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Hu Y, Badar IH, Liu Y, Zhu Y, Yang L, Kong B, Xu B. Advancements in production, assessment, and food applications of salty and saltiness-enhancing peptides: A review. Food Chem 2024; 453:139664. [PMID: 38761739 DOI: 10.1016/j.foodchem.2024.139664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/01/2024] [Accepted: 05/12/2024] [Indexed: 05/20/2024]
Abstract
Salt is important for food flavor, but excessive sodium intake leads to adverse health consequences. Thus, salty and saltiness-enhancing peptides are developed for sodium-reduction products. This review elucidates saltiness perception process and analyses correlation between the peptide structure and saltiness-enhancing ability. These peptides interact with taste receptors to produce saltiness perception, including ENaC, TRPV1, and TMC4. This review also outlines preparation, isolation, purification, characterization, screening, and assessment techniques of these peptides and discusses their potential applications. These peptides are from various sources and produced through enzymatic hydrolysis, microbial fermentation, or Millard reaction and then separated, purified, identified, and screened. Sensory evaluation, electronic tongue, bioelectronic tongue, and cell and animal models are the primary saltiness assessment approaches. These peptides can be used in sodium-reduction food products to produce "clean label" items, and the peptides with biological activity can also serve as functional ingredients, making them very promising for food industry.
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Affiliation(s)
- Yingying Hu
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui 230009, China; State Key Laboratory of Meat Quality Control and Cultured Meat Development, Jiangsu Yurun Meat Industry Group Co., Ltd, Nanjing, Jiangsu 210041, China
| | - Iftikhar Hussain Badar
- College of Food Science, Northeast Agricultural University, Harbin, Heilongjiang 150030, China; Department of Meat Science and Technology, University of Veterinary and Animal Sciences, Lahore 54000, Pakistan
| | - Yue Liu
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui 230009, China
| | - Yuan Zhu
- State Key Laboratory of Meat Quality Control and Cultured Meat Development, Jiangsu Yurun Meat Industry Group Co., Ltd, Nanjing, Jiangsu 210041, China
| | - Linwei Yang
- State Key Laboratory of Meat Quality Control and Cultured Meat Development, Jiangsu Yurun Meat Industry Group Co., Ltd, Nanjing, Jiangsu 210041, China
| | - Baohua Kong
- College of Food Science, Northeast Agricultural University, Harbin, Heilongjiang 150030, China.
| | - Baocai Xu
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui 230009, China.
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Moulahoum H, Ghorbanizamani F. Navigating the development of silver nanoparticles based food analysis through the power of artificial intelligence. Food Chem 2024; 445:138800. [PMID: 38382253 DOI: 10.1016/j.foodchem.2024.138800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/12/2024] [Accepted: 02/16/2024] [Indexed: 02/23/2024]
Abstract
In the ongoing pursuit of enhancing food safety and quality through advanced technologies, silver nanoparticles (AgNPs) stand out for their antimicrobial properties. Despite being overshadowed by other nanoparticles in food sensing applications, AgNPs possess inherent qualities that make them effective tools for rapid and selective contaminant detection in food matrices. This review aims to reinvigorate the interest in AgNPs in the food industry, emphasizing their sensing mechanism and the transformative potential of integrating them with artificial intelligence (AI) for enhanced food safety monitoring. It discusses key AI tools and principles in the food industry, demonstrating their positive impact on food analytical chemistry. The interplay between AI and biosensors offers many advantages and adaptability to dynamic analytical challenges, significantly improving food safety monitoring and potentially redefining the landscape of food safety and quality assurance.
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Affiliation(s)
- Hichem Moulahoum
- Department of Biochemistry, Faculty of Science, Ege University, 35100-Bornova, Izmir, Turkey.
| | - Faezeh Ghorbanizamani
- Department of Biochemistry, Faculty of Science, Ege University, 35100-Bornova, Izmir, Turkey.
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3
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Kuhnen G, Class LC, Badekow S, Hanisch KL, Rohn S, Kuballa J. Python workflow for the selection and identification of marker peptides-proof-of-principle study with heated milk. Anal Bioanal Chem 2024; 416:3349-3360. [PMID: 38607384 PMCID: PMC11106092 DOI: 10.1007/s00216-024-05286-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 03/26/2024] [Accepted: 04/02/2024] [Indexed: 04/13/2024]
Abstract
The analysis of almost holistic food profiles has developed considerably over the last years. This has also led to larger amounts of data and the ability to obtain more information about health-beneficial and adverse constituents in food than ever before. Especially in the field of proteomics, software is used for evaluation, and these do not provide specific approaches for unique monitoring questions. An additional and more comprehensive way of evaluation can be done with the programming language Python. It offers broad possibilities by a large ecosystem for mass spectrometric data analysis, but needs to be tailored for specific sets of features, the research questions behind. It also offers the applicability of various machine-learning approaches. The aim of the present study was to develop an algorithm for selecting and identifying potential marker peptides from mass spectrometric data. The workflow is divided into three steps: (I) feature engineering, (II) chemometric data analysis, and (III) feature identification. The first step is the transformation of the mass spectrometric data into a structure, which enables the application of existing data analysis packages in Python. The second step is the data analysis for selecting single features. These features are further processed in the third step, which is the feature identification. The data used exemplarily in this proof-of-principle approach was from a study on the influence of a heat treatment on the milk proteome/peptidome.
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Affiliation(s)
- Gesine Kuhnen
- GALAB Laboratories GmbH, Am Schleusengraben 7, 21029, Hamburg, Germany
- Department of Food Chemistry and Analysis, Institute of Food Technology and Food Chemistry, Technical University Berlin, Gustav Meyer Allee 25, 13355, Berlin, Germany
| | - Lisa-Carina Class
- GALAB Laboratories GmbH, Am Schleusengraben 7, 21029, Hamburg, Germany
- Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146, Hamburg, Germany
| | - Svenja Badekow
- GALAB Laboratories GmbH, Am Schleusengraben 7, 21029, Hamburg, Germany
| | - Kim Lara Hanisch
- GALAB Laboratories GmbH, Am Schleusengraben 7, 21029, Hamburg, Germany
| | - Sascha Rohn
- Department of Food Chemistry and Analysis, Institute of Food Technology and Food Chemistry, Technical University Berlin, Gustav Meyer Allee 25, 13355, Berlin, Germany
| | - Jürgen Kuballa
- GALAB Laboratories GmbH, Am Schleusengraben 7, 21029, Hamburg, Germany.
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Haider A, Iqbal SZ, Bhatti IA, Alim MB, Waseem M, Iqbal M, Mousavi Khaneghah A. Food authentication, current issues, analytical techniques, and future challenges: A comprehensive review. Compr Rev Food Sci Food Saf 2024; 23:e13360. [PMID: 38741454 DOI: 10.1111/1541-4337.13360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 03/29/2024] [Accepted: 04/16/2024] [Indexed: 05/16/2024]
Abstract
Food authentication and contamination are significant concerns, especially for consumers with unique nutritional, cultural, lifestyle, and religious needs. Food authenticity involves identifying food contamination for many purposes, such as adherence to religious beliefs, safeguarding health, and consuming sanitary and organic food products. This review article examines the issues related to food authentication and food fraud in recent periods. Furthermore, the development and innovations in analytical techniques employed to authenticate various food products are comprehensively focused. Food products derived from animals are susceptible to deceptive practices, which can undermine customer confidence and pose potential health hazards due to the transmission of diseases from animals to humans. Therefore, it is necessary to employ suitable and robust analytical techniques for complex and high-risk animal-derived goods, in which molecular biomarker-based (genomics, proteomics, and metabolomics) techniques are covered. Various analytical methods have been employed to ascertain the geographical provenance of food items that exhibit rapid response times, low cost, nondestructiveness, and condensability.
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Affiliation(s)
- Ali Haider
- Food Safety and Toxicology Lab, Department of Applied Chemistry, Government College University, Faisalabad, Punjab, Pakistan
| | - Shahzad Zafar Iqbal
- Food Safety and Toxicology Lab, Department of Applied Chemistry, Government College University, Faisalabad, Punjab, Pakistan
| | - Ijaz Ahmad Bhatti
- Department of Chemistry, University of Agriculture, Faisalabad, Pakistan
| | | | - Muhammad Waseem
- Food Safety and Toxicology Lab, Department of Applied Chemistry, Government College University, Faisalabad, Punjab, Pakistan
| | - Munawar Iqbal
- Department of Chemistry, Division of Science and Technology, University of Education, Lahore, Pakistan
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Kang X, Zhao Y, Yao L, Tan Z. Explainable machine learning for predicting the geographical origin of Chinese Oysters via mineral elements analysis. Curr Res Food Sci 2024; 8:100738. [PMID: 38659973 PMCID: PMC11039350 DOI: 10.1016/j.crfs.2024.100738] [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: 01/11/2024] [Revised: 04/06/2024] [Accepted: 04/12/2024] [Indexed: 04/26/2024] Open
Abstract
The traceability of geographic origin is essential for guaranteeing the quality, safety, and protection of oyster brands. However, the current outcomes of traceability lack credibility as they do not adequately explain the model's predictions. Consequently, we conducted a study to evaluate the efficacy of utilizing explainable machine learning combined with mineral elements analysis. The study findings revealed that 18 elements have the ability to determine regional orientation. Simultaneously, individuals should pay closer attention to the potential risks associated with oyster consumption due to the regional differences in essential and toxic elements they contain. Light gradient boosting machine (LightGBM) model exhibited indistinguishable performance, achieving flawless accuracy, precision, recall, F1 score and AUC, with values of 96.77%, 96.43%, 98.53%, 97.32% and 0.998, respectively. The SHapley Additive exPlanations (SHAP) method was used to evaluate the output of the LightGBM model, revealing differences in feature interactions among oysters from different provinces. Specifically, the features Na, Zn, V, Mg, and K were found to have a significant impact on the predictive process of the model. Consistent with existing research, the use of explainable machine learning techniques can provide insights into the complex connections between important product attributes and relevant geographical information.
