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Khan UM, Sameen A, Decker EA, Shabbir MA, Hussain S, Latif A, Abdi G, Aadil RM. Implementation of plant extracts for cheddar-type cheese production in conjunction with FTIR and Raman spectroscopy comparison. Food Chem X 2024; 22:101256. [PMID: 38495457 PMCID: PMC10943033 DOI: 10.1016/j.fochx.2024.101256] [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: 12/21/2023] [Revised: 02/13/2024] [Accepted: 02/25/2024] [Indexed: 03/19/2024] Open
Abstract
Plant extracts have demonstrated the ability to act as coagulants for milk coagulation at an adequate concentration, wide temperatures and pH ranges. This research is focused on the use of different vegetative extracts such as Citrus aurnatium flower extract (CAFE), bromelain, fig latex, and melon extract as economical and beneficial coagulants in the development of plant-based cheddar-type cheese. The cheddar-type cheese samples were subjected to physicochemical analysis in comparison to controlled cheese samples made from acetic acid and rennet. The fat, moisture, protein, and salt contents remained the same over the storage period, but a slight decline was observed in pH. The Ferric reducing antioxidant power (FRAP) increased with the passage of the ripening period. The FTIR and Raman spectra showed exponential changes and qualitative estimates in the binding and vibrational structure of lipids and protein in plant-based cheeses. The higher FTIR and Raman spectra bands were observed in acid, rennet, bromelain, and CAFE due to their firm and strong texture of cheese while lower spectra were observed in cheese made from melon extract due to weak curdling and textural properties. These plant extracts are economical and easily available alternative sources for cheese production with higher protein and nutritional contents.
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Affiliation(s)
- Usman Mir Khan
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan
| | - Aysha Sameen
- Department of Food Science and Technology, Government College Women University, Faisalabad 38000, Pakistan
| | - Eric Andrew Decker
- Department of Food Science, University of Massachusetts, Amherst, MA 01003, USA
| | - Muhammad Asim Shabbir
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan
| | - Shahzad Hussain
- Department of Food Science and Nutrition, College of Food and Agriculture, King Saud University, Riyadh 11451, Saudi Arabia
| | - Anam Latif
- Institute of Food Science and Nutrition, University of Sargodha, Sargodha 40100, Pakistan
| | - Gholamreza Abdi
- Department of Biotechnology, Persian Gulf Research Institute, Persian Gulf University, Bushehr 75169, Iran
| | - Rana Muhammad Aadil
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan
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2
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Nunes PP, Almeida MR, Pacheco FG, Fantini C, Furtado CA, Ladeira LO, Jorio A, Júnior APM, Santos RL, Borges ÁM. Detection of carbon nanotubes in bovine raw milk through Fourier transform Raman spectroscopy. J Dairy Sci 2024; 107:2681-2689. [PMID: 37923204 DOI: 10.3168/jds.2023-23481] [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: 03/12/2023] [Accepted: 10/11/2023] [Indexed: 11/07/2023]
Abstract
The potential use of carbon-based methodologies for drug delivery and reproductive biology in cows raises concerns about residues in milk and food safety. This study aimed to assess the potential of Fourier transform Raman spectroscopy and discriminant analysis using partial least squares (PLS-DA) to detect functionalized multiwalled carbon nanotubes (MWCNT) in bovine raw milk. Oxidized MWCNT were diluted in milk at different concentrations from 25.00 to 0.01 µg/mL. Raman spectroscopy measurements and PLS-DA were performed to identify low concentrations of MWCNT in milk samples. The PLS-DA model was characterized by the analysis of the variable importance in projection (VIP) scores. All the training samples were correctly classified by the model, resulting in no false-positive or false-negative classifications. For test samples, only one false-negative result was observed, for 0.01 µg/mL MWCNT dilution. The association between Raman spectroscopy and PLS-DA was able to identify MWCNT diluted in milk samples up to 0.1 µg/mL. The PLS-DA model was built and validated using a set of test samples and spectrally interpreted based on the highest VIP scores. This allowed the identification of the vibrational modes associated with the D and G bands of MWCNT, as well as the milk bands, which were the most important variables in this analysis.
