1
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Barea-Sepúlveda M, Calle JLP, Ferreiro-González M, Palma M. Development of a Novel HS-GC/MS Method Using the Total Ion Spectra Combined with Machine Learning for the Intelligent and Automatic Evaluation of Food-Grade Paraffin Wax Odor Level. Foods 2024; 13:1352. [PMID: 38731723 PMCID: PMC11083247 DOI: 10.3390/foods13091352] [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: 03/22/2024] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/13/2024] Open
Abstract
The intensity of the odor in food-grade paraffin waxes is a pivotal quality characteristic, with odor panel ratings currently serving as the primary criterion for its assessment. This study presents an innovative method for assessing odor intensity in food-grade paraffin waxes, employing headspace gas chromatography with mass spectrometry (HS/GC-MS) and integrating total ion spectra with advanced machine learning (ML) algorithms for enhanced detection and quantification. Optimization was conducted using Box-Behnken design and response surface methodology, ensuring precision with coefficients of variance below 9%. Analytical techniques, including hierarchical cluster analysis (HCA) and principal component analysis (PCA), efficiently categorized samples by odor intensity. The Gaussian support vector machine (SVM), random forest, partial least squares regression, and support vector regression (SVR) algorithms were evaluated for their efficacy in odor grade classification and quantification. Gaussian SVM emerged as superior in classification tasks, achieving 100% accuracy, while Gaussian SVR excelled in quantifying odor levels, with a coefficient of determination (R2) of 0.9667 and a root mean square error (RMSE) of 6.789. This approach offers a fast, reliable, robust, objective, and reproducible alternative to the current ASTM sensory panel assessments, leveraging the analytical capabilities of HS-GC/MS and the predictive power of ML for quality control in the petrochemical sector's food-grade paraffin waxes.
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Affiliation(s)
| | | | - Marta Ferreiro-González
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agri-Food Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain; (M.B.-S.); (J.L.P.C.); (M.P.)
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2
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Navarro Soto JP, Rico SI, Martínez Gila DM, Satorres Martínez S. Influence of the Degree of Fruitiness on the Quality Assessment of Virgin Olive Oils Using Electronic Nose Technology. SENSORS (BASEL, SWITZERLAND) 2024; 24:2565. [PMID: 38676183 PMCID: PMC11053873 DOI: 10.3390/s24082565] [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: 02/27/2024] [Revised: 04/12/2024] [Accepted: 04/12/2024] [Indexed: 04/28/2024]
Abstract
The electronic nose is a non-invasive technology suitable for the analysis of edible oils. One of the practical applications in the olive oil industry is the classification of virgin oils based on their sensory characteristics. Notwithstanding that this technology, at this stage, cannot realistically replace the currently used methods, it is fruitful for a preliminary analysis of the oil quality. This work makes use of this technology to develop a methodology for the detection of the threshold by which an extra-virgin olive oil (EVOO) drops into the virgin olive oil (VOO) category. With this aim, two features were studied: the level of fruitiness level and the type of defect. The results showed a greater influence of the level of fruitiness than the type of defect in the determination of the detection threshold. Furthermore, three of the sensors (S2, S7 and S9) of the commercial e-nose PEN3 were identified as the most discriminating in the classification between EVOO and VOO oils.
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Affiliation(s)
- Javiera P. Navarro Soto
- System Engineering and Automation Department, University of Jaén, 23071 Jaén, Spain; (J.P.N.S.); (S.I.R.); (S.S.M.)
| | - Sergio Illana Rico
- System Engineering and Automation Department, University of Jaén, 23071 Jaén, Spain; (J.P.N.S.); (S.I.R.); (S.S.M.)
| | - Diego M. Martínez Gila
- System Engineering and Automation Department, University of Jaén, 23071 Jaén, Spain; (J.P.N.S.); (S.I.R.); (S.S.M.)
- University Institute of Research on Olive Groves and Olive Oils, University of Jaén, 23071 Jaén, Spain
| | - Silvia Satorres Martínez
- System Engineering and Automation Department, University of Jaén, 23071 Jaén, Spain; (J.P.N.S.); (S.I.R.); (S.S.M.)
- University Institute of Research on Olive Groves and Olive Oils, University of Jaén, 23071 Jaén, Spain
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3
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Hong Y, Birse N, Quinn B, Li Y, Jia W, McCarron P, Wu D, da Silva GR, Vanhaecke L, van Ruth S, Elliott CT. Data fusion and multivariate analysis for food authenticity analysis. Nat Commun 2023; 14:3309. [PMID: 37291121 PMCID: PMC10250487 DOI: 10.1038/s41467-023-38382-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 04/27/2023] [Indexed: 06/10/2023] Open
Abstract
A mid-level data fusion coupled with multivariate analysis approach is applied to dual-platform mass spectrometry data sets using Rapid Evaporative Ionization Mass Spectrometry and Inductively Coupled Plasma Mass Spectrometry to determine the correct classification of salmon origin and production methods. Salmon (n = 522) from five different regions and two production methods are used in the study. The method achieves a cross-validation classification accuracy of 100% and all test samples (n = 17) have their origins correctly determined, which is not possible with single-platform methods. Eighteen robust lipid markers and nine elemental markers are found, which provide robust evidence of the provenance of the salmon. Thus, we demonstrate that our mid-level data fusion - multivariate analysis strategy greatly improves the ability to correctly identify the geographical origin and production method of salmon, and this innovative approach can be applied to many other food authenticity applications.
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Affiliation(s)
- Yunhe Hong
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Nicholas Birse
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Brian Quinn
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Yicong Li
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Wenyang Jia
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Philip McCarron
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Di Wu
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Gonçalo Rosas da Silva
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Lynn Vanhaecke
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
- Laboratory of Integrative Metabolomics, Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Saskia van Ruth
- Food Quality and Design Group, Wageningen University and Research, Wageningen, The Netherlands
- School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland
| | - Christopher T Elliott
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom.
