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Bilamjian S, Bahadi M, Ismail A, Tremblay C, Bayen S. Development of a method based on ATR-FTIR spectroscopy to detect maple syrup adulterated with added syrups. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:1768-1776. [PMID: 37872647 DOI: 10.1002/jsfa.13073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/09/2023] [Accepted: 10/24/2023] [Indexed: 10/25/2023]
Abstract
BACKGROUND Food adulteration is a global concern, whether it takes place intentionally or incidentally. In Canada, maple syrup is susceptible to being adulterated with cheaper syrups such as corn, beet, cane syrups, and many more due to its high price and economic importance. RESULTS In this study, the use of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy was investigated to detect maple syrups adulterated with 15 different sugar syrups at different concentration levels. The spectra were collected in the range of 4000-650 cm-1 in the absorbance unit. These spectra were used to build six libraries and three models. A method that is capable of performing a qualitative library search using a similarity search, which is based on the first derivative correlation search algorithm, was developed. This method was further evaluated and proved to be able to capture adulterated and reject non-adulterated maple syrups, belonging to the color grades golden and amber maple syrups, with an accuracy of 93.9% and 92.3%, respectively. However, for the maple syrup belonging to the dark color grade, this method demonstrated low specificity of 33.3%, and for this reason it was only able to adequately detect adulterated samples from the non-adulterated ones with an accuracy of 81.4%. CONCLUSION This simple and rapid method has strong potential for implementation in different stages of the maple syrup supply chain for early adulteration detection, particularly for golden and amber samples. Further evaluation and improvements are required for the dark color grade. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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Affiliation(s)
- Shaghig Bilamjian
- Department of Food Science and Agricultural Chemistry, McGill University, Montreal, Canada
| | - Mazen Bahadi
- Department of Food Science and Agricultural Chemistry, McGill University, Montreal, Canada
| | - Ashraf Ismail
- Department of Food Science and Agricultural Chemistry, McGill University, Montreal, Canada
| | | | - Stéphane Bayen
- Department of Food Science and Agricultural Chemistry, McGill University, Montreal, Canada
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2
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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: 9] [Impact Index Per Article: 9.0] [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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Limm W, Karunathilaka SR, Mossoba MM. Fourier transform infrared spectroscopy and chemometrics for the rapid screening of economically motivated adulteration of honey spiked with corn or rice syrup. J Food Prot 2023; 86:100054. [PMID: 37005034 DOI: 10.1016/j.jfp.2023.100054] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 01/18/2023] [Accepted: 01/21/2023] [Indexed: 01/29/2023]
Abstract
Due to its high price, increased consumption, and limited production, honey has been a main target for economically motivated adulteration (EMA). An approach combining Fourier-Transform infrared spectroscopy (FTIR) and chemometrics was evaluated to develop a rapid screening tool to detect potential EMA of honey with either rice or corn syrup. A single-class soft independent modeling of class analogy (SIMCA) model was developed using a diverse set of commercial honey products and an authentic set of honey samples collected at four different U.S. Department of Agriculture (USDA) honey sample collection locations. The SIMCA model was externally validated with a set of calibration-independent authentic honey, typical commercial honey control samples, and those spiked with rice and corn syrups in the 1-16% concentration range. The authentic honey and typical commercial honey test samples were correctly predicted with an 88.3% classification rate. High accuracy was found in predicting the rice and corn syrup spiked samples above the 7% concentration range, yielding 97.6% and 94.8% correct classification rates, respectively. This study demonstrated the potential for a rapid and accurate infrared and chemometrics method that can be used to rapidly screen for either rice or corn adulterants in honey in less than 5 min.
