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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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Rodríguez-Fernández R, Fernández-Gómez Á, Mejuto JC, Astray G. Modelling Polyphenol Extraction through Ultrasound-Assisted Extraction by Machine Learning in Olea europaea Leaves. Foods 2023; 12:4483. [PMID: 38137287 PMCID: PMC10742609 DOI: 10.3390/foods12244483] [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: 11/07/2023] [Revised: 12/05/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023] Open
Abstract
The study of the phenolic compounds present in olive leaves (Olea europaea) is of great interest due to their health benefits. In this research, different machine learning algorithms such as RF, SVM, and ANN, with temperature, time, and volume as input variables, were developed to model the extract yield and the total phenolic content (TPC) from experimental data reported in the literature. In terms of extract yield, the neural network-based ANNZ-L model presents the lowest root mean square error (RMSE) value in the validation phase (9.44 mg/g DL), which corresponds with a mean absolute percentage error (MAPE) of 3.7%. On the other hand, the best model to determine the TPC value was the neural network-based model ANNR, with an RMSE of 0.89 mg GAE/g DL in the validation phase (MAPE of 2.9%). Both models obtain, for the test phase, MAPE values of 4.9 and 3.5%, respectively. This affirms that ANN models would be good modelling tools to determine the extract yield and TPC value of the ultrasound-assisted extraction (UAE) process of olive leaves under different temperatures, times, and solvents.
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Affiliation(s)
| | | | | | - Gonzalo Astray
- Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, Spain; (R.R.-F.); (Á.F.-G.); (J.C.M.)
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3
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Application of stable isotope and mineral element fingerprint in identification of Hainan camellia oil producing area based on convolutional neural networks (CNN). Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
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Zhang W, Kasun LC, Wang QJ, Zheng Y, Lin Z. A Review of Machine Learning for Near-Infrared Spectroscopy. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249764. [PMID: 36560133 PMCID: PMC9784128 DOI: 10.3390/s22249764] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 12/04/2022] [Accepted: 12/05/2022] [Indexed: 06/01/2023]
Abstract
The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction.
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Affiliation(s)
- Wenwen Zhang
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
| | | | - Qi Jie Wang
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
| | - Yuanjin Zheng
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Zhiping Lin
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
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5
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Li X, Wang D, Ma F, Yu L, Mao J, Zhang W, Jiang J, Zhang L, Li P. Rapid detection of sesame oil multiple adulteration using a portable Raman spectrometer. Food Chem 2022; 405:134884. [DOI: 10.1016/j.foodchem.2022.134884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 11/02/2022] [Accepted: 11/03/2022] [Indexed: 11/14/2022]
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6
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Aznan A, Gonzalez Viejo C, Pang A, Fuentes S. Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies. Foods 2022; 11:1181. [PMID: 35563907 PMCID: PMC9105373 DOI: 10.3390/foods11091181] [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: 03/31/2022] [Revised: 04/18/2022] [Accepted: 04/18/2022] [Indexed: 12/10/2022] Open
Abstract
Aroma and other physicochemical parameters are important attributes influencing consumer perception and acceptance of rice. However, current methods using multiple instruments and laboratory analysis make these assessments costly and time-consuming. Therefore, this study aimed to assess rice quality traits of 17 commercial rice types using a low-cost electronic nose and portable near-infrared spectrometer coupled with machine learning (ML). Specifically, artificial neural networks (ANN) were used to classify the type of rice and predict rice quality traits (aromas, color, texture, and pH of cooked rice) as targets. The ML models developed showed that the chemometrics obtained from both sensor technologies successfully classified the rice (Model 1: 98.7%; Model 2: 98.6%) and predicted the peak area of aromas obtained by gas chromatography-mass spectroscopy found in raw (Model 3: R = 0.95; Model 6: R = 0.95) and cooked rice (Model 4: R = 0.98; Model 7: R = 0.96). Furthermore, a high R = 0.98 was obtained for Model 5 to estimate the color, texture, and pH of cooked rice. The proposed method is rapid, low-cost, reliable, and may help the rice industry increase high-quality rice production and accelerate the adoption of digital technologies and artificial intelligence to support the rice value chain.
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Affiliation(s)
- Aimi Aznan
- Digital Agriculture, Food and Wine Group (DAFW), School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia; (A.A.); (C.G.V.); (A.P.)
- Faculty of Chemical Engineering Technology, University Malaysia Perlis, Arau 02600, Perlis, Malaysia
| | - Claudia Gonzalez Viejo
- Digital Agriculture, Food and Wine Group (DAFW), School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia; (A.A.); (C.G.V.); (A.P.)
| | - Alexis Pang
- Digital Agriculture, Food and Wine Group (DAFW), School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia; (A.A.); (C.G.V.); (A.P.)
| | - Sigfredo Fuentes
- Digital Agriculture, Food and Wine Group (DAFW), School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia; (A.A.); (C.G.V.); (A.P.)
