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Yao Z, Zou W, Zhang X, Nie P, Lv H, Wang W, Zhao X, Yang Y, Yang L. Integrating mid-infrared spectroscopy, machine learning, and graphical bias correction for fatty acid prediction in water buffalo milk. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:6470-6482. [PMID: 38501395 DOI: 10.1002/jsfa.13471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 02/25/2024] [Accepted: 03/19/2024] [Indexed: 03/20/2024]
Abstract
BACKGROUND Buffalo milk, constituting 15% of global production, has higher fatty acids content than Holstein milk. Fourier-transform mid-infrared (FT-MIR) spectroscopy is widely used for dairy analysis, but its application to buffalo milk, with larger fat globules, remains understudied. The ultimate goal of this study is to develop machine learning models based on FT-MIR spectroscopy for predicting fatty acids in buffalo milk and to assess the accuracy of commercial milk analyzers. This research provides a convenient, fast, and environmentally friendly method for detecting the fatty acid composition in buffalo milk. RESULTS We employed six machine learning algorithms to establish a detection model for 34 fatty acids in buffalo milk. The predictive models demonstrated robust capabilities for high-content fatty acids [C14:0, C15:0, C16:0, C17:0, C18:0, C18:1, saturated fatty acid (SFA), monounsaturated fatty acid (MUFA)], with errors within a 15% range. Traditional FT6000 detection methods exhibited limitations in measuring SFAs and polyunsaturated fatty acids (PUFA). Implementing a mean difference correction of 0.21 for MUFAs and applying regression equations (SFA × 1.0639 + 0.0705; PUFA × 0.5472 + 0.0047) significantly improved measurement accuracy. CONCLUSION This study successfully developed a predictive model for fatty acids in Mediterranean buffalo milk based on FT-MIR spectroscopy. Additionally, a correction was applied to the existing measurement device, FT6000, enabling more accurate measurements of fatty acids in buffalo milk. The findings have practical implications for the food industry, offering a faster and more reliable approach to assess and monitor fatty acid composition in buffalo milk, potentially influencing product development and quality control processes. © 2024 Society of Chemical Industry.
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Affiliation(s)
- Zhiqiu Yao
- International Joint Research Center for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Wenna Zou
- International Joint Research Center for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Xinxin Zhang
- International Joint Research Center for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Pei Nie
- International Joint Research Center for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Hunan Agricultural University, Changsha, China
| | - Haimiao Lv
- International Joint Research Center for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Wei Wang
- International Joint Research Center for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Xuhong Zhao
- International Joint Research Center for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Ying Yang
- International Joint Research Center for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Liguo Yang
- International Joint Research Center for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
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Cardin M, Mounier J, Coton E, Cardazzo B, Perini M, Bertoldi D, Pianezze S, Segato S, Di Camillo B, Cappellato M, Coton M, Carraro L, Currò S, Lucchini R, Mohammadpour H, Novelli E. Discriminative power of DNA-based, volatilome, near infrared spectroscopy, elements and stable isotopes methods for the origin authentication of typical Italian mountain cheese using sPLS-DA modeling. Food Res Int 2024; 178:113975. [PMID: 38309918 DOI: 10.1016/j.foodres.2024.113975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/29/2023] [Accepted: 01/03/2024] [Indexed: 02/05/2024]
Abstract
Origin authentication methods are pivotal in counteracting frauds and provide evidence for certification systems. For these reasons, geographical origin authentication methods are used to ensure product origin. This study focused on the origin authentication (i.e. at the producer level) of a typical mountain cheese origin using various approaches, including shotgun metagenomics, volatilome, near infrared spectroscopy, stable isotopes, and elemental analyses. DNA-based analysis revealed that viral communities achieved a higher classification accuracy rate (97.4 ± 2.6 %) than bacterial communities (96.1 ± 4.0 %). Non-starter lactic acid bacteria and phages specific to each origin were identified. Volatile organic compounds exhibited potential clusters according to cheese origin, with a classification accuracy rate of 90.0 ± 11.1 %. Near-infrared spectroscopy showed lower discriminative power for cheese authentication, yielding only a 76.0 ± 31.6 % classification accuracy rate. Model performances were influenced by specific regions of the infrared spectrum, possibly associated with fat content, lipid profile and protein characteristics. Furthermore, we analyzed the elemental composition of mountain Caciotta cheese and identified significant differences in elements related to dairy equipment, macronutrients, and rare earth elements among different origins. The combination of elements and isotopes showed a decrease in authentication performance (97.0 ± 3.1 %) compared to the original element models, which were found to achieve the best classification accuracy rate (99.0 ± 0.01 %). Overall, our findings emphasize the potential of multi-omics techniques in cheese origin authentication and highlight the complexity of factors influencing cheese composition and hence typicity.