- School of Food Science and Technology, Faculty of Science and Technology, Thammasat University, 99 Mhu 18, Pahonyothin Road, Khong Luang, Pathum Thani, 12120, Thailand.
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4
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Ding R, Yu L, Wang C, Zhong S, Gu R. Quality assessment of traditional Chinese medicine based on data fusion combined with machine learning: A review. Crit Rev Anal Chem 2023:1-18. [PMID: 36966435 DOI: 10.1080/10408347.2023.2189477] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2023]
Abstract
The authenticity and quality of traditional Chinese medicine (TCM) directly impact clinical efficacy and safety. Quality assessment of traditional Chinese medicine (QATCM) is a global concern due to increased demand and shortage of resources. Recently, modern analytical technologies have been extensively investigated and utilized to analyze the chemical composition of TCM. However, a single analytical technique has some limitations, and judging the quality of TCM only from the characteristics of the components is not enough to reflect the overall view of TCM. Thus, the development of multi-source information fusion technology and machine learning (ML) has further improved QATCM. Data information from different analytical instruments can better understand the connection between herbal samples from multiple aspects. This review focuses on the use of data fusion (DF) and ML in QATCM, including chromatography, spectroscopy, and other electronic sensors. The common data structures and DF strategies are introduced, followed by ML methods, including fast-growing deep learning. Finally, DF strategies combined with ML methods are discussed and illustrated for research on applications such as source identification, species identification, and content prediction in TCM. This review demonstrates the validity and accuracy of QATCM-based DF and ML strategies and provides a reference for developing and applying QATCM methods.
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Affiliation(s)
- Rong Ding
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Lianhui Yu
- Chengdu Pushi Pharmaceutical Technology Co., Ltd, Chengdu, China
| | - Chenghui Wang
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Shihong Zhong
- School of Pharmacy, Southwest Minzu University, Chengdu, China
| | - Rui Gu
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
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5
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Quintanilla-Casas B, Rinnan Å, Romero A, Guardiola F, Tres A, Vichi S, Bro R. Using fluorescence excitation-emission matrices to predict bitterness and pungency of virgin olive oil: A feasibility study. Food Chem 2022; 395:133602. [DOI: 10.1016/j.foodchem.2022.133602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 05/30/2022] [Accepted: 06/27/2022] [Indexed: 11/29/2022]
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6
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Guo F, Ma M, Yu M, Bian Q, Hui J, Pan X, Su X, Wu J. Classification of chinese fragrant rapeseed oil based on sensory evaluation and gas chromatography-olfactometry. Front Nutr 2022; 9:945144. [PMID: 35990337 PMCID: PMC9381969 DOI: 10.3389/fnut.2022.945144] [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: 05/16/2022] [Accepted: 07/01/2022] [Indexed: 11/13/2022] Open
Abstract
Fragrant rapeseed oils and traditional pressed oils are increasingly popular in China owing to their sensory advantages. Many fragrant rapeseed oils are labeled by different fragrance types; however, the scientific basis for these differences is lacking. To identify the distinctive aroma and achieve fragrance classification, the sensory characteristics and aroma components of nine different fragrant rapeseed oils were analyzed via sensory evaluation and gas-chromatography-mass spectrometry-olfactometry. A total of 35 aroma compounds were found to contribute to the overall aroma. By using chemometrics methods, rapeseed oils were categorized into three fragrance styles: “strong fragrance,” “umami fragrance,” and “delicate fragrance.” In total, 10 aroma compounds were predicted to be the most effective compounds for distinguishing sensory characteristics of fragrant rapeseed oil. According to our results, this approach has excellent potential for the fragrance classification and quality control of rapeseed oil.
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Affiliation(s)
- Fei Guo
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China.,COFCO Nutrition and Health Research Institute Co., Ltd., Beijing, China
| | - Mingjuan Ma
- COFCO Nutrition and Health Research Institute Co., Ltd., Beijing, China.,Beijing Key Laboratory of Nutrition & Health and Food Safety, Beijing, China.,Beijing Engineering Laboratory for Geriatric Nutrition Food Research, Beijing, China
| | - Miao Yu
- COFCO Nutrition and Health Research Institute Co., Ltd., Beijing, China.,Beijing Key Laboratory of Nutrition & Health and Food Safety, Beijing, China.,Beijing Engineering Laboratory for Geriatric Nutrition Food Research, Beijing, China
| | - Qi Bian
- COFCO Nutrition and Health Research Institute Co., Ltd., Beijing, China.,Beijing Key Laboratory of Nutrition & Health and Food Safety, Beijing, China.,Beijing Engineering Laboratory for Geriatric Nutrition Food Research, Beijing, China
| | - Ju Hui
- COFCO Nutrition and Health Research Institute Co., Ltd., Beijing, China.,Beijing Key Laboratory of Nutrition & Health and Food Safety, Beijing, China.,Beijing Engineering Laboratory for Geriatric Nutrition Food Research, Beijing, China
| | - Xin Pan
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China
| | - Xiaoxia Su
- COFCO Nutrition and Health Research Institute Co., Ltd., Beijing, China.,Beijing Key Laboratory of Nutrition & Health and Food Safety, Beijing, China.,Beijing Engineering Laboratory for Geriatric Nutrition Food Research, Beijing, China
| | - Jihong Wu
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China.,National Engineering Research Center for Fruit and Vegetable Processing, Beijing, China.,Key Laboratory of Fruit and Vegetable Processing, Ministry of Agriculture, Beijing, China.,Beijing Key Laboratory for Food Non-thermal Processing, Beijing, China
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7
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Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review. Molecules 2022; 27:molecules27154846. [PMID: 35956791 PMCID: PMC9369811 DOI: 10.3390/molecules27154846] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/20/2022] [Accepted: 07/22/2022] [Indexed: 12/03/2022] Open
Abstract
The release of the FDA’s guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.