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7
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Li Q, Wu W, Fang X, Chen H, Han Y, Liu R, Niu B, Gao H. Structural characterization of a polysaccharide from bamboo (Phyllostachys edulis) shoot and its prevention effect on colitis mouse. Food Chem 2022; 387:132807. [PMID: 35397273 DOI: 10.1016/j.foodchem.2022.132807] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/20/2022] [Accepted: 03/23/2022] [Indexed: 12/31/2022]
Abstract
A water-soluble dietary fiber named BSDF-1 (polysaccharide) was isolated from the bamboo (Phyllostachys edulis) shoot. BSDF-1was characterized as a backbone consisting predominately of 1,4-linked Glcp, and the protective effects and mechanisms of the anti-inflammatory activity were investigated using a dextran sulfate sodium (DSS)-induced colitis mouse model. BSDF-1 administration significantly reduced colonic pathological damage, inhibited the activation of inflammatory signaling pathways, including nuclear factor-kappa B and NLR family pyrin domain containing 3 inflammasomes pathways. It restored the mRNA expression of tight junction proteins, including zonula occludens-1, claudin-1, and occludin. Furthermore, BSDF-1 treatment reduced Parabacteroides, Mucispirillum, Helicobacter, Bacteroides, and Streptococcus levels, whereas high-dose BSDF-1 treatment increased Prevotella, Alitipes, Anaerostipes, Odoribacter, Bifidobacterium, Butyricimonas, and Lactobacillus levels. In conclusion, BSDF-1 can inhibit the activation of inflammatory signaling pathways and restore the intestinal barrier function. Thus, BSDF-1 may be a valuable food supplement or nutraceutical to manage and prevent ulcerative colitis.
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Affiliation(s)
- Qi Li
- Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; Key Laboratory of Post-Harvest Handling of Fruits, Ministry of Agriculture and Rural Affairs, Hangzhou 310021, China; Key Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang Province, Hangzhou 310021, China; Key Laboratory of Postharvest Preservation and Processing of Fruits and Vegetables, China National Light Industry, Hangzhou 310021, China
| | - Weijie Wu
- Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; Key Laboratory of Post-Harvest Handling of Fruits, Ministry of Agriculture and Rural Affairs, Hangzhou 310021, China; Key Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang Province, Hangzhou 310021, China; Key Laboratory of Postharvest Preservation and Processing of Fruits and Vegetables, China National Light Industry, Hangzhou 310021, China
| | - Xiangjun Fang
- Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; Key Laboratory of Post-Harvest Handling of Fruits, Ministry of Agriculture and Rural Affairs, Hangzhou 310021, China; Key Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang Province, Hangzhou 310021, China; Key Laboratory of Postharvest Preservation and Processing of Fruits and Vegetables, China National Light Industry, Hangzhou 310021, China
| | - Hangjun Chen
- Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; Key Laboratory of Post-Harvest Handling of Fruits, Ministry of Agriculture and Rural Affairs, Hangzhou 310021, China; Key Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang Province, Hangzhou 310021, China; Key Laboratory of Postharvest Preservation and Processing of Fruits and Vegetables, China National Light Industry, Hangzhou 310021, China
| | - Yanchao Han
- Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; Key Laboratory of Post-Harvest Handling of Fruits, Ministry of Agriculture and Rural Affairs, Hangzhou 310021, China; Key Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang Province, Hangzhou 310021, China; Key Laboratory of Postharvest Preservation and Processing of Fruits and Vegetables, China National Light Industry, Hangzhou 310021, China
| | - Ruiling Liu
- Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; Key Laboratory of Post-Harvest Handling of Fruits, Ministry of Agriculture and Rural Affairs, Hangzhou 310021, China; Key Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang Province, Hangzhou 310021, China; Key Laboratory of Postharvest Preservation and Processing of Fruits and Vegetables, China National Light Industry, Hangzhou 310021, China
| | - Ben Niu
- Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; Key Laboratory of Post-Harvest Handling of Fruits, Ministry of Agriculture and Rural Affairs, Hangzhou 310021, China; Key Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang Province, Hangzhou 310021, China; Key Laboratory of Postharvest Preservation and Processing of Fruits and Vegetables, China National Light Industry, Hangzhou 310021, China
| | - Haiyan Gao
- Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; Key Laboratory of Post-Harvest Handling of Fruits, Ministry of Agriculture and Rural Affairs, Hangzhou 310021, China; Key Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang Province, Hangzhou 310021, China; Key Laboratory of Postharvest Preservation and Processing of Fruits and Vegetables, China National Light Industry, Hangzhou 310021, China.
