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Hassan OB, ELkady EF, El-Zaher AA, Sayed RM. Resolution of overlapping spectra using chemometric manipulations of UV-spectrophotometric data for the determination of Atenolol, Losartan, and Hydrochlorothiazide in pharmaceutical dosage form. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 318:124471. [PMID: 38776669 DOI: 10.1016/j.saa.2024.124471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 04/24/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024]
Abstract
Simultaneous determination of atenolol (ATN), losartan potassium (LOS), and hydrochlorothiazide (HCZ) in presence of HCZ impurity B was conducted by chemometric approaches and radial basis function network (RBFN) using UV-spectrophotometry without preliminary separation. Three chemometric models namely, classical least-squares (CLS), principal component regression (PCR), and partial least-squares (PLS) along with RBFN were utilized using the ternary mixtures of the three drugs. The multivariate calibrations were obtained by measuring the zero-order absorbance of the mixtures from 250 to 270 nm at the interval of 0.2 nm. The models were built covering the concentration range of (4.0 to 20.0), (3.8 to 20.2), and (0.9 to 50.1) μg mL-1 for ATN, LOS, and HCZ, respectively. The regression coefficient was calculated between the actual and predicted concentrations of the 3 drugs using CLS, PCR, PLS and RBFN. The accuracy of the developed models was evaluated using the root mean square error of prediction (RMSEP) giving satisfactory results. The proposed methods were simple, accurate, precise and were applied efficiently for the quantitation of the three components in laboratory-prepared mixtures, and in dosage form showing good recovery values. In addition, the obtained results were compared statistically with each other using ANOVA test showing non-significant difference between them.
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Affiliation(s)
- Omnia Bassam Hassan
- Faculty of Biotechnology, October University for Modern Sciences and Arts (MSA), Giza 12451, Egypt
| | - Ehab F ELkady
- Pharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St., Cairo 11562, Egypt
| | - Asmaa A El-Zaher
- Pharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St., Cairo 11562, Egypt
| | - Rawda M Sayed
- Pharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St., Cairo 11562, Egypt.
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2
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Lončar B, Pezo L, Knežević V, Nićetin M, Filipović J, Petković M, Filipović V. Enhancing Cookie Formulations with Combined Dehydrated Peach: A Machine Learning Approach for Technological Quality Assessment and Optimization. Foods 2024; 13:782. [PMID: 38472895 DOI: 10.3390/foods13050782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 02/27/2024] [Accepted: 02/29/2024] [Indexed: 03/14/2024] Open
Abstract
This study focuses on predicting and optimizing the quality parameters of cookies enriched with dehydrated peach through the application of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. The purpose of the study is to employ advanced machine learning techniques to understand the intricate relationships between input parameters, such as the presence of dehydrated peach and treatment methods (lyophilization and lyophilization with osmotic pretreatment), and output variables representing various quality aspects of cookies. For each of the 32 outputs, including the parameters of the basic chemical compositions of the cookie samples, selected mineral contents, moisture contents, baking characteristics, color properties, sensorial attributes, and antioxidant properties, separate models were constructed using SVMs and ANNs. Results showcase the efficiency of ANN models in predicting a diverse set of quality parameters with r2 up to 1.000, with SVM models exhibiting slightly higher coefficients of determination for specific variables with r2 reaching 0.981. The sensitivity analysis underscores the pivotal role of dehydrated peach and the positive influence of osmotic pretreatment on specific compositional attributes. Utilizing established Artificial Neural Network models, multi-objective optimization was conducted, revealing optimal formulation and factor values in cookie quality optimization. The optimal quantity of lyophilized peach with osmotic pretreatment for the cookie formulation was identified as 15%.