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Affiliation(s)
- Philipe P Nunes
- Department of Veterinary Clinic and Surgery, Veterinary School, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil
| | - Mariana R Almeida
- Department of Chemistry, Institute of Exact Science, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil
| | - Flávia G Pacheco
- Laboratory of Carbon Nanostructure Chemistry, Nuclear Technology Development Center, Belo Horizonte, MG 31270-901, Brazil
| | - Cristiano Fantini
- Department of Physics, Institute of Exact Science, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil
| | - Clascídia A Furtado
- Laboratory of Carbon Nanostructure Chemistry, Nuclear Technology Development Center, Belo Horizonte, MG 31270-901, Brazil
| | - Luiz O Ladeira
- Department of Physics, Institute of Exact Science, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil
| | - Ado Jorio
- Department of Physics, Institute of Exact Science, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil
| | - Antônio P M Júnior
- Department of Veterinary Clinic and Surgery, Veterinary School, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil
| | - Renato L Santos
- Department of Veterinary Clinic and Surgery, Veterinary School, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil
| | - Álan M Borges
- Department of Veterinary Clinic and Surgery, Veterinary School, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil.
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3
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Stocco G, Gómez-Mascaraque LG, Deshwal GK, Sanchez JC, Molle A, Pizzamiglio V, Berzaghi P, Gergov G, Cipolat-Gotet C. Exploring the use of NIR and Raman spectroscopy for the prediction of quality traits in PDO cheeses. Front Nutr 2024; 11:1327301. [PMID: 38379551 PMCID: PMC10876835 DOI: 10.3389/fnut.2024.1327301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 01/04/2024] [Indexed: 02/22/2024] Open
Abstract
The aims of this proof of principle study were to compare two different chemometric approaches using a Bayesian method, Partial Least Square (PLS) and PLS-discriminant analysis (DA), for the prediction of the chemical composition and texture properties of the Grana Padano (GP) and Parmigiano Reggiano (PR) PDO cheeses by using NIR and Raman spectra and quantify their ability to distinguish between the two PDO and among their ripening periods. For each dairy chain consortium, 9 cheese samples from 3 dairy industries were collected for a total of 18 cheese samples. Three seasoning times were chosen for each dairy industry: 12, 20, and 36 months for GP and 12, 24, and 36 months for PR. A portable NIR instrument (spectral range: 950-1,650 nm) was used on 3 selected spots on the paste of each cheese sample, for a total of 54 spectra collected. An Alpha300 R confocal Raman microscope was used to collect 10 individual spectra for each cheese sample in each spot for a total of 540 Raman spectra collected. After the detection of eventual outliers, the spectra were also concatenated together (NIR + Raman). All the cheese samples were assessed in terms of chemical composition and texture properties following the official reference methods. A Bayesian approach and PLS-DA were applied to the NIR, Raman, and fused spectra to predict the PDO type and seasoning time. The PLS-DA reached the best performances, with 100% correctly identified PDO type using Raman only. The fusion of the data improved the results in 60% of the cases with the Bayesian and of 40% with the PLS-DA approach. A Bayesian approach and a PLS procedure were applied to the NIR, Raman, and fused spectra to predict the chemical composition of the cheese samples and their texture properties. In this case, the best performance in validation was reached with the Bayesian method on Raman spectra for fat (R2VAL = 0.74). The fusion of the data was not always helpful in improving the prediction accuracy. Given the limitations associated with our sample set, future studies will expand the sample size and incorporate diverse PDO cheeses.
