1
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Parsain T, Tripathi A, Tiwari A. Detection of milk adulteration using coffee ring effect and convolutional neural network. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2024; 41:730-741. [PMID: 38814700 DOI: 10.1080/19440049.2024.2358518] [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: 03/15/2024] [Accepted: 05/15/2024] [Indexed: 05/31/2024]
Abstract
A low-cost and effective method is reported to identify water and synthetic milk adulteration of cow's milk using coffee ring patterns. The cow's milk samples were diluted with tap water (TW), distilled water (DW) and mineral water (MW) and drop cast onto glass slides to observe coffee ring patterns. The area of the ring, total particle area and average particle diameter were extracted from these patterns. For each ring, the ratio of total particle area versus total ring area was calculated. The area ratio, regardless of water adulterants, follows an exponential model with respect to average particle diameter. Unlike TW, the ratio for DW and MW adulterated milk are clustered and classified together with respect to the particle diameter. These results were independent of dilution level and are used for adulterant classification. The ring of milk adulterated using synthetic milk gave multiple concentric rings, flower-like structures, and oil globules throughout the dilution level. An Alexnet model was used to classify water and synthetic milk adulterants in authentic milk. The trained model could achieve 96.7% and 95.8% accuracy for binary and tertiary classification respectively. These results enable us to distinguish synthetic milk from pure milk and segregate DW and MW with respect to TW adulterated milk.
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Affiliation(s)
- Tapan Parsain
- Department of Physics, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India
| | - Ajay Tripathi
- Department of Physics, Sikkim University, Gangtok, Sikkim, India
| | - Archana Tiwari
- Department of Physics, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India
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2
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Sharifi F, Naderi-Boldaji M, Ghasemi-Varnamkhasti M, Kheiralipour K, Ghasemi M, Maleki A. Feasibility study of detecting some milk adulterations using a LED-based Vis-SWNIR photoacoustic spectroscopy system. Food Chem 2023; 424:136411. [PMID: 37229900 DOI: 10.1016/j.foodchem.2023.136411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 05/11/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023]
Abstract
The aim of this study is to evaluate a previousely developed photoacoustic spectroscopy system with light sources of visible to short-wave near infrared (Vis-SWNIR, 395-940 nm) for detection of adulterations in cow's milk including formalin, urea, hydrogen peroxide, starch, sodium hypochlorite, and detergent powder. The results of principal component analysis (PCA) showed a very good visual differentiation of different adulterations. The artificial neural networks (ANN) showed the highest classification accuracy (97.6 %) in detection of adulteration type and adulteration level (nearly 100 %). It can be generally concluded that the Vis-SWNIR photoacoustic spectroscopy system is a reliable and potent instrument for detecting various types of milk adulterations. Further studies are suggested with including cow's milk of different sources with probable variations in composition to generalize the findings of the present study. With the extension of the light sources to the range of long-wave NIR, the system can be applied as a diagnostic tool for quality evaluation of other liquid foods.
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Affiliation(s)
- Fatemeh Sharifi
- Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Shahrekord University, Shahrekord 88186-34141, Iran; Bakhtar Higher Education Institution, Ilam 69313-83638, Iran
| | - Mojtaba Naderi-Boldaji
- Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Shahrekord University, Shahrekord 88186-34141, Iran.
| | - Mahdi Ghasemi-Varnamkhasti
- Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Shahrekord University, Shahrekord 88186-34141, Iran
| | - Kamran Kheiralipour
- Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Ilam University, Ilam 69391-77111, Iran
| | - Mohsen Ghasemi
- Department of Physics, Faculty of Basic Sciences, Shahrekord University, Shahrekord 88186-34141, Iran
| | - Ali Maleki
- Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Shahrekord University, Shahrekord 88186-34141, Iran
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3
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Zhang L, Zhu Y, Guo Z, You L, Zhang C, Chen X. Colorimetric Sensing of the Peroxide Number of Milk Powder Using CsPbBr 3 Perovskite Nanocrystals. BIOSENSORS 2023; 13:bios13040493. [PMID: 37185568 PMCID: PMC10137039 DOI: 10.3390/bios13040493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/14/2023] [Accepted: 04/17/2023] [Indexed: 05/17/2023]
Abstract
In this study, a wavelength-shift-based colorimetric sensing approach for the peroxide number of milk powder using CsPbBr3 perovskite nanocrystals (CsPbBr3 NCs) has been developed. Through the fat extraction, REDOX reactions and halogen exchange, as well as the optimized experimental conditions, a colorimetric sensing method was established to determine the peroxide number of milk powder samples. The integrated process of milk powder fat extraction and the REDOX process greatly shortened the determination time. This colorimetric method has a good linear correlation in the range of the peroxide number from 0.02 to 1.96 mmol/kg, and the detection limit was found to be 3 μmol/kg. This study further deepens the application prospect of wavelength-shift-based colorimetric sensing using CsPbBr3 NCs.
