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Kanwal N, Musharraf SG. Analytical approaches for the determination of adulterated animal fats and vegetable oils in food and non-food samples. Food Chem 2024; 460:140786. [PMID: 39142208 DOI: 10.1016/j.foodchem.2024.140786] [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: 04/17/2024] [Revised: 08/01/2024] [Accepted: 08/05/2024] [Indexed: 08/16/2024]
Abstract
Edible oils and fats are crucial components of everyday cooking and the production of food products, but their purity has been a major issue for a long time. High-quality edible oils are contaminated with low- and cheap-quality edible oils to increase profits. The adulteration of edible oils and fats also produces many health risks. Detection of main and minor components can identify adulterations using various techniques, such as GC, HPLC, TLC, FTIR, NIR, NMR, direct mass spectrometry, PCR, E-Nose, and DSC. Each detection technique has its advantages and disadvantages. For example, chromatography offers high precision but requires extensive sample preparation, while spectroscopy is rapid and non-destructive but may lack resolution. Direct mass spectrometry is faster and simpler than chromatography-based MS, eliminating complex preparation steps. DNA-based oil authentication is effective but hindered by laborious extraction processes. E-Nose only distinguishes odours, and DSC directly studies lipid thermal properties without derivatization or solvents. Mass spectrometry-based techniques, particularly GC-MS is found to be highly effective for detecting adulteration of oils and fats in food and non-food samples. This review summarizes the benefits and drawbacks of these analytical approaches and their use in conjunction with chemometric tools to detect the adulteration of animal fats and vegetable oils. This combination provides a powerful technique with enormous chemotaxonomic potential that includes the detection of adulterations, quality assurance, assessment of geographical origin, assessment of the process, and classification of the product in complex matrices from food and non-food samples.
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Affiliation(s)
- Nayab Kanwal
- H. E. J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi 75270, Pakistan
| | - Syed Ghulam Musharraf
- H. E. J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi 75270, Pakistan; Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi 75270, Pakistan..
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Ozen B, Cavdaroglu C, Tokatli F. Trends in authentication of edible oils using vibrational spectroscopic techniques. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024; 16:4216-4233. [PMID: 38899503 DOI: 10.1039/d4ay00562g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
The authentication of edible oils has become increasingly important for ensuring product quality, safety, and compliance with regulatory standards. Some prevalent authenticity issues found in edible oils include blending expensive oils with cheaper substitutes or lower-grade oils, incorrect labeling regarding the oil's source or type, and falsely stating the oil's origin. Vibrational spectroscopy techniques, such as infrared (IR) and Raman spectroscopy, have emerged as effective tools for rapidly and non-destructively analyzing edible oils. This review paper offers a comprehensive overview of recent advancements in using vibrational spectroscopy for authenticating edible oils. The fundamental principles underlying vibrational spectroscopy are introduced and chemometric approaches that enhance the accuracy and reliability of edible oil authentication are summarized. Recent research trends highlighted in the review include authenticating newly introduced oils, identifying oils based on their specific origins, adopting handheld/portable spectrometers and hyperspectral imaging, and integrating modern data handling techniques into the use of vibrational spectroscopic techniques for edible oil authentication. Overall, this review provides insights into the current state-of-the-art techniques and prospects for utilizing vibrational spectroscopy in the authentication of edible oils, thereby facilitating quality control and consumer protection in the food industry.
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Affiliation(s)
- Banu Ozen
- Izmir Institute of Technology, Department of Food Engineering, Urla, Izmir, Turkiye.
| | - Cagri Cavdaroglu
- Izmir Institute of Technology, Department of Food Engineering, Urla, Izmir, Turkiye.
| | - Figen Tokatli
- Izmir Institute of Technology, Department of Food Engineering, Urla, Izmir, Turkiye.