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Affiliation(s)
- William Limm
- U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Regulatory Science, 5001 Campus Drive, College Park, MD 20740, USA.
| | - Sanjeewa R Karunathilaka
- University of Maryland, Joint Institute for Food Safety and Applied Nutrition, 2134 Patapsco Building, College Park, MD 20742, USA
| | - Magdi M Mossoba
- U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Regulatory Science, 5001 Campus Drive, College Park, MD 20740, USA
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Islam MK, Sostaric T, Lim LY, Hammer K, Locher C. A Comprehensive HPTLC-Based Analysis of the Impacts of Temperature on the Chemical Properties and Antioxidant Activity of Honey. Molecules 2022; 27:molecules27238491. [PMID: 36500584 PMCID: PMC9737681 DOI: 10.3390/molecules27238491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022] Open
Abstract
Honeys are commonly subjected to a series of post-harvest processing steps, such as filtration and/or radiation treatment and heating to various temperatures, which might affect their physicochemical properties and bioactivity levels. Therefore, there is a need for robust quality control assessments after honey processing and storage to ensure that the exposure to higher temperatures, for example, does not compromise the honey's chemical composition and/or antioxidant activity. This paper describes a comprehensive short-term (48 h) and long-term (5 months) study of the effects of temperature (40 °C, 60 °C and 80 °C) on three commercial honeys (Manuka, Marri and Coastal Peppermint) and an artificial honey, using high-performance thin-layer chromatography (HPTLC) analysis. Samples were collected at baseline, at 6 h, 12 h, 24 h and 48 h, and then monthly for five months. Then, they were analysed for potential changes in their organic extract HPTLC fingerprints, in their HPTLC-DPPH total band activities, in their major sugar composition and in their hydroxymethylfurfural (HMF) content. It was found that, while all the assessed parameters changed over the monitoring period, changes were moderate at 40 °C but increased significantly with increasing temperature, especially the honeys' HPTLC-DPPH total band activity and HMF content.
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Affiliation(s)
- Md Khairul Islam
- Cooperative Research Centre for Honey Bee Products Limited (CRC HBP), University of Western Australia, Perth 6009, Australia
- Division of Pharmacy, School of Allied Health, University of Western Australia, Perth 6009, Australia
| | - Tomislav Sostaric
- Division of Pharmacy, School of Allied Health, University of Western Australia, Perth 6009, Australia
| | - Lee Yong Lim
- Division of Pharmacy, School of Allied Health, University of Western Australia, Perth 6009, Australia
| | - Katherine Hammer
- Cooperative Research Centre for Honey Bee Products Limited (CRC HBP), University of Western Australia, Perth 6009, Australia
- School of Biomedical Sciences, University of Western Australia, Perth 6009, Australia
| | - Cornelia Locher
- Cooperative Research Centre for Honey Bee Products Limited (CRC HBP), University of Western Australia, Perth 6009, Australia
- Division of Pharmacy, School of Allied Health, University of Western Australia, Perth 6009, Australia
- Correspondence:
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5
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Qualitative and Quantitative Detection of Acacia Honey Adulteration with Glucose Syrup Using Near-Infrared Spectroscopy. SEPARATIONS 2022. [DOI: 10.3390/separations9100312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Honey adulteration with cheap sweeteners such as corn syrup or invert syrup results in honey of lesser quality that can harm the objectives of both manufacturers and consumers. Therefore, there is a growing interest for the development of a fast and simple method for adulteration detection. In this work, near-infrared spectroscopy (NIR) was used for the detection of honey adulteration and changes in the physical and chemical properties of the prepared adulterations. Fifteen (15) acacia honey samples were adulterated with glucose syrup in a range from 10% to 90%. Raw and pre-processed NIR spectra of pure honey samples and prepared adulterations were subjected to Principal Component Analysis (PCA), Partial Least Squares (PLS) regression, and Artificial Neural Network (ANN) modeling. The results showed that PCA ensures distinct grouping of samples in pure honey samples, honey adulterations, and pure adulteration using NIR spectra after the Multiplicative Scatter Correction (MSC) method. Furthermore, PLS models developed for the prediction of the added adulterant amount, moisture content, and conductivity can be considered sufficient for screening based on RPD and RER values (1.7401 < RPD < 2.7601; 7.7128 < RER < 8.7157) (RPD of 2.7601; RER of 8.7157) and can be moderately used in practice. The R2validation of the developed ANN models was greater than 0.86 for all outputs examined. Based on the obtained results, it can be concluded that NIR coupled with ANN modeling can be considered an efficient tool for honey adulteration quantification.