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8
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COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11062884] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The COVID-19 disease has spread all over the world, representing an intriguing challenge for humanity as a whole. The efficient diagnosis of humans infected by COVID-19 still remains an increasing need worldwide. The chest X-ray imagery represents, among others, one attractive means to detect COVID-19 cases efficiently. Many studies have reported the efficiency of using deep learning classifiers in diagnosing COVID-19 from chest X-ray images. They conducted several comparisons among a subset of classifiers to identify the most accurate. In this paper, we investigate the potential of the combination of state-of-the-art classifiers in achieving the highest possible accuracy for the detection of COVID-19 from X-ray. For this purpose, we conducted a comprehensive comparison study among 16 state-of-the-art classifiers. To the best of our knowledge, this is the first study considering this number of classifiers. This paper’s innovation lies in the methodology that we followed to develop the inference system that allows us to detect COVID-19 with high accuracy. The methodology consists of three steps: (1) comprehensive comparative study between 16 state-of-the-art classifiers; (2) comparison between different ensemble classification techniques, including hard/soft majority, weighted voting, Support Vector Machine, and Random Forest; and (3) finding the combination of deep learning models and ensemble classification techniques that lead to the highest classification confidence on three classes. We found that using the Majority Voting approach is an adequate strategy to adopt in general cases for this task and may achieve an average accuracy up to 99.314%.
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Braghini F, Biluca FC, Schulz M, Gonzaga LV, Costa ACO, Fett R. Stingless bee honey: a precious but unregulated product - reality and expectations. FOOD REVIEWS INTERNATIONAL 2021. [DOI: 10.1080/87559129.2021.1884875] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Francieli Braghini
- Department of Food Science and Technology, Federal University of Santa Catarina, Florianópolis, SC, Brazil
| | - Fabíola C. Biluca
- Department of Food Science and Technology, Federal University of Santa Catarina, Florianópolis, SC, Brazil
| | - Mayara Schulz
- Department of Food Science and Technology, Federal University of Santa Catarina, Florianópolis, SC, Brazil
| | - Luciano V. Gonzaga
- Department of Food Science and Technology, Federal University of Santa Catarina, Florianópolis, SC, Brazil
| | - Ana C. O. Costa
- Department of Food Science and Technology, Federal University of Santa Catarina, Florianópolis, SC, Brazil
| | - Roseane Fett
- Department of Food Science and Technology, Federal University of Santa Catarina, Florianópolis, SC, Brazil
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10
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Zarezadeh MR, Aboonajmi M, Ghasemi Varnamkhasti M. Fraud detection and quality assessment of olive oil using ultrasound. Food Sci Nutr 2021; 9:180-189. [PMID: 33473282 PMCID: PMC7802576 DOI: 10.1002/fsn3.1980] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 09/23/2020] [Accepted: 10/16/2020] [Indexed: 02/05/2023] Open
Abstract
Today, food safety is recognized as one of the most important human priorities, so effective and new policies have been implemented to improve and develop the position of effective laws in the food industry. Extra virgin olive oil (EVOO) has many amazing benefits for human body's health. Due to the nutritional value and high price of EVOO, there is a lot of cheating in it. The ultrasound approach has many advantages in the food studies, and it is fast and nondestructive for quality evaluation. In this study, to fraud detection of EVOO four ultrasonic properties of oil in five levels of adulteration (5%, 10%, 20%, 35%, and 50%) were extracted. The 2 MHz ultrasonic probes were used in the DOI 1,000 STARMANS diagnostic ultrasonic device in a "probe holding mechanism." The four extracted ultrasonic features include the following: "percentage of amplitude reduction, time of flight (TOF), the difference between the first and second maximum amplitudes of the domain (in the time-amplitude diagram), and the ratio of the first and second maximum of amplitude." Seven classification algorithms including "Naïve Bayes, support vector machine, gradient boosting classifier, K-nearest neighbors, artificial neural network, logistic regression, and AdaBoost" were used to classify the preprocessed data. Results showed that the Naïve Bayes algorithm with 90.2% provided the highest accuracy among the others, and the support vector machine and gradient boosting classifier with 88.2% were in the next ranks.