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Hachem M, Ahmmed MK, Nacir-Delord H. Phospholipidomics in Clinical Trials for Brain Disorders: Advancing our Understanding and Therapeutic Potentials. Mol Neurobiol 2024; 61:3272-3295. [PMID: 37981628 PMCID: PMC11087356 DOI: 10.1007/s12035-023-03793-y] [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: 05/19/2023] [Accepted: 10/31/2023] [Indexed: 11/21/2023]
Abstract
Phospholipidomics is a specialized branch of lipidomics that focuses on the characterization and quantification of phospholipids. By using sensitive analytical techniques, phospholipidomics enables researchers to better understand the metabolism and activities of phospholipids in brain disorders such as Alzheimer's and Parkinson's diseases. In the brain, identifying specific phospholipid biomarkers can offer valuable insights into the underlying molecular features and biochemistry of these diseases through a variety of sensitive analytical techniques. Phospholipidomics has emerged as a promising tool in clinical studies, with immense potential to advance our knowledge of neurological diseases and enhance diagnosis and treatment options for patients. In the present review paper, we discussed numerous applications of phospholipidomics tools in clinical studies, with a particular focus on the neurological field. By exploring phospholipids' functions in neurological diseases and the potential of phospholipidomics in clinical research, we provided valuable insights that could aid researchers and clinicians in harnessing the full prospective of this innovative practice and improve patient outcomes by providing more potent treatments for neurological diseases.
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Affiliation(s)
- Mayssa Hachem
- Department of Chemistry and Healthcare Engineering Innovation Center, Khalifa University of Sciences and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates.
| | - Mirja Kaizer Ahmmed
- Department of Fishing and Post-Harvest Technology, Chattogram Veterinary and Animal Sciences University, Chattogram, Bangladesh
- Riddet Institute, Massey University, Palmerston North, New Zealand
| | - Houda Nacir-Delord
- Department of Chemistry, Khalifa University of Sciences and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates
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Gao Y, Carne A, Young W, Burrow K, Naji S, Fraser-Miller SJ, Gordon KC, Bekhit AEDA. Effect of consumption of sheep and cow milk on rat brain fatty acid and phospholipid composition. Food Chem 2024; 439:138056. [PMID: 38035492 DOI: 10.1016/j.foodchem.2023.138056] [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: 07/24/2023] [Revised: 11/02/2023] [Accepted: 11/20/2023] [Indexed: 12/02/2023]
Abstract
The effect of sheep milk and cow milk on the lipid composition of rat brain was investigated in two feeding experiments of 28-days duration. Total lipids of the rat brain were extracted using ethanol-hexane, and the fatty acids and phospholipid contents analysed using gas chromatography with flame ionization detection (GC-FID) and phosphorus-31 nuclear magnetic resonance (31P NMR). Furthermore, freeze-dried pooled samples were analysed using attenuated total reflectance Fourier Transform Infrared and Fourier Transform Raman Spectroscopy and analysed with multivariate methods. A significantly (P < 0.05) higher C18:2 content was found in the cow milk group compared with sheep milk-treated groups in Study one. In Study two, a significantly (P < 0.05) lower C16:0 content was present in the sheep milk-treated group compared to the control low Ca/P group. No significant (P > 0.05) differences were observed in the spectroscopy analyses. It is concluded that sheep and cow milks fed to rats for 28-days had a low effect on the brain lipidome.
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Affiliation(s)
- Yutong Gao
- Food Science Department, University of Otago, P.O. Box 56, Dunedin 9016, New Zealand
| | - Alan Carne
- Biochemistry Department, University of Otago, P.O. Box 56, Dunedin 9016, New Zealand
| | - Wayne Young
- AgResearch Ltd, Tennent Drive, Palmerston North 4442, New Zealand
| | - Keegan Burrow
- Department of Wine, Food and Molecular Biosciences, RFH Building, Lincoln University, PO Box 85084, Lincoln 7647, Christchurch, New Zealand
| | - Samer Naji
- Te Whai Ao - Dodd-Walls Centre for Photonic and Quantum Technologies, Department of Chemistry, University of Otago, P.O. Box 56, Dunedin 9016, New Zealand
| | - Sara J Fraser-Miller
- Te Whai Ao - Dodd-Walls Centre for Photonic and Quantum Technologies, Department of Chemistry, University of Otago, P.O. Box 56, Dunedin 9016, New Zealand
| | - Keith C Gordon
- Te Whai Ao - Dodd-Walls Centre for Photonic and Quantum Technologies, Department of Chemistry, University of Otago, P.O. Box 56, Dunedin 9016, New Zealand
| | - Alaa El-Din A Bekhit
- Food Science Department, University of Otago, P.O. Box 56, Dunedin 9016, New Zealand.