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Affiliation(s)
- Xuming Kang
- Key Laboratory of Testing and Evaluation for Aquatic Product Safety and Quality, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, 266071, China
| | - Yanfang Zhao
- Key Laboratory of Testing and Evaluation for Aquatic Product Safety and Quality, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, 266071, China
| | - Lin Yao
- Key Laboratory of Testing and Evaluation for Aquatic Product Safety and Quality, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, 266071, China
| | - Zhijun Tan
- Key Laboratory of Testing and Evaluation for Aquatic Product Safety and Quality, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, 266071, China
- Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, 266071, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian Polytechnic University, Dalian, 116034, China
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Hou Z, Xia R, Li Y, Xu H, Wang Y, Feng Y, Pan S, Wang Z, Ren H, Qian G, Wang H, Zhu J, Xin G. Key components, formation pathways, affecting factors, and emerging analytical strategies for edible mushrooms aroma: A review. Food Chem 2024; 438:137993. [PMID: 37992603 DOI: 10.1016/j.foodchem.2023.137993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 11/09/2023] [Accepted: 11/12/2023] [Indexed: 11/24/2023]
Abstract
Aroma is one of the decisive factors affecting the quality and consumer acceptance of edible mushrooms. This review summarized the key components and formation pathways of edible mushroom aroma. It also elaborated on the affecting factors and emerging analytical strategies of edible mushroom aroma. A total of 1308 volatile organic compounds identified in edible mushrooms, 61 were key components. The formation of these compounds is closely related to fatty acid metabolism, amino acid metabolism, lentinic acid metabolism, and terpenoid metabolism. The aroma profiles of edible mushrooms were affected by genetic background, preharvest factors, and preservation methods. Molecular sensory science and omics techniques are emerging analytical strategies to reveal aroma information of edible mushrooms. This review would provide valuable data and insights for future research on edible mushroom aroma.
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Affiliation(s)
- Zhenshan Hou
- Shenyang Agricultural University, College of Food Science, Shenyang 110866, Liaoning, China
| | - Rongrong Xia
- Shenyang Agricultural University, College of Food Science, Shenyang 110866, Liaoning, China
| | - Yunting Li
- Shenyang Agricultural University, College of Food Science, Shenyang 110866, Liaoning, China
| | - Heran Xu
- Shenyang Agricultural University, College of Food Science, Shenyang 110866, Liaoning, China
| | - Yafei Wang
- Shenyang Agricultural University, College of Food Science, Shenyang 110866, Liaoning, China
| | - Yao Feng
- Shenyang Agricultural University, College of Food Science, Shenyang 110866, Liaoning, China
| | - Song Pan
- Shenyang Agricultural University, College of Food Science, Shenyang 110866, Liaoning, China
| | - Zijian Wang
- Shenyang Agricultural University, College of Food Science, Shenyang 110866, Liaoning, China
| | - Hongli Ren
- Shenyang Agricultural University, College of Food Science, Shenyang 110866, Liaoning, China
| | - Guanlin Qian
- Shenyang Agricultural University, College of Food Science, Shenyang 110866, Liaoning, China
| | - Huanyu Wang
- Shenyang Agricultural University, College of Food Science, Shenyang 110866, Liaoning, China
| | - Jiayi Zhu
- Shenyang Agricultural University, College of Food Science, Shenyang 110866, Liaoning, China
| | - Guang Xin
- Shenyang Agricultural University, College of Food Science, Shenyang 110866, Liaoning, China; Liaoning Key Laboratory of Development and Utilization for Natural Products Active Molecules, Anshan 114007, Liaoning, China.
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Bischof G, Januschewski E, Juadjur A. Authentication of Laying Hen Housing Systems Based on Egg Yolk Using 1H NMR Spectroscopy and Machine Learning. Foods 2024; 13:1098. [PMID: 38611402 PMCID: PMC11011716 DOI: 10.3390/foods13071098] [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: 03/08/2024] [Revised: 03/29/2024] [Accepted: 04/01/2024] [Indexed: 04/14/2024] Open
Abstract
(1) Background: The authenticity of eggs in relation to the housing system of laying hens is susceptible to food fraud due to the potential for egg mislabeling. (2) Methods: A total of 4188 egg yolks, obtained from four different breeds of laying hens housed in colony cage, barn, free-range, and organic systems, were analyzed using 1H NMR spectroscopy. The data of the resulting 1H NMR spectra were used for different machine learning methods to build classification models for the four housing systems. (3) Results: The comparison of the seven computed models showed that the support vector machine (SVM) model gave the best results with a cross-validation accuracy of 98.5%. The test of classification models with eggs from supermarkets showed that only a maximum of 62.8% of samples were classified according to the housing system labeled on the eggs. (4) Conclusion: The classification models developed in this study included the largest sample size compared to the literature. The SVM model is most suitable for evaluating 1H NMR data in terms of the hen housing system. The test with supermarket samples showed that more authentic samples to analyze influencing factors such as breed, feeding, and housing changes are required.
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Affiliation(s)
- Greta Bischof
- Chemical Analytics, German Institute of Food Technologies (DIL e.V.), Prof.-v.-Klitzing-Str. 7, 49610 Quakenbrück, Germany (A.J.)
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Chen L, Kuuliala L, Somrani M, Walgraeve C, Demeestere K, De Baets B, Devlieghere F. Rapid and non-destructive microbial quality prediction of fresh pork stored under modified atmospheres by using selected-ion flow-tube mass spectrometry and machine learning. Meat Sci 2024; 213:109505. [PMID: 38579509 DOI: 10.1016/j.meatsci.2024.109505] [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: 12/04/2023] [Revised: 03/08/2024] [Accepted: 03/27/2024] [Indexed: 04/07/2024]
Abstract
Volatile organic compounds (VOCs) indicative of pork microbial spoilage can be quantified rapidly at trace levels using selected-ion flow-tube mass spectrometry (SIFT-MS). Packaging atmosphere is one of the factors influencing VOC production patterns during storage. On this basis, machine learning would help to process complex volatolomic data and predict pork microbial quality efficiently. This study focused on (1) investigating model generalizability based on different nested cross-validation settings, and (2) comparing the predictive power and feature importance of nine algorithms, including Artificial Neural Network (ANN), k-Nearest Neighbors, Support Vector Regression, Decision Tree, Partial Least Squares Regression, and four ensemble learning models. The datasets used contain 37 VOCs' concentrations (input) and total plate counts (TPC, output) of 350 pork samples with different storage times, including 225 pork loin samples stored under three high-O2 and three low-O2 conditions, and 125 commercially packaged products. An appropriate choice of cross-validation strategies resulted in trustworthy and relevant predictions. When trained on all possible selections of two high-O2 and two low-O2 conditions, ANNs produced satisfactory TPC predictions of unseen test scenarios (one high-O2 condition, one low-O2 condition, and the commercial products). ANN-based bagging outperformed other employed models, when TPC exceeded ca. 6 log CFU/g. VOCs including benzaldehyde, 3-methyl-1-butanol, ethanol and methyl mercaptan were identified with high feature importance. This elaborated case study illustrates great prospects of real-time detection techniques and machine learning in meat quality prediction. Further investigations on handling low VOC levels would enhance the model performance and decision making in commercial meat quality control.
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Affiliation(s)
- Linyun Chen
- Research Unit Food Microbiology and Food Preservation (FMFP), Department of Food Technology, Safety and Health, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium.
| | - Lotta Kuuliala
- Research Unit Food Microbiology and Food Preservation (FMFP), Department of Food Technology, Safety and Health, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium; Research Group NutriFOODchem, Department of Food Technology, Safety and Health, Ghent University, Coupure links 653, 9000 Ghent, Belgium
| | - Mariem Somrani
- Research Unit Food Microbiology and Food Preservation (FMFP), Department of Food Technology, Safety and Health, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium; Departamento de Ingeniería Agronómica, Instituto de Biotecnología Vegetal, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| | - Christophe Walgraeve
- Research Group Environmental Organic Chemistry and Technology (EnVOC), Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium
| | - Kristof Demeestere
- Research Group Environmental Organic Chemistry and Technology (EnVOC), Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium
| | - Bernard De Baets
- Research Unit Knowledge-based Systems (KERMIT), Department of Data Analysis and Mathematical Modelling, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium
| | - Frank Devlieghere
- Research Unit Food Microbiology and Food Preservation (FMFP), Department of Food Technology, Safety and Health, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium
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Nitika N, Keerthiveena B, Thakur G, Rathore AS. Convolutional Neural Networks Guided Raman Spectroscopy as a Process Analytical Technology (PAT) Tool for Monitoring and Simultaneous Prediction of Monoclonal Antibody Charge Variants. Pharm Res 2024; 41:463-479. [PMID: 38366234 DOI: 10.1007/s11095-024-03663-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 01/18/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND Charge related heterogeneities of monoclonal antibody (mAb) based therapeutic products are increasingly being considered as a critical quality attribute (CQA). They are typically estimated using analytical cation exchange chromatography (CEX), which is time consuming and not suitable for real time control. Raman spectroscopy coupled with artificial intelligence (AI) tools offers an opportunity for real time monitoring and control of charge variants. OBJECTIVE We present a process analytical technology (PAT) tool for on-line and real-time charge variant determination during process scale CEX based on Raman spectroscopy employing machine learning techniques. METHOD Raman spectra are collected from a reference library of samples with distribution of acidic, main, and basic species from 0-100% in a mAb concentration range of 0-20 g/L generated from process-scale CEX. The performance of different machine learning techniques for spectral processing is compared for predicting different charge variant species. RESULT A convolutional neural network (CNN) based model was successfully calibrated for quantification of acidic species, main species, basic species, and total protein concentration with R2 values of 0.94, 0.99, 0.96 and 0.99, respectively, and the Root Mean Squared Error (RMSE) of 0.1846, 0.1627, and 0.1029 g/L, respectively, and 0.2483 g/L for the total protein concentration. CONCLUSION We demonstrate that Raman spectroscopy combined with AI-ML frameworks can deliver rapid and accurate determination of product related impurities. This approach can be used for real time CEX pooling decisions in mAb production processes, thus enabling consistent charge variant profiles to be achieved.