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Affiliation(s)
- Li Zhang
- Institute of Analytical Technology and Smart Instruments, College of Environment and Public Healthy, Xiamen Huaxia University, Xiamen 361024, China
| | - Yimeng Zhu
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361005, China
| | - Zhiyong Guo
- Institute of Analytical Technology and Smart Instruments, College of Environment and Public Healthy, Xiamen Huaxia University, Xiamen 361024, China
| | - Longjie You
- National Quality Supervision and Inspection Center for Incense Products, Yongchun 362600, China
| | - Chen Zhang
- Institute of Analytical Technology and Smart Instruments, College of Environment and Public Healthy, Xiamen Huaxia University, Xiamen 361024, China
| | - Xi Chen
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361005, China
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4
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Galindo-Luján R, Pont L, Sanz-Nebot V, Benavente F. Protein profiling and classification of commercial quinoa grains by MALDI-TOF-MS and chemometrics. Food Chem 2023; 398:133895. [DOI: 10.1016/j.foodchem.2022.133895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 07/28/2022] [Accepted: 08/06/2022] [Indexed: 11/29/2022]
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5
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Bhandari SD, Gallegos-Peretz T, Wheat T, Jaudzems G, Kouznetsova N, Petrova K, Shah D, Hengst D, Vacha E, Lu W, Moore JC, Metra P, Xie Z. Amino Acid Fingerprinting of Authentic Nonfat Dry Milk and Skim Milk Powder and Effects of Spiking with Selected Potential Adulterants. Foods 2022; 11:foods11182868. [PMID: 36140996 PMCID: PMC9498471 DOI: 10.3390/foods11182868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/17/2022] [Accepted: 09/02/2022] [Indexed: 11/27/2022] Open
Abstract
A collaborative study was undertaken in which five international laboratories participated to determine amino acid fingerprints in 39 authentic nonfat dry milk (NFDM)/skim milk powder (SMP) samples. A rapid method of amino acid analysis involving microwave-assisted hydrolysis followed by ultra-high performance liquid chromatography-ultraviolet detection (UHPLC-UV) was used for quantitation of amino acids and to calculate their distribution. The performance of this rapid method of analysis was evaluated and was used to determine the amino acid fingerprint of authentic milk powders. The distribution of different amino acids and their predictable upper and lower tolerance limits in authentic NFDM/SMP samples were established as a reference. Amino acid fingerprints of NFDM/SMP were compared with selected proteins and nitrogen rich compounds (proteins from pea, soy, rice, wheat, whey, and fish gelatin) which can be potential economically motivated adulterants (EMA). The amino acid fingerprints of NFDM/SMP were found to be affected by spiking with pea, soy, rice, whey, fish gelatin and arginine among the investigated adulterants but not by wheat protein and melamine. The study results establish an amino acid fingerprint of authentic NFDM/SMP and demonstrate the utility of this method as a tool in verifying the authenticity of milk powders and detecting their adulteration.
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Affiliation(s)
- Sneh D. Bhandari
- Merieux NutriSciences, 3600 Eagle Nest Drive, Crete, IL 60417, USA
| | | | - Thomas Wheat
- Waters Corporation, 34 Maple Street, Milford, MA 01757, USA
| | - Gregory Jaudzems
- Nestlé Quality Assurance Center, 6625 Eiterman Rd., Dublin, OH 43017, USA
| | - Natalia Kouznetsova
- United States Pharmacopeia (USP), 12601 Twinbrook Parkway, Rockville, MD 20852, USA
| | - Katya Petrova
- United States Pharmacopeia (USP), 12601 Twinbrook Parkway, Rockville, MD 20852, USA
| | - Dimple Shah
- Waters Corporation, 34 Maple Street, Milford, MA 01757, USA
| | - Daniel Hengst
- Eurofins Food Integrity and Innovation, Madison, WI 53704, USA
| | - Erika Vacha
- Eurofins Food Integrity and Innovation, Madison, WI 53704, USA
| | - Weiying Lu
- Institute of Food and Nutraceutical Science, Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jeffrey C. Moore
- United States Pharmacopeia (USP), 12601 Twinbrook Parkway, Rockville, MD 20852, USA
| | - Pierre Metra
- Merieux NutriSciences Corporation, 113 Route de Paris, 69160 Tassin la Demi-Lune, France
| | - Zhuohong Xie
- United States Pharmacopeia (USP), 12601 Twinbrook Parkway, Rockville, MD 20852, USA
- Correspondence: ; Tel.: +1-240-221-2052
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Zhang D, Nie J, Niu X, Chen F, Hu Z, Wen X, Li Y, Guo L. Time-resolved spectral-image laser-induced breakdown spectroscopy for precise qualitative and quantitative analysis of milk powder quality by fully excavating the matrix information. Food Chem 2022; 386:132763. [PMID: 35364495 DOI: 10.1016/j.foodchem.2022.132763] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 02/17/2022] [Accepted: 03/19/2022] [Indexed: 11/25/2022]
Abstract
A novel and effective method named time-resolved spectral-image laser-induced breakdown spectroscopy (TRSI-LIBS) was proposed to achieve precise qualitative and quantitative analysis of milk powder quality. To verify the feasibility of TRSI-LIBS, qualitative and quantitative analysis of milk powder quality was carried out. For qualitative analysis of foreign protein adulteration, the accuracy of models based on TRSI-LIBS was higher than those based on LIBS, with an accuracy improvement of about 5% to 10%. For the quantitative analysis of foreign protein adulteration and element content, the quantitative analysis models based on TSRI-LIBS also had better effect. For instance, limit of detection (LOD),determination coefficient of prediction (R2p), root-mean-square error of prediction (RMSEP) and average relative error of prediction (AREP) of quantitative model of calcium (Ca) content based on TRSI-LIBS improved from 1.47 mg/g, 0.95, 0.35 mg/g and 23.29% to 0.81 mg/g, 0.98, 0.20 mg/g and 12.60%.