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Affiliation(s)
- Giorgia Stocco
- Department of Veterinary Science, University of Parma, Parma, Italy
| | - Laura G. Gómez-Mascaraque
- Department of Food Chemistry and Technology, Teagasc Food Research Centre Moorepark, Fermoy, Ireland
| | - Gaurav Kr Deshwal
- Department of Food Chemistry and Technology, Teagasc Food Research Centre Moorepark, Fermoy, Ireland
| | | | - Arnaud Molle
- Department of Veterinary Science, University of Parma, Parma, Italy
| | | | - Paolo Berzaghi
- Department of Animal Medicine, Production and Health, University of Padova, Padova, Italy
| | - Georgi Gergov
- Institute of Chemical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria
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4
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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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5
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Shin S, Doh IJ, Okeyo K, Bae E, Robinson JP, Rajwa B. Hybrid Raman and Laser-Induced Breakdown Spectroscopy for Food Authentication Applications. Molecules 2023; 28:6087. [PMID: 37630339 PMCID: PMC10458226 DOI: 10.3390/molecules28166087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 08/06/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
The issue of food fraud has become a significant global concern as it affects both the quality and safety of food products, ultimately resulting in the loss of customer trust and brand loyalty. To address this problem, we have developed an innovative approach that can tackle various types of food fraud, including adulteration, substitution, and dilution. Our methodology utilizes an integrated system that combines laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. Although both techniques emerged as valuable tools for food analysis, they have until now been used separately, and their combined potential in food fraud has not been thoroughly tested. The aim of our study was to demonstrate the potential benefits of integrating Raman and LIBS modalities in a portable system for improved product classification and subsequent authentication. In pursuit of this objective, we designed and tested a compact, hybrid Raman/LIBS system, which exhibited distinct advantages over the individual modalities. Our findings illustrate that the combination of these two modalities can achieve higher accuracy in product classification, leading to more effective and reliable product authentication. Overall, our research highlights the potential of hybrid systems for practical applications in a variety of industries. The integration and design were mainly focused on the detection and characterization of both elemental and molecular elements in various food products. Two different sets of solid food samples (sixteen Alpine-style cheeses and seven brands of Arabica coffee beans) were chosen for the authentication analysis. Class detection and classification were accomplished through the use of multivariate feature selection and machine-learning procedures. The accuracy of classification was observed to improve by approximately 10% when utilizing the hybrid Raman/LIBS spectra, as opposed to the analysis of spectra from the individual methods. This clearly demonstrates that the hybrid system can significantly improve food authentication accuracy while maintaining the portability of the combined system. Thus, the successful implementation of a hybrid Raman-LIBS technique is expected to contribute to the development of novel portable devices for food authentication in food as well as other various industries.
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Affiliation(s)
- Sungho Shin
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA; (I.-J.D.); (J.P.R.)
| | - Iyll-Joon Doh
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA; (I.-J.D.); (J.P.R.)
| | - Kennedy Okeyo
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA;
| | - Euiwon Bae
- School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA;
| | - J. Paul Robinson
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA; (I.-J.D.); (J.P.R.)
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA;
| | - Bartek Rajwa
- Bindley Bioscience Center, Discovery Park, Purdue University, West Lafayette, IN 47907, USA
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6
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Kharbach M, Alaoui Mansouri M, Taabouz M, Yu H. Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches. Foods 2023; 12:2753. [PMID: 37509845 PMCID: PMC10379817 DOI: 10.3390/foods12142753] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 06/30/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.
