Academic Journal
Data‐Driven Automated Chemometric FTIR Analysis for Rapid Carbohydrate Quantification in Milk Powder: Validation Against Conventional Methods.
| Τίτλος: | Data‐Driven Automated Chemometric FTIR Analysis for Rapid Carbohydrate Quantification in Milk Powder: Validation Against Conventional Methods. |
|---|---|
| Συγγραφείς: | Lorena, Torres Armendáriz Neyba, Hugo, Ramos Sánchez Víctor, Eduardo, Orozco Mena Raúl, Samuel, Pérez Vega, Quintero Ramos, Armando, Johan, Mendoza Chacón, Iván, Salmerón |
| Πηγή: | Journal of Food Science (John Wiley & Sons, Inc.); Jun2026, Vol. 91 Issue 6, p1-13, 13p |
| Θεματικοί όροι: | Fourier transform infrared spectroscopy, Chemometrics, Scientific method, Spectrum analysis, Dairy processing, Electronic data processing, Dried milk, Sugar analysis |
| Περίληψη: | Accurate quantification of carbohydrates in dairy products is essential for regulatory compliance and process control. Conventional chromatographic and colorimetric techniques, although reliable, are often limited by long analysis times, complex sample preparation, and high operational costs. In this study, data‐driven automated chemometric FTIR analysis developed in the Google Colab computational environment was evaluated as an alternative for carbohydrate quantification in milk powder. The approach is based on automated spectral preprocessing by baseline correction, Savitzky‐Golay smoothing, and normalization, feature extraction within the 899–955 cm−1 region associated with lactose, glucose, and galactose, and univariate regression analysis. Calibration was performed using mixtures of solely standards in an inert matrix (KBr), ranging from 0% to 100%, and validated against high‐performance anion‐exchange chromatography with pulsed amperometric detection (HPAEC‐PAD) and the dinitrosalicylic acid (DNS) method. The FTIR approach demonstrated strong analytical performance for carbohydrate quantification, particularly for lactose, with a coefficient of determination R2 ≥ 0.98, accuracy values of 114.95%, and high precision (%RSD < 0.1), comparable to reference methods under controlled conditions. It was observed that during the analysis of commercial milk powder samples, lactose quantification remained accurate; however, matrix effects and spectral overlap influenced glucose quantitative performance in the presence of starch. These results indicate that FTIR spectroscopy combined with automated spectral processing is better suited as a rapid screening and decision‐support tool for dairy quality control. Furthermore, the chemically informed preprocessing strategy established in this study provides an analytical foundation for the future development of advanced predictive models for multicomponent food analysis. Practical Applications: Automated data‐processing‐assisted FTIR spectroscopy provides a rapid, non‐destructive approach for monitoring carbohydrate content in milk powder with minimal sample preparation. This method enables faster decision‐making in quality control compared to conventional techniques and can be implemented as a routine screening tool in dairy processing environments. It is particularly useful for verifying product consistency and supporting process monitoring, while complementing established analytical methods. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Food Science (John Wiley & Sons, Inc.) is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=00221147&ISBN=&volume=91&issue=6&date=20260601&spage=1&pages=1-13&title=Journal of Food Science (John Wiley & Sons, Inc.)&atitle=Data%E2%80%90Driven%20Automated%20Chemometric%20FTIR%20Analysis%20for%20Rapid%20Carbohydrate%20Quantification%20in%20Milk%20Powder%3A%20Validation%20Against%20Conventional%20Methods.&aulast=Lorena%2C%20Torres%20Armend%C3%A1riz%20Neyba&id=DOI:10.1111/1750-3841.71231 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: Data‐Driven Automated Chemometric FTIR Analysis for Rapid Carbohydrate Quantification in Milk Powder: Validation Against Conventional Methods. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lorena%2C+Torres+Armendáriz+Neyba%22">Lorena, Torres Armendáriz Neyba</searchLink><br /><searchLink fieldCode="AR" term="%22Hugo%2C+Ramos+Sánchez+Víctor%22">Hugo, Ramos Sánchez Víctor</searchLink><br /><searchLink fieldCode="AR" term="%22Eduardo%2C+Orozco+Mena+Raúl%22">Eduardo, Orozco Mena Raúl</searchLink><br /><searchLink fieldCode="AR" term="%22Samuel%2C+Pérez+Vega%22">Samuel, Pérez Vega</searchLink><br /><searchLink fieldCode="AR" term="%22Quintero+Ramos%2C+Armando%22">Quintero Ramos, Armando</searchLink><br /><searchLink fieldCode="AR" term="%22Johan%2C+Mendoza+Chacón%22">Johan, Mendoza Chacón</searchLink><br /><searchLink