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Affiliation(s)
- Biljana Lončar
- Faculty of Technology Novi Sad, University of Novi Sad, Bulevar Cara Lazara 1, 21000 Novi Sad, Serbia
| | - Lato Pezo
- Institute of General and Physical Chemistry, Studentski trg 12/V, 11000 Belgrade, Serbia
| | - Violeta Knežević
- Faculty of Technology Novi Sad, University of Novi Sad, Bulevar Cara Lazara 1, 21000 Novi Sad, Serbia
| | - Milica Nićetin
- Faculty of Technology Novi Sad, University of Novi Sad, Bulevar Cara Lazara 1, 21000 Novi Sad, Serbia
| | - Jelena Filipović
- Institute of Food Technology in Novi Sad, University of Novi Sad, Bulevar Cara Lazara 1, 21000 Novi Sad, Serbia
| | - Marko Petković
- Faculty of Agronomy, University of Kragujevac, Cara Dušana 34, 32102 Čačak, Serbia
| | - Vladimir Filipović
- Faculty of Technology Novi Sad, University of Novi Sad, Bulevar Cara Lazara 1, 21000 Novi Sad, Serbia
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3
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Zhang S, Wu SQY, Hum M, Perumal J, Tan EY, Lee ASG, Teng J, Dinish US, Olivo M. Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis. RSC Adv 2024; 14:3599-3610. [PMID: 38264270 PMCID: PMC10804230 DOI: 10.1039/d3ra05723b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 11/17/2023] [Indexed: 01/25/2024] Open
Abstract
Breast cancer is a prevalent form of cancer worldwide, and the current standard screening method, mammography, often requires invasive biopsy procedures for further assessment. Recent research has explored microRNAs (miRNAs) in circulating blood as potential biomarkers for early breast cancer diagnosis. In this study, we employed a multi-modal spectroscopy approach, combining attenuated total reflection Fourier transform infrared (ATR-FTIR) and surface-enhanced Raman scattering (SERS) to comprehensively characterize the full-spectrum fingerprints of RNA biomarkers in the blood serum of breast cancer patients. The sensitivity of conventional FTIR and Raman spectroscopy was enhanced by ATR-FTIR and SERS through the utilization of a diamond ATR crystal and silver-coated silicon nanopillars, respectively. Moreover, a wider measurement wavelength range was achieved with the multi-modal approach than with a single spectroscopic method alone. We have shown the results on 91 clinical samples, which comprised 44 malignant and 47 benign cases. Principal component analysis (PCA) was performed on the ATR-FTIR, SERS, and multi-modal data. From the peak analysis, we gained insights into biomolecular absorption and scattering-related features, which aid in the differentiation of malignant and benign samples. Applying 32 machine learning algorithms to the PCA results, we identified key molecular fingerprints and demonstrated that the multi-modal approach outperforms individual techniques, achieving higher average validation accuracy (95.1%), blind test accuracy (91.6%), specificity (94.7%), sensitivity (95.5%), and F-score (94.8%). The support vector machine (SVM) model showed the best area under the curve (AUC) characterization value of 0.9979, indicating excellent performance. These findings highlight the potential of the multi-modal spectroscopy approach as an accurate, reliable, and rapid method for distinguishing between malignant and benign breast tumors in women. Such a label-free approach holds promise for improving early breast cancer diagnosis and patient outcomes.