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Affiliation(s)
- Nitika Nitika
- Department of Chemical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India
| | - B Keerthiveena
- School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi, India
| | - Garima Thakur
- Department of Chemical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India
| | - Anurag S Rathore
- Department of Chemical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
- School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi, India.
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10
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Liang F, Huang Y, Miao J, Lai K. A simple and efficient alginate hydrogel combined with surface-enhanced Raman spectroscopy for quantitative analysis of sodium nitrite in meat products. Analyst 2024; 149:1518-1526. [PMID: 38265063 DOI: 10.1039/d3an01771k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
Sodium nitrite is a commonly used preservative and color protectant in the food industry. Conventional analytical methods are highly susceptible to food matrix interference, time-consuming and costly. In this study, the ion cross-linking method was employed to prepare alginate hydrogel substrates, and phenosafranin was chosen as a single-molecule probe to analyze sodium nitrite. Our investigation centered on elucidating the effects of alginate and cross-linking ion concentrations on Raman signal characteristics. The optimal Raman response was observed in the precursor solution with 1% sodium alginate and 0.1 mol L-1 cross-linking ions. The relative standard deviations (RSDs) of the feature peaks from the three substrate batches ranged from 1.22% to 16.30%, attesting the robustness and consistency of the substrates. The signal reduction of the substrates after a four-week storage period remained below 10%, indicating that the substrates had good reproducibility and stability. The limits of detection (LODs) for sodium nitrite in extracts from cured meat, luncheon meat, and sliced ham were determined to range from 3.75 mg kg-1 to 8.11 mg kg-1, with low interference from the food matrix. The support vector machine algorithm was utilized to train and predict the data, which proved to be more accurate (98.6%-99.8% recovery) than the traditional linear regression model (81.9%-112.7% recovery) in predicting the spiked samples. The application of hydrogel-based surface-enhanced Raman spectroscopy (SERS) substrates for nitrite detection in food, combined with machine learning for regression prediction in data processing, collectively augmented the potential of SERS technology in the field of food analysis.
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Affiliation(s)
- Fengnian Liang
- College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, China.
- Engineering Research Center of Food Thermal - Processing Technology, Shanghai, 201306, China
| | - Yiqun Huang
- School of Food Science and Bioengineering, Changsha University of Science and Technology, Hunan, 410076, China
| | - Junjian Miao
- College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, China.
- Engineering Research Center of Food Thermal - Processing Technology, Shanghai, 201306, China
| | - Keqiang Lai
- College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, China.
- Engineering Research Center of Food Thermal - Processing Technology, Shanghai, 201306, China
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11
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Medina-García M, Jiménez-Carvelo AM, Bagur-González MG, González-Casado A. Innovative non-targeted liquid chromatography fingerprinting approach for authenticating tigernuts under Protected Designation of Origin quality seal. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:1638-1644. [PMID: 37850307 DOI: 10.1002/jsfa.13054] [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: 07/09/2023] [Revised: 09/13/2023] [Accepted: 10/18/2023] [Indexed: 10/19/2023]
Abstract
BACKGROUND Tigernut is a typical foodstuff from a specific region of Valencia (Spain) called 'L'Horta Nord', where it is commercialized under a Protected Designation of Origin (PDO) as Chufa de Valencia ('Valencia's tigernut'). PDO-recognized tigernuts present unique characteristics associated with their particular production region. Increasing demand and the associated expansion of its cultivation area has made necessary an exhaustive quality control to check the geographical origin and quality seal. RESULTS In this work, a new multivariate analytical method capable of authenticating the PDO quality seal of tigernut samples was developed. Tigernut fat fraction was extracted under optimal conditions, applying the methodology of design of experiments. The analytical method combined fingerprinting methodology and chemometric tools to observe the natural grouping of samples using the exploratory analysis method and to develop classification models (partial least squares-discriminatory analysis; PLS-DA) to discriminate between two sample categories: (i) PDO tigernuts; and (ii) NON-PDO tigernuts. CONCLUSION The built PLS-DA model demonstrated 100% accuracy, high sensitivity and specificity, revealing that the tigernut fat fraction can be applied to authenticate the PDO quality seal. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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Affiliation(s)
- Miriam Medina-García
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Granada, Spain
| | - Ana M Jiménez-Carvelo
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Granada, Spain
| | - María G Bagur-González
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Granada, Spain
| | - Antonio González-Casado
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Granada, Spain
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12
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Long P, Li Y, Han Z, Zhu M, Zhai X, Jiang Z, Wen M, Ho CT, Zhang L. Discovery of color compounds: Integrated multispectral omics on exploring critical colorant compounds of black tea infusion. Food Chem 2024; 432:137185. [PMID: 37633133 DOI: 10.1016/j.foodchem.2023.137185] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/22/2023] [Accepted: 08/15/2023] [Indexed: 08/28/2023]
Abstract
The present study provided a highly efficient and systematic workflow for identifying colorants of food and beverage. Generally, the objective colorimeter and subjective human eye had different systems to identify colors, which makes the color description very challenging. Here, the Lab/LCH color system was applied to clearly illustrate color changes. Our workflow was applied to determine and verify the differential colorant substances between two groups of black tea infusions. Regarding color parameters, the infusions of black tea from Camellia sinensis and Camellia assamica differed significantly. The differential substances between black tea infusions were correlated to color parameters by mass spectrometry and nuclear magnetic resonance based multivariate statistical analysis and verified by machine learning tool. Pyroglutamic acid-glucose Amadori product, quercetin-3-O-glucoside, quinic acid and theabrownins were identified as main color contributors to black teas' color difference, which were also verified by addition test with standard black tea infusion.
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Affiliation(s)
- Piaopiao Long
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Yaxin Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Zisheng Han
- Department of Food Science, Rutgers University, New Brunswick, NJ 08901, USA
| | - Mengting Zhu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Xiaoting Zhai
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Zongde Jiang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Mingchun Wen
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Chi-Tang Ho
- Department of Food Science, Rutgers University, New Brunswick, NJ 08901, USA.
| | - Liang Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China.
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13
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Liu S, Rong Y, Chen Q, Ouyang Q. Colorimetric sensor array combined with chemometric methods for the assessment of aroma produced during the drying of tencha. Food Chem 2024; 432:137190. [PMID: 37633147 DOI: 10.1016/j.foodchem.2023.137190] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/24/2023] [Accepted: 08/16/2023] [Indexed: 08/28/2023]
Abstract
The aroma produced during drying is an important indicator of tencha and needs to be monitored. This study constructed an olfactory visualization system for assessing tencha aroma using colorimetric sensor array (CSA) combined with chemometric methods. The 16 chemically responsive dyes were selected to obtain aroma information of tencha samples and extracted image data of aroma information by machine vision algorithms. Subsequently, k-nearest neighbor, support vector machine, classification and regression tree, and random forest (RF), four qualitative models were applied to build the mathematical models. The RF model with nine principal components was preferred, with recognition rate of 100.00% and 91.07% for the training and prediction sets, respectively. The experimental results showed that CSA combined with the RF model can be effectively applied to assess tencha aroma. This study provided a scientific and novel method to maintain the stability of tencha quality in the production process.
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Affiliation(s)
- Shuangshuang Liu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Yanna Rong
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Quansheng Chen
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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14
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Arroyo-Cerezo A, Yang X, Jiménez-Carvelo AM, Pellegrino M, Felicita Savino A, Berzaghi P. Assessment of extra virgin olive oil quality by miniaturized near infrared instruments in a rapid and non-destructive procedure. Food Chem 2024; 430:137043. [PMID: 37541043 DOI: 10.1016/j.foodchem.2023.137043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 07/25/2023] [Accepted: 07/26/2023] [Indexed: 08/06/2023]
Abstract
Food fraud in olive oil is a major concern for consumers and authorities due to the health risks and economic impacts. Common frauds include blending with other cheaper non-olive oils, or misleading labelling. The main issue is that legislation and methods presently used in routine laboratories are not always up to date with current fraudulent practices, making detection difficult, so new analytical methods development is required. This study focuses on developing an affordable and non-destructive analysis method based on NIR spectroscopy and chemometrics for EVOO quality assessment, specifically by monitoring 7 parameters of interest in EVOO measured by official methods and used to develop calibrations through NIR data. For this, two NIR low-cost portable instruments were employed, studied in-depth and compared with a NIR benchtop instrument. Calibration results enabled detection of atypical olive oils and excellent accuracy, especially for palmitic and oleic acid predictions, demonstrating the potential of the instruments.