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Dumancas GG, Ellis H. Comprehensive examination and comparison of machine learning techniques for the quantitative determination of adulterants in honey using Fourier infrared spectroscopy with attenuated total reflectance accessory. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 276:121186. [PMID: 35405374 DOI: 10.1016/j.saa.2022.121186] [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: 01/29/2022] [Revised: 03/13/2022] [Accepted: 03/22/2022] [Indexed: 06/14/2023]
Abstract
Facile, robust, and accurate analyses of honey adulterants are required in the honey industry to assess its purity for commercialization purposes. A stacked regression ensemble approach using Fourier transform infrared spectroscopic method was developed for the quantitative determination of corn, cane, beet, and rice syrup adulterants in honey. A training set (n=81) was used to predict the percent adulterant composition of the aforementioned constituents in an independent test set (n=32). A comprehensive comparison of the performance of various machine learning techniques including support vector regression using linear function, least absolute shrinkage and selection operator, ride regression, elastic net, partial least squares, random forests, recursive partitioning and regression trees, gradient boosting, and gaussian process regression was assessed. The predictive performance of the aforementioned machine learning approaches was then compared with stacked regression, an ensemble learning technique which collates the performance of the various abovementioned techniques. Results show that stacked regression did not primarily outperform other techniques across all four syrup adulterant constituents in the testing set data. Further, elastic net generalized linear model generated the optimum results (Rootmeansquareerrorofprediction(RMSEP)average=0.0107,Raverage2=0.809) across all four honey adulterant constituents. Elastic net coupled with Fourier transform infrared spectroscopy may offer a novel, direct, and accurate method of simultaneously quantifying corn, cane, beet, and rice syrup adulterants in honey.
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Affiliation(s)
- Gerard G Dumancas
- Department of Chemistry, Loyola Science Center, The University of Scranton, Scranton, PA 18510, USA.
| | - Helena Ellis
- Department of Mathematics and Physical Sciences, Louisiana State University - Alexandria, Alexandria, LA 71302, USA
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Godoy CA, Valderrama P, Boroski M. HMF Monitoring: Storage Condition and Honey Quality. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02358-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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8
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Qualitative and Quantitative Detection of Monofloral, Polyfloral, and Honeydew Honeys Adulteration by Employing Mid-Infrared Spectroscopy and Chemometrics. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02266-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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9
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Multivariate analysis of food fraud: A review of NIR based instruments in tandem with chemometrics. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2021.104343] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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10
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Comparison of Various Signal Processing Techniques and Spectral Regions for the Direct Determination of Syrup Adulterants in Honey Using Fourier Transform Infrared Spectroscopy and Chemometrics. CHEMOSENSORS 2022. [DOI: 10.3390/chemosensors10020051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Honey consumption has become increasingly popular worldwide. However, the increase in demand for honey has also caused an increase in its adulteration, a deliberate fraud which involves adding of other substances to pure honey for economic purposes. This process not only lowers the quality of honey, but also has potential health risks, including high blood sugar, increased risk of diabetes, and weight gain. Herein, we develop an easy-to-use and direct method of quantifying corn, cane, beet, and rice syrup adulterants in honey using Fourier transform infrared spectroscopy and chemometrics. Various signal processing techniques, including derivatives, moving average, binning, Savitzky–Golay, and standard normal variate using the entire spectral region (3996–650 cm−1) and specific spectral region (1501–799 cm−1), were compared. Optimum results were obtained using first derivative signal processing for both the entire and specific spectral regions. The first derivative signal processing technique garnered the most optimum results using the specific spectral range (1501–799 cm−1) (RMSECVaverage = 0.021, RMSEPaverage = 0.014, R2average = 0.859) across all syrup adulterants. An exploratory analysis to assess the utility of this specific spectral region in pattern recognition of samples based on their adulterant content show that this region is effective in discriminating samples according to the presence or absence of honey syrup adulterants.