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Affiliation(s)
- Shuyan Zhang
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR) 2 Fusionopolis Way, Innovis #08-03 Singapore 138634 Republic of Singapore
| | - Steve Qing Yang Wu
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR) 2 Fusionopolis Way, Innovis #08-03 Singapore 138634 Republic of Singapore
| | - Melissa Hum
- Division of Cellular and Molecular Research, Humphrey Oei Institute of Cancer Research, National Cancer Centre Singapore (NCCS) 30 Hospital Boulevard Singapore 168583 Republic of Singapore
| | - Jayakumar Perumal
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR) 2 Fusionopolis Way, Innovis #08-03 Singapore 138634 Republic of Singapore
| | - Ern Yu Tan
- Breast & Endocrine Surgery, Tan Tock Seng Hospital (TTSH) 11 Jln Tan Tock Seng Singapore 308433 Republic of Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Republic of Singapore
| | - Ann Siew Gek Lee
- Division of Cellular and Molecular Research, Humphrey Oei Institute of Cancer Research, National Cancer Centre Singapore (NCCS) 30 Hospital Boulevard Singapore 168583 Republic of Singapore
- SingHealth Duke-NUS Oncology Academic Clinical Programme (ONCO ACP), Duke-NUS Medical School Singapore 169857 Republic of Singapore
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore Singapore 117593 Republic of Singapore
| | - Jinghua Teng
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR) 2 Fusionopolis Way, Innovis #08-03 Singapore 138634 Republic of Singapore
| | - U S Dinish
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR) 2 Fusionopolis Way, Innovis #08-03 Singapore 138634 Republic of Singapore
| | - Malini Olivo
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR) 2 Fusionopolis Way, Innovis #08-03 Singapore 138634 Republic of Singapore
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4
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Jazmin Hidalgo M, Emilio Gaiad J, Casimiro Goicoechea H, Mendoza A, Pérez-Rodríguez M, Gerardo Pellerano R. Geographical origin identification of mandarin fruits by analyzing fingerprint signatures based on multielemental composition. Food Chem X 2023; 20:101040. [PMID: 38144842 PMCID: PMC10740036 DOI: 10.1016/j.fochx.2023.101040] [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: 09/25/2023] [Revised: 11/21/2023] [Accepted: 11/28/2023] [Indexed: 12/26/2023] Open
Abstract
Given rising traders and consumers concerns, the global food industry is increasingly demanding authentic and traceable products. Consequently, there is a heightened focus on verifying geographical authenticity as food quality assurance. In this work, we assessed pattern recognition approaches based on elemental predictors to discern the provenance of mandarin juices from three distinct citrus-producing zones located in the Northeast region of Argentina. A total of 202 samples originating from two cultivars were prepared through microwave-assisted acid digestion and analyzed by microwave plasma atomic emission spectroscopy (MP-AES). Later, we applied linear discriminant analysis (LDA), k-nearest neighbor (k-NN), support vector machine (SVM), and random forest (RF) to the element data obtained. SVM accomplished the best classification performance with a 95.1% success rate, for which it was selected for citrus samples authentication. The proposed method highlights the capability of mineral profiles in accurately identifying the genuine origin of mandarin juices. By implementing this model in the food supply chain, it can prevent mislabeling fraud, thereby contributing to consumer protection.
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Affiliation(s)
- Melisa Jazmin Hidalgo
- Instituto de Química Básica y Aplicada del Nordeste Argentino (IQUIBA-NEA), UNNE-CONICET, Facultad de Ciencias Exactas y Naturales y Agrimensura, Ave. Libertad 5400, Corrientes 3400, Argentina
| | - José Emilio Gaiad
- Instituto de Química Básica y Aplicada del Nordeste Argentino (IQUIBA-NEA), UNNE-CONICET, Facultad de Ciencias Exactas y Naturales y Agrimensura, Ave. Libertad 5400, Corrientes 3400, Argentina
| | - Héctor Casimiro Goicoechea
- Laboratorio de Desarrollo Analítico y Quimiometría (LADAQ), Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Ciudad Universitaria, Santa Fe 3000, Argentina
| | - Alberto Mendoza
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, N.L., Mexico
| | - Michael Pérez-Rodríguez
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, N.L., Mexico
| | - Roberto Gerardo Pellerano
- Instituto de Química Básica y Aplicada del Nordeste Argentino (IQUIBA-NEA), UNNE-CONICET, Facultad de Ciencias Exactas y Naturales y Agrimensura, Ave. Libertad 5400, Corrientes 3400, Argentina
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5