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Affiliation(s)
- Mourad Kharbach
- Department of Food and Nutrition, University of Helsinki, 00014 Helsinki, Finland
- Department of Computer Sciences, University of Helsinki, 00560 Helsinki, Finland
| | - Mohammed Alaoui Mansouri
- Nano and Molecular Systems Research Unit, University of Oulu, 90014 Oulu, Finland
- Research Unit of Mathematical Sciences, University of Oulu, 90014 Oulu, Finland
| | - Mohammed Taabouz
- Biopharmaceutical and Toxicological Analysis Research Team, Laboratory of Pharmacology and Toxicology, Faculty of Medicine and Pharmacy, University Mohammed V in Rabat, Rabat BP 6203, Morocco
| | - Huiwen Yu
- Shenzhen Hospital, Southern Medical University, Shenzhen 518005, China
- Chemometrics group, Faculty of Science, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg, Denmark
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7
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Teng Y, Wang Z, Zuo S, Li X, Chen Y. Identification of antibiotic residues in aquatic products with surface-enhanced Raman scattering powered by 1-D convolutional neural networks. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 289:122195. [PMID: 36549071 DOI: 10.1016/j.saa.2022.122195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 11/22/2022] [Accepted: 11/26/2022] [Indexed: 06/17/2023]
Abstract
Universal and fast antibiotic residues detection technology is imperative for the control of food safety in aquatic products. However, accurate surface-enhanced Raman scattering (SERS) quantitative detection of complicated samples is still a challenge. A recognition method powered by deep learning and took advantage of the unique fingerprint information merits of SERS was proposed. Herein, the spectra were collected by Ag nanofilm SERS substrate prepared by self-assembly of Ag nanoparticles on water/oil interface. A SERS-based database of commonly used antibiotics in aquatic products was set up, which is suitable for employed as input data for learning and training. The results show that the five types of antibiotics are successfully distinguished through principal component analysis (PCA) and each antibiotic in every type was successfully distinguished. Furthermore, one-dimensional convolutional neural networks (1-D CNN) was used to distinguish the antibiotics, and the results show that all the test samples were correctly predicted by 1-D CNN model. The results of this research suggest the great potential of the combination of SERS spectra with deep learning as a method for rapid and highly accurate identification of antibiotic residues in aquatic products.
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Affiliation(s)
- Yuanjie Teng
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China.
| | - Zhenni Wang
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
| | - Shaohua Zuo
- Engineering Research Center for Nanophotonics & Advanced Instrument, Ministry of Education, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China; Engineering Research Center of Nanoelectronic Integration and Advanced Equipment, Ministry of Education, China.
| | - Xin Li
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
| | - Yinxin Chen
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
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Arroyo-Cerezo A, Jimenez-Carvelo AM, Gonzalez-Casado A, Ruisanchez I, Cuadros-Rodriguez L. The potential of the spatially offset Raman spectroscopy (SORS) for implementing rapid and non-invasive in-situ authentication methods of plastic-packaged commodity foods – Application to sliced cheeses. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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9
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Costa JR, Pereira DA, de Paula IL, de Abreu LR, Pinto SM, Edwards HG, Stephani R, de Oliveira LF. The taste of a champion: Characterization of artisanal cheeses from the Minas Gerais region (Brazil) by Raman spectroscopy and microstructural analysis. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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10
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Hebling E Tavares JP, da Silva Medeiros ML, Barbin DF. Near-infrared techniques for fraud detection in dairy products: A review. J Food Sci 2022; 87:1943-1960. [PMID: 35362099 DOI: 10.1111/1750-3841.16143] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 03/09/2022] [Accepted: 03/16/2022] [Indexed: 01/14/2023]
Abstract
The dairy products sector is an important part of the food industry, and their consumption is expected to grow in the next 10 years. Therefore, the authentication of these products in a faster and precise way is required for the sake of public health. This review proposes the use of near-infrared techniques for the detection of food fraud in dairy products as they are faster, nondestructive, environmentally friendly, do not require sample preparation, and allow multiconstituent analysis. First, we have described frequent forms of food fraud in dairy products and the application of traditional techniques for their detection, highlighting gaps and counterproductive characteristics for the actual global food chain, as longer sample preparation time and use of reagents. Then, the application of near-infrared spectroscopy and hyperspectral imaging for the detection of food fraud mainly in cheese, butter, and yogurt are described. As these techniques depend on model development, the coverage of different dairy products by the literature will promote the identification of food fraud in a faster and reliable way.