fieldCode="AR" term="%22Iván%2C+Salmerón%22">Iván, Salmerón</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Food Science (John Wiley & Sons, Inc.); Jun2026, Vol. 91 Issue 6, p1-13, 13p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Fourier+transform+infrared+spectroscopy%22">Fourier transform infrared spectroscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Chemometrics%22">Chemometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Scientific+method%22">Scientific method</searchLink><br /><searchLink fieldCode="DE" term="%22Spectrum+analysis%22">Spectrum analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Dairy+processing%22">Dairy processing</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Dried+milk%22">Dried milk</searchLink><br /><searchLink fieldCode="DE" term="%22Sugar+analysis%22">Sugar analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurate quantification of carbohydrates in dairy products is essential for regulatory compliance and process control. Conventional chromatographic and colorimetric techniques, although reliable, are often limited by long analysis times, complex sample preparation, and high operational costs. In this study, data‐driven automated chemometric FTIR analysis developed in the Google Colab computational environment was evaluated as an alternative for carbohydrate quantification in milk powder. The approach is based on automated spectral preprocessing by baseline correction, Savitzky‐Golay smoothing, and normalization, feature extraction within the 899–955 cm−1 region associated with lactose, glucose, and galactose, and univariate regression analysis. Calibration was performed using mixtures of solely standards in an inert matrix (KBr), ranging from 0% to 100%, and validated against high‐performance anion‐exchange chromatography with pulsed amperometric detection (HPAEC‐PAD) and the dinitrosalicylic acid (DNS) method. The FTIR approach demonstrated strong analytical performance for carbohydrate quantification, particularly for lactose, with a coefficient of determination R2 ≥ 0.98, accuracy values of 114.95%, and high precision (%RSD < 0.1), comparable to reference methods under controlled conditions. It was observed that during the analysis of commercial milk powder samples, lactose quantification remained accurate; however, matrix effects and spectral overlap influenced glucose quantitative performance in the presence of starch. These results indicate that FTIR spectroscopy combined with automated spectral processing is better suited as a rapid screening and decision‐support tool for dairy quality control. Furthermore, the chemically informed preprocessing strategy established in this study provides an analytical foundation for the future development of advanced predictive models for multicomponent food analysis. Practical Applications: Automated data‐processing‐assisted FTIR spectroscopy provides a rapid, non‐destructive approach for monitoring carbohydrate content in milk powder with minimal sample preparation. This method enables faster decision‐making in quality control compared to conventional techniques and can be implemented as a routine screening tool in dairy processing environments. It is particularly useful for verifying product consistency and supporting process monitoring, while complementing established analytical methods. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Food Science (John Wiley & Sons, Inc.) is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/1750-3841.71231 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Subjects: – SubjectFull: Fourier transform infrared spectroscopy Type: general – SubjectFull: Chemometrics Type: general – SubjectFull: Scientific method Type: general – SubjectFull: Spectrum analysis Type: general – SubjectFull: Dairy processing Type: general – SubjectFull: Electronic data processing Type: general – SubjectFull: Dried milk Type: general – SubjectFull: Sugar analysis Type: general Titles: – TitleFull: Data‐Driven Automated Chemometric FTIR Analysis for Rapid Carbohydrate Quantification in Milk Powder: Validation Against Conventional Methods. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lorena, Torres Armendáriz Neyba – PersonEntity: Name: NameFull: Hugo, Ramos Sánchez Víctor – PersonEntity: Name: NameFull: Eduardo, Orozco Mena Raúl – PersonEntity: Name: NameFull: Samuel, Pérez Vega – PersonEntity: Name: NameFull: Quintero Ramos, Armando – PersonEntity: Name: NameFull: Johan, Mendoza Chacón – PersonEntity: Name: NameFull: Iván, Salmerón IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00221147 Numbering: – Type: volume Value: 91 – Type: issue Value: 6 Titles: – TitleFull: Journal of Food Science (John Wiley & Sons, Inc.) Type: main |
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