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Ong P, Yeh CW, Tsai IL, Lee WJ, Wang YJ, Chuang YK. Evaluation of convolutional neural network for non-destructive detection of imidacloprid and acetamiprid residues in chili pepper (Capsicum frutescens L.) based on visible near-infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 303:123214. [PMID: 37531681 DOI: 10.1016/j.saa.2023.123214] [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: 04/14/2023] [Revised: 07/14/2023] [Accepted: 07/26/2023] [Indexed: 08/04/2023]
Abstract
Consumption of agricultural products with pesticide residue is risky and can negatively affect health. This study proposed a nondestructive method of detecting pesticide residues in chili pepper based on the combination of visible and near-infrared (VIS/NIR) spectroscopy (400-2498 nm) and deep learning modeling. The obtained spectra of chili peppers with two types of pesticide residues (acetamiprid and imidacloprid) were analyzed using a one-dimensional convolutional neural network (1D-CNN). Compared with the commonly used partial least squares regression model, the 1D-CNN approach yielded higher prediction accuracy, with a root mean square error of calibration of 0.23 and 0.28 mg/kg and a root mean square error of prediction of 0.55 and 0.49 mg/kg for the acetamiprid and imidacloprid data sets, respectively. Overall, the results indicate that the combination of the 1D-CNN model and VIS/NIR spectroscopy is a promising nondestructive method of identifying pesticide residues in chili pepper.
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Affiliation(s)
- Pauline Ong
- Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia.
| | - Ching-Wen Yeh
- Master's Program in Food Safety, College of Nutrition, Taipei Medical University, 250 Wusing Street, Taipei 11031, Taiwan.
| | - I-Lin Tsai
- Department of Biochemistry and Molecular Cell Biology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan.
| | - Wei-Ju Lee
- School of Food Safety, College of Nutrition, Taipei Medical University, Taipei 11031, Taiwan.
| | - Yu-Jen Wang
- Department of Radiation Oncology, Fu Jen Catholic University Hospital, New Taipei City, Taiwan; School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan; Department of Radiation Oncology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.
| | - Yung-Kun Chuang
- Master's Program in Food Safety, College of Nutrition, Taipei Medical University, 250 Wusing Street, Taipei 11031, Taiwan; School of Food Safety, College of Nutrition, Taipei Medical University, Taipei 11031, Taiwan; Nutrition Research Center, Taipei Medical University Hospital, Taipei 11031, Taiwan.
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6
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Abi-Rizk H, Jouan-Rimbaud Bouveresse D, Chamberland J, Cordella CBY. Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:5410-5440. [PMID: 37818969 DOI: 10.1039/d3ay01132a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
A greater demand for high-quality food is being driven by the growth of economic and technological advancements. In this context, consumers are currently paying special attention to organoleptic characteristics such as smell, taste, and appearance. Motivated to mimic human senses, scientists developed electronic devices such as e-noses, e-tongues, and e-eyes, to spot signals relative to different chemical substances prevalent in food systems. To interpret the information provided by the sensors' responses, multiple chemometric approaches are used depending on the aim of the study. This review based on the Web of Science database, endeavored to scrutinize three e-sensing systems coupled to chemometric approaches for food quality evaluation. A total of 122 eligible articles pertaining to the e-nose, e-tongue and e-eye devices were selected to conduct this review. Most of the performed studies used exploratory analysis based on linear factorial methods, while classification and regression techniques came in the second position. Although their applications have been less common in food science, it is to be noted that nonlinear approaches based on artificial intelligence and machine learning deployed in a big-data context have generally yielded better results for classification and regression purposes, providing new perspectives for future studies.
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Affiliation(s)
- Hala Abi-Rizk
- LAboratoire de Recherche et de Traitement de l'Information Chimiosensorielle - LARTIC, Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, G1V 0A6, Canada.
| | | | - Julien Chamberland
- Department of Food Sciences, STELA Dairy Research Center, Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, G1V 0A6, Canada
| | - Christophe B Y Cordella
- LAboratoire de Recherche et de Traitement de l'Information Chimiosensorielle - LARTIC, Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, G1V 0A6, Canada.
