ATR-MIR spectroscopy as a process analytical technology in wine alcoholic fermentation – A tutorial

Schorn-García, Daniel; Cavaglia, Julieta; Giussani, Barbara; Busto, Olga; Aceña, Laura; Mestres, Montserrat; Boqué, Ricard

doi:10.1016/j.microc.2021.106215

Cited by 14 publications

(8 citation statements)

References 41 publications

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“…Concerning the factor “Fermentation,” the evolution of the first score value (98.26% of variance) over time shows a sigmoidal trend (Figure S1). We have previously reported this behavior for the first component in a PCA analysis of MIR spectra 9,10 . The loading of this factor shows that the region between 1150 and 1000 cm −1 is the most related to this factor.…”

Section: Resultsmentioning

confidence: 62%

“…We have previously reported this behavior for the first component in a PCA analysis of MIR spectra. 9,10 The loading of this factor shows that the region between 1150 and 1000 cm À1 is the most related to this factor. Literature reports absorptions in this region related to ethanol and sugars in wine alcoholic fermentation and explained by the stretching modes of C-C and C-O bonds.…”

Section: Asca With Ik â J Matrix Unfoldingmentioning

confidence: 99%

“…9 In the case of alcoholic fermentation, the transformation of sugars (glucose and fructose) into ethanol and CO 2 accounts for most of the variability in the MIR spectra. 10 Although this is valuable information, it hinders the detection of other minor sources of variability, such as the production of some organic acids that might be detrimental for the wine, as it is the case for acetic acid.…”

Section: Introductionmentioning

confidence: 99%

“…In the food industry, when using vibrational spectroscopy for process control, it is common that the phenomenon we are interested in is hidden by other sources of variation from the main process 9 . In the case of alcoholic fermentation, the transformation of sugars (glucose and fructose) into ethanol and CO 2 accounts for most of the variability in the MIR spectra 10 . Although this is valuable information, it hinders the detection of other minor sources of variability, such as the production of some organic acids that might be detrimental for the wine, as it is the case for acetic acid.…”

Section: Introductionmentioning

confidence: 99%

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Methodologies based on ASCA to elucidate the influence of a subprocess: Vinification as a case of study

Schorn-García

Giussani

Busto

et al. 2023

Journal of Chemometrics

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In food manufacturing and processing, food matrix complexity usually makes it difficult to detect unwanted subprocesses, which can impact the quality of the final product. In the case of wine alcoholic fermentation, the main process is the conversion of sugars into ethanol and carbon dioxide, but the presence of some unwanted microorganisms could lead to wine contamination by production of undesired minor compounds. In the study we present, an intentional contamination of the vinification process by the addition of acetic acid bacteria was studied using a portable Fourier transform infrared (FT-IR) spectrometer. ANOVA simultaneous component analysis (ASCA) was used to unravel these minor variability sources. However, as the subprocess is two orders of magnitude lower in concentration than the main process, different methodologies were used to enhance the ASCA results, such as to select a specific spectral region related to acetic acid bacteria metabolism, to divide the process in time intervals related to the different phases, or to unfold the data matrix in different ways. In addition, spectral preprocessing was optimized to scale up small peaks related to the subprocess. Our results show that several methodologies to build ASCA models can be applied to emphasize and better characterize bacteria contamination subprocesses.

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Section: Resultsmentioning

confidence: 62%

Section: Asca With Ik â J Matrix Unfoldingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Methodologies based on ASCA to elucidate the influence of a subprocess: Vinification as a case of study

Schorn-García

Giussani

Busto

et al. 2023

Journal of Chemometrics

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“…Schorn-García et al. 3 and references therein and Gorla et al. 4 ), the experience we have with NIR spectroscopy is quite young and is mainly focused on the use of miniaturized NIR instruments.…”

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confidence: 99%

Smart analytical methods and applied chemometrics group – Department of Science and High Technology, University of Insubria in Como

Giussani

2021

NIR news

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Predicting the Specific Gravity of Must During Fermentation Using Machine Learning Models

Kovačević,

Orić,

Tolić

et al. 2024

Lecture Notes in Networks and Systems

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ATR-MIR spectroscopy as a process analytical technology in wine alcoholic fermentation – A tutorial

Cited by 14 publications

References 41 publications

Methodologies based on ASCA to elucidate the influence of a subprocess: Vinification as a case of study

Methodologies based on ASCA to elucidate the influence of a subprocess: Vinification as a case of study

Smart analytical methods and applied chemometrics group – Department of Science and High Technology, University of Insubria in Como

Predicting the Specific Gravity of Must During Fermentation Using Machine Learning Models

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