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Affiliation(s)
- Alejandra Arroyo-Cerezo
- Department of Analytical Chemistry, University of Granada, C/ Fuentenueva s/n, 18071 Granada, Spain
| | - Xueping Yang
- Department of Animal Medicine, Production and Health, University of Padua, Via Dell'Università 16, 35020 Legnaro, Italy
| | - Ana M Jiménez-Carvelo
- Department of Analytical Chemistry, University of Granada, C/ Fuentenueva s/n, 18071 Granada, Spain.
| | - Marina Pellegrino
- Department of Animal Medicine, Production and Health, University of Padua, Via Dell'Università 16, 35020 Legnaro, Italy; Laboratorio di Perugia -ICQRF-MASAF, Via della Madonna Alta 138c/d, 06128 Perugia, Italy
| | - Angela Felicita Savino
- Laboratorio di Perugia -ICQRF-MASAF, Via della Madonna Alta 138c/d, 06128 Perugia, Italy
| | - Paolo Berzaghi
- Department of Animal Medicine, Production and Health, University of Padua, Via Dell'Università 16, 35020 Legnaro, Italy
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15
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Eshawu AB, Ghalsasi VV. Metabolomics of natural samples: A tutorial review on the latest technologies. J Sep Sci 2024; 47:e2300588. [PMID: 37942863 DOI: 10.1002/jssc.202300588] [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: 08/13/2023] [Revised: 10/29/2023] [Accepted: 11/06/2023] [Indexed: 11/10/2023]
Abstract
Metabolomics is the study of metabolites present in a living system. It is a rapidly growing field aimed at discovering novel compounds, studying biological processes, diagnosing diseases, and ensuring the quality of food products. Recently, the analysis of natural samples has become important to explore novel bioactive compounds and to study how environment and genetics affect living systems. Various metabolomics techniques, databases, and data analysis tools are available for natural sample metabolomics. However, choosing the right method can be a daunting exercise because natural samples are heterogeneous and require untargeted approaches. This tutorial review aims to compile the latest technologies to guide an early-career scientist on natural sample metabolomics. First, different extraction methods and their pros and cons are reviewed. Second, currently available metabolomics databases and data analysis tools are summarized. Next, recent research on metabolomics of milk, honey, and microbial samples is reviewed. Finally, after reviewing the latest trends in technologies, a checklist is presented to guide an early-career researcher on how to design a metabolomics project. In conclusion, this review is a comprehensive resource for a researcher planning to conduct their first metabolomics analysis. It is also useful for experienced researchers to update themselves on the latest trends in metabolomics.
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Affiliation(s)
- Ali Baba Eshawu
- School of Biotechnology, Faculty of Applied Sciences and Biotechnology, Shoolini University of Biotechnology and Management Sciences, Solan, India
| | - Vihang Vivek Ghalsasi
- School of Biotechnology, Faculty of Applied Sciences and Biotechnology, Shoolini University of Biotechnology and Management Sciences, Solan, India
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16
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Agnoletti BZ, Pereira LL, Alves EA, Rocha RB, Debona DG, Lyrio MVV, Moreira TR, de Castro EVR, da S Oliveira EC, Filgueiras PR. The terroir of Brazilian Coffea canephora: Characterization of the chemical composition. Food Res Int 2024; 176:113814. [PMID: 38163718 DOI: 10.1016/j.foodres.2023.113814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/25/2023] [Accepted: 12/02/2023] [Indexed: 01/03/2024]
Abstract
FTIR spectroscopy and multivariate analysis were used in the chemical study of the terroirs of Coffea canephora. Conilon coffees from Espírito Santo and Amazon robusta from Matas of Rondônia, were separated by PCA, with lipids and caffeine being the markers responsible for the separation. Coffees from Bahia, Minas Gerais, and São Paulo did not exhibit separation, indicating that the botanical variety had a greater effect on the terroir than geographic origin. Thus, the genetic factor was investigated considering the conilon and robusta botanical varieties. This last group was composed of hybrid robusta and apoatã. The DD-SIMCA favored the identification of the genetic predominance of the samples. PLS-DA had a high classification performance regarding the conilon, hybrid robusta, and apoatã genetic nature. Lipids, caffeine, chlorogenic acids, quinic acid, trigonelline, proteins, amino acids, and carbohydrates were identified as chemical markers that discriminated the genetic groups.
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Affiliation(s)
- Bárbara Zani Agnoletti
- Federal University of Espírito Santo/ UFES, Department of Chemistry, Goiabeiras Campus, Av. Fernando Ferrari, 514, ZIP code: 29075-110 Vitória, Espírito Santo, Brazil.
| | - Lucas Louzada Pereira
- Federal Institute of Espírito Santo, Department of Food Science and Technology, Av. Elizabeth Minete Perim, S/N, Bairro São Rafael, ZIP code: 29375-000 Venda Nova do Imigrante, Espírito Santo, Brazil
| | - Enrique Anastácio Alves
- Brazilian Agricultural Research Corporation - EMBRAPA Rondônia, Porto Velho, Rondônia, Brazil
| | - Rodrigo Barros Rocha
- Brazilian Agricultural Research Corporation - EMBRAPA Rondônia, Porto Velho, Rondônia, Brazil
| | - Danieli Gracieri Debona
- Federal University of Espírito Santo/ UFES, Department of Chemistry, Goiabeiras Campus, Av. Fernando Ferrari, 514, ZIP code: 29075-110 Vitória, Espírito Santo, Brazil
| | - Marcos Valério Vieira Lyrio
- Federal University of Espírito Santo/ UFES, Department of Chemistry, Goiabeiras Campus, Av. Fernando Ferrari, 514, ZIP code: 29075-110 Vitória, Espírito Santo, Brazil
| | - Taís Rizzo Moreira
- Federal University of Espirito Santo/UFES, Department of Forest and Wood Sciences, Center of Agrarian Sciences and Engineering, Av. Governador Lindemberg, 316, CEP: 29550-000 Jerônimo Monteiro, Espirito Santo, Brazil
| | - Eustáquio Vinicius Ribeiro de Castro
- Federal University of Espírito Santo/ UFES, Department of Chemistry, Goiabeiras Campus, Av. Fernando Ferrari, 514, ZIP code: 29075-110 Vitória, Espírito Santo, Brazil
| | - Emanuele Catarina da S Oliveira
- Federal Institute of Espírito Santo, Department of Food Science and Technology, Av. Elizabeth Minete Perim, S/N, Bairro São Rafael, ZIP code: 29375-000 Venda Nova do Imigrante, Espírito Santo, Brazil
| | - Paulo Roberto Filgueiras
- Federal University of Espírito Santo/ UFES, Department of Chemistry, Goiabeiras Campus, Av. Fernando Ferrari, 514, ZIP code: 29075-110 Vitória, Espírito Santo, Brazil
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17
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Díaz EO, Iino H, Koyama K, Kawamura S, Koseki S, Lyu S. Non-destructive quality classification of rice taste properties based on near-infrared spectroscopy and machine learning algorithms. Food Chem 2023; 429:136907. [PMID: 37487393 DOI: 10.1016/j.foodchem.2023.136907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 06/28/2023] [Accepted: 07/10/2023] [Indexed: 07/26/2023]
Abstract
The taste quality of rice is determined by protein and amylose percentages, with low levels indicating high-quality taste in Japan. However, accurate non-destructive screening remains a challenge for the industry. We explored the use of machine learning models and near-infrared spectra to classify rice taste quality. Three models were optimized using 796 brown rice samples from Hokkaido, Japan, produced between 2008 and 2016, and tested on 278 distinct samples from the same region produced between 2017 and 2019. Logistic regression and support vector machine models outperformed the partial least-squares discriminant analysis model, achieving high accuracy (94%), f1-score (90%), average precision (0.94), and low classification error (4%) and allowing accurate non-destructive classification of rice quality. These results not only improve rice quality, post-harvest technology, and producer output in Japan but also could enhance quality control processes and foster the production of high-quality products for other agricultural goods and food commodities worldwide.
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Affiliation(s)
- Edenio Olivares Díaz
- Graduate School of Agricultural Science, Hokkaido University, Kita-9 Nishi-9 Kita-Ku, Sapporo 060-8589, Japan.
| | - Haruka Iino
- Graduate School of Agricultural Science, Hokkaido University, Kita-9 Nishi-9 Kita-Ku, Sapporo 060-8589, Japan
| | - Kento Koyama
- Graduate School of Agricultural Science, Hokkaido University, Kita-9 Nishi-9 Kita-Ku, Sapporo 060-8589, Japan
| | - Shuso Kawamura
- Graduate School of Agricultural Science, Hokkaido University, Kita-9 Nishi-9 Kita-Ku, Sapporo 060-8589, Japan
| | - Shigenobu Koseki
- Graduate School of Agricultural Science, Hokkaido University, Kita-9 Nishi-9 Kita-Ku, Sapporo 060-8589, Japan
| | - Suxing Lyu
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa 277-8561, Japan
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18
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Skiada V, Katsaris P, Kambouris ME, Gkisakis V, Manoussopoulos Y. Classification of olive cultivars by machine learning based on olive oil chemical composition. Food Chem 2023; 429:136793. [PMID: 37535989 DOI: 10.1016/j.foodchem.2023.136793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 06/15/2023] [Accepted: 07/01/2023] [Indexed: 08/05/2023]
Abstract
Extra virgin olive oil traceability and authenticity are important quality indicators, and are currently the subject of exhaustive research, for developing methods to secure olive oil origin-related issues. The aim of this study was the development of a classification model capable of olive cultivar identification based on olive oil chemical composition. To achieve our aim, 385 samples of two Greek and three Italian olive cultivars were collected during two successive crop years from different locations in the coastline part of western Greece and southern Italy and analyzed for their chemical characteristics. Principal Component Analysis showed trends of differentiation among olive cultivars within or between the crop years. Artificial intelligence model of the XGBoost machine learning algorithm showed high performance in classifying the five olive cultivars from the pooled samples.
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Affiliation(s)
- Vasiliki Skiada
- Institute of Olive Tree, Subtropical Crops and Viticulture, Hellenic Agricultural Organization-DEMETER, 24100 Kalamata, Greece
| | - Panagiotis Katsaris
- Institute of Olive Tree, Subtropical Crops and Viticulture, Hellenic Agricultural Organization-DEMETER, 24100 Kalamata, Greece
| | | | - Vasileios Gkisakis
- Institute of Olive Tree, Subtropical Crops and Viticulture, Hellenic Agricultural Organization-DEMETER, 24100 Kalamata, Greece
| | - Yiannis Manoussopoulos
- Plant Protection Division of Patras, Hellenic Agricultural Organization - DEMETER, N.E.O & Amerikis, 264 42 Patras, Greece.