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Affiliation(s)
- Deng Zhang
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Junfei Nie
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Xuechen Niu
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Feng Chen
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Zhenlin Hu
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Xuelin Wen
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Yuqiong Li
- Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, PR China.
| | - Lianbo Guo
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China.
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7
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Zhao C, Wang T, Chen F, Sun Y, Chen G. 13C NMR detection of non-protein nitrogen substance adulteration in animal feed. Anal Bioanal Chem 2022; 414:2453-2460. [PMID: 35122142 DOI: 10.1007/s00216-022-03886-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 12/27/2021] [Accepted: 01/07/2022] [Indexed: 11/28/2022]
Abstract
Illegal adulteration of melamine in animal feed and food has been widely studied. However, the risk of using substitute non-protein nitrogen substances still exists. In this study, we developed the 13C NMR method for the detection of non-protein nitrogen substance adulteration in animal feed. Three compounds, i.e., urea, melamine, and biuret, were used for method development. We found that the chemical shifts of the characteristic peaks in the carbon spectra of high-nitrogen adulterants were all between 150 and 170 ppm, whereas the chemical shifts of real protein peptide bonds (-CO-NH-) were between 170 and 180 ppm, demonstrating a good distinction between non-protein nitrogen and authentic protein. The method for analyzing melamine, urea, and biuret was validated. The R2 values were all above 0.99 within the calibration range of 0.05-2% (w/w). The limits of quantification of urea, melamine, and biuret were 0.0120%, 0.0660%, and 0.0806%, respectively. This method involves simple sample pretreatment and rapid detection while also providing high accuracy. All the sample information obtained by NMR detection does not require strict impurity removal. Compared with a previously reported 1H NMR method, the developed 13C NMR method does not require strict moisture removal to avoid active hydrogen exchange, and the interfering peak overlap is mitigated.
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Affiliation(s)
- Chengxiang Zhao
- College of Chemistry and Chemical Engineering, Tianjin University of Technology, Tianjin, 300384, China.,Institute of Agricultural Quality Standards and Testing Technology, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Tongtong Wang
- Institute of Agricultural Quality Standards and Testing Technology, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Furong Chen
- Institute of Agricultural Quality Standards and Testing Technology, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yongyue Sun
- College of Chemistry and Chemical Engineering, Tianjin University of Technology, Tianjin, 300384, China.
| | - Gang Chen
- Institute of Agricultural Quality Standards and Testing Technology, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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8
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Shenashen MA, Emran MY, El Sabagh A, Selim MM, Elmarakbi A, El-Safty SA. Progress in sensory devices of pesticides, pathogens, coronavirus, and chemical additives and hazards in food assessment: Food safety concerns. PROGRESS IN MATERIALS SCIENCE 2022; 124:100866. [DOI: 10.1016/j.pmatsci.2021.100866] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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9
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Negi A, Lakshmi P, Praba K, Meenatchi R, Pare A. Detection of Food Adulterants in Different Foodstuff. Food Chem 2021. [DOI: 10.1002/9781119792130.ch5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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10
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Nagraik R, Sharma A, Kumar D, Chawla P, Kumar AP. Milk adulterant detection: Conventional and biosensor based approaches: A review. SENSING AND BIO-SENSING RESEARCH 2021. [DOI: 10.1016/j.sbsr.2021.100433] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
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11
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Galindo-Luján R, Pont L, Sanz-Nebot V, Benavente F. Classification of quinoa varieties based on protein fingerprinting by capillary electrophoresis with ultraviolet absorption diode array detection and advanced chemometrics. Food Chem 2020; 341:128207. [PMID: 33035861 DOI: 10.1016/j.foodchem.2020.128207] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 08/10/2020] [Accepted: 09/23/2020] [Indexed: 12/24/2022]
Abstract
Quinoa (Chenopodium quinoa Willd.) is an andean grain with exceptional nutritional properties that has been progressively introduced in western countries as a protein-rich super food with a broad amino acid spectrum. Quinoa is consumed as whole grain, but it is also milled to produce high-value flour, which is susceptible to adulteration. Therefore, there is a growing interest in developing novel analytical methods to get further information about quinoa at the chemical level. In this study, we developed a rapid and simple capillary electrophoresis-ultraviolet absorption diode array detection (CE-UV-DAD) method to obtain characteristic multiwavelength electrophoretic profiles of soluble protein extracts from different quinoa grain varieties. Then, advanced chemometric methods (i.e. multivariate curve resolution alternating least squares, MCR-ALS, followed by principal component analysis, PCA, and partial least squares discriminant analysis, PLS-DA) were applied to deconvolute the components present in the electropherograms and classify the quinoa varieties according to their differential protein composition.