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11
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Detection of honey adulterated with agave, corn, inverted sugar, maple and rice syrups using FTIR analysis. Food Control 2021. [DOI: 10.1016/j.foodcont.2021.108266] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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12
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Islam MK, Vinsen K, Sostaric T, Lim LY, Locher C. Detection of syrup adulterants in manuka and jarrah honey using HPTLC-multivariate data analysis. PeerJ 2021; 9:e12186. [PMID: 34616629 PMCID: PMC8464195 DOI: 10.7717/peerj.12186] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 08/30/2021] [Indexed: 11/25/2022] Open
Abstract
High-Performance Thin-Layer Chromatography (HPTLC) was used in a chemometric investigation of the derived sugar and organic extract profiles of two different honeys (Manuka and Jarrah) with adulterants. Each honey was adulterated with one of six different sugar syrups (rice, corn, golden, treacle, glucose and maple syrups) in five different concentrations (10%, 20%, 30%, 40%, and 50% w/w). The chemometric analysis was based on the combined sugar and organic extract profiles’ datasets. To obtain the respective sugar profiles, the amount of fructose, glucose, maltose, and sucrose present in the honey was quantified and for the organic extract profile, the honey’s dichloromethane extract was investigated at 254 and 366 nm, as well as at T (Transmittance) white light and at 366 nm after derivatisation. The presence of sugar syrups, even at a concentration of only 10%, significantly influenced the honeys’ sugar and organic extract profiles and multivariate data analysis of these profiles, in particular cluster analysis (CA), principal component analysis (PCA), principal component regression (PCR), partial least-squares regression (PLSR) and Machine Learning using an artificial neural network (ANN), were able to detect post-harvest syrup adulterations and to discriminate between neat and adulterated honey samples. Cluster analysis and principal component analysis, for instance, could easily differentiate between neat and adulterated honeys through the use of CA or PCA plots. In particular the presence of excess amounts of maltose and sucrose allowed for the detection of sugar adulterants and adulterated honeys by HPTLC-multivariate data analysis. Partial least-squares regression and artificial neural networking were employed, with augmented datasets, to develop optimal calibration for the adulterated honeys and to predict those accurately, which suggests a good predictive capacity of the developed model.
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Affiliation(s)
- Md Khairul Islam
- Division of Pharmacy, School of Allied Health, University of Western Australia, Crawley, WA, Australia.,Cooperative Research Centre for Honey Bee Products Limited (CRC HBP), Perth, WA, Australia
| | - Kevin Vinsen
- International Centre for Radio Astronomy Research (ICRAR), University of Western Australia, Crawley, WA, Australia
| | - Tomislav Sostaric
- Division of Pharmacy, School of Allied Health, University of Western Australia, Crawley, WA, Australia
| | - Lee Yong Lim
- Division of Pharmacy, School of Allied Health, University of Western Australia, Crawley, WA, Australia
| | - Cornelia Locher
- Division of Pharmacy, School of Allied Health, University of Western Australia, Crawley, WA, Australia.,Cooperative Research Centre for Honey Bee Products Limited (CRC HBP), Perth, WA, Australia
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Hosseini E, Ghasemi JB, Daraei B, Asadi G, Adib N. Application of genetic algorithm and multivariate methods for the detection and measurement of milk-surfactant adulteration by attenuated total reflection and near-infrared spectroscopy. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:2696-2703. [PMID: 33073373 DOI: 10.1002/jsfa.10894] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 08/18/2020] [Accepted: 10/19/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND The adulteration of milk by hazardous chemicals like surfactants has recently increased. It conceals the quality of the product to gain profit. As milk and milk-based products are consumed by many people, novel analytical procedures are needed to detect these adulterants. This study focused on Fourier-transform infrared (FTIR) spectroscopy equipped with an attenuated total reflection (ATR) accessory, and near-infrared (NIR) spectroscopy for the determination of milk-surfactant adulteration using a genetic algorithm (GA) coupled with multivariate methods. The model surfactant was sodium dodecyl sulfate (SDS), and its concentration varied from 1.94-19.4 gkg-1 in adulterated samples. RESULTS Prominent peaks in the spectral range of 5500-6400 cm-1 , 1160-1260 cm-1 and 1049-1080 cm-1 may correspond to the sulfonate group in SDS. A genetic algorithm could significantly reduce the number of variables to almost one third by selecting the specific wavenumber region. Principal component analysis (PCA) for ATR and NIR data indicated separate clusters of samples in terms of the concentration level of SDS (P ≤ 0.05). Partial least squares regression (PLSR) was used to determine the maximum R2 value for ATR and NIR data for calibration, cross-validation and prediction, which were 0.980, 0.972, 0.980, and 0.970, 0.937, and 0.956 respectively. The results showed apparent differences between unadulterated and adulterated samples using partial least squares-discriminant analysis (PLS-DA), which was validated by the permutation test. CONCLUSION The results clearly show the successful application of the proposed methods with multivariate analysis in the selection of variables, classification, clustering, and identification of the adulterant in amounts as low as 1.94 gkg-1 in milk. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Elahesadat Hosseini
- Department of Food Science and Technology, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Jahan B Ghasemi
- School of Chemistry, College of Science, University of Tehran, Tehran, Iran
| | - Bahram Daraei
- Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Gholamhassan Asadi
- Department of Food Science and Technology, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Nooshin Adib
- Food and Drug Laboratory Research Center, Food and Drug Organization, Tehran, Iran
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Padiso T, Keiphetlhetswe M, Donald Chinyama M, Molwantwa M, Sichilongo K. Physicochemical characterization and adulteration detection of selected commercial and natural honeys from Zambia and Botswana. J FOOD PROCESS PRES 2021. [DOI: 10.1111/jfpp.15371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Tumelo Padiso
- Department of Chemistry Faculty of Science University of Botswana Gaborone Botswana
| | | | | | - Mompoloki Molwantwa
- Department of Chemistry Faculty of Science University of Botswana Gaborone Botswana
| | - Kwenga Sichilongo
- Department of Chemistry Faculty of Science University of Botswana Gaborone Botswana
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15
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Taylan O, Cebi N, Yilmaz MT, Sagdic O, Ozdemir D, Balubaid M. Rapid detection of green-pea adulteration in pistachio nuts using Raman spectroscopy and chemometrics. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:1699-1708. [PMID: 33006383 DOI: 10.1002/jsfa.10845] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 09/25/2020] [Accepted: 10/02/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Ground pistachio nut is prone to adulteration because of its high economic value and wide usage. Green pea is known as the main adulterant in frauds involving pistachio nuts. The present study developed a new, rapid, reliable and low-cost methodology by using a portable Raman spectrometer in combination with chemometrics for the detection of green pea in pistachio nuts. RESULTS Three different methods of Raman spectroscopy-based chemometrics analysis were developed for the determination of green-pea adulteration in pistachio nuts. The first method involved the development of hierarchical cluster analysis (HCA) and principal component analysis (PCA), which differentiated authentic pistachio nuts from green pea and green pea-adulterated samples. The best classification pattern was observed in the adulteration range of 20-80% (w/w). In addition to classification methods, partial least squares regression (PLSR) and genetic algorithm-based inverse least squares (GILS) were also used to develop multivariate calibration models to determine quantitatively the degree of green-pea adulteration in grounded pistachio nuts. The spectral range of 1790-283 cm-1 was used in the case of multivariate data analysis. A green-pea adulteration level of 5-80% (w/w) was successfully identified by PLSR and GILS. The correlation coefficient of determination (R2 ) was determined as 0.91 and 0.94 for the PLSR and GILS analyses, respectively. CONCLUSION A Raman spectrometer combined with chemometrics has a high capability with regard to the detection of adulteration in pistachio nuts, combined with low cost, strong reliability, a high level of accuracy, rapidity of analysis, and minimum sample preparation. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Osman Taylan
- Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Nur Cebi
- Davutpaşa Campus, Chemical and Metallurgical Engineering Faculty, Food Engineering Department, Yıldız Technical University, Istanbul, Turkey
| | - Mustafa Tahsin Yilmaz
- Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Osman Sagdic
- Davutpaşa Campus, Chemical and Metallurgical Engineering Faculty, Food Engineering Department, Yıldız Technical University, Istanbul, Turkey
| | - Durmus Ozdemir
- Faculty of Science, Department of Chemistry, İzmir Institute of Technology, Izmir, Turkey
| | - Mohammed Balubaid
- Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
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Mendes E, Duarte N. Mid-Infrared Spectroscopy as a Valuable Tool to Tackle Food Analysis: A Literature Review on Coffee, Dairies, Honey, Olive Oil and Wine. Foods 2021; 10:foods10020477. [PMID: 33671755 PMCID: PMC7926530 DOI: 10.3390/foods10020477] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/15/2021] [Accepted: 02/17/2021] [Indexed: 12/12/2022] Open