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8
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Discriminant analysis of vegetable oils by thermogravimetric-gas chromatography/mass spectrometry combined with data fusion and chemometrics without sample pretreatment. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113403] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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9
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de Aguiar LM, Galvan D, Bona E, Colnago LA, Killner MHM. Data fusion of middle-resolution NMR spectroscopy and low-field relaxometry using the Common Dimensions Analysis (ComDim) to monitor diesel fuel adulteration. Talanta 2022; 236:122838. [PMID: 34635228 DOI: 10.1016/j.talanta.2021.122838] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/28/2021] [Accepted: 08/30/2021] [Indexed: 12/28/2022]
Abstract
Medium-resolution (MR-NMR) and time-domain NMR relaxometry (TD-NMR) using benchtop and low-field NMR instruments are powerful tools to tackle fuel adulteration issues. In this work, for the first time, we investigate the possibility of enhancing the low-field NMR capability on fuel analysis using data fusion of MR and TD-NMR. We used the ComDim (Common Dimensions Analysis) multi-block analysis to join the data, which allowed exploration, classification, and quantification of common adulterations of diesel fuel by vegetable oils, biodiesel, and diesel of different sources as well as the sulfur content. After data exploration using ComDim, classification (applying linear discriminant analysis, LDA), and regression (applying multiple linear regression, MLR), models were built using ComDim scores as input variables on the LDA and MLR analyses. This approach enabled 100% of accuracy in classifying diesel fuel source (refinery), sulfur content (S10 or S500), vegetable oil, and biodiesel source. Moreover, in the quantification step, all MLR models showed a root mean square error of prediction (RMSEP) and the residual prediction deviation (RPD) values comparable to the literature for determining diesel, vegetable oil, and biodiesel contents.
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Affiliation(s)
| | - Diego Galvan
- Universidade Estadual de Londrina, Departamento de Química, P.O. Box 10.011, 86.057-970, Londrina, Brazil
| | - Evandro Bona
- Programa de Pós-Graduação em Tecnologia de Alimentos, Universidade Tecnológica Federal do Paraná, Campus - Campo Mourão, 87.301 899, Campo Mourão, Brazil
| | - Luiz Alberto Colnago
- Embrapa Instrumentação, Rua XV de Novembro, 1452, São Carlos, SP, 13560-970, Brazil
| | - Mario Henrique M Killner
- Universidade Estadual de Londrina, Departamento de Química, P.O. Box 10.011, 86.057-970, Londrina, Brazil.
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10
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Sensory Panel Performance Evaluation—Comprehensive Review of Practical Approaches. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112411977] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Sensory assessors determine the result of sensory analysis; therefore, investigation of panel performance is inevitable to obtain well-established results. In the last few decades, numerous publications examine the performance of both panelists and panels. The initial point of any panelist measures are the applied selection methods, which are chosen according to the purpose (general suitability or product-specific skills). A practical overview is given on the available solutions, methods, protocols and software relating to all major panelist and panel measure indices (agreement, discrimination, repeatability, reproducibility and scale usage), with special focus on the utilized statistical methods. The novel approach of the presented methods is multi-faceted, concerning time factor (measuring performance at a given moment or over a period), the level of integration in the sensory testing procedure and the target of the measurements (panelist versus panel). The present paper supports the choice of the performance parameter and its related statistical procedure. Available software platforms, their accessibility (open-source status) and their functions are thoroughly analyzed concerning panelist or whole panel evaluation. The applied sensory test method strongly defines the applicable performance evaluation tools; therefore, these aspects are also discussed. A special field is related to proficiency testing. With the focus on special activities (product competitions, expert panels, food and horticultural goods), practical examples are given. In our research, special attention was given to sensory activity in companies and product experts or product-specific panels. Emerging future trends in this field will involve meta-analyses, application of AI and integration of psychophysics.
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11
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Azcarate SM, Ríos-Reina R, Amigo JM, Goicoechea HC. Data handling in data fusion: Methodologies and applications. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116355] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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12
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Wang Z, Cao J, Li W, Wang Y, Luo G, Qiao Y, Zhang Y, Xu B. Using a material database and data fusion method to accelerate the process model development of high shear wet granulation. Sci Rep 2021; 11:16514. [PMID: 34389766 PMCID: PMC8363627 DOI: 10.1038/s41598-021-96097-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 08/04/2021] [Indexed: 11/09/2022] Open
Abstract
High shear wet granulation (HSWG) has been wildly used in manufacturing of oral solid dosage (OSD) forms, and process modeling is vital to understanding and controlling this complex process. In this paper, data fusion and multivariate modeling technique were applied to develop a formulation-process-quality model for HSWG process. The HSWG experimental data from both literature and the authors' laboratory were fused into a single and formatted representation. A material database and material matching method were used to compensate the incomplete physical characterization of literature formulation materials, and dimensionless parameters were utilized to reconstruct process variables at different granulator scales. The exploratory study on input materials properties by principal component analysis (PCA) revealed that the formulation data collected from different articles generated a formulation library which was full of diversity. In prediction of the median granule size, the partial least squares (PLS) regression models derived from literature data only and a combination of literature data and laboratory data were compared. The results demonstrated that incorporating a small number of laboratory data into the multivariate calibration model could help significantly reduce the prediction error, especially at low level of liquid to solid ratio. The proposed data fusion methodology was beneficial to scientific development of HSWG formulation and process, with potential advantages of saving both experimental time and cost.