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7
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Islam M, Kaczmarek A, Montowska M, Tomaszewska-Gras J. Comparing Different Chemometric Approaches to Detect Adulteration of Cold-Pressed Flaxseed Oil with Refined Rapeseed Oil Using Differential Scanning Calorimetry. Foods 2023; 12:3352. [PMID: 37761061 PMCID: PMC10530209 DOI: 10.3390/foods12183352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 08/31/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
Flaxseed oil is one of the best sources of n-3 fatty acids, thus its adulteration with refined oils can lead to a reduction in its nutritional value and overall quality. The purpose of this study was to compare different chemometric models to detect adulteration of flaxseed oil with refined rapeseed oil (RP) using differential scanning calorimetry (DSC). Based on the melting phase transition curve, parameters such as peak temperature (T), peak height (h), and percentage of area (P) were determined for pure and adulterated flaxseed oils with an RP concentration of 5, 10, 20, 30, and 50% (w/w). Significant linear correlations (p ≤ 0.05) between the RP concentration and all DSC parameters were observed, except for parameter h1 for the first peak. In order to assess the usefulness of the DSC technique for detecting adulterations, three chemometric approaches were compared: (1) classification models (linear discriminant analysis-LDA, adaptive regression splines-MARS, support vector machine-SVM, and artificial neural networks-ANNs); (2) regression models (multiple linear regression-MLR, MARS, SVM, ANNs, and PLS); and (3) a combined model of orthogonal partial least squares discriminant analysis (OPLS-DA). With the LDA model, the highest accuracy of 99.5% in classifying the samples, followed by ANN > SVM > MARS, was achieved. Among the regression models, the ANN model showed the highest correlation between observed and predicted values (R = 0.996), while other models showed goodness of fit as following MARS > SVM > MLR. Comparing OPLS-DA and PLS methods, higher values of R2X(cum) = 0.986 and Q2 = 0.973 were observed with the PLS model than OPLS-DA. This study demonstrates the usefulness of the DSC technique and importance of an appropriate chemometric model for predicting the adulteration of cold-pressed flaxseed oil with refined rapeseed oil.
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Affiliation(s)
- Mahbuba Islam
- Department of Food Quality and Safety Management, Poznań University of Life Sciences, ul. Wojska Polskiego 31/33, 60-624 Poznań, Poland; (M.I.); (A.K.)
| | - Anna Kaczmarek
- Department of Food Quality and Safety Management, Poznań University of Life Sciences, ul. Wojska Polskiego 31/33, 60-624 Poznań, Poland; (M.I.); (A.K.)
| | - Magdalena Montowska
- Department of Meat Technology, Poznan University of Life Sciences, ul. Wojska Polskiego 31/33, 60-624 Poznań, Poland;
| | - Jolanta Tomaszewska-Gras
- Department of Food Quality and Safety Management, Poznań University of Life Sciences, ul. Wojska Polskiego 31/33, 60-624 Poznań, Poland; (M.I.); (A.K.)