Abstract
Nowadays, food adulteration and authentication are topics of utmost importance for consumers, food producers, business operators and regulatory agencies. Therefore, there is an increasing search for rapid, robust and accurate analytical techniques to determine the authenticity and to detect adulteration and misrepresentation. Mid-infrared spectroscopy (MIR), often associated with chemometric techniques, offers a fast and accurate method to detect and predict food adulteration based on the fingerprint characteristics of the food matrix. In the first part of this review the basic concepts of infrared spectroscopy, sampling techniques, as well as an overview of chemometric tools are summarized. In the second part, recent applications of MIR spectroscopy to the analysis of foods such as coffee, dairy products, honey, olive oil and wine are discussed, covering a timespan from 2010 to mid-2020. The literature gathered in this article clearly reveals that the MIR spectroscopy associated with attenuated total reflection acquisition mode and different chemometric tools have been broadly applied to address quality, authenticity and adulteration issues. This technique has the advantages of being simple, fast and easy to use, non-destructive, environmentally friendly and, in the future, it can be applied in routine analyses and official food control.
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Rapid Screening of Mentha spicata Essential Oil and L-Menthol in Mentha piperita Essential Oil by ATR-FTIR Spectroscopy Coupled with Multivariate Analyses. Foods 2021; 10:foods10020202. [PMID: 33498340 PMCID: PMC7909401 DOI: 10.3390/foods10020202] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 01/10/2021] [Accepted: 01/11/2021] [Indexed: 01/13/2023] Open
Abstract
Mentha piperita essential oil (EO) has high economic importance because of its wide usage area and health-beneficial properties. Besides health-beneficial properties, Mentha piperita EO has great importance in the flavor and food industries because of its unique sensory and quality properties. High-valued essential oils are prone to being adulterated with economic motivations. This kind of adulteration deteriorates the quality of authentic essential oil, injures the consumers, and causes negative effects on the whole supply chain from producer to the consumer. The current research used fast, economic, robust, reliable, and effective ATR-FTIR spectroscopy coupled chemometrics of hierarchical cluster analysis(HCA), principal component analysis (PCA), partial least squares regression (PLSR) and principal component regression (PCR) for monitoring of Mentha spicata EO and L-menthol adulteration in Mentha piperita EOs. Adulterant contents (Mentha spicata and L-menthol) were successfully calculated using PLSR and PCR models. Standard error of the cross-validation SECV values changed between 0.06 and 2.14. Additionally, bias and press values showed alteration between 0.06 and1.43 and 0.03 and 41.15, respectively. Authentic Mentha piperita was successfully distinguished from adulterated samples, Mentha spicata and L-menthol, by HCA and PCA analysis. The results showed that attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometrics could be effectively used for monitoring various adulterants in essential oils.
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18
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Fakhlaei R, Selamat J, Khatib A, Razis AFA, Sukor R, Ahmad S, Babadi AA. The Toxic Impact of Honey Adulteration: A Review. Foods 2020; 9:E1538. [PMID: 33114468 PMCID: PMC7692231 DOI: 10.3390/foods9111538] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/10/2020] [Accepted: 09/11/2020] [Indexed: 12/11/2022] Open
Abstract
Honey is characterized as a natural and raw foodstuff that can be consumed not only as a sweetener but also as medicine due to its therapeutic impact on human health. It is prone to adulterants caused by humans that manipulate the quality of honey. Although honey consumption has remarkably increased in the last few years all around the world, the safety of honey is not assessed and monitored regularly. Since the number of consumers of honey adulteration have increased in recent years, their trust and interest in this valuable product has decreased. Honey adulterants are any substances that are added to the pure honey. In this regard, this paper provides a comprehensive and critical review of the different types of adulteration, common sugar adulterants and detection methods, and draws a clear perspective toward the impact of honey adulteration on human health. Adulteration increases the consumer's blood sugar, which can cause diabetes, abdominal weight gain, and obesity, raise the level of blood lipids and can cause high blood pressure. The most common organ affected by honey adulterants is the liver followed by the kidney, heart, and brain, as shown in several in vivo research designs.