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Affiliation(s)
- Zheng Wang
- Department of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No.11, North Third Ring East Road, Beijing, 100029, People's Republic of China
| | - Junjie Cao
- Department of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No.11, North Third Ring East Road, Beijing, 100029, People's Republic of China
| | - Wanting Li
- Department of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No.11, North Third Ring East Road, Beijing, 100029, People's Republic of China
| | - Yawen Wang
- Department of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No.11, North Third Ring East Road, Beijing, 100029, People's Republic of China
| | - Gan Luo
- Department of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No.11, North Third Ring East Road, Beijing, 100029, People's Republic of China
| | - Yanjiang Qiao
- Department of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No.11, North Third Ring East Road, Beijing, 100029, People's Republic of China.,Beijing Key Laboratory of Chinese Medicine Manufacturing Process Control and Quality Evaluation, Beijing, 100029, People's Republic of China
| | - Yanling Zhang
- Department of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No.11, North Third Ring East Road, Beijing, 100029, People's Republic of China. .,Beijing Key Laboratory of Chinese Medicine Manufacturing Process Control and Quality Evaluation, Beijing, 100029, People's Republic of China.
| | - Bing Xu
- Department of Chinese Medicine Informatics, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No.11, North Third Ring East Road, Beijing, 100029, People's Republic of China. .,Beijing Key Laboratory of Chinese Medicine Manufacturing Process Control and Quality Evaluation, Beijing, 100029, People's Republic of China.
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13
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14
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Galvan D, Aquino A, Effting L, Mantovani ACG, Bona E, Conte-Junior CA. E-sensing and nanoscale-sensing devices associated with data processing algorithms applied to food quality control: a systematic review. Crit Rev Food Sci Nutr 2021; 62:6605-6645. [PMID: 33779434 DOI: 10.1080/10408398.2021.1903384] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed.
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Affiliation(s)
- Diego Galvan
- Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, 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, Brazil.,Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
| | - Adriano Aquino
- Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, 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, Brazil.,Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
| | - Luciane Effting
- Chemistry Department, State University of Londrina (UEL), Londrina, PR, Brazil
| | | | - Evandro Bona
- Post-Graduation Program of Food Technology (PPGTA), Federal University of Technology Paraná (UTFPR), Campo Mourão, PR, Brazil
| | - Carlos Adam Conte-Junior
- Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, 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, Brazil.,Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
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15
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Zaroual H, Chénè C, El Hadrami EM, Karoui R. Application of new emerging techniques in combination with classical methods for the determination of the quality and authenticity of olive oil: a review. Crit Rev Food Sci Nutr 2021; 62:4526-4549. [DOI: 10.1080/10408398.2021.1876624] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Hicham Zaroual
- Université d'Artois, UMRT 1158 BioEcoAgro, ICV-Institut Charles VIOLLETTE, Lens, France
- Sidi Mohamed Ben Abdellah University, Applied Organic Chemistry Laboratory, Fez, Morocco
| | | | - El Mestafa El Hadrami
- Sidi Mohamed Ben Abdellah University, Applied Organic Chemistry Laboratory, Fez, Morocco
| | - Romdhane Karoui
- Université d'Artois, UMRT 1158 BioEcoAgro, ICV-Institut Charles VIOLLETTE, Lens, France
- INRA, USC 1281,Lille, France
- Yncréa, Lille, France
- University of the Littoral Opal Coast (ULCO), Boulogne sur Mer, France
- University of Lille, Lille, France
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16
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Conte L, Bendini A, Valli E, Lucci P, Moret S, Maquet A, Lacoste F, Brereton P, García-González DL, Moreda W, Gallina Toschi T. Olive oil quality and authenticity: A review of current EU legislation, standards, relevant methods of analyses, their drawbacks and recommendations for the future. Trends Food Sci Technol 2020. [DOI: 10.1016/j.tifs.2019.02.025] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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17
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Quintanilla-Casas B, Marin M, Guardiola F, García-González DL, Barbieri S, Bendini A, Gallina Toschi T, Vichi S, Tres A. Supporting the Sensory Panel to Grade Virgin Olive Oils: An In-House-Validated Screening Tool by Volatile Fingerprinting and Chemometrics. Foods 2020; 9:foods9101509. [PMID: 33096623 PMCID: PMC7593957 DOI: 10.3390/foods9101509] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 10/12/2020] [Accepted: 10/19/2020] [Indexed: 12/16/2022] Open
Abstract
The commercial category of virgin olive oil is currently assigned on the basis of chemical-physical and sensory parameters following official methods. Considering the limited number of samples that can be analysed daily by a sensory panel, an instrumental screening tool could be supportive by reducing the assessors’ workload and improving their performance. The present work aims to in-house validate a screening strategy consisting of two sequential binary partial least squares-discriminant analysis (PLS-DA) models that was suggested to be successful in a proof-of-concept study. This approach is based on the volatile fraction fingerprint obtained by HS-SPME–GC–MS from more than 300 virgin olive oils from two crop seasons graded by six different sensory panels into extra virgin, virgin or lampante categories. Uncertainty ranges were set for the binary classification models according to sensitivity and specificity by means of receiver operating characteristics (ROC) curves, aiming to identify boundary samples. Thereby, performing the screening approach, only the virgin olive oils classified as uncertain (23.3%) would be assessed by a sensory panel, while the rest would be directly classified into a given commercial category (78.9% of correct classification). The sensory panel’s workload would be reduced to less than one-third of the samples. A highly reliable classification of samples would be achieved (84.0%) by combining the proposed screening tool with the reference method (panel test) for the assessment of uncertain samples.
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Affiliation(s)
- Beatriz Quintanilla-Casas
- Departament de Nutrició, Ciències de l’Alimentació i Gastronomia, Campus de l’Alimentació de Torribera, Facultat de Farmacia i Ciències de l’Alimentació, Universitat de Barcelona, 08921 Santa Coloma de Gramenet, Spain; (B.Q.-C.); (M.M.); (F.G.); (A.T.)
- Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Universitat de Barcelona (UB), 08921 Santa Coloma de Gramenet, Spain
| | - Marco Marin
- Departament de Nutrició, Ciències de l’Alimentació i Gastronomia, Campus de l’Alimentació de Torribera, Facultat de Farmacia i Ciències de l’Alimentació, Universitat de Barcelona, 08921 Santa Coloma de Gramenet, Spain; (B.Q.-C.); (M.M.); (F.G.); (A.T.)
- Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Universitat de Barcelona (UB), 08921 Santa Coloma de Gramenet, Spain
| | - Francesc Guardiola
- Departament de Nutrició, Ciències de l’Alimentació i Gastronomia, Campus de l’Alimentació de Torribera, Facultat de Farmacia i Ciències de l’Alimentació, Universitat de Barcelona, 08921 Santa Coloma de Gramenet, Spain; (B.Q.-C.); (M.M.); (F.G.); (A.T.)
- Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Universitat de Barcelona (UB), 08921 Santa Coloma de Gramenet, Spain
| | | | - Sara Barbieri
- Department of Agricultural and Food Science, Alma Mater Studiorum-Università di Bologna, 47521 Cesena, Italy; (S.B.); (A.B.); (T.G.T.)
| | - Alessandra Bendini
- Department of Agricultural and Food Science, Alma Mater Studiorum-Università di Bologna, 47521 Cesena, Italy; (S.B.); (A.B.); (T.G.T.)
| | - Tullia Gallina Toschi
- Department of Agricultural and Food Science, Alma Mater Studiorum-Università di Bologna, 47521 Cesena, Italy; (S.B.); (A.B.); (T.G.T.)
| | - Stefania Vichi
- Departament de Nutrició, Ciències de l’Alimentació i Gastronomia, Campus de l’Alimentació de Torribera, Facultat de Farmacia i Ciències de l’Alimentació, Universitat de Barcelona, 08921 Santa Coloma de Gramenet, Spain; (B.Q.-C.); (M.M.); (F.G.); (A.T.)
- Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Universitat de Barcelona (UB), 08921 Santa Coloma de Gramenet, Spain
- Correspondence:
| | - Alba Tres
- Departament de Nutrició, Ciències de l’Alimentació i Gastronomia, Campus de l’Alimentació de Torribera, Facultat de Farmacia i Ciències de l’Alimentació, Universitat de Barcelona, 08921 Santa Coloma de Gramenet, Spain; (B.Q.-C.); (M.M.); (F.G.); (A.T.)
- Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Universitat de Barcelona (UB), 08921 Santa Coloma de Gramenet, Spain
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18
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Yi Y, Hua H, Sun X, Guan Y, Chen C. Rapid determination of polysaccharides and antioxidant activity of Poria cocos using near-infrared spectroscopy combined with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 240:118623. [PMID: 32599484 DOI: 10.1016/j.saa.2020.118623] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 06/12/2020] [Accepted: 06/14/2020] [Indexed: 06/11/2023]
Abstract
This study evaluates the feasibility of near-infrared (NIR) spectroscopy combined with chemometrics as a fast and efficient technique to predict the polysaccharide content and antioxidant activity of Poria cocos. The reference values of polysaccharide content were determined by the phenol-sulfuric acid method, and the antioxidant activities were determined by the DPPH scavenge assay, FRAP scavenge assay and ABTS scavenge assay, respectively. The partial least squares regression algorithm was used to relate the spectra to the reference values. Various methods for spectra pretreatment and variable selection were optimized to improve the predictability and stability of the models. As a result, the best models yielded very satisfying results, of which the values of coefficients of determination were all >0.94, and the values of residual predictive deviation were all >4. Such results confirmed that the present method is robust and applicable, and thus has good potential for rapid quality evaluation of Poria cocos.
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Affiliation(s)
- Yuan Yi
- School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou 510006, PR China
| | - Haimin Hua
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China
| | - Xuefen Sun
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China
| | - Ying Guan
- School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou 510006, PR China
| | - Chao Chen
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China; Key Laboratory of Digitalization Quality Evaluation of Chinese Materia Medica of SATCM, Guangzhou 510006, PR China; Research Center for Quality Engineering & Technology of Chinese Materia Medica in Guangdong Universities, Guangzhou 510006, PR China; Research Center for Quality Engineering & Technology of Chinese Materia Medica of Guangdong Province, Guangzhou 510006, PR China.
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19
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Zhou L, Zhang C, Qiu Z, He Y. Information fusion of emerging non-destructive analytical techniques for food quality authentication: A survey. Trends Analyt Chem 2020. [DOI: 10.1016/j.trac.2020.115901] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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20
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Quintanilla-Casas B, Bustamante J, Guardiola F, García-González DL, Barbieri S, Bendini A, Toschi TG, Vichi S, Tres A. Virgin olive oil volatile fingerprint and chemometrics: Towards an instrumental screening tool to grade the sensory quality. Lebensm Wiss Technol 2020. [DOI: 10.1016/j.lwt.2019.108936] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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21
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Li C, Zong B, Guo H, Luo Z, He P, Gong S, Fan F. Discrimination of white teas produced from fresh leaves with different maturity by near-infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 227:117697. [PMID: 31699592 DOI: 10.1016/j.saa.2019.117697] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 10/15/2019] [Accepted: 10/24/2019] [Indexed: 06/10/2023]
Abstract
White tea is a special tea product with increasing market demand. The assessment of white tea quality is mainly based on panel sensory by sensory evaluation experts, which is time costly and is limited by many uncertainties. This study established a rapid and accurate method for classification of white teas produced from buds and young leaves and that produced from mature leaves and shoots using near-infrared spectroscopy (NIR). Back propagation neural network modelling and support vector machine (SVM) modelling were compared with six pre-processing methods. The best performance was provided by SVM with particle swarm optimization combined with Savitzky-Golay filter pre-processing method, achieving the accuracy of 98.92% in test samples. The NIR-related chemical compounds of two categories of white teas produced from fresh leaves with different maturity were analyzed, including catechins, alkaloids, amino acids and flavonol glycosides. Compared with chemical component concentration, NIR absorbance had a distinct advantage in quick classification of white teas based on the principal components analysis. In addition, the sensory characteristics of two categories white teas produced from fresh leaves with different maturity were also assessed by panelist. The result showed that characteristics of "umami-like" and "smooth" were more likely present in white teas produced from buds and young leaves, while "woody" and "coarse" characteristics were usually present in white teas produced from mature leaves and shoots. Thus, NIR technique is a rapid and reliable method for discrimination of white teas produced from fresh leaves with different maturity, and is a potential method to discriminate sensory characteristics of white teas.