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Affiliation(s)
- Rocío Galindo-Luján
- Department of Chemical Engineering and Analytical Chemistry, Institute for Research on Nutrition and Food Safety (INSA·UB), University of Barcelona, 08028 Barcelona, Spain
| | - Laura Pont
- Department of Chemical Engineering and Analytical Chemistry, Institute for Research on Nutrition and Food Safety (INSA·UB), University of Barcelona, 08028 Barcelona, Spain
| | - Victoria Sanz-Nebot
- Department of Chemical Engineering and Analytical Chemistry, Institute for Research on Nutrition and Food Safety (INSA·UB), University of Barcelona, 08028 Barcelona, Spain
| | - Fernando Benavente
- Department of Chemical Engineering and Analytical Chemistry, Institute for Research on Nutrition and Food Safety (INSA·UB), University of Barcelona, 08028 Barcelona, Spain.
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12
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Esteki M, Shahsavari Z, Simal-Gandara J. Food identification by high performance liquid chromatography fingerprinting and mathematical processing. Food Res Int 2019; 122:303-317. [DOI: 10.1016/j.foodres.2019.04.025] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 04/09/2019] [Accepted: 04/10/2019] [Indexed: 01/31/2023]
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13
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Bergana MM, Adams KM, Harnly J, Moore JC, Xie Z. Non-targeted detection of milk powder adulteration by 1H NMR spectroscopy and conformity index analysis. J Food Compost Anal 2019. [DOI: 10.1016/j.jfca.2019.01.016] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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14
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Yang J, Zheng N, Soyeurt H, Yang Y, Wang J. Detection of plant protein in adulterated milk using nontargeted nano-high-performance liquid chromatography-tandem mass spectroscopy combined with principal component analysis. Food Sci Nutr 2019; 7:56-64. [PMID: 30680159 PMCID: PMC6341172 DOI: 10.1002/fsn3.791] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 08/07/2018] [Accepted: 08/07/2018] [Indexed: 12/20/2022] Open
Abstract
The objective of this study was to detect plant protein adulterated in fluid milk using nano-high-performance liquid chromatography (HPLC)-tandem mass spectroscopy (LC-MS/MS) combined with proteomics. Unadulterated milk and samples adulterated with soy protein, pea protein, hydrolyzed wheat protein, and hydrolyzed rice protein were prepared, with plant protein level ranged from 0.5% to 8% in total protein. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gels clearly revealed that centrifugation at 20,000 g for 60 min would reduce band intensity of casein and albumin in milk. Results of nano-HPLC-MS/MS indicated the major proteins of soy (β-conglycinin, glycinin), pea (vincilin, convicilin, legumin), and wheat (glutenin and gliadin) in adulterated milks, allowing detection of soy protein and hydrolyzed wheat protein at the level above 0.5% in total protein and pea protein at the level of 2 and 4%. No rice protein was identified in milk samples adulterated with hydrolyzed rice protein. Combined with principal component analysis, nano-HPLC-MS/MS could discriminate all the adulterated samples from authentic milk. This study demonstrated the feasibility of nano-HPLC-MS/MS on the detection of (hydrolyzed) plant protein adulterated in milk.