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Guo M, Wang K, Lin H, Wang L, Cao L, Sui J. Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives. Compr Rev Food Sci Food Saf 2024; 23:e13301. [PMID: 38284587 DOI: 10.1111/1541-4337.13301] [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: 07/24/2023] [Revised: 11/27/2023] [Accepted: 12/31/2023] [Indexed: 01/30/2024]
Abstract
In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low-level, mid-level, and high-level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra-to-spectra, spectra-to-machine vision, spectra-to-electronic nose, and spectra-to-nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real-world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone-based multi-sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.
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Affiliation(s)
- Minqiang Guo
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
- College of Food Science and Engineering, Xinjiang Institute of Technology, Aksu, Xinjiang, China
| | - Kaiqiang Wang
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Hong Lin
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Lei Wang
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Limin Cao
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Jianxin Sui
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
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Li Q, Zhang Z, Ma Z. Raman spectral pattern recognition of breast cancer: A machine learning strategy based on feature fusion and adaptive hyperparameter optimization. Heliyon 2023; 9:e18148. [PMID: 37501962 PMCID: PMC10368853 DOI: 10.1016/j.heliyon.2023.e18148] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 07/08/2023] [Accepted: 07/10/2023] [Indexed: 07/29/2023] Open
Abstract
Raman spectroscopy, as a kind of molecular vibration spectroscopy, provides abundant information for measuring components and molecular structure in the early detection and diagnosis of breast cancer. Currently, portable Raman spectrometers have simplified and made equipment application more affordable, albeit at the cost of sacrificing the signal-to-noise ratio (SNR). Consequently, this necessitates a higher recognition rate from pattern recognition algorithms. Our study employs a feature fusion strategy to reduce the dimensionality of high-dimensional Raman spectra and enhance the discriminative information between normal tissues and tumors. In the conducted random experiment, the classifier achieved a performance of over 96% for all three average metrics: accuracy, sensitivity, and specificity. Additionally, we propose a multi-parameter serial encoding evolutionary algorithm (MSEA) and integrate it into the Adaptive Local Hyperplane K-nearest Neighbor classification algorithm (ALHK) for adaptive hyperparameter optimization. The implementation of serial encoding tackles the predicament of parallel optimization in multi-hyperparameter vector problems. To bolster the convergence of the optimization algorithm towards a global optimal solution, an exponential viability function is devised for nonlinear processing. Moreover, an improved elitist strategy is employed for individual selection, effectively eliminating the influence of probability factors on the robustness of the optimization algorithm. This study further optimizes the hyperparameter space through sensitivity analysis of hyperparameters and cross-validation experiments, leading to superior performance compared to the ALHK algorithm with manual hyperparameter configuration.
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Affiliation(s)
- Qingbo Li
- School of Instrumentation and Optoelectronic Engineering, Precision Opto-Mechatronics Technology Key Laboratory of Education Ministry, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing, 100191, China
| | - Zhixiang Zhang
- School of Instrumentation and Optoelectronic Engineering, Precision Opto-Mechatronics Technology Key Laboratory of Education Ministry, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing, 100191, China
| | - Zhenhe Ma
- Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Detection Technology, Northeastern University, Qinhuangdao Campus, Qinhuangdao, 066004, China
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Ahmmed F, Gordon KC, Killeen DP, Fraser-Miller SJ. Detection and Quantification of Adulteration in Krill Oil with Raman and Infrared Spectroscopic Methods. Molecules 2023; 28:molecules28093695. [PMID: 37175105 PMCID: PMC10180486 DOI: 10.3390/molecules28093695] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 04/14/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023] Open
Abstract
Raman and infrared spectroscopy, used as individual and low-level fused datasets, were evaluated to identify and quantify the presence of adulterants (palm oil, PO; ω-3 concentrates in ethyl ester, O3C and fish oil, FO) in krill oil. These datasets were qualitatively analysed with principal component analysis (PCA) and classified as adulterated or unadulterated using support vector machines (SVM). Using partial least squares regression (PLSR), it was possible to identify and quantify the adulterant present in the KO mixture. Raman spectroscopy performed better (r2 = 0.98; RMSEP = 2.3%) than IR spectroscopy (r2 = 0.91; RMSEP = 4.2%) for quantification of O3C in KO. A data fusion approach further improved the analysis with model performance for quantification of PO (r2 = 0.98; RMSEP = 2.7%) and FO (r2 = 0.76; RMSEP = 9.1%). This study demonstrates the potential use of Raman and IR spectroscopy to quantify adulterants present in KO.