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Affiliation(s)
- Chunlin Li
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China
| | - Bangzheng Zong
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China
| | - Haowei Guo
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China
| | - Zhou Luo
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China
| | - Puming He
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China
| | - Shuying Gong
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China.
| | - Fangyuan Fan
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China.
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22
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Contreras MDM, Jurado-Campos N, Arce L, Arroyo-Manzanares N. A robustness study of calibration models for olive oil classification: Targeted and non-targeted fingerprint approaches based on GC-IMS. Food Chem 2019; 288:315-324. [DOI: 10.1016/j.foodchem.2019.02.104] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 02/19/2019] [Accepted: 02/22/2019] [Indexed: 12/19/2022]
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23
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Gonçalves TR, Rosa LN, Torquato AS, da Silva LFO, Março PH, Gomes STM, Matsushita M, Valderrama P. Assessment of Brazilian Monovarietal Olive Oil in Two Different Package Systems by Using Data Fusion and Chemometrics. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-019-01511-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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24
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Wu XM, Zhang QZ, Wang YZ. Traceability the provenience of cultivated Paris polyphylla Smith var. yunnanensis using ATR-FTIR spectroscopy combined with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2019; 212:132-145. [PMID: 30639599 DOI: 10.1016/j.saa.2019.01.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 12/19/2018] [Accepted: 01/02/2019] [Indexed: 05/20/2023]
Abstract
The conventional procedures, based on attenuated total reflectance-Fourier transform infrared spectrometry (ATR-FTIR), have been developed for the origins traceability of cultivated Paris polyphylla Smith var. yunnanensis (PPY) samples with the help of partial least square discriminant analysis (PLS-DA) and random forest. In this study, a set of 219 batch cultivated PPY samples, containing the cultivation years of 5, 6 and 7, and covering the municipal districts of Chuxiong, Dali, Honghe, Lijiang and Yuxi in Yunnan Province, China, were used to build the discrimination models. Firstly, a visualized analysis was carried out by t-distributed stochastic neighbor embedding (t-SNE) to reduce each data point in a two-dimensional map and make a knowledge of the sample distribution tendency. Secondly, the single spectra data sets of Paridis rhizome and leaf tissues, and the combination of these two data sets with variable selection (mid-level data fusion strategy), were used to establish PLS-DA and random forest models, and parallelly compared the model performance. Results demonstrated that the discrimination ability of PLS-DA preceded the random forest model, and the classification performance was remarkably improved after mid-level data fusion. These results verified each other by 5-, 6- and 7-year old Paridis samples and indicated that the model performance established in the present study was reliable. Besides, five agronomic characters, including the plant height, dry weight of rhizome and leaf tissues, and the allocation of rhizome and leaf were determined and analyzed, results of which indicated that the dry weight and their allocation was significantly different among various origins and fluctuated with the cultivation years. This study was using a comprehensive and green analytical method to discriminate the cultivated Paridis according to their provenances, which was simultaneously benefited for the appropriate cultivation areas selection based on the dry weight of rhizome tissues.
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Affiliation(s)
- Xue-Mei Wu
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China; College of Traditional Chinese Medicine, Yunnan University of Traditional Chinese Medicine, Kunming 650500, China
| | - Qing-Zhi Zhang
- College of Traditional Chinese Medicine, Yunnan University of Traditional Chinese Medicine, Kunming 650500, China
| | - Yuan-Zhong Wang
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
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25
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Rodrigues N, Marx ÍMG, Casal S, Dias LG, Veloso ACA, Pereira JA, Peres AM. Application of an electronic tongue as a single-run tool for olive oils' physicochemical and sensory simultaneous assessment. Talanta 2019; 197:363-373. [PMID: 30771949 DOI: 10.1016/j.talanta.2019.01.055] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Revised: 01/08/2019] [Accepted: 01/14/2019] [Indexed: 10/27/2022]
Abstract
Olive oil is highly appreciated due to its nutritional and organoleptic characteristics. However, a huge compositional variation is observed between olive oils, requiring the use of diverse analytical techniques for its classification including titration, spectrophotometry and chromatography, as well as sensory analysis. Chemical analysis is usually time-consuming, expensive and require skilled technicians, while the sensorial ones are dependent upon individual subjective evaluations, even if performed by trained panellists. This work evaluated and demonstrated the feasibility of using a potentiometric electronic tongue, comprising non-specific lipid polymeric and cross-sensitive sensor membranes, coupled with chemometric tools based on different sub-sets of sensors (from 11 to 14 sensors), to predict key quality parameters of olive oils based on single-run assays. The multivariate linear models established for 23 centenarian olive trees from different cultivars allowed predicting peroxide value, oxidative stability, total phenols and tocopherols contents, CIELAB scale parameters (L*, a* and b* values), as well as 11 gustatory-retronasal positive attributes (green, sweet, bitter, pungent, tomato and tomato leaves, apple, banana, cabbage, fresh herbs and dry fruits) with satisfactory accuracy (0.90 ± 0.07 ≤ R2 ≤ 0.98 ± 0.02 for the repeated K-fold-CV procedure, which ensured that 25% of the data was used for internal-validation purposes). The electronic tongue device had an accuracy statistically similar to that achieved with standard analytical techniques, pointing out the versatility of the device for the fast and simultaneous chemical and sensory analysis of olive oil.