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Affiliation(s)
- Jinhui Yang
- Ministry of Agriculture – Milk Risk Assessment LaboratoryInstitute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
- Ministry of Agriculture – Milk and Dairy Product Inspection CenterBeijingChina
- State Key Laboratory of Animal NutritionInstitute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
- AGROBIOCHEM Department and Teaching and Research Centre (TERRA)Gembloux Agro‐Bio TechUniversity of LiègeGemblouxBelgium
| | - Nan Zheng
- Ministry of Agriculture – Milk Risk Assessment LaboratoryInstitute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
- Ministry of Agriculture – Milk and Dairy Product Inspection CenterBeijingChina
- State Key Laboratory of Animal NutritionInstitute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
| | - Hélène Soyeurt
- AGROBIOCHEM Department and Teaching and Research Centre (TERRA)Gembloux Agro‐Bio TechUniversity of LiègeGemblouxBelgium
| | - Yongxin Yang
- Institute of Animal Husbandry and Veterinary MedicineAnhui Academy of Agricultural SciencesHefeiChina
| | - Jiaqi Wang
- Ministry of Agriculture – Milk Risk Assessment LaboratoryInstitute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
- Ministry of Agriculture – Milk and Dairy Product Inspection CenterBeijingChina
- State Key Laboratory of Animal NutritionInstitute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
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15
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A review on the application of chromatographic methods, coupled to chemometrics, for food authentication. Food Control 2018. [DOI: 10.1016/j.foodcont.2018.06.015] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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16
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Karunathilaka SR, Yakes BJ, He K, Brückner L, Mossoba MM. First use of handheld Raman spectroscopic devices and on-board chemometric analysis for the detection of milk powder adulteration. Food Control 2018. [DOI: 10.1016/j.foodcont.2018.04.046] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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17
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Harnly J, Bergana MM, Adams KM, Xie Z, Moore JC. Variance of Commercial Powdered Milks Analyzed by Proton Nuclear Magnetic Resonance and Impact on Detection of Adulterants. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2018; 66:8478-8488. [PMID: 29697263 DOI: 10.1021/acs.jafc.8b00432] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Proton nuclear magnetic resonance spectra for 66 commercial powdered milk samples were analyzed by principal component analysis, soft independent modeling of class analogy, and pooled, crossed analysis of variance. It was found that the sample type (skim milk powder or non-fat dry milk), the supplier, the production site, the processing temperature (high, medium, or low temperature), and the day of analysis provided statistically significant sources of variation. Interestingly, inexact alignment (deviations of ±0.002 ppm) of the spectral reference peak was a significant source of variation, and fine alignment was necessary before the variation arising from the other experimental factors could be accurately evaluated. Using non-targeted analysis, the lowest detectable adulteration for dicyandiamide, melamine, and sucrose was 0.05%, the lowest detectable adulteration for maltodextrin and urea was 0.5%, the lowest detectable adulteration for ammonium sulfate and whey was 5%, and the lowest adulteration for soy protein isolate was undetectable using methods described herein. The measurement of variance and detection of adulteration were relatively unaffected by the resolution. Similar results were obtained with unbinned data (0.0003 ppm resolution) and binning of 333 data points (0.1 ppm resolution).
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Affiliation(s)
- James Harnly
- Food Composition and Methods Development Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service , United States Department of Agriculture , Building 161, BARC-East, Beltsville , Maryland 20705 , United States
| | - Marti Mamula Bergana
- United States Pharmacopeia , 12601 Twinbrook Parkway , Rockville , Maryland 20852 , United States
| | - Kristie M Adams
- United States Pharmacopeia , 12601 Twinbrook Parkway , Rockville , Maryland 20852 , United States
| | - Zhuohong Xie
- United States Pharmacopeia , 12601 Twinbrook Parkway , Rockville , Maryland 20852 , United States
| | - Jeffrey C Moore
- United States Pharmacopeia , 12601 Twinbrook Parkway , Rockville , Maryland 20852 , United States
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18
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Du L, Lu W, Cai Z(J, Bao L, Hartmann C, Gao B, Yu L(L. Rapid detection of milk adulteration using intact protein flow injection mass spectrometric fingerprints combined with chemometrics. Food Chem 2018; 240:573-578. [DOI: 10.1016/j.foodchem.2017.07.107] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Revised: 07/19/2017] [Accepted: 07/24/2017] [Indexed: 01/06/2023]
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19
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Gautam PB, Sharma R, Lata K, Rajput YS, Mann B. Construction of a lateral flow strip for detection of soymilk in milk. Journal of Food Science and Technology 2017; 54:4213-4219. [PMID: 29184227 DOI: 10.1007/s13197-017-2890-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 07/10/2017] [Accepted: 09/22/2017] [Indexed: 01/27/2023]
Abstract
A lateral flow based detection method for ascertaining the presence of soymilk in whole bovine milk has been described. The method uses commercially available rabbit anti-soy protein antibodies conjugated to gold nanoparticles (AuNPs) wherein soymilk protein in adulterated milk and soymilk protein at test line competes for limited antibodies. At control line, anti-rabbit immunoglobulin was immobilized for ensuring flow properties of antibody-conjugated AuNPs. Absence or diminished intensity of band at test line indicates presence of soymilk in milk. The soymilk detection limit was 1.75% (v/v) in whole bovine milk and results are available in 5 min. Constructed lateral flow device can be used for on-spot examination of soymilk in milk.
