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Millatina NRN, Calle JLP, Barea-Sepúlveda M, Setyaningsih W, Palma M. Detection and quantification of cocoa powder adulteration using Vis-NIR spectroscopy with chemometrics approach. Food Chem 2024; 449:139212. [PMID: 38583399 DOI: 10.1016/j.foodchem.2024.139212] [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: 12/01/2023] [Revised: 03/12/2024] [Accepted: 03/31/2024] [Indexed: 04/09/2024]
Abstract
The rising demand for cocoa powder has resulted in an upsurge in market prices, leading to the emergence of adulteration practices aimed at achieving economic benefits. This study aimed to detect and quantify cocoa powder adulteration using visible and near-infrared spectroscopy (Vis-NIRS). The adulterants used in this study were powdered carob, cocoa shell, foxtail millet, soybean, and whole wheat. The NIRS data could not be resolved using Savitzky-Golay smoothing. Nevertheless, the application of a random forest and support vector machine successfully classified the samples with 100% accuracy. Quantification of adulteration using partial least squares (PLS), Lasso, Ridge, elastic Net, and RF regressions provided R2 higher than 0.96 and root mean square error <2.6. Coupling PLS with the Boruta algorithm produced the most reliable regression model (R2 = 1, RMSE = 0.0000). Finally, an online application was prepared to facilitate the determination of adulterants in the cocoa powder.
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Affiliation(s)
- Nela Rifda Nur Millatina
- Department of Food and Agricultural Product Technology, Faculty of Agricultural Technology, Universitas Gadjah Mada, Jalan Flora, Bulaksumur, 55281 Yogyakarta, Indonesia
| | - José Luis Pérez Calle
- Department of Analytical Chemistry, Faculty of Sciences, IVAGRO, University of Cadiz, Campus de Excelencia Internacional Agroalimentario (CeiA3), Campus del Rio San Pedro, 11510, Puerto Real, Cádiz, Spain
| | - Marta Barea-Sepúlveda
- Department of Analytical Chemistry, Faculty of Sciences, IVAGRO, University of Cadiz, Campus de Excelencia Internacional Agroalimentario (CeiA3), Campus del Rio San Pedro, 11510, Puerto Real, Cádiz, Spain
| | - Widiastuti Setyaningsih
- Department of Food and Agricultural Product Technology, Faculty of Agricultural Technology, Universitas Gadjah Mada, Jalan Flora, Bulaksumur, 55281 Yogyakarta, Indonesia..
| | - Miguel Palma
- Department of Analytical Chemistry, Faculty of Sciences, IVAGRO, University of Cadiz, Campus de Excelencia Internacional Agroalimentario (CeiA3), Campus del Rio San Pedro, 11510, Puerto Real, Cádiz, Spain
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Nath PC, Mishra AK, Sharma R, Bhunia B, Mishra B, Tiwari A, Nayak PK, Sharma M, Bhuyan T, Kaushal S, Mohanta YK, Sridhar K. Recent advances in artificial intelligence towards the sustainable future of agri-food industry. Food Chem 2024; 447:138945. [PMID: 38461725 DOI: 10.1016/j.foodchem.2024.138945] [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: 01/04/2024] [Revised: 02/26/2024] [Accepted: 03/02/2024] [Indexed: 03/12/2024]
Abstract
Artificial intelligence has the potential to alter the agricultural and food processing industries, with significant ramifications for sustainability and global food security. The integration of artificial intelligence in agriculture has witnessed a significant uptick in recent years. Therefore, comprehensive understanding of these techniques is needed to broaden its application in agri-food supply chain. In this review, we explored cutting-edge artificial intelligence methodologies with a focus on machine learning, neural networks, and deep learning. The application of artificial intelligence in agri-food industry and their quality assurance throughout the production process is thoroughly discussed with an emphasis on the current scientific knowledge and future perspective. Artificial intelligence has played a significant role in transforming agri-food systems by enhancing efficiency, sustainability, and productivity. Many food industries are implementing the artificial intelligence in modelling, prediction, control tool, sensory evaluation, quality control, and tackling complicated challenges in food processing. Similarly, artificial intelligence applied in agriculture to improve the entire farming process, such as crop yield optimization, use of herbicides, weeds identification, and harvesting of fruits. In summary, the integration of artificial intelligence in agri-food systems offers the potential to address key challenges in agriculture, enhance sustainability, and contribute to global food security.