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Affiliation(s)
| | - Mohammad Aboonajmi
- Department of Agro‐technologyCollege of AburaihanUniversity of TehranTehranIran
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11
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Ciulu M, Oertel E, Serra R, Farre R, Spano N, Caredda M, Malfatti L, Sanna G. Classification of Unifloral Honeys from SARDINIA (Italy) by ATR-FTIR Spectroscopy and Random Forest. Molecules 2020; 26:E88. [PMID: 33375521 PMCID: PMC7794911 DOI: 10.3390/molecules26010088] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 12/17/2020] [Accepted: 12/25/2020] [Indexed: 12/14/2022] Open
Abstract
Nowadays, the mislabeling of honey floral origin is a very common fraudulent practice. The scientific community is intensifying its efforts to provide the bodies responsible for controlling the authenticity of honey with fast and reliable analytical protocols. In this study, the classification of various monofloral honeys from Sardinia, Italy, was attempted by means of ATR-FTIR spectroscopy and random forest. Four different floral origins were considered: strawberry-tree (Arbutus Unedo L.), asphodel (Asphodelus microcarpus), thistle (Galactites tormentosa), and eucalyptus (Eucalyptus calmadulensis). Training a random forest on the infrared spectra allowed achieving an average accuracy of 87% in a cross-validation setting. The identification of the significant wavenumbers revealed the important role played by the region 1540-1175 cm-1 and, to a lesser extent, the region 1700-1600 cm-1. The contribution of the phenolic fraction was identified as the main responsible for this observation.
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Affiliation(s)
- Marco Ciulu
- Department of Animal Sciences, University of Göttingen, Kellnerweg 6, 37077 Göttingen, Germany;
| | - Elisa Oertel
- Department of Animal Sciences, University of Göttingen, Kellnerweg 6, 37077 Göttingen, Germany;
| | - Rosanna Serra
- Dipartimento di Chimica e Farmacia, Università degli studi di Sassari, Via Vienna 2, 07100 Sassari, Italy; (R.S.); (R.F.); (N.S.); (L.M.); (G.S.)
| | - Roberta Farre
- Dipartimento di Chimica e Farmacia, Università degli studi di Sassari, Via Vienna 2, 07100 Sassari, Italy; (R.S.); (R.F.); (N.S.); (L.M.); (G.S.)
| | - Nadia Spano
- Dipartimento di Chimica e Farmacia, Università degli studi di Sassari, Via Vienna 2, 07100 Sassari, Italy; (R.S.); (R.F.); (N.S.); (L.M.); (G.S.)
| | - Marco Caredda
- AGRIS Sardegna, Loc. Bonassai S.S. 291 Km 18.6, 07100 Sassari, Italy;
| | - Luca Malfatti
- Dipartimento di Chimica e Farmacia, Università degli studi di Sassari, Via Vienna 2, 07100 Sassari, Italy; (R.S.); (R.F.); (N.S.); (L.M.); (G.S.)
| | - Gavino Sanna
- Dipartimento di Chimica e Farmacia, Università degli studi di Sassari, Via Vienna 2, 07100 Sassari, Italy; (R.S.); (R.F.); (N.S.); (L.M.); (G.S.)
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Yu Z, Jung D, Park S, Hu Y, Huang K, Rasco BA, Wang S, Ronholm J, Lu X, Chen J. Smart traceability for food safety. Crit Rev Food Sci Nutr 2020; 62:905-916. [DOI: 10.1080/10408398.2020.1830262] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Zhilong Yu
- Food Nutrition and Health Program, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, Canada
- Department of Food Science and Agricultural Chemistry, Faculty of Agricultural and Environmental Sciences, McGill University, Quebec, Canada
| | - Dongyun Jung
- Department of Food Science and Agricultural Chemistry, Faculty of Agricultural and Environmental Sciences, McGill University, Quebec, Canada
| | - Soyoun Park
- Department of Food Science and Agricultural Chemistry, Faculty of Agricultural and Environmental Sciences, McGill University, Quebec, Canada
| | - Yaxi Hu
- Food Nutrition and Health Program, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, Canada
| | - Kang Huang
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand
| | - Barbara A. Rasco
- College of Agriculture and Natural Resources, University of Wyoming, Laramie, Wyoming, USA
| | - Shuo Wang
- Tianjin Key Laboratory of Food Science and Health, School of Medicine, Nankai University, Tianjin, China
| | - Jennifer Ronholm
- Department of Food Science and Agricultural Chemistry, Faculty of Agricultural and Environmental Sciences, McGill University, Quebec, Canada
- Department of Animal Science, Faculty of Agricultural and Environmental Sciences, McGill University, Quebec, Canada
| | - Xiaonan Lu
- Food Nutrition and Health Program, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, Canada
- Department of Food Science and Agricultural Chemistry, Faculty of Agricultural and Environmental Sciences, McGill University, Quebec, Canada
| | - Juhong Chen
- Department of Biological Systems Engineering, Virginia Tech, Blacksburg, Virginia, USA
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