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Affiliation(s)
- Priyae Brath Gautam
- Department of Dairy Chemistry, National Dairy Research Institute, Karnal, 132001 India
| | - Rajan Sharma
- Department of Dairy Chemistry, National Dairy Research Institute, Karnal, 132001 India
| | - Kiran Lata
- Department of Dairy Chemistry, National Dairy Research Institute, Karnal, 132001 India
| | - Y S Rajput
- Department of Animal Biochemistry, National Dairy Research Institute, Karnal, 132001 India
| | - Bimlesh Mann
- Department of Dairy Chemistry, National Dairy Research Institute, Karnal, 132001 India
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20
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Agrimonti C, Bottari B, Sardaro MLS, Marmiroli N. Application of real-time PCR (qPCR) for characterization of microbial populations and type of milk in dairy food products. Crit Rev Food Sci Nutr 2017; 59:423-442. [DOI: 10.1080/10408398.2017.1375893] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Caterina Agrimonti
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma, Italy
| | - Benedetta Bottari
- Department of Food and Drug Science, University of Parma, Parma, Italy
| | - Maria Luisa Savo Sardaro
- Department of Food and Drug Science, University of Parma, Parma, Italy; Department of Nutrition and Gastronomy, University San Raffaele Roma Srl, Rome, Italy
| | - Nelson Marmiroli
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma, Italy
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21
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Lu W, Liu J, Gao B, Lv X, Yu L(L. Technical note: Nontargeted detection of adulterated plant proteins in raw milk by UPLC-quadrupole time-of-flight mass spectrometric proteomics combined with chemometrics. J Dairy Sci 2017; 100:6980-6986. [DOI: 10.3168/jds.2017-12574] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Accepted: 05/15/2017] [Indexed: 11/19/2022]
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22
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Hong E, Lee SY, Jeong JY, Park JM, Kim BH, Kwon K, Chun HS. Modern analytical methods for the detection of food fraud and adulteration by food category. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2017; 97:3877-3896. [PMID: 28397254 DOI: 10.1002/jsfa.8364] [Citation(s) in RCA: 153] [Impact Index Per Article: 21.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2016] [Revised: 03/23/2017] [Accepted: 04/04/2017] [Indexed: 06/07/2023]
Abstract
This review provides current information on the analytical methods used to identify food adulteration in the six most adulterated food categories: animal origin and seafood, oils and fats, beverages, spices and sweet foods (e.g. honey), grain-based food, and others (organic food and dietary supplements). The analytical techniques (both conventional and emerging) used to identify adulteration in these six food categories involve sensory, physicochemical, DNA-based, chromatographic and spectroscopic methods, and have been combined with chemometrics, making these techniques more convenient and effective for the analysis of a broad variety of food products. Despite recent advances, the need remains for suitably sensitive and widely applicable methodologies that encompass all the various aspects of food adulteration. © 2017 Society of Chemical Industry.
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Affiliation(s)
- Eunyoung Hong
- Advanced Food Safety Research Group, BK21 Plus, School of Food Science and Technology, Chung-Ang University, Gyeonggi-do, Republic of Korea
| | - Sang Yoo Lee
- Advanced Food Safety Research Group, BK21 Plus, School of Food Science and Technology, Chung-Ang University, Gyeonggi-do, Republic of Korea
| | - Jae Yun Jeong
- Science and Technology Management Policy, University of Science & Technology, Gyeonggi-do, Republic of Korea
- R&D Strategy, Korea Food Research Institute, Gyeonggi-do, Republic of Korea
| | - Jung Min Park
- Science and Technology Management Policy, University of Science & Technology, Gyeonggi-do, Republic of Korea
- R&D Strategy, Korea Food Research Institute, Gyeonggi-do, Republic of Korea
| | - Byung Hee Kim
- Department of Food Science and Nutrition, Sookmyung Women's University, Seoul, Republic of Korea
| | - Kisung Kwon
- New Hazardous Substances Team, National Institute of Food and Drug Safety Evaluation, Chungcheongbuk-do, Republic of Korea
| | - Hyang Sook Chun
- Advanced Food Safety Research Group, BK21 Plus, School of Food Science and Technology, Chung-Ang University, Gyeonggi-do, Republic of Korea
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23
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Methodologies for the Characterization of the Quality of Dairy Products. ADVANCES IN FOOD AND NUTRITION RESEARCH 2017; 82:237-275. [PMID: 28427534 DOI: 10.1016/bs.afnr.2016.12.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The growing interest of consumers in food quality and safety issues has contributed to the increasing demand for sensitive and rapid analytical technologies. Physicochemical, textural, sensory, etc., methods have been used to evaluate the quality and authenticity of milk and dairy products. Despite the importance of these standard methods, they are expensive and time consuming. Recently, spectroscopic methods have shown great potential due to speed of analysis, minimal sample preparation, high repeatability, low cost, and, most of all, the fact that these techniques are noninvasive and nondestructive and, therefore, could be applied to any on-line monitoring system. This chapter gave examples of the application of the most commonly traditional methods for the determination of the quality of milk and dairy products. A special focus is devoted to the use of infrared and fluorescence spectroscopies for the evaluation of the quality of dairy products.