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Affiliation(s)
| | | | - Douglas Fernandes Barbin
- Department of Food Engineering, School of Food Engineering, University of Campinas, Campinas, Brazil
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11
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Katsara K, Kenanakis G, Viskadourakis Z, Papadakis VM. Polyethylene Migration from Food Packaging on Cheese Detected by Raman and Infrared (ATR/FT-IR) Spectroscopy. MATERIALS 2021; 14:ma14143872. [PMID: 34300791 PMCID: PMC8303366 DOI: 10.3390/ma14143872] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 07/01/2021] [Accepted: 07/06/2021] [Indexed: 11/16/2022]
Abstract
For multiple years, food packaging migration has been a major concern in food and health sciences. Plastics, such as polyethylene, are continuously utilized in food packaging for preservation and easy handling purposes during transportation and storage. In this work, three types of cheese, Edam, Kefalotyri and Parmesan, of different hardness were studied under two complementary vibrational spectroscopy methods, ATR-FTIR and Raman spectroscopy, to determine the migration of low-density polyethylene from plastic packaging to the surface of cheese samples. The experimental duration of this study was set to 28 days due to the degradation time of the selected cheese samples, which is clearly visible after 1 month in refrigerated conditions at 4 °C. Raman and ATR-FTIR measurements were performed at a 4–3–4–3 day pattern to obtain comparative results. Initially, consistency/repeatability measurement tests were performed on Day0 for each sample of all cheese specimens to understand if there is any overlap between the characteristic Raman and ATR-FTIR peaks of the cheese with the ones from the low-density polyethylene package. We provide evidence that on Day14, peaks of low-density polyethylene appeared due to polymeric migration in all three cheese types we tested. In all cheese samples, microbial outgrowth started to develop after Day21, as observed visually and under the bright-field microscope, causing peak reverse. Food packaging migration was validated using two different approaches of vibrational spectroscopy (Raman and FT-IR), revealing that cheese needs to be consumed within a short time frame in refrigerated conditions at 4 °C.
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Affiliation(s)
- Klytaimnistra Katsara
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, N. Plastira 100, GR-70013 Heraklion, Greece;
| | - George Kenanakis
- Institute of Electronic Structure and Laser, Foundation for Research and Technology-Hellas, N. Plastira 100, GR-70013 Heraklion, Greece; (G.K.); (Z.V.)
| | - Zacharias Viskadourakis
- Institute of Electronic Structure and Laser, Foundation for Research and Technology-Hellas, N. Plastira 100, GR-70013 Heraklion, Greece; (G.K.); (Z.V.)
| | - Vassilis M. Papadakis
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, N. Plastira 100, GR-70013 Heraklion, Greece;
- Correspondence: ; Tel.: +30-281-03-912-67
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12
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Guerrero‐Pérez MO, Patience GS, Bañares MA. Experimental methods in chemical engineering:
Raman
spectroscopy. CAN J CHEM ENG 2020. [DOI: 10.1002/cjce.23884] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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13
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Li Vigni M, Durante C, Michelini S, Nocetti M, Cocchi M. Preliminary Assessment of Parmigiano Reggiano Authenticity by Handheld Raman Spectroscopy. Foods 2020; 9:foods9111563. [PMID: 33126689 PMCID: PMC7692761 DOI: 10.3390/foods9111563] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Revised: 10/22/2020] [Accepted: 10/26/2020] [Indexed: 11/16/2022] Open
Abstract
Raman spectroscopy, and handheld spectrometers in particular, are gaining increasing attention in food quality control as a fast, portable, non-destructive technique. Furthermore, this technology also allows for measuring the intact sample through the packaging and, with respect to near infrared spectroscopy, it is not affected by the water content of the samples. In this work, we evaluate the potential of the methodology to model, by multivariate data analysis, the authenticity of Parmigiano Reggiano cheese, which is one of the most well-known and appreciated hard cheeses worldwide, with protected denomination of origin (PDO). On the other hand, it is also highly subject to counterfeiting. In particular, it is critical to assess the authenticity of grated cheese, to which, under strictly specified conditions, the PDO is extended. To this aim, it would be highly valuable to develop an authenticity model based on a fast, non-destructive technique. In this work, we present preliminary results obtained by a handheld Raman spectrometer and class-modeling (Soft Independent Modeling of Class Analogy, SIMCA), which are extremely promising, showing sensitivity and specificity of 100% for the test set. Moreover, another salient issue, namely the percentage of rind in grated cheese, was addressed by developing a multivariate calibration model based on Raman spectra. It was possible to obtain a prediction error around 5%, with 18% being the maximum content allowed by the production protocol.
