small number of optimal variables (under 88 variables). The SCARS-PLS model outperformed the conventional PLS model, and achieved excellent accuracy in accordance with ISO 12099:2017 for the four prediction models of acetic acid, ethanol, caffeic acid, and gallic acid, with root-mean-square error of prediction values of 0.137%, 0.178%, 0.637 μg/mL and 0.640 μg/mL, respectively. In contrast, only an acetic acid content prediction model constructed via the conventional PLS method and using the whole spectral region (949 variables) could pass with acceptable accuracy. These results indicate that the NIR optical probe coupled with SCARS is an appropriate method for the continuous monitoring of multianalytes during vinegar fermentation, particularly acetic acid and ethanol contents, which are indicators of the finished fermentation of pineapple vinegar.

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