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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: 0] [Impact Index Per Article: 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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Abdous B, Sajjadi SM, Bagheri A. Predicting the aggregation number of cationic surfactants based on ANN-QSAR modeling approaches: understanding the impact of molecular descriptors on aggregation numbers. RSC Adv 2022; 12:33666-33678. [PMID: 36505704 PMCID: PMC9685374 DOI: 10.1039/d2ra06064g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 11/03/2022] [Indexed: 11/25/2022] Open
Abstract
In this work, a quantitative structure-activity relationship (QSAR) study is performed on some cationic surfactants to evaluate the relationship between the molecular structures of the compounds with their aggregation numbers (AGGNs) in aqueous solution at 25 °C. An artificial neural network (ANN) model is combined with the QSAR study to predict the aggregation number of the surfactants. In the ANN analysis, four out of more than 3000 molecular descriptors were used as input variables, and the complete set of 41 cationic surfactants was randomly divided into a training set of 29, a test set of 6, and a validation set of 6 molecules. After that, a multiple linear regression (MLR) analysis was utilized to build a linear model using the same descriptors and the results were compared statistically with those of the ANN analysis. The square of the correlation coefficient (R 2) and root mean square error (RMSE) of the ANN and MLR models (for the whole data set) were 0.9392, 7.84, and 0.5010, 22.52, respectively. The results of the comparison revealed the efficiency of ANN in detecting a correlation between the molecular structure of surfactants and their AGGN values with a high predictive power due to the non-linearity in the studied data. Based on the ANN algorithm, the relative importance of the selected descriptors was computed and arranged in the following descending order: H-047 > ESpm12x > JGI6> Mor20p. Then, the QSAR data was interpreted and the impact of each descriptor on the AGGNs of the molecules were thoroughly discussed. The results showed there is a correlation between each selected descriptor and the AGGN values of the surfactants.
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Affiliation(s)
- Behnaz Abdous
- Faculty of Chemistry, Semnan UniversitySemnanIran+98-23-33384110+98-23-31533192
| | - S. Maryam Sajjadi
- Faculty of Chemistry, Semnan UniversitySemnanIran+98-23-33384110+98-23-31533192
| | - Ahmad Bagheri
- Faculty of Chemistry, Semnan UniversitySemnanIran+98-23-33384110+98-23-31533192
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10
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MSGWO-MKL-SVM: A Missing Link Prediction Method for UAV Swarm Network Based on Time Series. MATHEMATICS 2022. [DOI: 10.3390/math10142535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Missing link prediction technology (MLP) is always a hot research area in the field of complex networks, and it has been extensively utilized in UAV swarm network reconstruction recently. UAV swarm is an artificial network with strong randomness, in the face of which prediction methods based on network similarity often perform poorly. To solve those problems, this paper proposes a Multi Kernel Learning algorithm with a multi-strategy grey wolf optimizer based on time series (MSGWO-MKL-SVM). The Multiple Kernel Learning (MKL) method is adopted in this algorithm to extract the advanced features of time series, and the Support Vector Machine (SVM) algorithm is used to determine the hyperplane of threshold value in nonlinear high dimensional space. Besides that, we propose a new measurable indicator of Multiple Kernel Learning based on cluster, transforming a Multiple Kernel Learning problem into a multi-objective optimization problem. Some adaptive neighborhood strategies are used to enhance the global searching ability of grey wolf optimizer algorithm (GWO). Comparison experiments were conducted on the standard UCI datasets and the professional UAV swarm datasets. The classification accuracy of MSGWO-MKL-SVM on UCI datasets is improved by 6.2% on average, and the link prediction accuracy of MSGWO-MKL-SVM on professional UAV swarm datasets is improved by 25.9% on average.
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11
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Mafata M, Brand J, Medvedovici A, Buica A. Chemometric and sensometric techniques in enological data analysis. Crit Rev Food Sci Nutr 2022; 63:10995-11009. [PMID: 35730201 DOI: 10.1080/10408398.2022.2089624] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Enological evaluations capture the chemical and sensory space of wine using different techniques; many sensory methods as well as a variety of analytical chemistry techniques contribute to the amount of information generated. Data fusion, especially integrating data sets, is important when working with complex systems. The success reported when trying to integrate different modalities is generally low and has been attributed to the lack of statistically considerate strategies focusing on the data handling process. Multiple stages of data handling must be carefully considered when dealing with multi-modal data. In this review, the different stages in the data analysis process were examined. The study revealed misconceptions surrounding the process and elucidated rules for purpose-driven approaches by examining the complexities of each stage and the impact the decisions made at each stage have on the resulting models. The two major modeling approaches are either supervised (discrimination, classification, prediction) or unsupervised (exploration). Supervised approaches were emphatic on the pre-processing steps and prioritized increasing performance. Unsupervised approaches were mostly used for preliminary steps. The review found aspects often neglected when it came to the data collection and capturing which in the end contributed to the low success in combining sensory and chemistry data.