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Affiliation(s)
- Mario Li Vigni
- Dipartimento Scienze Chimiche e Geologiche, Università di Modena e Reggio Emilia, Via Campi 103, 41125 Modena, Italy; (M.L.V.); (C.D.)
| | - Caterina Durante
- Dipartimento Scienze Chimiche e Geologiche, Università di Modena e Reggio Emilia, Via Campi 103, 41125 Modena, Italy; (M.L.V.); (C.D.)
| | - Sara Michelini
- Consorzio Parmigiano Reggiano, Via Kennedy 18, 42124 Reggio Emilia, Italy; (S.M.); (M.N.)
| | - Marco Nocetti
- Consorzio Parmigiano Reggiano, Via Kennedy 18, 42124 Reggio Emilia, Italy; (S.M.); (M.N.)
| | - Marina Cocchi
- Dipartimento Scienze Chimiche e Geologiche, Università di Modena e Reggio Emilia, Via Campi 103, 41125 Modena, Italy; (M.L.V.); (C.D.)
- Correspondence: ; Tel.: +39-0592058554
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14
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Lei T, Lin XH, Sun DW. Rapid classification of commercial Cheddar cheeses from different brands using PLSDA, LDA and SPA–LDA models built by hyperspectral data. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2019. [DOI: 10.1007/s11694-019-00234-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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15
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Developments of nondestructive techniques for evaluating quality attributes of cheeses: A review. Trends Food Sci Technol 2019. [DOI: 10.1016/j.tifs.2019.04.013] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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16
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Raman spectroscopy for the differentiation of Arabic coffee genotypes. Food Chem 2019; 288:262-267. [PMID: 30902291 DOI: 10.1016/j.foodchem.2019.02.093] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 01/31/2019] [Accepted: 02/21/2019] [Indexed: 12/20/2022]
Abstract
The objective of this study was to evaluate the ability of Raman spectroscopy to identify the genotype of green coffee beans. Four genotypes of Arabic coffee: one Mundo Novo line (G1) and three Bourbon lines (G2, G3, and G4). The harvest was selected using a wet processing method. Raman spectra of the samples were obtained using a FT-Raman RFS/100 spectrometer in the spectral range of 3500-400 cm-1. The data were treated using chemometric unsupervised classification tools and supervised analysis. Using the unsupervised analysis (PCA), the apparent tendency of agglomeration between samples G1 and G3 was verified. These differences were present in the spectral bands that are characteristic of fatty acids and kahweol. Based on this information, a classification model to discriminate (PLS-DA) the Mundo Novo and Bourbon samples was utilized. Raman spectroscopy allowed the building of an adequate model to differentiate between coffee genotypes.
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17
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Abreu GF, Borém FM, Oliveira LFC, Almeida MR, Alves APC. Raman spectroscopy: A new strategy for monitoring the quality of green coffee beans during storage. Food Chem 2019; 287:241-248. [PMID: 30857695 DOI: 10.1016/j.foodchem.2019.02.019] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 02/05/2019] [Accepted: 02/07/2019] [Indexed: 01/18/2023]
Abstract
Raman spectroscopy was used to identify chemical changes associated sensory quality of coffee beans, for natural and pulped natural coffee stored in different packaging. The green beans of natural coffee and pulped natural coffee were stored in three types of packaging materials in a commercial warehouse. Sensory analyses were performed, and Raman spectra were collected after 0, 3, 6, 9, 12, and 18 storage months. Raman spectra were used to construct multivariate control charts. The charts, which were constructed using principal component analysis, can only be used to identify chemical changes in the green beans from pulped natural coffee stored in different packaging materials. Raman spectroscopy is more sensitive than sensory analysis for detecting chemical changes in stored pulped natural coffee. The measured changes ultimately affect the quality of the beverage because samples stored for six months in paper packaging were determined to no longer meet the quality control requirements.