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Affiliation(s)
- Pinku Chandra Nath
- Bioproducts Processing Research Laboratory (BPRL), Department of Bio Engineering, National Institute of Technology, Agartala 799046, India; Department of Applied Biology, University of Science and Technology Meghalaya, Baridua 793101, India
| | - Awdhesh Kumar Mishra
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Gyeongbuk, Republic of Korea
| | - Ramesh Sharma
- Bioproducts Processing Research Laboratory (BPRL), Department of Bio Engineering, National Institute of Technology, Agartala 799046, India; Sri Shakthi Institute of Engineering and Technology, Chinniyampalayam, 641062 Coimbatore, India
| | - Biswanath Bhunia
- Bioproducts Processing Research Laboratory (BPRL), Department of Bio Engineering, National Institute of Technology, Agartala 799046, India
| | - Bishwambhar Mishra
- Department of Biotechnology, Chaitanya Bharathi Institute of Technology, Hyderabad 500075, India
| | - Ajita Tiwari
- Department of Agricultural Engineering, Assam University, Silchar 788011, India
| | - Prakash Kumar Nayak
- Department of Food Engineering and Technology, Central Institute of Technology Kokrajhar, Kokrajhar 783370, India
| | - Minaxi Sharma
- Department of Applied Biology, University of Science and Technology Meghalaya, Baridua 793101, India
| | - Tamanna Bhuyan
- Department of Applied Biology, University of Science and Technology Meghalaya, Baridua 793101, India
| | - Sushant Kaushal
- Department of Tropical Agriculture and International Cooperation, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
| | - Yugal Kishore Mohanta
- Department of Applied Biology, University of Science and Technology Meghalaya, Baridua 793101, India; Centre for Herbal Pharmacology and Environmental Sustainability, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam 603103, India.
| | - Kandi Sridhar
- Department of Food Technology, Karpagam Academy of Higher Education (Deemed to be University), Coimbatore 641021, India.
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Sharma R, Nath PC, Lodh BK, Mukherjee J, Mahata N, Gopikrishna K, Tiwari ON, Bhunia B. Rapid and sensitive approaches for detecting food fraud: A review on prospects and challenges. Food Chem 2024; 454:139817. [PMID: 38805929 DOI: 10.1016/j.foodchem.2024.139817] [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: 11/25/2023] [Revised: 05/13/2024] [Accepted: 05/22/2024] [Indexed: 05/30/2024]
Abstract
Precise and reliable analytical techniques are required to guarantee food quality in light of the expanding concerns regarding food safety and quality. Because traditional procedures are expensive and time-consuming, quick food control techniques are required to ensure product quality. Various analytical techniques are used to identify and detect food fraud, including spectroscopy, chromatography, DNA barcoding, and inotrope ratio mass spectrometry (IRMS). Due to its quick findings, simplicity of use, high throughput, affordability, and non-destructive evaluations of numerous food matrices, NI spectroscopy and hyperspectral imaging are financially preferred in the food business. The applicability of this technology has increased with the development of chemometric techniques and near-infrared spectroscopy-based instruments. The current research also discusses the use of several multivariate analytical techniques in identifying food fraud, such as principal component analysis, partial least squares, cluster analysis, multivariate curve resolutions, and artificial intelligence.
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Affiliation(s)
- Ramesh Sharma
- Bioproducts Processing Research Laboratory (BPRL), Department of Bio Engineering, National Institute of Technology, Agartala 799046, India; Department of Food Technology, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu-641062, India.
| | - Pinku Chandra Nath
- Bioproducts Processing Research Laboratory (BPRL), Department of Bio Engineering, National Institute of Technology, Agartala 799046, India.
| | - Bibhab Kumar Lodh
- Department of Chemical Engineering, National Institute of Technology, Agartala-799046, India.
| | - Jayanti Mukherjee
- Department of Pharmaceutical Chemistry, CMR College of Pharmacy, Hyderabad- 501401, Telangana, India.
| | - Nibedita Mahata
- Department of Biotechnology, National Institute of Technology Durgapur, Durgapur-713209.
| | - Konga Gopikrishna
- SEED Division, Department of Science and Technology, New Delhi, 110016, India.
| | - Onkar Nath Tiwari
- Centre for Conservation and Utilisation of Blue Green Algae (CCUBGA), Division of Microbiology, ICAR-Indian Agricultural Research Institute (IARI), New Delhi, 110012, India.
| | - Biswanath Bhunia
- Bioproducts Processing Research Laboratory (BPRL), Department of Bio Engineering, National Institute of Technology, Agartala 799046, India.