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19
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Santos GRD, Maia LC, Lobo FA, Santiago ADF, Silva GAD. A model based on a multivariate classification for assessing impacts on water quality in a DOCE river watershed after the Fundão tailings dam failure. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 334:122174. [PMID: 37451586 DOI: 10.1016/j.envpol.2023.122174] [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: 04/26/2023] [Revised: 06/19/2023] [Accepted: 07/09/2023] [Indexed: 07/18/2023]
Abstract
The main purpose of this study was to build multivariate classification models using water quality monitoring data for the hydrographic basin of the Gualaxo do Norte River, Minas Gerais state, Brazil, which was impacted in 2015 by the rupture of a containment structure for iron ore tailings. A total of 27 points were evaluated, covering areas affected and unaffected by the disaster, with monitoring of chemical, physical, and microbiological variables during the period from July 2016 to June 2017. Multivariate classification techniques were applied to the data, with the aim of developing models to determine when the impacted locations would present characteristics equivalent to those existing prior to the rupture. Classification models constructed using PLS-DA and LDA were able to predict three classes: unaffected main river, affected main river, and tributaries. The first technique was able to clearly differentiate the three classes for the data evaluated, achieving averages corresponding to 90% accuracy. The second method was consistent with the first, identifying the chloride content, conductivity, turbidity, and alkalinity as discriminatory variables, among those monitored, with the relationships among the parameters being coherent with the environmental conditions of the region. The model, with a correct classification rate of 91.67%, enabled identification of the behavior of new samples, using only these easily measured variables. In summary, application of the multivariate statistical tools allowed the development of models capable of providing information about the recovery process of an ecosystem impacted by the greatest environmental disaster to have occurred in Brazil.
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Affiliation(s)
- Grazielle Rocha Dos Santos
- Department of Environmental Engineering, Federal University of Ouro Preto, Ouro Preto, MG, 35400-000, Brazil.
| | - Luisa Cardoso Maia
- Department of Environmental Engineering, Federal University of Ouro Preto, Ouro Preto, MG, 35400-000, Brazil
| | - Fabiana Aparecida Lobo
- Department of Chemistry, Federal University of Ouro Preto, Ouro Preto, MG, 35400-000, Brazil
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20
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Li C, Yao S, Song B, Zhao L, Hou B, Zhang Y, Zhang F, Qi X. Evaluation of Cooked Rice for Eating Quality and Its Components in Geng Rice. Foods 2023; 12:3267. [PMID: 37685200 PMCID: PMC10486766 DOI: 10.3390/foods12173267] [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: 08/05/2023] [Revised: 08/20/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023] Open
Abstract
At present, ''eating well" is increasingly desired by people instead of merely ''being full". Rice provides the majority of daily caloric needs for half of the global human population. However, eating quality is difficult to objectively evaluate in rice breeding programs. This study was carried out to objectively quantify and predict eating quality in Geng rice. First, eating quality and its components were identified by trained panels. Analysis of variance and broad-sense heritability showed that variation among varieties was significant for all traits except hardness. Among them, viscosity, taste, and appearance were significantly correlated with eating quality. We established an image acquisition and processing system to quantify cooked rice appearance and optimized the process of measuring cooked rice viscosity with a texture analyzer. The results show that yellow areas of the images were significantly correlated with appearance, and adhesiveness was significantly correlated with viscosity. Based on these results, multiple regression analysis was used to predict eating quality: eating quality = 0.37 × adhesiveness - 0.71 × yellow area + 0.89 × taste - 0.34, R2 = 0.85. The correlation coefficient between the predicted and actual values was 0.86. We anticipate that this predictive model will be useful in future breeding programs for high-eating-quality rice.
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Affiliation(s)
- Cui Li
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Nanxincun 20, Fragrant Hill, Beijing 100093, China; (C.L.); (S.Y.); (B.S.); (B.H.); (F.Z.)
- University of Chinese Academy of Sciences, Yuquan Road 19, Beijing 100049, China
- China National Botanical Garden, Nanxincun 20, Fragrant Hill, Beijing 100093, China
| | - Shujun Yao
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Nanxincun 20, Fragrant Hill, Beijing 100093, China; (C.L.); (S.Y.); (B.S.); (B.H.); (F.Z.)
- University of Chinese Academy of Sciences, Yuquan Road 19, Beijing 100049, China
- China National Botanical Garden, Nanxincun 20, Fragrant Hill, Beijing 100093, China
| | - Bo Song
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Nanxincun 20, Fragrant Hill, Beijing 100093, China; (C.L.); (S.Y.); (B.S.); (B.H.); (F.Z.)
- China National Botanical Garden, Nanxincun 20, Fragrant Hill, Beijing 100093, China
| | - Lei Zhao
- Tonghua Academy of Agricultural Sciences, Hailong Town, Meihekou 135007, China;
| | - Bingzhu Hou
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Nanxincun 20, Fragrant Hill, Beijing 100093, China; (C.L.); (S.Y.); (B.S.); (B.H.); (F.Z.)
- China National Botanical Garden, Nanxincun 20, Fragrant Hill, Beijing 100093, China
| | - Yong Zhang
- LUSTER LightTech Co., Ltd., Yard No.13, Cuihu Nanhuan Road, Beijing 100094, China;
| | - Fan Zhang
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Nanxincun 20, Fragrant Hill, Beijing 100093, China; (C.L.); (S.Y.); (B.S.); (B.H.); (F.Z.)
- China National Botanical Garden, Nanxincun 20, Fragrant Hill, Beijing 100093, China
| | - Xiaoquan Qi
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Nanxincun 20, Fragrant Hill, Beijing 100093, China; (C.L.); (S.Y.); (B.S.); (B.H.); (F.Z.)
- China National Botanical Garden, Nanxincun 20, Fragrant Hill, Beijing 100093, China
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21
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Yang H, Wang Y, Zhao J, Li P, Li L, Wang F. A machine learning method for juice human sensory hedonic prediction using electronic sensory features. Curr Res Food Sci 2023; 7:100576. [PMID: 37691694 PMCID: PMC10485034 DOI: 10.1016/j.crfs.2023.100576] [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: 07/02/2023] [Revised: 08/03/2023] [Accepted: 08/24/2023] [Indexed: 09/12/2023] Open
Abstract
This study proposed a method that combines fused electronic sensory analysis technology with artificial neural network to predict the human sensory hedonic of fruit juice. Quantitative descriptive analysis (QDA) and the scoring test method were utilized for human sensory evaluation. The first step involved modeling the fused e-sensory features with human sensory attributes, followed by establishing a fitting model of human sensory attributes and acceptance. The R2 and RMSE values obtained were 0.77 and 0.42 (QDA method), and 0.63 and 0.63 (scoring test method). Finally, the relationship between the fusion e-sensory features and the human sensory hedonic was established. Model-1 achieved an R2 of 0.95 and an RMSE of 0.04, while model-2 achieved an R2 value of 0.88 and an RMSE value of 0.21. This study demonstrates the potential of fusing e-sensory technologies to replace human senses, which may lead to the development of devices with simultaneous multiple senses.
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Affiliation(s)
- Huihui Yang
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, PR China
- Weifang Institute of Food Science and Processing Technology, Weifang, 261000, PR China
| | - Yutang Wang
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, PR China
| | - Jinyong Zhao
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, PR China
| | - Ping Li
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, PR China
| | - Long Li
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, PR China
- Weifang Institute of Food Science and Processing Technology, Weifang, 261000, PR China
| | - Fengzhong Wang
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, PR China
- Weifang Institute of Food Science and Processing Technology, Weifang, 261000, PR China
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22
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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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23
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Jin G, Zhu Y, Cui C, Yang C, Hu S, Cai H, Ning J, Wei C, Li A, Hou R. Tracing the origin of Taiping Houkui green tea using 1H NMR and HS-SPME-GC-MS chemical fingerprints, data fusion and chemometrics. Food Chem 2023; 425:136538. [PMID: 37300997 DOI: 10.1016/j.foodchem.2023.136538] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 05/17/2023] [Accepted: 06/02/2023] [Indexed: 06/12/2023]
Abstract
The narrow geographical traceability of green tea is both important and challenging. This study aimed to establish multi-technology metabolomic and chemometric approaches to finely discriminate the geographic origins of green teas. Taiping Houkui green tea samples were analyzed by headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry and 1H NMR of polar (D2O) and non-polar (CDCl3). Common dimension, low-level and mid-level data fusion approaches were tested to verify if the combination of several analytical sources can improve the classification ability of samples from different origins. In assessments of tea from six origins, the single instrument data test set results in 40.00% to 80.00% accuracy. Data fusion improved single-instrument performance classification with mid-level data fusion to obtain 93.33% accuracy in the test set. These results provide comprehensive metabolomic insights into the origin of TPHK fingerprinting and open up new metabolomic approaches for quality control in the tea industry.
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Affiliation(s)
- Ge Jin
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei 230036, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, Hefei 230036, China
| | - Yuanyuan Zhu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei 230036, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, Hefei 230036, China
| | - Chuanjian Cui
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei 230036, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, Hefei 230036, China
| | - Chen Yang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei 230036, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, Hefei 230036, China
| | - Shaode Hu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei 230036, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, Hefei 230036, China
| | - Huimei Cai
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei 230036, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, Hefei 230036, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei 230036, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, Hefei 230036, China
| | - Chaoling Wei
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei 230036, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, Hefei 230036, China
| | - Aoxia Li
- Anhui Lanxiang Houkui Tea Co., Ltd., Huangshan, Anhui 245703, China
| | - Ruyan Hou
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei 230036, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, Hefei 230036, China.