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Affiliation(s)
- Mpho Mafata
- South African Grape and Wine Research Institute, Department of Viticulture and Oenology, Stellenbosch University, Stellenbosch, South Africa
- School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Jeanne Brand
- South African Grape and Wine Research Institute, Department of Viticulture and Oenology, Stellenbosch University, Stellenbosch, South Africa
| | - Andrei Medvedovici
- Department of Analytical Chemistry, Faculty of Chemistry, University of Bucharest, Bucharest, Romania
| | - Astrid Buica
- South African Grape and Wine Research Institute, Department of Viticulture and Oenology, Stellenbosch University, Stellenbosch, South Africa
- School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
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12
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Inobeme A, Nayak V, Mathew TJ, Okonkwo S, Ekwoba L, Ajai AI, Bernard E, Inobeme J, Mariam Agbugui M, Singh KR. Chemometric approach in environmental pollution analysis: A critical review. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 309:114653. [PMID: 35176568 DOI: 10.1016/j.jenvman.2022.114653] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 01/18/2022] [Accepted: 01/31/2022] [Indexed: 06/14/2023]
Abstract
With the ever-increasing global population and industrialization, it has become a call of the hour to start taking care of the environment to balance the ecosystem. For this, effective monitoring and assessment are required, which involves collecting and measuring environmental details, temporal and spatial readings of environmental data, and parameters. However, assessment of the environment is very tedious as it includes monitoring target analytes, identifying their sources, and reporting, which invariably implies that detailed environmental monitoring would be an intricate and expensive process. The traditional protocols in environmental measures are often manual and time demanding, which makes it further difficult. Moreover, several changes also occur within the environment, which could be chemical, physical, or biological, and since these environmental impacts are often cumulative, it becomes difficult to measure an isolated system. Furthermore, the chances of skipping significant results and trends become high. Also, experimental data obtained from the environmental analysis are usually non-linear and multi-variant due to different associations among various contributing variables. Therefore, it is implied that accurate measurements and environment monitoring are not using traditional analytical protocols. Thus, the need for a chemometric approach in environmental pollution analysis becomes paramount due to the inherent limitations associated with the conventional approach of analyzing environmental datasets. Chemometrics has appeared as a potential technique, which enhances the particulars of the chemical datasets by using statistical and mathematical analysis methods to analyze chemical data beyond univariate analysis. Utilizing chemometrics to study the environmental data is a revolutionary idea as it helps identify the relationship between sources of contaminations, environmental drivers, and their impact on the environment. Hence, this review critically explores the concept of chemometrics and its application in environmental pollution analysis by briefly highlighting the idea of chemometrics, its types, applications, advantages, and limitations in the environmental domain. An attempt is also made to present future trends in applications of chemometrics in environmental pollution analysis.
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Affiliation(s)
- Abel Inobeme
- Department of Chemistry, Edo University Iyamho, Edo State, Nigeria.
| | - Vanya Nayak
- Department of Chemistry, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India
| | - Tsado John Mathew
- Department of Chemistry, Ibrahim Badamosi Babangida University Lapai, Nigeria
| | - Stanley Okonkwo
- Department of Chemistry, Osaka Kyoiku University, Osaka, Japan
| | - Lucky Ekwoba
- Department of Pure and Industrial Chemistry, Kogi State University, Anyigba, Nigeria
| | | | - Esther Bernard
- Department of Chemical Engineering, Federal University of Technology Minna, Nigeria
| | | | - M Mariam Agbugui
- Department of Biological Science, Edo University Iyamho, Nigeria
| | - Kshitij Rb Singh
- Department of Chemistry, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India.