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Affiliation(s)
- Fatema Ahmmed
- Te Whai Ao-Dodd-Walls Centre for Photonic and Quantum Technologies, Department of Chemistry, University of Otago, P.O. Box 56, Dunedin 9016, New Zealand
| | - Keith C Gordon
- Te Whai Ao-Dodd-Walls Centre for Photonic and Quantum Technologies, Department of Chemistry, University of Otago, P.O. Box 56, Dunedin 9016, New Zealand
| | - Daniel P Killeen
- The New Zealand Institute for Plant and Food Research Limited, P.O. Box 5114, Port Nelson, Nelson 7043, New Zealand
| | - Sara J Fraser-Miller
- Te Whai Ao-Dodd-Walls Centre for Photonic and Quantum Technologies, Department of Chemistry, University of Otago, P.O. Box 56, Dunedin 9016, New Zealand
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Koster HJ, Guillen-Perez A, Gomez-Diaz JS, Navas-Moreno M, Birkeland AC, Carney RP. Fused Raman spectroscopic analysis of blood and saliva delivers high accuracy for head and neck cancer diagnostics. Sci Rep 2022; 12:18464. [PMID: 36323705 PMCID: PMC9630497 DOI: 10.1038/s41598-022-22197-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 10/11/2022] [Indexed: 11/25/2022] Open
Abstract
As a rapid, label-free, non-destructive analytical measurement requiring little to no sample preparation, Raman spectroscopy shows great promise for liquid biopsy cancer detection and diagnosis. We carried out Raman analysis and mass spectrometry of plasma and saliva from more than 50 subjects in a cohort of head and neck cancer patients and benign controls (e.g., patients with benign oral masses). Unsupervised data models were built to assess diagnostic performance. Raman spectra collected from either biofluid provided moderate performance to discriminate cancer samples. However, by fusing together the Raman spectra of plasma and saliva for each patient, subsequent analytical models delivered an impressive sensitivity, specificity, and accuracy of 96.3%, 85.7%, and 91.7%, respectively. We further confirmed that the metabolites driving the differences in Raman spectra for our models are among the same ones that drive mass spectrometry models, unifying the two techniques and validating the underlying ability of Raman to assess metabolite composition. This study bolsters the relevance of Raman to provide additive value by probing the unique chemical compositions across biofluid sources. Ultimately, we show that a simple data augmentation routine of fusing plasma and saliva spectra provided significantly higher clinical value than either biofluid alone, pushing forward the potential of clinical translation of Raman spectroscopy for liquid biopsy cancer diagnostics.
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Affiliation(s)
- Hanna J. Koster
- grid.27860.3b0000 0004 1936 9684Biomedical Engineering, University of California, Davis, CA USA
| | - Antonio Guillen-Perez
- grid.27860.3b0000 0004 1936 9684Electrical and Computer Engineering, University of California, Davis, CA USA
| | - Juan Sebastian Gomez-Diaz
- grid.27860.3b0000 0004 1936 9684Electrical and Computer Engineering, University of California, Davis, CA USA
| | | | - Andrew C. Birkeland
- grid.27860.3b0000 0004 1936 9684Department of Otolaryngology, University of California, CA Davis, USA
| | - Randy P. Carney
- grid.27860.3b0000 0004 1936 9684Biomedical Engineering, University of California, Davis, CA USA
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