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24
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Karunathilaka SR, Farris S, Mossoba MM, Moore JC, Yakes BJ. Non-targeted detection of milk powder adulteration using Raman spectroscopy and chemometrics: melamine case study. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2016; 34:170-182. [DOI: 10.1080/19440049.2016.1260168] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Sanjeewa R. Karunathilaka
- US Food and Drug Administration (USFDA), Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, MD, USA
| | - Samantha Farris
- US Food and Drug Administration (USFDA), Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, MD, USA
| | - Magdi M. Mossoba
- US Food and Drug Administration (USFDA), Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, MD, USA
| | | | - Betsy Jean Yakes
- US Food and Drug Administration (USFDA), Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, MD, USA
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25
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Azad T, Ahmed S. Common milk adulteration and their detection techniques. INTERNATIONAL JOURNAL OF FOOD CONTAMINATION 2016. [DOI: 10.1186/s40550-016-0045-3] [Citation(s) in RCA: 98] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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26
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Poonia A, Jha A, Sharma R, Singh HB, Rai AK, Sharma N. Detection of adulteration in milk: A review. INT J DAIRY TECHNOL 2016. [DOI: 10.1111/1471-0307.12274] [Citation(s) in RCA: 93] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Amrita Poonia
- Centre of Food Science and Technology; Banaras Hindu University; Varanasi 221 005 India
| | - Alok Jha
- Centre of Food Science and Technology; Banaras Hindu University; Varanasi 221 005 India
| | - Rajan Sharma
- Division of Dairy Chemistry; National Dairy Research Institute; Karnal 132 001 India
| | | | - Ashwini Kumar Rai
- Department of Botany; Banaras Hindu University; Varanasi 221 005 India
| | - Nitya Sharma
- Department of Farm Engineering; Banaras Hindu University; Varanasi 221 005 India
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27
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Karunathilaka SR, Kia ARF, Srigley C, Chung JK, Mossoba MM. Nontargeted, Rapid Screening of Extra Virgin Olive Oil Products for Authenticity Using Near-Infrared Spectroscopy in Combination with Conformity Index and Multivariate Statistical Analyses. J Food Sci 2016; 81:C2390-C2397. [PMID: 27626761 DOI: 10.1111/1750-3841.13432] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Revised: 07/15/2016] [Accepted: 07/27/2016] [Indexed: 01/27/2023]
Abstract
A rapid tool for evaluating authenticity was developed and applied to the screening of extra virgin olive oil (EVOO) retail products by using Fourier-transform near infrared (FT-NIR) spectroscopy in combination with univariate and multivariate data analysis methods. Using disposable glass tubes, spectra for 62 reference EVOO, 10 edible oil adulterants, 20 blends consisting of EVOO spiked with adulterants, 88 retail EVOO products and other test samples were rapidly measured in the transmission mode without any sample preparation. The univariate conformity index (CI) and the multivariate supervised soft independent modeling of class analogy (SIMCA) classification tool were used to analyze the various olive oil products which were tested for authenticity against a library of reference EVOO. Better discrimination between the authentic EVOO and some commercial EVOO products was observed with SIMCA than with CI analysis. Approximately 61% of all EVOO commercial products were flagged by SIMCA analysis, suggesting that further analysis be performed to identify quality issues and/or potential adulterants. Due to its simplicity and speed, FT-NIR spectroscopy in combination with multivariate data analysis can be used as a complementary tool to conventional official methods of analysis to rapidly flag EVOO products that may not belong to the class of authentic EVOO.
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Affiliation(s)
- Sanjeewa R Karunathilaka
- Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, Md, U.S.A
| | - Ali-Reza Fardin Kia
- Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, Md, U.S.A
| | - Cynthia Srigley
- Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, Md, U.S.A
| | - Jin Kyu Chung
- Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, Md, U.S.A
| | - Magdi M Mossoba
- Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, Md, U.S.A.
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28
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Review on proteomics for food authentication. J Proteomics 2016; 147:212-225. [PMID: 27389853 DOI: 10.1016/j.jprot.2016.06.033] [Citation(s) in RCA: 114] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Revised: 06/21/2016] [Accepted: 06/28/2016] [Indexed: 12/24/2022]
Abstract
UNLABELLED Consumers have the right to know what is in the food they are eating. Accordingly, European and global food regulations require that the provenance of the food can be guaranteed from farm to fork. Many different instrumental techniques have been proposed for food authentication. Although traditional methods are still being used, new approaches such as genomics, proteomics, and metabolomics are helping to complement existing methodologies for verifying the claims made about certain food products. During the last decade, proteomics (the large-scale analysis of proteins in a particular biological system at a particular time) has been applied to different research areas within food technology. Since proteins can be used as markers for many properties of a food, even indicating processes to which the food has been subjected, they can provide further evidence of the foods labeling claim. This review is a comprehensive and updated overview of the applications, drawbacks, advantages, and challenges of proteomics for food authentication in the assessment of the foods compliance with labeling regulations and policies. SIGNIFICANCE This review paper provides a comprehensive and critical overview of the application of proteomics approaches to determine the authenticity of several food products updating the performances and current limitations of the applied techniques in both laboratory and industrial environments.