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Muguruma Y, Nunome M, Inoue K. A Review on the Foodomics Based on Liquid Chromatography Mass Spectrometry. Chem Pharm Bull (Tokyo) 2022; 70:12-18. [PMID: 34980727 DOI: 10.1248/cpb.c21-00765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Due to the globalization of food production and distribution, the food chain has become increasingly complex, making it more difficult to evaluate unexpected food changes. Therefore, establishing sensitive, robust, and cost-effective analytical platforms to efficiently extract and analyze the food-chemicals in complex food matrices is essential, however, challenging. LC/MS-based metabolomics is the key to obtain a broad overview of human metabolism and understand novel food science. Various metabolomics approaches (e.g., targeted and/or untargeted) and sample preparation techniques in food analysis have their own advantages and limitations. Selecting an analytical platform that matches the characteristics of the analytes is important for food analysis. This review highlighted the recent trends and applications of metabolomics based on "foodomics" by LC-MS and provides the perspectives and insights into the methodology and various sample preparation techniques in food analysis.
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Affiliation(s)
- Yoshio Muguruma
- Graduate School of Pharmaceutical Sciences, Ritsumeikan University
| | - Mari Nunome
- Graduate School of Pharmaceutical Sciences, Ritsumeikan University
| | - Koichi Inoue
- Graduate School of Pharmaceutical Sciences, Ritsumeikan University
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Smartphone-based method for the determination of chlorophyll and carotenoid contents in olive and avocado oils: An approach with calibration transfer. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.104164] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Galvan D, Effting L, Cremasco H, Adam Conte-Junior C. Can Socioeconomic, Health, and Safety Data Explain the Spread of COVID-19 Outbreak on Brazilian Federative Units? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E8921. [PMID: 33266276 PMCID: PMC7730726 DOI: 10.3390/ijerph17238921] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 11/12/2020] [Accepted: 11/14/2020] [Indexed: 12/15/2022]
Abstract
Infinite factors can influence the spread of COVID-19. Evaluating factors related to the spread of the disease is essential to point out measures that take effect. In this study, the influence of 14 variables was assessed together by Artificial Neural Networks (ANN) of the type Self-Organizing Maps (SOM), to verify the relationship between numbers of cases and deaths from COVID-19 in Brazilian states for 110 days. The SOM analysis showed that the variables that presented a more significant relationship with the numbers of cases and deaths by COVID-19 were influenza vaccine applied, Intensive Care Unit (ICU), ventilators, physicians, nurses, and the Human Development Index (HDI). In general, Brazilian states with the highest rates of influenza vaccine applied, ICU beds, ventilators, physicians, and nurses, per 100,000 inhabitants, had the lowest number of cases and deaths from COVID-19, while the states with the lowest rates were most affected by the disease. According to the SOM analysis, other variables such as Personal Protective Equipment (PPE), tests, drugs, and Federal funds, did not have as significant effect as expected.
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Affiliation(s)
- Diego Galvan
- COVID-19 Research Group, Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Cidade Universitária, Rio de Janeiro RJ 21941-598, Brazil;
- Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro RJ 21941-909, Brazil
- Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro RJ 20020-000, Brazil
| | - Luciane Effting
- Chemistry Department, State University of Londrina (UEL), Londrina PR 86057-970, Brazil; (L.E.); (H.C.)
| | - Hágata Cremasco
- Chemistry Department, State University of Londrina (UEL), Londrina PR 86057-970, Brazil; (L.E.); (H.C.)
| | - Carlos Adam Conte-Junior
- COVID-19 Research Group, Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Cidade Universitária, Rio de Janeiro RJ 21941-598, Brazil;
- Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro RJ 21941-909, Brazil
- Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro RJ 20020-000, Brazil
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