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Affiliation(s)
- Giselle Figueiredo Abreu
- Department of Agricultural Engineering, Federal University of Lavras, POB 3037, 37.200-000 Lavras, MG, Brazil.
| | - Flávio Meira Borém
- Department of Agricultural Engineering, Federal University of Lavras, POB 3037, 37.200-000 Lavras, MG, Brazil.
| | | | - Mariana Ramos Almeida
- Department of Chemistry, Federal University of Minas Gerais, 31.270-901 Belo Horizonte, MG, Brazil
| | - Ana Paula Carvalho Alves
- Department of Agricultural Engineering, Federal University of Lavras, POB 3037, 37.200-000 Lavras, MG, Brazil
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18
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He H, Sun DW, Pu H, Chen L, Lin L. Applications of Raman spectroscopic techniques for quality and safety evaluation of milk: A review of recent developments. Crit Rev Food Sci Nutr 2019; 59:770-793. [PMID: 30614242 DOI: 10.1080/10408398.2018.1528436] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Milk is a complete nutrient source for humans. The quality and safety of milk are critical for both producers and consumers, thereby the dairy industry requires rapid and nondestructive methods to ensure milk quality and safety. However, conventional methods are time-consuming and laborious, and require complicated preparation procedures. Therefore, the exploration of new milk analytical methods is essential. This current review introduces the principles of Raman spectroscopy and presents recent advances since 2012 of Raman spectroscopic techniques mainly involving surface-enhanced Raman spectroscopy (SERS), fourier-transform (FT) Raman spectroscopy, near-infrared (NIR) Raman spectroscopy, and micro-Raman spectroscopy for milk analysis including milk compositions, microorganisms and antibiotic residues in milk, as well as milk adulterants. Additionally, some challenges and future outlooks are proposed. The current review shows that Raman spectroscopic techniques have the promising potential for providing rapid and nondestructive detection of milk parameters. However, the application of Raman spectroscopy on milk analysis is not common yet since some limitations of Raman spectroscopy need to be overcome before making it a routine tool for the dairy industry.
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Affiliation(s)
- Huirong He
- a School of Food Science and Engineering , South China University of Technology , Guangzhou 510641 , China.,b Academy of Contemporary Food Engineering , South China University of Technology, Guangzhou Higher Education Mega Centre , Guangzhou 510006 , China.,c Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods , Guangzhou Higher Education Mega Centre , Guangzhou 510006 , China
| | - Da-Wen Sun
- a School of Food Science and Engineering , South China University of Technology , Guangzhou 510641 , China.,b Academy of Contemporary Food Engineering , South China University of Technology, Guangzhou Higher Education Mega Centre , Guangzhou 510006 , China.,c Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods , Guangzhou Higher Education Mega Centre , Guangzhou 510006 , China.,d Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre , University College Dublin, National University of Ireland , Dublin 4 , Ireland
| | - Hongbin Pu
- a School of Food Science and Engineering , South China University of Technology , Guangzhou 510641 , China.,b Academy of Contemporary Food Engineering , South China University of Technology, Guangzhou Higher Education Mega Centre , Guangzhou 510006 , China.,c Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods , Guangzhou Higher Education Mega Centre , Guangzhou 510006 , China
| | - Lijun Chen
- e Beijing Sanyuan Foods Co., Ltd , Beijing , China
| | - Li Lin
- e Beijing Sanyuan Foods Co., Ltd , Beijing , China
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19
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Smithson SC, Fakayode BD, Henderson S, Nguyen J, Fakayode SO. Detection, Purity Analysis, and Quality Assurance of Adulterated Peanut (Arachis Hypogaea) Oils. Foods 2018; 7:E122. [PMID: 30065168 PMCID: PMC6112014 DOI: 10.3390/foods7080122] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Revised: 07/25/2018] [Accepted: 07/27/2018] [Indexed: 11/25/2022] Open
Abstract
The intake of adulterated and unhealthy oils and trans-fats in the human diet has had negative health repercussions, including cardiovascular disease, causing millions of deaths annually. Sadly, a significant percentage of all consumable products including edible oils are neither screened nor monitored for quality control for various reasons. The prospective intake of adulterated oils and the associated health impacts on consumers is a significant public health safety concern, necessitating the need for quality assurance checks of edible oils. This study reports a simple, fast, sensitive, accurate, and low-cost chemometric approach to the purity analysis of highly refined peanut oils (HRPO) that were adulterated either with vegetable oil (VO), canola oil (CO), or almond oil (AO) for food quality assurance purposes. The Fourier transform infrared spectra of the pure oils and adulterated HRPO samples were measured and subjected to a partial-least-square (PLS) regression analysis. The obtained PLS regression figures-of-merit were incredible, with remarkable linearity (R² = 0.994191 or better). The results of the score plots of the PLS regressions illustrate pattern recognition of the adulterated HRPO samples. Importantly, the PLS regressions accurately determined percent compositions of adulterated HRPOs, with an overall root-mean-square-relative-percent-error of 5.53% and a limit-of-detection as low as 0.02% (wt/wt). The developed PLS regressions continued to predict the compositions of newly prepared adulterated HRPOs over a period of two months, with incredible accuracy without the need for re-calibration. The accuracy, sensitivity, and robustness of the protocol make it desirable and potentially adoptable by health departments and local enforcement agencies for fast screening and quality assurance of consumable products.