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Affiliation(s)
- Marco Cardin
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020, Legnaro, PD, Italy; Univ Brest, INRAE, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, F-29280 Plouzané, France
| | - Jérôme Mounier
- Univ Brest, INRAE, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, F-29280 Plouzané, France
| | - Emmanuel Coton
- Univ Brest, INRAE, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, F-29280 Plouzané, France
| | - Barbara Cardazzo
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020, Legnaro, PD, Italy
| | - Matteo Perini
- Centro Trasferimento Tecnologico, Fondazione Edmund Mach, Via E. Mach, 1, 38098 San Michele all'Adige, Italy
| | - Daniela Bertoldi
- Centro Trasferimento Tecnologico, Fondazione Edmund Mach, Via E. Mach, 1, 38098 San Michele all'Adige, Italy
| | - Silvia Pianezze
- Centro Trasferimento Tecnologico, Fondazione Edmund Mach, Via E. Mach, 1, 38098 San Michele all'Adige, Italy
| | - Severino Segato
- Department of Animal Medicine, Production and Health, University of Padova, Viale Università 16, 35020 Legnaro, PD, Italy
| | - Barbara Di Camillo
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020, Legnaro, PD, Italy; Department of Information Engineering, University of Padova, Via Gradenigo 6/b, 35131 Padova, Italy
| | - Marco Cappellato
- Department of Information Engineering, University of Padova, Via Gradenigo 6/b, 35131 Padova, Italy
| | - Monika Coton
- Univ Brest, INRAE, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, F-29280 Plouzané, France
| | - Lisa Carraro
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020, Legnaro, PD, Italy
| | - Sarah Currò
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020, Legnaro, PD, Italy
| | - Rosaria Lucchini
- Italian Health Authority and Research Organization for Animal Health and Food Safety (Istituto zooprofilattico sperimentale delle Venezie), Viale Università 10, 35020 Legnaro, PD, Italy
| | - Hooriyeh Mohammadpour
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020, Legnaro, PD, Italy
| | - Enrico Novelli
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020, Legnaro, PD, Italy.
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Dewantier GR, Torley PJ, Blanch EW. Identifying Chemical Differences in Cheddar Cheese Based on Maturity Level and Manufacturer Using Vibrational Spectroscopy and Chemometrics. Molecules 2023; 28:8051. [PMID: 38138541 PMCID: PMC10745544 DOI: 10.3390/molecules28248051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/03/2023] [Accepted: 12/07/2023] [Indexed: 12/24/2023] Open
Abstract
Cheese is a nutritious dairy product and a valuable commodity. Internationally, cheddar cheese is produced and consumed in large quantities, and it is the main cheese variety that is exported from Australia. Despite its importance, the analytical methods to that are used to determine cheese quality rely on traditional approaches that require time, are invasive, and which involve potentially hazardous chemicals. In contrast, spectroscopic techniques can rapidly provide molecular information and are non-destructive, fast, and chemical-free methods. Combined with partner recognition methods (chemometrics), they can identify small changes in the composition or condition of cheeses. In this work, we combined FTIR and Raman spectroscopies with principal component analysis (PCA) to investigate the effects of aging in commercial cheddar cheeses. Changes in the amide I and II bands were the main spectral characteristics responsible for classifying commercial cheddar cheeses based on the ripening time and manufacturer using FTIR, and bands from lipids, including β'-polymorph of fat crystals, were more clearly determined through changes in the Raman spectra.
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Affiliation(s)
- Gerson R. Dewantier
- Applied Chemistry and Environmental Science, School of Science, Royal Melbourne Institute of Technology University, Melbourne, VIC 3001, Australia;
| | - Peter J. Torley
- Biosciences and Food Technology, School of Science, Royal Melbourne Institute of Technology University, Bundoora, VIC 3083, Australia;
| | - Ewan W. Blanch
- Applied Chemistry and Environmental Science, School of Science, Royal Melbourne Institute of Technology University, Melbourne, VIC 3001, Australia;
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Rosa Silva LK, Cardim de Jesus J, Vieira Onelli RR, Conceição DG, Santos LS, Barbosa Ferrão SP. Spectroscopy (MIR), chromatography (RP-HPLC) and chemometrics applied to soluble peptides to discriminate the geographic origin of coalho cheese. BIOCATALYSIS AND AGRICULTURAL BIOTECHNOLOGY 2023. [DOI: 10.1016/j.bcab.2023.102678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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Use of MIR spectroscopy associated with chemometric techniques to verify the authenticity of prato cheese. Int Dairy J 2023. [DOI: 10.1016/j.idairyj.2023.105643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
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Hassoun A, Jagtap S, Garcia-Garcia G, Trollman H, Pateiro M, Lorenzo JM, Trif M, Rusu AV, Aadil RM, Šimat V, Cropotova J, Câmara JS. Food quality 4.0: From traditional approaches to digitalized automated analysis. J FOOD ENG 2023. [DOI: 10.1016/j.jfoodeng.2022.111216] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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