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Affiliation(s)
- Nuno Rodrigues
- Centro de Investigação de Montanha (CIMO), ESA, Instituto Politécnico de Bragança, Campus Santa Apolónia, 5300-253 Bragança, Portugal
| | - Ítala M G Marx
- Laboratory of Separation and Reaction Engineering - Laboratory of Catalysis and Materials (LSRE-LCM), ESA, Instituto Politécnico de Bragança, Campus Santa Apolónia, 5300-253 Bragança, Portugal
| | - Susana Casal
- LAQV/REQUIMTE, Laboratory of Bromatology and Hydrology, Faculty of Pharmacy, University of Porto, Rua de Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal
| | - Luís G Dias
- Centro de Investigação de Montanha (CIMO), ESA, Instituto Politécnico de Bragança, Campus Santa Apolónia, 5300-253 Bragança, Portugal
| | - Ana C A Veloso
- Instituto Politécnico de Coimbra, ISEC, DEQB, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal; CEB - Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - José A Pereira
- Centro de Investigação de Montanha (CIMO), ESA, Instituto Politécnico de Bragança, Campus Santa Apolónia, 5300-253 Bragança, Portugal
| | - António M Peres
- Centro de Investigação de Montanha (CIMO), ESA, Instituto Politécnico de Bragança, Campus Santa Apolónia, 5300-253 Bragança, Portugal; Laboratory of Separation and Reaction Engineering - Laboratory of Catalysis and Materials (LSRE-LCM), ESA, Instituto Politécnico de Bragança, Campus Santa Apolónia, 5300-253 Bragança, Portugal.
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26
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Tahir HE, Arslan M, Mahunu GK, Shi J, Zou X, Gasmalla MAA, Mariod AA. Data Fusion Approach Improves the Prediction of Single Phenolic Compounds in Honey: A Study of NIR and Raman Spectroscopies. EFOOD 2019. [DOI: 10.2991/efood.k.191018.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
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27
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Advanced Classification of Coffee Beans with Fatty Acids Profiling to Block Information Loss. Symmetry (Basel) 2018. [DOI: 10.3390/sym10100529] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Classification is a kernel process in the standardization, grading, and sensory aspects of coffee industries. The chemometric data of fatty acids and crude fat are used to characterize the varieties of coffee. Two category classifiers were used to distinguish the species and roasting degree of coffee beans. However, the fatty acid profiling with normalized data gave a bad discriminant result in the classification study with mixed dimensions in species and roasted degree. The result of the predictive model is in conflict with the context of human cognition, since roasted coffee beans are easily visually distinguished from green coffee beans. By exploring the effects of error analysis and information processing technologies, the lost information was identified as a bias–variance tradeoff derived from the percentile normalization. The roasting degree as extensive information was attenuated by the percentile normalization, but the cultivars as intensive information were enhanced. An informational spiking technique is proposed to patch the dataset and block the information loss. The identified blocking of informational loss could be available for multidimensional classification systems based on the chemometric data.
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28
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Gonçalves TR, Rosa LN, Gonçalves RP, Torquato AS, Março PH, Marques Gomes ST, Matsushita M, Valderrama P. Monitoring the Oxidative Stability of Monovarietal Extra Virgin Olive Oils by UV–Vis Spectroscopy and MCR–ALS. FOOD ANAL METHOD 2018. [DOI: 10.1007/s12161-018-1149-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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29
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Olive Oil Total Phenolic Contents and Sensory Sensations Trends during Oven and Microwave Heating Processes and Their Discrimination Using an Electronic Tongue. J FOOD QUALITY 2018. [DOI: 10.1155/2018/7826428] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Olive oil has unique organoleptic attributes and its consumption is associated with nutritional and health benefits, which are mainly related to its rich composition in phenolic and volatile compounds. The use of olive oil in heat-induced cooking leads to deep reduction of phenolic and volatile concentrations and to changes of the sensory profiles. This work confirmed that oven and microwave heating significantly reduced total phenolic contents (P value < 0.0001, one-way ANOVA), more pronounced in the latter, together with a significant reduction of the intensity of fruity, sweet, bitter, pungent, and green attributes (P value < 0.0001, Kruskal-Wallis test), particularly for fruity and green sensations. Besides, bitter, fruity, green, and pungent intensities showed a linear dependency with the total phenolic contents (0.8075≤R-Pearson ≤ 0.9694). Finally, the potentiometric electronic tongue together with linear discriminant analysis-simulated annealing algorithm allowed satisfactory discrimination (sensitivities of 94±4%, for repeated K-fold cross-validation) of olive oils subjected to intense microwave heating (5–10 min, 160–205°C) from those processed under usual cooking conditions (oven heating during 15–60 min or microwave heating during 1.5–3 min, 72–165°C). This could be due to the different responses of the electronic tongue towards olive oils with diverse phenolic and sensory profiles.