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24
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Abdollahi SA, Andarkhor A, Pourahmad A, Alibak AH, Alobaid F, Aghel B. Simulating and Comparing CO 2/CH 4 Separation Performance of Membrane-Zeolite Contactors by Cascade Neural Networks. MEMBRANES 2023; 13:membranes13050526. [PMID: 37233587 DOI: 10.3390/membranes13050526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/02/2023] [Accepted: 05/17/2023] [Indexed: 05/27/2023]
Abstract
Separating carbon dioxide (CO2) from gaseous streams released into the atmosphere is becoming critical due to its greenhouse effect. Membrane technology is one of the promising technologies for CO2 capture. SAPO-34 filler was incorporated in polymeric media to synthesize mixed matrix membrane (MMM) and enhance the CO2 separation performance of this process. Despite relatively extensive experimental studies, there are limited studies that cover the modeling aspects of CO2 capture by MMMs. This research applies a special type of machine learning modeling scenario, namely, cascade neural networks (CNN), to simulate as well as compare the CO2/CH4 selectivity of a wide range of MMMs containing SAPO-34 zeolite. A combination of trial-and-error analysis and statistical accuracy monitoring has been applied to fine-tune the CNN topology. It was found that the CNN with a 4-11-1 topology has the highest accuracy for the modeling of the considered task. The designed CNN model is able to precisely predict the CO2/CH4 selectivity of seven different MMMs in a broad range of filler concentrations, pressures, and temperatures. The model predicts 118 actual measurements of CO2/CH4 selectivity with an outstanding accuracy (i.e., AARD = 2.92%, MSE = 1.55, R = 0.9964).
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Affiliation(s)
| | - AmirReza Andarkhor
- Department of Chemistry, Payam Noor University (Bushehr Branch), Bushehr 1688, Iran
| | - Afham Pourahmad
- Department of Polymer Engineering, Amirkabir University of Technology, Tehran 1591634311, Iran
| | - Ali Hosin Alibak
- Chemical Engineering Department, Faculty of Engineering, Soran University, Soran 44008, Iraq
- Faculty of Chemical and Petroleum Engineering, University of Tabriz, Tabriz 5166616471, Iran
| | - Falah Alobaid
- Institut Energiesysteme und Energietechnik, Technische Universität Darmstadt, Otto-Berndt-Straße 2, 64287 Darmstadt, Germany
| | - Babak Aghel
- Institut Energiesysteme und Energietechnik, Technische Universität Darmstadt, Otto-Berndt-Straße 2, 64287 Darmstadt, Germany
- Department of Chemical Engineering, Faculty of Energy, Kermanshah University of Technology, Kermanshah 6715685420, Iran
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25
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Atmospheric solids analysis probe-mass spectrometry (ASAP-MS) as a rapid fingerprinting technique to differentiate the harvest seasons of Tieguanyin oolong teas. Food Chem 2023; 408:135135. [PMID: 36527922 DOI: 10.1016/j.foodchem.2022.135135] [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: 08/29/2022] [Revised: 11/13/2022] [Accepted: 11/29/2022] [Indexed: 12/12/2022]
Abstract
Atmospheric solids analysis probe-mass spectrometry (ASAP-MS), an ambient mass spectrometry technique, was used to differentiate spring and autumn Tieguanyin teas. Two configurations were used to obtain their chemical fingerprints - ASAP attached to a high-resolution quadrupole time-of-flight mass spectrometer (i.e., ASAP-QTOF) and to a single-quadrupole mass spectrometer (i.e., Radian™ ASAP™ mass spectrometer). Then, orthogonal projections to latent structures-discriminant analysis was conducted to identify features that held promise in differentiating harvest seasons. Four machine learning models - decision tree, linear discriminant analysis, support vector machine, and k-nearest neighbour - were built using these features, and high classification accuracy of up to 100% was achieved. The markers were putatively identified using their accurate masses and MS/MS fragmentation patterns from ASAP-QTOF. This approach was successfully transferred to the Radian ASAP MS, which is more deployable in the field. Overall, this study demonstrated the potential of ASAP-MS as a rapid fingerprinting tool for differentiating spring and autumn Tieguanyin.
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26
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Geng T, Wang Y, Yin XL, Chen W, Gu HW. A Comprehensive Review on the Excitation-Emission Matrix Fluorescence Spectroscopic Characterization of Petroleum-Containing Substances: Principles, Methods, and Applications. Crit Rev Anal Chem 2023:1-23. [PMID: 37155146 DOI: 10.1080/10408347.2023.2205500] [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: 05/10/2023]
Abstract
Petroleum-containing substance (PCS) is a general term used for petroleum and its derivatives. A comprehensive characterization of PCSs is crucial for resource exploitation, economic development and environmental protection. Fluorescence spectroscopy, especially excitation-emission matrix fluorescence (EEMF) spectroscopy, has been proved to be a powerful tool to characterize PCSs since its remarkable sensitivity, selectivity, simplicity and high efficiency. However, there is a lack of systematic review focusing on this field in the literature. This paper reviews the fundamental principles and measurements of EEMF for characterizing PCSs, and makes a systematic introduction to various information mining methods including basic peak information extraction, spectral parameterization and some commonly used chemometric methods. In addition, recent advances in applying EEMF to characterize PCSs during the whole life-cycle process of petroleum are also revisited. Furthermore, the current limitations of EEMF in the measurement and characterization of PCSs are discussed and corresponding solutions are provided. For promoting the future development of this field, the urgent need to build a relatively complete EEMF fingerprint library to trace PCSs, not only pollutants but also crude oil and petroleum products, is proposed. Finally, the extensions of EEMF to high-dimensional chemometrics and deep learning are prospected, with the expectation of solving more complex systems and problems.
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Affiliation(s)
- Tao Geng
- Hubei Engineering Research Center for Clean Production and Pollutant Control of Oil and Gas Fields, College of Chemistry and Environmental Engineering, Yangtze University, Jingzhou, China
| | - Yan Wang
- Hubei Engineering Research Center for Clean Production and Pollutant Control of Oil and Gas Fields, College of Chemistry and Environmental Engineering, Yangtze University, Jingzhou, China
| | - Xiao-Li Yin
- College of Life Sciences, Yangtze University, Jingzhou, China
| | - Wu Chen
- Hubei Engineering Research Center for Clean Production and Pollutant Control of Oil and Gas Fields, College of Chemistry and Environmental Engineering, Yangtze University, Jingzhou, China
- State Key Laboratory of Petroleum Pollution Control, CNPC Research Institute of Safety and Environmental Technology, Beijing, China
| | - Hui-Wen Gu
- Hubei Engineering Research Center for Clean Production and Pollutant Control of Oil and Gas Fields, College of Chemistry and Environmental Engineering, Yangtze University, Jingzhou, China
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27
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Deng F, Lu H, Yuan Y, Chen H, Li Q, Wang L, Tao Y, Zhou W, Cheng H, Chen Y, Lei X, Li G, Li M, Ren W. Accurate prediction of the eating and cooking quality of rice using artificial neural networks and the texture properties of cooked rice. Food Chem 2023; 407:135176. [PMID: 36512909 DOI: 10.1016/j.foodchem.2022.135176] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/27/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022]
Abstract
Accurate prediction of the eating and cooking quality (ECQ) of rice is of great importance. Statistical and machine learning models were developed to predict the overall acceptability of cooked rice. The results showed that the models developed using stepwise multiple linear regression, principal component analysis plus multiple linear regression, partial least square regression, k-nearest neighbor, random forest, and gradient boosted decision tree had determination coefficients (R2) of 0.156-0.452, 0.357, 0.160-0.460, 0.192-0.746, 0.453-0.708, and 0.469-0.880, respectively, which were improved to 0.675-0.979 by artificial neural networks (ANN) models. The ANN models also had lower root mean square errors (0.574-1.32). Further, the ANN model using textural properties could accurately predict 92.1 % of overall acceptability, which could be improved to >96 % using the components and/or pasting characteristics. Overall, the accuracy of ECQ prediction was substantially improved by the model developed using ANN with texture properties of rice.
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Affiliation(s)
- Fei Deng
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China/Key Laboratory of Crop Ecophysiology and Farming Systems in Southwest China, Ministry of Agriculture and Rural Affairs/College of Agronomy, Sichuan Agricultural University, Chengdu 611130, China
| | - Hui Lu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China/Key Laboratory of Crop Ecophysiology and Farming Systems in Southwest China, Ministry of Agriculture and Rural Affairs/College of Agronomy, Sichuan Agricultural University, Chengdu 611130, China
| | - Yujie Yuan
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China/Key Laboratory of Crop Ecophysiology and Farming Systems in Southwest China, Ministry of Agriculture and Rural Affairs/College of Agronomy, Sichuan Agricultural University, Chengdu 611130, China
| | - Hong Chen
- College of Life Science and Engineering, Southwest University of Science and Technology, Mianyang, Sichuan 621010, China
| | - Qiuping Li
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China/Key Laboratory of Crop Ecophysiology and Farming Systems in Southwest China, Ministry of Agriculture and Rural Affairs/College of Agronomy, Sichuan Agricultural University, Chengdu 611130, China
| | - Li Wang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China/Key Laboratory of Crop Ecophysiology and Farming Systems in Southwest China, Ministry of Agriculture and Rural Affairs/College of Agronomy, Sichuan Agricultural University, Chengdu 611130, China
| | - Youfeng Tao
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China/Key Laboratory of Crop Ecophysiology and Farming Systems in Southwest China, Ministry of Agriculture and Rural Affairs/College of Agronomy, Sichuan Agricultural University, Chengdu 611130, China
| | - Wei Zhou
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China/Key Laboratory of Crop Ecophysiology and Farming Systems in Southwest China, Ministry of Agriculture and Rural Affairs/College of Agronomy, Sichuan Agricultural University, Chengdu 611130, China
| | - Hong Cheng
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China/Key Laboratory of Crop Ecophysiology and Farming Systems in Southwest China, Ministry of Agriculture and Rural Affairs/College of Agronomy, Sichuan Agricultural University, Chengdu 611130, China
| | - Yong Chen
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China/Key Laboratory of Crop Ecophysiology and Farming Systems in Southwest China, Ministry of Agriculture and Rural Affairs/College of Agronomy, Sichuan Agricultural University, Chengdu 611130, China
| | - Xiaolong Lei
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an 625014, China
| | - Guiyong Li
- Food Crops Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650221, China
| | - Min Li
- Rice Research Institute of Guizhou Province, Guiyang 550025, China
| | - Wanjun Ren
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China/Key Laboratory of Crop Ecophysiology and Farming Systems in Southwest China, Ministry of Agriculture and Rural Affairs/College of Agronomy, Sichuan Agricultural University, Chengdu 611130, China.