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Affiliation(s)
- Rafieh Fakhlaei
- Food Safety and Food Integrity (FOSFI), Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia;
| | - Jinap Selamat
- Food Safety and Food Integrity (FOSFI), Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia;
- Department of Food Science, Faculty of Food Science and Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; (A.F.A.R.); (R.S.)
| | - Alfi Khatib
- Pharmacognosy Research Group, Department of Pharmaceutical Chemistry, Kulliyyah of Pharmacy, International Islamic University Malaysia, Kuantan 25200, Pahang Darul Makmur, Malaysia;
- Faculty of Pharmacy, Airlangga University, Surabaya 60155, Indonesia
| | - Ahmad Faizal Abdull Razis
- Department of Food Science, Faculty of Food Science and Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; (A.F.A.R.); (R.S.)
- Natural Medicines and Products Research Laboratory, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
| | - Rashidah Sukor
- Department of Food Science, Faculty of Food Science and Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; (A.F.A.R.); (R.S.)
| | - Syahida Ahmad
- Department of Biochemistry, Faculty of Biotechnology & Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia;
| | - Arman Amani Babadi
- School of Energy and Power Engineering, Jiangsu University, Zhenjiang 212013, China;
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19
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Islam MK, Sostaric T, Lim LY, Hammer K, Locher C. A validated method for the quantitative determination of sugars in honey using high-performance thin-layer chromatography. JPC-J PLANAR CHROMAT 2020. [DOI: 10.1007/s00764-020-00054-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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20
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Non-targeted method to detect honey adulteration: Combination of electrochemical and spectrophotometric responses with principal component analysis. J Food Compost Anal 2020. [DOI: 10.1016/j.jfca.2020.103466] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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21
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Application of Quality-by-Design Approach in the Analytical Method Development for Quantification of Sugars in Sugarcane Honey by Reversed-Phase Liquid Chromatography. FOOD ANAL METHOD 2020. [DOI: 10.1007/s12161-020-01767-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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22
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Liu W, Zhang Y, Li M, Han D, Liu W. Determination of invert syrup adulterated in acacia honey by terahertz spectroscopy with different spectral features. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2020; 100:1913-1921. [PMID: 31846080 DOI: 10.1002/jsfa.10202] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 11/24/2019] [Accepted: 12/17/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Invert syrup is a common adulterant in honey falsification, thus generating risk for consumers. Most of the methods developed are tedious and time-consuming for manufactures and consumers. However, terahertz spectroscopy provides analytical information in a simple, rapid, and environmentally friendly manner. Subsequently, 3 kinds of terahertz spectroscopic characteristics data, the absorption coefficient, the slope of the absorption coefficient spectra, and the area of the absorption coefficient spectra, were employed for determination of acacia honey adulterated with invert syrup. RESULTS Single linear regression (SLR) models with different terahertz spectroscopic features were adopted to predict the syrup adulterant proportion in acacia honey. The best SLR model used the area of the absorption coefficient, displaying an adjusted correlation coefficient of 0.985 and a root-mean-square error of 3.201. Meanwhile, multiple linear regression (MLR) models using a successive projections algorithm for variables selection were implemented. The MLR model considered the integral area of the absorption coefficient spectra, as the inputs yielded the best result with less variables selected, higher R c 2 and R p 2 , lower root-mean-square error of calibration and prediction, as well as higher residual predictive deviation. CONCLUSIONS The results indicate terahertz spectroscopy combined with the integral area of the absorption coefficient spectra is reliable enough for invert syrup proportion quantification in acacia honey and is also a rapid and nondestructive determination method for other honey adulterants. © 2019 Society of Chemical Industry.