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29
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Karunathilaka SR, Farris S, Mossoba MM, Moore JC, Yakes BJ. Characterising variances of milk powder and instrumentation for the development of a non-targeted, Raman spectroscopy and chemometrics detection method for the evaluation of authenticity. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2016; 33:921-32. [PMID: 27167451 PMCID: PMC4918629 DOI: 10.1080/19440049.2016.1188437] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Accepted: 04/29/2016] [Indexed: 01/20/2023]
Abstract
There is a need to develop rapid tools to screen milk products for economically motivated adulteration. An understanding of the physiochemical variability within skim milk powder (SMP) and non-fat dry milk (NFDM) is the key to establishing the natural differences of these commodities prior to the development of non-targeted detection methods. This study explored the sources of variance in 71 commercial SMP and NFDM samples using Raman spectroscopy and principal component analysis (PCA) and characterised the largest number of commercial milk powders acquired from a broad number of international manufacturers. Spectral pre-processing using a gap-segment derivative transformation (gap size = 5, segment width = 9, fourth derivative) in combination with sample normalisation was necessary to reduce the fluorescence background of the milk powder samples. PC scores plots revealed no clear trends for various parameters, including day of analysis, powder type, supplier and processing temperatures, while the largest variance was due to irreproducibility in sample positioning. Significant chemical sources of variances were explained by using the spectral features in the PC loadings plots where four samples from the same manufacturer were determined to likely contain an additional component or lactose anomers, and one additional sample was identified as an outlier and likely containing an adulterant or differing quality components. The variance study discussed herein with this large, diverse set of milk powders holds promise for future use as a non-targeted screening method that could be applied to commercial milk powders.
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Affiliation(s)
- Sanjeewa R. Karunathilaka
- US Food and Drug Administration (USFDA), Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, MD, USA
| | - Samantha Farris
- US Food and Drug Administration (USFDA), Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, MD, USA
| | - Magdi M. Mossoba
- US Food and Drug Administration (USFDA), Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, MD, USA
| | | | - Betsy Jean Yakes
- US Food and Drug Administration (USFDA), Center for Food Safety and Applied Nutrition, Office of Regulatory Science, College Park, MD, USA
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30
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Handford CE, Campbell K, Elliott CT. Impacts of Milk Fraud on Food Safety and Nutrition with Special Emphasis on Developing Countries. Compr Rev Food Sci Food Saf 2015; 15:130-142. [DOI: 10.1111/1541-4337.12181] [Citation(s) in RCA: 131] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Accepted: 09/29/2015] [Indexed: 01/19/2023]
Affiliation(s)
- Caroline E. Handford
- the Inst. for Global Food Security, School of Biological Sciences; Queen's Univ. Belfast; 18-30 Malone Rd. Belfast Northern Ireland BT9 5BN United Kingdom
| | - Katrina Campbell
- the Inst. for Global Food Security, School of Biological Sciences; Queen's Univ. Belfast; 18-30 Malone Rd. Belfast Northern Ireland BT9 5BN United Kingdom
| | - Christopher T. Elliott
- the Inst. for Global Food Security, School of Biological Sciences; Queen's Univ. Belfast; 18-30 Malone Rd. Belfast Northern Ireland BT9 5BN United Kingdom
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31
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Kamal M, Karoui R. Analytical methods coupled with chemometric tools for determining the authenticity and detecting the adulteration of dairy products: A review. Trends Food Sci Technol 2015. [DOI: 10.1016/j.tifs.2015.07.007] [Citation(s) in RCA: 133] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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32
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Lu W, Lv X, Gao B, Shi H, Yu LL. Differentiating Milk and Non-milk Proteins by UPLC Amino Acid Fingerprints Combined with Chemometric Data Analysis Techniques. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2015; 63:3996-4002. [PMID: 25835028 DOI: 10.1021/acs.jafc.5b00702] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Amino acid fingerprinting combined with chemometric data analysis was used to differentiate milk and non-milk proteins in this study. Microwave-assisted hydrolysis and ultraperformance liquid chromatography (UPLC) were used to obtain the amino acid fingerprints. Both univariate and multivariate chemometrics methods were applied for differentiation. The confidence boundary of amino acid concentration, principal component analysis (PCA), and partial least-squares-discriminant analysis (PLS-DA) of the amino acid fingerprints demonstrated that there were significant differences between milk proteins and inexpensive non-milk protein powders from other biological sources including whey, peanut, corn, soy, fish, egg yolk, beef extract, collagen, and cattle bone. The results indicate that the amino acid compositions with the chemometric techniques could be applied for the detection of potential protein adulterants in milk.
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Affiliation(s)
- Weiying Lu
- †Institute of Food and Nutraceutical Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiaxia Lv
- †Institute of Food and Nutraceutical Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Boyan Gao
- §Department of Nutrition and Food Science, University of Maryland, College Park, Maryland 20742, United States
| | - Haiming Shi
- †Institute of Food and Nutraceutical Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Liangli Lucy Yu
- §Department of Nutrition and Food Science, University of Maryland, College Park, Maryland 20742, United States
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