f0b673ce609d4747fea255597c54aa83 2026_Guerra_FOODS_Plant-based_beverages_IR.pdf ea0b0f500425ddaf3dd42c787e0ef1cf2679b2bb 2026_Guerra_FOODS_Plant-based_beverages_IR.pdf 25d692b33f04889b5d670aafc12d7eb6e444127eefda3ca72293987925196b2a 2026_Guerra_FOODS_Plant-based_beverages_IR.pdf Title: Assessing the Feasibility of Mid- and Near-Infrared Spectroscopy for Classifying Plant-Based Beverages and Predicting Nutrient Content Subject: The increasing demand for plant-based beverages as alternatives to dairy milk requires rapid and reliable methods to assess their composition and authenticity. This study investigated the feasibility of mid-infrared (MIR) and near-infrared (NIR) spectroscopy for classifying plant-based beverages and predicting their nutritional profile. A total of 57 commercial beverages from five categories (oat, almond, soybean, rice, and coconut) were analyzed. Canonical discriminant analysis was used to discriminate among beverage categories, and modified partial least-squares regression models were developed using reference chemical analyses and spectral data to predict protein, fat, sugars, ash, acidity traits, minerals, and amino acid composition. Both MIR and NIR successfully discriminated among the five beverage categories. Quantitative prediction models were generally more accurate with MIR than with NIR. The most robust MIR models were obtained for protein, fat, glucose, ash, and selected minerals (P and K), reaching accuracy levels suitable for quality-control applications. Protein was predicted with excellent accuracy by both technologies. The most robust prediction models were achieved for amino acid composition, with all amino acids except phenylalanine showing satisfactory predictive performance, particularly with MIR spectroscopy. In conclusion, these results demonstrate the feasibility of infrared spectroscopy for the authentication and compositional assessment of plant-based beverages. In particular, MIR spectroscopy showed considerable potential for integration into routine quality-control workflows, similar to those currently implemented for dairy milk analysis. Keywords: compositional analysis; infrared spectroscopy; quality control; plant-based beverages Author: Alberto Guerra, Massimo De Marchi, Marta Pozza and Carmen L. Manuelian Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25; modified using OpenPDF 1.4.2 CreationDate: Thu Jul 23 13:52:57 2026 CEST ModDate: Thu Jul 23 14:40:43 2026 CEST Custom Metadata: yes Metadata Stream: yes Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 16 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 762413 bytes Optimized: yes PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- GADHWU+VnURWPalladioL Type 1 Custom yes yes yes 513 0 NYEIUO+URWPalladioL-Roma Type 1 Custom yes yes yes 517 0 ZVQDAR+URWPalladioL-Bold Type 1 Custom yes yes yes 520 0 JDZWZJ+URWPalladioL-Ital Type 1 Custom yes yes yes 523 0 EUQOAW+EURM7 Type 1 Builtin yes yes yes 300 0 UWJUYN+CMSY10 Type 1 Builtin yes yes yes 303 0 OVNEXW+EURM10 Type 1 Builtin yes yes yes 306 0 JCMNAL+PalatinoLinotype TrueType WinAnsi yes yes no 161 0 JCMNAJ+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 167 0 JCMNAL+PalatinoLinotype TrueType WinAnsi yes yes no 169 0 URYLRZ+URWPalladioL-BoldItal Type 1 Custom yes yes yes 308 0 XHAQEC+CMBSY10 Type 1 Builtin yes yes yes 307 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2026-10-06 04:00:48 CEST RepresentationInformation: 2026_Guerra_FOODS_Plant-based_beverages_IR.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2026-10-05 06:49:39 CEST Size: 762413 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf Profile: Linearized PDF PDFMetadata: Objects: 0 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: ViewerPreferences: HideToolbar: false HideMenubar: false HideWindowUI: false FitWindow: true CenterWindow: false DisplayDocTitle: false NonFullScreenPageMode: UseNone Direction: L2R ViewArea: CropBox ViewClip: CropBox PrintArea: CropBox PageClip: CropBox PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Item: Title: Materials and Methods Children: Item: Title: Sample Collection Item: Title: Spectral Collection Item: Title: Chemical Analysis Item: Title: Statistical Analysis and Development of Prediction Models Item: Title: Results and Discussion Children: Item: Title: Study Limitations Item: Title: Spectra and Canonical Discrimination Analysis Item: Title: Prediction Models for Gross Composition and Acidity Traits Item: Title: Prediction Models for Detailed Composition Item: Title: Conclusions Item: Title: References Filters: FilterPipeline: FlateDecode Fonts: Type1: Font: BaseFont: GADHWU+VnURWPalladioL FontSubset: true FirstChar: 160 LastChar: 225 FontDescriptor: FontName: GADHWU+VnURWPalladioL Flags: Symbolic FontBBox: -166, -283, 1021, 943 FontFile: true EncodingDictionary: Differences: true ToUnicode: true Font: BaseFont: OVNEXW+EURM10 FontSubset: true FirstChar: 22 LastChar: 22 FontDescriptor: FontName: OVNEXW+EURM10 Flags: Symbolic FontBBox: -32, -243, 1060, 720 FontFile: true ToUnicode: true Font: BaseFont: XHAQEC+CMBSY10 FontSubset: true FirstChar: 14 LastChar: 14 FontDescriptor: FontName: XHAQEC+CMBSY10 Flags: Symbolic FontBBox: -26, -940, 1332, 825 FontFile: true ToUnicode: true Font: BaseFont: URYLRZ+URWPalladioL-BoldItal FontSubset: true FirstChar: 110 LastChar: 110 FontDescriptor: FontName: URYLRZ+URWPalladioL-BoldItal Flags: Symbolic FontBBox: -170, -300, 1073, 935 FontFile: true EncodingDictionary: Differences: true ToUnicode: true Font: BaseFont: NYEIUO+URWPalladioL-Roma FontSubset: true FirstChar: 1 LastChar: 252 FontDescriptor: FontName: NYEIUO+URWPalladioL-Roma Flags: Symbolic FontBBox: -166, -283, 1021, 943 FontFile: true EncodingDictionary: Differences: true ToUnicode: true Font: BaseFont: ZVQDAR+URWPalladioL-Bold FontSubset: true FirstChar: 3 LastChar: 122 FontDescriptor: FontName: ZVQDAR+URWPalladioL-Bold Flags: Symbolic FontBBox: -152, -301, 1000, 935 FontFile: true EncodingDictionary: Differences: true ToUnicode: true Font: BaseFont: JDZWZJ+URWPalladioL-Ital FontSubset: true FirstChar: 2 LastChar: 122 FontDescriptor: FontName: JDZWZJ+URWPalladioL-Ital Flags: Symbolic FontBBox: -170, -305, 1010, 941 FontFile: true EncodingDictionary: Differences: true ToUnicode: true Font: BaseFont: EUQOAW+EURM7 FontSubset: true FirstChar: 22 LastChar: 22 FontDescriptor: FontName: EUQOAW+EURM7 Flags: Symbolic FontBBox: -5, -243, 1229, 720 FontFile: true ToUnicode: true Font: BaseFont: UWJUYN+CMSY10 FontSubset: true FirstChar: 0 LastChar: 21 FontDescriptor: FontName: UWJUYN+CMSY10 Flags: Symbolic FontBBox: -29, -960, 1116, 775 FontFile: true ToUnicode: true XMP: application/pdf Alberto Guerra, Massimo De Marchi, Marta Pozza and Carmen L. Manuelian The increasing demand for plant-based beverages as alternatives to dairy milk requires rapid and reliable methods to assess their composition and authenticity. This study investigated the feasibility of mid-infrared (MIR) and near-infrared (NIR) spectroscopy for classifying plant-based beverages and predicting their nutritional profile. A total of 57 commercial beverages from five categories (oat, almond, soybean, rice, and coconut) were analyzed. Canonical discriminant analysis was used to discriminate among beverage categories, and modified partial least-squares regression models were developed using reference chemical analyses and spectral data to predict protein, fat, sugars, ash, acidity traits, minerals, and amino acid composition. Both MIR and NIR successfully discriminated among the five beverage categories. Quantitative prediction models were generally more accurate with MIR than with NIR. The most robust MIR models were obtained for protein, fat, glucose, ash, and selected minerals (P and K), reaching accuracy levels suitable for quality-control applications. Protein was predicted with excellent accuracy by both technologies. The most robust prediction models were achieved for amino acid composition, with all amino acids except phenylalanine showing satisfactory predictive performance, particularly with MIR spectroscopy. In conclusion, these results demonstrate the feasibility of infrared spectroscopy for the authentication and compositional assessment of plant-based beverages. In particular, MIR spectroscopy showed considerable potential for integration into routine quality-control workflows, similar to those currently implemented for dairy milk analysis. Assessing the Feasibility of Mid- and Near-Infrared Spectroscopy for Classifying Plant-Based Beverages and Predicting Nutrient Content 2026-07-23T19:52:57+08:00 LaTeX with hyperref 2026-07-23T14:40:43+02:00 2026-07-23T14:40:43+02:00 compositional analysis; infrared spectroscopy; quality control; plant-based beverages pdfTeX-1.40.25; modified using OpenPDF 1.4.2 False This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5 uuid:a181a454-c236-4772-af6a-e65c9a1f078b uuid:97d27e61-5b5a-4115-8ec7-e3569b2b8830 Pages: Page: Sequence: 1 Annotations: Annotation: Subtype: Link Rect: 34, 204, 124, 223 Annotation: Subtype: Link Rect: 88, 77, 152, 84 Annotation: Subtype: Link Rect: 34, 64, 119, 72 Annotation: Subtype: Link Rect: 34, 773, 117, 809 Annotation: Subtype: Link Rect: 515, 773, 560, 809 Annotation: Subtype: Link Rect: 110, 643, 121, 656 Annotation: Subtype: Link Rect: 223, 643, 234, 656 Annotation: Subtype: Link Rect: 300, 643, 311, 656 Annotation: Subtype: 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