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28
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Haseli G, Ranjbarzadeh R, Hajiaghaei-Keshteli M, Jafarzadeh Ghoushchi S, Hasani A, Deveci M, Ding W. HECON: Weight assessment of the product loyalty criteria considering the customer decision's halo effect using the convolutional neural networks. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2022.12.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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29
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Chen R, Liu F, Zhang C, Wang W, Yang R, Zhao Y, Peng J, Kong W, Huang J. Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy. FRONTIERS IN PLANT SCIENCE 2023; 14:1128300. [PMID: 37025139 PMCID: PMC10072231 DOI: 10.3389/fpls.2023.1128300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/27/2023] [Indexed: 06/19/2023]
Abstract
Herbs have been used as natural remedies for disease treatment, prevention, and health care. Some herbs with functional properties are also used as food or food additives for culinary purposes. The quality and safety inspection of herbs are influenced by various factors, which need to be assessed in each operation across the whole process of herb production. Traditional analysis methods are time-consuming and laborious, without quick response, which limits industry development and digital detection. Considering the efficiency and accuracy, faster, cheaper, and more environment-friendly techniques are highly needed to complement or replace the conventional chemical analysis methods. Infrared (IR) and Raman spectroscopy techniques have been applied to the quality control and safety inspection of herbs during the last several decades. In this paper, we generalize the current application using IR and Raman spectroscopy techniques across the whole process, from raw materials to patent herbal products. The challenges and remarks were proposed in the end, which serve as references for improving herb detection based on IR and Raman spectroscopy techniques. Meanwhile, make a path to driving intelligence and automation of herb products factories.
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Affiliation(s)
- Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Wei Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Rui Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Yiying Zhao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Jiyu Peng
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Wenwen Kong
- College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou, China
| | - Jing Huang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
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30
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Sicard J, Barbe S, Boutrou R, Bouvier L, Delaplace G, Lashermes G, Théron L, Vitrac O, Tonda A. A primer on predictive techniques for food and bioresources transformation processes. J FOOD PROCESS ENG 2023. [DOI: 10.1111/jfpe.14325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023]
Affiliation(s)
| | | | | | - Laurent Bouvier
- UMET Université de Lille, CNRS, Centrale Lille, INRAE Villeneuve‐D'Ascq France
| | - Guillaume Delaplace
- UMET Université de Lille, CNRS, Centrale Lille, INRAE Villeneuve‐D'Ascq France
| | | | | | - Olivier Vitrac
- SayFood, INRAE, AgroParisTech Université Paris Saclay Massy France
| | - Alberto Tonda
- MIA‐Paris, AgroParisTech, INRAE Université Paris Saclay Paris France
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31
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Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis. Foods 2023; 12:foods12061242. [PMID: 36981168 PMCID: PMC10048131 DOI: 10.3390/foods12061242] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/06/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023] Open
Abstract
Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.
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32
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Combining untargeted profiling of phenolics and sterols, supervised multivariate class modelling and artificial neural networks for the origin and authenticity of extra-virgin olive oil: A case study on Taggiasca Ligure. Food Chem 2023; 404:134543. [DOI: 10.1016/j.foodchem.2022.134543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/25/2022] [Accepted: 10/05/2022] [Indexed: 11/06/2022]
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33
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Stój A, Czernecki T, Domagała D. Authentication of Polish Red Wines Produced from Zweigelt and Rondo Grape Varieties Based on Volatile Compounds Analysis in Combination with Machine Learning Algorithms: Hotrienol as a Marker of the Zweigelt Variety. Molecules 2023; 28:molecules28041961. [PMID: 36838950 PMCID: PMC9967794 DOI: 10.3390/molecules28041961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/10/2023] [Accepted: 02/16/2023] [Indexed: 02/22/2023] Open
Abstract
The aim of this study was to determine volatile compounds in red wines of Zweigelt and Rondo varieties using HS-SPME/GC-MS and to find a marker and/or a classification model for the assessment of varietal authenticity. The wines were produced by using five commercial yeast strains and two types of malolactic fermentation. Sixty-seven volatile compounds were tentatively identified in the test wines; they represented several classes: 9 acids, 24 alcohols, 2 aldehydes, 19 esters, 2 furan compounds, 2 ketones, 1 sulfur compound and 8 terpenes. 3,7-dimethyl-1,5,7-octatrien-3-ol (hotrienol) was found to be a variety marker for Zweigelt wines, since it was detected in all the Zweigelt wines, but was not present in the Rondo wines at all. The relative concentrations of volatiles were used as an input data set, divided into two subsets (training and testing), to the support vector machine (SVM) and k-nearest neighbor (kNN) algorithms. Both machine learning methods yielded models with the highest possible classification accuracy (100%) when the relative concentrations of all the test compounds or alcohols alone were used as input data. An evaluation of the importance value of subsets consisting of six volatile compounds with the highest potential to distinguish between the Zweigelt and Rondo varieties revealed that SVM and kNN yielded the best classification models (F-score of 1, accuracy of 100%) when 3-ethyl-4-methylpentan-1-ol or 3,7-dimethyl-1,5,7-octatrien-3-ol (hotrienol) or subsets containing one or both of them were used. Moreover, the best SVM model (F-score of 1) was built with a subset containing 2-phenylethyl acetate and 3-(methylsulfanyl)propan-1-ol.
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Affiliation(s)
- Anna Stój
- Department of Biotechnology, Microbiology and Human Nutrition, Faculty of Food Science and Biotechnology, University of Life Sciences, 8 Skromna Street, 20-704 Lublin, Poland
- Correspondence: (A.S.); (D.D.)
| | - Tomasz Czernecki
- Department of Biotechnology, Microbiology and Human Nutrition, Faculty of Food Science and Biotechnology, University of Life Sciences, 8 Skromna Street, 20-704 Lublin, Poland
| | - Dorota Domagała
- Department of Applied Mathematics and Computer Science, Faculty of Production Engineering, University of Life Sciences in Lublin, 28 Głęboka Street, 20-612 Lublin, Poland
- Correspondence: (A.S.); (D.D.)
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34
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Jiang L, Zheng K. Towards the intelligent antioxidant activity evaluation of green tea products during storage: A joint cyclic voltammetry and machine learning study. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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35
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Rapid Identification of Insecticide- and Herbicide-Tolerant Genetically Modified Maize Using Mid-Infrared Spectroscopy. Processes (Basel) 2022. [DOI: 10.3390/pr11010090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Genetically modified (GM) technology is of great significance for increasing crop production, protecting biodiversity, and reducing environmental pollution. However, with the frequent occurrence of safety events regarding GM foods, more and more disputes have arisen over the potential safety of transgenic technology. It is particularly necessary to find a fast and accurate method for transgenic product identification. In this research, mid-infrared spectroscopy, coupled with chemometric methods, was applied to discriminate GM maize from its non-GM parent. A total of 120 GM maize and 120 non-GM maize samples were prepared, and the spectral information in the range of 400–4000 cm−1 was collected. After acquiring the spectra, wavelet transform (WT) was used to preprocess the data, and k-means was carried out to split all samples into calibration and prediction sets in the ratio of 2:1. Principal component analysis (PCA) was then conducted to qualitatively distinguish the two types of samples, and an apparent cluster was observed. Since the full spectrum covered a large amount of data and redundant information, we adopted the successive projections algorithm (SPA) to select optimal wavelengths for further analysis. Chemometrics, including partial least squares-discriminant analysis (PLS-DA), the k-nearest neighbor algorithm (KNN), and the extreme learning machine (ELM), were performed to establish classification models based on full spectra and optimal wavelengths. The overall results indicated that ELM models based on full spectra and optimal spectra showed better accuracy and reliability, with a 100% recognition rate in the calibration set and a 98.75% recognition rate in the prediction set. It has been confirmed that mid-infrared spectroscopy, combined with chemometric methods, can be a novel approach to identify transgenic maize.
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36
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Grassi S, Tarapoulouzi M, D’Alessandro A, Agriopoulou S, Strani L, Varzakas T. How Chemometrics Can Fight Milk Adulteration. Foods 2022; 12:foods12010139. [PMID: 36613355 PMCID: PMC9819000 DOI: 10.3390/foods12010139] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 12/10/2022] [Accepted: 12/22/2022] [Indexed: 12/29/2022] Open
Abstract
Adulteration and fraud are amongst the wrong practices followed nowadays due to the attitude of some people to gain more money or their tendency to mislead consumers. Obviously, the industry follows stringent controls and methodologies in order to protect consumers as well as the origin of the food products, and investment in these technologies is highly critical. In this context, chemometric techniques proved to be very efficient in detecting and even quantifying the number of substances used as adulterants. The extraction of relevant information from different kinds of data is a crucial feature to achieve this aim. However, these techniques are not always used properly. In fact, training is important along with investment in these technologies in order to cope effectively and not only reduce fraud but also advertise the geographical origin of the various food and drink products. The aim of this paper is to present an overview of the different chemometric techniques (from clustering to classification and regression applied to several analytical data) along with spectroscopy, chromatography, electrochemical sensors, and other on-site detection devices in the battle against milk adulteration. Moreover, the steps which should be followed to develop a chemometric model to face adulteration issues are carefully presented with the required critical discussion.