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30
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Veloso ACA, Silva LM, Rodrigues N, Rebello LPG, Dias LG, Pereira JA, Peres AM. Perception of olive oils sensory defects using a potentiometric taste device. Talanta 2017; 176:610-618. [PMID: 28917798 DOI: 10.1016/j.talanta.2017.08.066] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 08/15/2017] [Accepted: 08/20/2017] [Indexed: 11/18/2022]
Abstract
The capability of perceiving olive oils sensory defects and intensities plays a key role on olive oils quality grade classification since olive oils can only be classified as extra-virgin if no defect can be perceived by a human trained sensory panel. Otherwise, olive oils may be classified as virgin or lampante depending on the median intensity of the defect predominantly perceived and on the physicochemical levels. However, sensory analysis is time-consuming and requires an official sensory panel, which can only evaluate a low number of samples per day. In this work, the potential use of an electronic tongue as a taste sensor device to identify the defect predominantly perceived in olive oils was evaluated. The potentiometric profiles recorded showed that intra- and inter-day signal drifts could be neglected (i.e., relative standard deviations lower than 25%), being not statistically significant the effect of the analysis day on the overall recorded E-tongue sensor fingerprints (P-value = 0.5715, for multivariate analysis of variance using Pillai's trace test), which significantly differ according to the olive oils' sensory defect (P-value = 0.0084, for multivariate analysis of variance using Pillai's trace test). Thus, a linear discriminant model based on 19 potentiometric signal sensors, selected by the simulated annealing algorithm, could be established to correctly predict the olive oil main sensory defect (fusty, rancid, wet-wood or winey-vinegary) with average sensitivity of 75 ± 3% and specificity of 73 ± 4% (repeated K-fold cross-validation variant: 4 folds×10 repeats). Similarly, a linear discriminant model, based on 24 selected sensors, correctly classified 92 ± 3% of the olive oils as virgin or lampante, being an average specificity of 93 ± 3% achieved. The overall satisfactory predictive performances strengthen the feasibility of the developed taste sensor device as a complementary methodology for olive oils' defects analysis and subsequent quality grade classification. Furthermore, the capability of identifying the type of sensory defect of an olive oil may allow establishing helpful insights regarding bad practices of olives or olive oils production, harvesting, transport and storage.
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Affiliation(s)
- Ana C A Veloso
- Instituto Politécnico de Coimbra, ISEC, DEQB, Rua Pedro Nunes, Quinta da Nora 3030-199, Coimbra, Portugal; CEB - Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Lucas M Silva
- School of Agriculture, Polytechnic Institute of Bragança, Campus Santa Apolónia, 5300-253 Bragança, Portugal; Instituto de Educação, Ciência e Tecnologia Fluminense - Campus Bom Jesus do Itabapoana, Brazil
| | - Nuno Rodrigues
- Centro de Investigação de Montanha (CIMO), ESA, Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal; Universidad de Léon, Departamento de Ingeniería Agrária, Av. Portugal, n° 41, 24071 Léon, Spain
| | - Ligia P G Rebello
- Instituto de Educação, Ciência e Tecnologia Fluminense - Campus Bom Jesus do Itabapoana, Brazil
| | - Luís G Dias
- School of Agriculture, Polytechnic Institute of Bragança, Campus Santa Apolónia, 5300-253 Bragança, Portugal; CQ-VR, Centro de Química - Vila Real, University of Trás-os-Montes e Alto Douro, Apartado 1013, 5001-801 Vila Real, Portugal
| | - José A Pereira
- Centro de Investigação de Montanha (CIMO), ESA, Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - António M Peres
- Laboratory of Separation and Reaction Engineering - Laboratory of Catalysis and Materials (LSRE-LCM), Escola Superior Agrária, Instituto Politécnico de Bragança, Campus Santa Apolónia, 5300-253 Bragança, Portugal.
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31
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Discriminative capacities of infrared spectroscopy and e-nose on Turkish olive oils. Eur Food Res Technol 2017. [DOI: 10.1007/s00217-017-2909-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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32
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Zhang H, Tian JY, Huang J, Huang XH, Quan GJ, Yan S, Liu PR. Rapid and pollution-free characterization of intracellular polyphosphate and orthophosphate using mid-infrared spectroscopy combined with chemometrics in the denitrifying phosphorus removal process. RSC Adv 2016. [DOI: 10.1039/c6ra23756h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Content of the intracellular Poly-P and orthophosphate variation may be predicted rapidly by mid-infrared spectroscopy and PLS method in denitrifying phosphorus removal process.
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Affiliation(s)
- H. Zhang
- Key Laboratory of Anhui Province of Water Pollution Control and Wastewater Reuse
- School of Environment and Energy Engineering
- Anhui Jianzhu University
- Hefei
- China
| | - J. Y. Tian
- Key Laboratory of Anhui Province of Water Pollution Control and Wastewater Reuse
- School of Environment and Energy Engineering
- Anhui Jianzhu University
- Hefei
- China
| | - J. Huang
- Key Laboratory of Anhui Province of Water Pollution Control and Wastewater Reuse
- School of Environment and Energy Engineering
- Anhui Jianzhu University
- Hefei
- China
| | - X. H. Huang
- Key Laboratory of Anhui Province of Water Pollution Control and Wastewater Reuse
- School of Environment and Energy Engineering
- Anhui Jianzhu University
- Hefei
- China
| | - G. J. Quan
- Key Laboratory of Anhui Province of Water Pollution Control and Wastewater Reuse
- School of Environment and Energy Engineering
- Anhui Jianzhu University
- Hefei
- China
| | - S. Yan
- Key Laboratory of Anhui Province of Water Pollution Control and Wastewater Reuse
- School of Environment and Energy Engineering
- Anhui Jianzhu University
- Hefei
- China
| | - P. R. Liu
- Key Laboratory of Anhui Province of Water Pollution Control and Wastewater Reuse
- School of Environment and Energy Engineering
- Anhui Jianzhu University
- Hefei
- China
| |
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