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Affiliation(s)
- Wen Liu
- School of Chemical Engineering, Xiangtan University, Xiangtan, China
| | - Yuying Zhang
- Market Supervision and Administration of Xihu District, Hangzhou, China
| | - Ming Li
- Tianjin Key Laboratory of Food Biotechnology, College of Biotechnology and Food Science, Tianjin University of Commerce, Tianjin, China
| | - Donghai Han
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China
| | - Wenjie Liu
- School of Chemical Engineering, Xiangtan University, Xiangtan, China
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23
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Untargeted headspace gas chromatography – Ion mobility spectrometry analysis for detection of adulterated honey. Talanta 2019; 205:120123. [DOI: 10.1016/j.talanta.2019.120123] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 07/03/2019] [Accepted: 07/05/2019] [Indexed: 11/24/2022]
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24
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Tanner N, Lichtenberg‐Kraag B. Identification and Quantification of Single and Multi‐Adulteration of Beeswax by FTIR‐ATR Spectroscopy. EUR J LIPID SCI TECH 2019. [DOI: 10.1002/ejlt.201900245] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Norman Tanner
- Institute for Bee Research Hohen Neuendorf Friedrich‐Engels‐Str. 32 16540 Hohen Neuendorf Germany
- Institute of Biochemistry and Biology, University of Potsdam Karl‐Liebknecht‐Str. 24–25 14476 Potsdam Germany
| | - Birgit Lichtenberg‐Kraag
- Institute for Bee Research Hohen Neuendorf Friedrich‐Engels‐Str. 32 16540 Hohen Neuendorf Germany
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25
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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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26
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Yaman N, Durakli Velioglu S. Use of Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy in Combination with Multivariate Methods for the Rapid Determination of the Adulteration of Grape, Carob and Mulberry Pekmez. Foods 2019; 8:E231. [PMID: 31261701 PMCID: PMC6678892 DOI: 10.3390/foods8070231] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 06/20/2019] [Accepted: 06/20/2019] [Indexed: 11/16/2022] Open
Abstract
Pekmez, a traditional Turkish food generally produced by concentration of fruit juices, is subjected to fraudulent activities like many other foodstuffs. This study reports the use of Fourier transform infrared spectroscopy (FTIR) in combination with chemometric methods for the detection of fraudulent addition of glucose syrup to traditional grape, carob and mulberry pekmez. FTIR spectra of samples were taken in mid-infrared (MIR) range of 400-4000 cm-1 using attenuated total reflectance (ATR) sample accessory. Partial least squares-discriminant analysis (PLS-DA) and PLS chemometric methods were built for qualitative and quantitative analysis of pekmez samples, respectively. PLS-DA models were successfully used for the discrimination of pure pekmez samples and the adulterated pekmez samples with glucose syrup. Sensitivity and specificity of 100%, and model efficiency of 100% were obtained in PLS-DA models for all pekmez groups. Detection of the adulteration ratio of pekmez samples was also accomplished using ATR-FTIR spectroscopy in combination with PLS. As a result, it was shown that ATR-FTIR spectroscopy along with chemometric methods had a great potential for determination of pekmez adulteration with glucose syrup.
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Affiliation(s)
- Nihal Yaman
- Department of Food Engineering, Faculty of Agriculture, Tekirdag Namik Kemal University, Tekirdag 59030, Turkey
- Malatya Directorate of Provincial Agriculture and Forestry, Ministry of Agriculture and Forestry, Malatya 44200, Turkey
| | - Serap Durakli Velioglu
- Department of Food Engineering, Faculty of Agriculture, Tekirdag Namik Kemal University, Tekirdag 59030, Turkey.
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27
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Su WH, Sun DW. Mid-infrared (MIR) Spectroscopy for Quality Analysis of Liquid Foods. FOOD ENGINEERING REVIEWS 2019. [DOI: 10.1007/s12393-019-09191-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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