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Affiliation(s)
- Silvia Grassi
- Department of Food, Environmental and Nutritional Sciences (DeFENS), Università degli Studi di Milano, Via Celoria, 2, 20133 Milano, Italy
| | - Maria Tarapoulouzi
- Department of Chemistry, Faculty of Pure and Applied Science, University of Cyprus, P.O. Box 20537, Nicosia CY-1678, Cyprus
| | - Alessandro D’Alessandro
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Sofia Agriopoulou
- Department of Food Science and Technology, University of the Peloponnese, Antikalamos, 24100 Kalamata, Greece
| | - Lorenzo Strani
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
- Correspondence: (L.S.); (T.V.)
| | - Theodoros Varzakas
- Department of Food Science and Technology, University of the Peloponnese, Antikalamos, 24100 Kalamata, Greece
- Correspondence: (L.S.); (T.V.)
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Non-targeting metabolite profiling and chemometric approaches for the discrimination and authentication analyses of whole-wheat flours from Tunisian durum wheat landraces (Triticum turgidum ssp. durum). JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01759-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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38
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Wang XZ, Wu HL, Wang T, Chen AQ, Sun HB, Ding ZW, Chang HY, Yu RQ. Rapid identification and semi-quantification of adulteration in walnut oil by using excitation–emission matrix fluorescence spectroscopy coupled with chemometrics and ensemble learning. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.105094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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39
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Improvement of blueberry freshness prediction based on machine learning and multi-source sensing in the cold chain logistics. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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40
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Zani Agnoletti B, dos Santos Gomes W, Falquetto de Oliveira G, Henrique da Cunha P, Helena Cassago Nascimento M, Cunha Neto Á, Louzada Pereira L, Vinicius Ribeiro de Castro E, Catarina da Silva Oliveira E, Roberto Filgueiras P. Effect of fermentation on the quality of conilon coffee (Coffea canephora): Chemical and sensory aspects. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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41
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Unravelling the physicochemical features of US wheat flours over the past two decades by machine learning analysis. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.114036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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42
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PÉREZ-BELTRÁN CH, PÉREZ–CABALLERO G, ANDRADE JM, CUADROS-RODRÍGUEZ L, JIMÉNEZ-CARVELO AM. Non-targeted Spatially Offset Raman Spectroscopy-based vanguard analytical method to authenticate spirits: White Tequilas as a case study. Microchem J 2022. [DOI: 10.1016/j.microc.2022.108126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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43
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Yang C, Zhou S, Zhu J, Sheng H, Mao W, Fu Z, Chen Z. Plasma Lipid-based Machine Learning Models Provides a Potential Diagnostic Tool for Colorectal Cancer Patients. Clin Chim Acta 2022; 536:191-199. [PMID: 36191612 DOI: 10.1016/j.cca.2022.09.002] [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: 04/23/2022] [Revised: 08/23/2022] [Accepted: 09/01/2022] [Indexed: 11/29/2022]
Abstract
Colorectal cancer is the second leading cause of cancer-related death across the world. So far, screening methods for colorectal cancer are limited to blood test, imaging test, and digital rectal examination, that are either invasive or ineffective. So, this study aims to explore novel, more convenient and effective diagnostic methods for colorectal cancer. First, the experiment cohort was randomly split to train set and test set, and LC-MS-based plasma lipidomics was applied to identify lipid features in colorectal cancer. Second, univariate and multivariate analyses were performed to screen for significantly differentially expressed lipids. Third, single-lipid-based ROC analysis and multiple-lipid-based machine learning modelling were conducted to assess differential lipids' diagnostic performance. Lastly, survival analyses were used to evaluate lipids' prognostic values. In total, 41 differential lipids were screened out, 10 were upregulated and 31 were downregulated in CRC. Only CerP(d15:0_22:0+O) showed fine predictive accuracy in single-lipid-base ROC analysis. Among the four machine learning models, SVM showed best predictive performance with accuracy (in predicting test set) of 1.0000 (95%CI: 0.8806, 1.0000), that can be reached by modelling with only 14 lipids. Four lipids had significant prognostic values, that were TG(11:0_18:0_18:0) (HR: 0.34), TG(18:0_18:0_18:1) (HR: 0.34), PC(22:1_12:3) (HR: 2.22), LPC(17:0) (HR: 3.16). In conclusion, this study discovered novel lipid features that has potential diagnostic and prognostic values, and showed combination of plasma lipidomics and machine learning modelling could have outstanding diagnostic performance and may serve as a convenient and more accessible way to aid clinical diagnosis of colorectal cancer.
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Affiliation(s)
- Chenxi Yang
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Sicheng Zhou
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Jing Zhu
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Huaying Sheng
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Weimin Mao
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory of Diagnosis & Treatment Technology on Thoracic Oncology (Lung and Esophagus), Hangzhou, Zhejiang, China
| | - Zhixuan Fu
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Zhongjian Chen
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory of Diagnosis & Treatment Technology on Thoracic Oncology (Lung and Esophagus), Hangzhou, Zhejiang, China.
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44
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IoT-based food traceability system: Architecture, technologies, applications, and future trends. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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45
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Li H, Wu X, Wu S, Chen L, Kou X, Zeng Y, Li D, Lin Q, Zhong H, Hao T, Dong B, Chen S, Zheng J. Machine learning directed discrimination of virgin and recycled poly(ethylene terephthalate) based on non-targeted analysis of volatile organic compounds. JOURNAL OF HAZARDOUS MATERIALS 2022; 436:129116. [PMID: 35569370 DOI: 10.1016/j.jhazmat.2022.129116] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 04/22/2022] [Accepted: 05/06/2022] [Indexed: 06/15/2023]
Abstract
The use of non-decontaminated recycled poly(ethylene terephthalate) (PET) in food packages arouses consumer safety concerns, and thus is a major obstacle hindering PET bottle-to-bottle recycling in many developing regions. Herein, machine learning (ML) algorithms were employed for the discrimination of 127 batches of virgin PET and recycled PET (rPET) samples based on 1247 volatile organic compounds (VOCs) tentatively identified by headspace solid-phase microextraction comprehensive two-dimensional gas chromatography quadrupole-time-of-flight mass spectrometry. 100% prediction accuracy was achieved for PET discrimination using random forest (RF) and support vector machine (SVM) algorithms. The features of VOCs bearing high variable contributions to the RF prediction performance characterized by mean decrease Gini and variable importance were summarized as high occurrence rate, dominant appearance and distinct instrument response. Further, RF and SVM were employed for PET discrimination using the simplified input datasets composed of 62 VOCs with the highest contributions to the RF prediction performance derived by the AUCRF algorithm, by which over 99% prediction accuracy was achieved. Our results demonstrated ML algorithms were reliable and powerful to address PET adulteration and were beneficial to boost food-contact applications of rPET bottles.
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Affiliation(s)
- Hanke Li
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China; School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510641, China
| | - Xuefeng Wu
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
| | - Siliang Wu
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
| | - Lichang Chen
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Xiaoxue Kou
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
| | - Ying Zeng
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
| | - Dan Li
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
| | - Qinbao Lin
- Key Laboratory of Product Packaging and Logistics, Packaging Engineering Institute, Jinan University, Zhuhai 519070, China
| | - Huaining Zhong
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China.
| | - Tianying Hao
- Key Laboratory of Product Packaging and Logistics, Packaging Engineering Institute, Jinan University, Zhuhai 519070, China
| | - Ben Dong
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China.
| | - Sheng Chen
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
| | - Jianguo Zheng
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
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46
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Fengou LC, Liu Y, Roumani D, Tsakanikas P, Nychas GJE. Spectroscopic Data for the Rapid Assessment of Microbiological Quality of Chicken Burgers. Foods 2022; 11:foods11162386. [PMID: 36010385 PMCID: PMC9407583 DOI: 10.3390/foods11162386] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/31/2022] [Accepted: 08/04/2022] [Indexed: 11/27/2022] Open
Abstract
The rapid assessment of the microbiological quality of highly perishable food commodities is of great importance. Spectroscopic data coupled with machine learning methods have been investigated intensively in recent years, because of their rapid, non-destructive, eco-friendly qualities and their potential to be used on-, in- or at-line. In the present study, the microbiological quality of chicken burgers was evaluated using Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI) in tandem with machine learning algorithms. Six independent batches were purchased from a food industry and stored at 0, 4, and 8 °C. At regular time intervals (specifically every 24 h), duplicate samples were subjected to microbiological analysis, FTIR measurements, and MSI sampling. The samples (n = 274) acquired during the data collection were classified into three microbiological quality groups: “satisfactory”: 4−7 log CFU/g, “acceptable”: 7−8 log CFU/g, and “unacceptable”: >8 logCFU/g. Subsequently, classification models were trained and tested (external validation) with several machine learning approaches, namely partial least squares discriminant analysis (PLSDA), support vector machine (SVM), random forest (RF), logistic regression (LR), and ordinal logistic regression (OLR). Accuracy scores were attained for the external validation, exhibiting FTIR data values in the range of 79.41−89.71%, and, for the MSI data, in the range of 74.63−85.07%. The performance of the models showed merit in terms of the microbiological quality assessment of chicken burgers.
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Affiliation(s)
- Lemonia-Christina Fengou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
- Correspondence:
| | - Yunge Liu
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
- Laboratory of Beef Processing and Quality Control, College of Food Science and Engineering, Shandong Agricultural University, Tai’an 271018, China
| | - Danai Roumani
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
| | - Panagiotis Tsakanikas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
| | - George-John E. Nychas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
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47
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Varrà MO, Ghidini S, Fabrile MP, Ianieri A, Zanardi E. Country of origin label monitoring of musky and common octopuses (Eledone spp. and Octopus vulgaris) by means of a portable near-infrared spectroscopic device. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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48
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Bi K, Zhang S, Zhang C, Qiu T. Consumer-oriented sensory optimization of yogurt: An artificial intelligence approach. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.108995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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49
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50
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Study on the detection of heavy metal lead (Pb) in mussels based on near-infrared spectroscopy technology and a REELM classifier. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107394] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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