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Affiliation(s)
- Shayla C Smithson
- Department of Physical Sciences, University of Arkansas Fort Smith, 5210 Grand Avenue, P.O. Box 3649, Fort Smith, AR 72913-3649, USA.
| | - Boluwatife D Fakayode
- Department of Physical Sciences, University of Arkansas Fort Smith, 5210 Grand Avenue, P.O. Box 3649, Fort Smith, AR 72913-3649, USA.
| | - Siera Henderson
- Department of Physical Sciences, University of Arkansas Fort Smith, 5210 Grand Avenue, P.O. Box 3649, Fort Smith, AR 72913-3649, USA.
| | - John Nguyen
- Department of Physical Sciences, University of Arkansas Fort Smith, 5210 Grand Avenue, P.O. Box 3649, Fort Smith, AR 72913-3649, USA.
| | - Sayo O Fakayode
- Department of Physical Sciences, University of Arkansas Fort Smith, 5210 Grand Avenue, P.O. Box 3649, Fort Smith, AR 72913-3649, USA.
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20
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Valdés A, Beltrán A, Mellinas C, Jiménez A, Garrigós MC. Analytical methods combined with multivariate analysis for authentication of animal and vegetable food products with high fat content. Trends Food Sci Technol 2018. [DOI: 10.1016/j.tifs.2018.05.014] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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21
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Botanical authentication of honeys based on Raman spectra. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2017. [DOI: 10.1007/s11694-017-9666-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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22
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Efenberger-Szmechtyk M, Nowak A, Kregiel D. Implementation of chemometrics in quality evaluation of food and beverages. Crit Rev Food Sci Nutr 2017; 58:1747-1766. [PMID: 28128644 DOI: 10.1080/10408398.2016.1276883] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Conventional methods for food quality evaluation based on chemical or microbiological analysis followed by traditional univariate statistics such as ANOVA are considered insufficient for some purposes. More sophisticated instrumental methods including spectroscopy and chromatography, in combination with multivariate analysis-chemometrics, can be used to determine food authenticity, identify adulterations or mislabeling and determine food safety. The purpose of this review is to present the current state of knowledge on the use of chemometric tools for evaluating quality of food products of animal and plant origin and beverages. The article describes applications of several multivariate techniques in food and beverages research, showing their role in adulteration detection, authentication, quality control, differentiation of samples and comparing their classification and prediction ability.
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Affiliation(s)
| | - Agnieszka Nowak
- a Institute of Fermentation Technology and Microbiology, Lodz University of Technology , Lodz , Poland
| | - Dorota Kregiel
- a Institute of Fermentation Technology and Microbiology, Lodz University of Technology , Lodz , Poland
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23
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Determination of adulterated neem and flaxseed oil compositions by FTIR spectroscopy and multivariate regression analysis. Food Control 2016. [DOI: 10.1016/j.foodcont.2016.04.008] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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