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Tantinantrakun A, Thompson AK, Terdwongworakul A, Teerachaichayut S. Assessment of Nitrite Content in Vienna Chicken Sausages Using Near-Infrared Hyperspectral Imaging. Foods 2023; 12:2793. [PMID: 37509885 PMCID: PMC10379852 DOI: 10.3390/foods12142793] [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: 06/17/2023] [Revised: 07/16/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Sodium nitrite is a food additive commonly used in sausages, but legally, the unsafe levels of nitrite in sausage should be less than 80 mg/kg, since higher levels can be harmful to consumers. Consumers must rely on processors to conform to these levels. Therefore, the determination of nitrite content in chicken sausages using near infrared hyperspectral imaging (NIR-HSI) was investigated. A total of 140 chicken sausage samples were produced by adding sodium nitrite in various levels. The samples were divided into a calibration set (n = 94) and a prediction set (n = 46). Quantitative analysis, to detect nitrate in the sausages, and qualitative analysis, to classify nitrite levels, were undertaken in order to evaluate whether individual sausages had safe levels or non-safe levels of nitrite. NIR-HSI was preprocessed to obtain the optimum conditions for establishing the models. The results showed that the model from the partial least squares regression (PLSR) gave the most reliable performance, with a coefficient of determination of prediction (Rp) of 0.92 and a root mean square error of prediction (RMSEP) of 15.603 mg/kg. The results of the classification using the partial least square-discriminant analysis (PLS-DA) showed a satisfied accuracy for prediction of 91.30%. It was therefore concluded that they were sufficiently accurate for screening and that NIR-HSI has the potential to be used for the fast, accurate and reliable assessment of nitrite content in chicken sausages.
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Affiliation(s)
- Achiraya Tantinantrakun
- Department of Food Science, School of Food-Industry, King Mongkut's Institute of Technology Ladkrabang, Chalongkrung Road, Ladkrabang, Bangkok 10520, Thailand
| | - Anthony Keith Thompson
- Department of Postharvest Technology, Cranfield University, College Road, Cranfield, Bedford MK430AL, UK
| | - Anupun Terdwongworakul
- Department of Agricultural Engineering, Faculty of Engineering at Kamphaeng Saen, Kasetsart University, Kamphaeng Saen, Nakhon Pathom 73140, Thailand
| | - Sontisuk Teerachaichayut
- Department of Food Process Engineering, School of Food-Industry, King Mongkut's Institute of Technology Ladkrabang, Chalongkrung Road, Ladkrabang, Bangkok 10520, Thailand
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Suratno, Windarsih A, Warmiko HD, Khasanah Y, Indrianingsih AW, Rohman A. Metabolomics and Proteomics Approach Using LC-Orbitrap HRMS for the Detection of Pork in Tuna Meat for Halal Authentication. FOOD ANAL METHOD 2023. [DOI: 10.1007/s12161-023-02472-x] [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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Qiu R, Zhao Y, Kong D, Wu N, He Y. Development and comparison of classification models on VIS-NIR hyperspectral imaging spectra for qualitative detection of the Staphylococcus aureus in fresh chicken breast. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121838. [PMID: 36108407 DOI: 10.1016/j.saa.2022.121838] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/26/2022] [Accepted: 09/02/2022] [Indexed: 06/15/2023]
Abstract
Chicken is at risk of contaminated foodborne pathogens in the production process. Timely and nondestructive detection of foodborne pathogens in chicken is essential for food security. The study aims to explore the feasibility of developing efficient classification models for qualitative detection of Staphylococcus aureus in chicken breast using the hyperspectral imaging technique. Principal component analysis was used to process the full spectral information and three wavelength selection methods (competitive adaptive reweighted sampling, genetic algorithm, and successive projections algorithm) were applied to extract effective wavelengths. These methods were combined with the support vector machine algorithm to develop conventional classification models, respectively. In addition, a convolutional neural network model based on deep learning was designed and trained for comparison. The performance of the convolutional neural network model was significantly better than that of conventional classification models. The overall accuracy for chicken sample classifications was improved from 83.88% to 91.38%. The results demonstrated that deep learning can effectively extract spectral features and promote the application of hyperspectral imaging in foodborne pathogens detection of chicken products.
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Affiliation(s)
- Ruicheng Qiu
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Yinglei Zhao
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou 310000, China
| | - Dandan Kong
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Na Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
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Hu Y, Huang P, Wang Y, Sun J, Wu Y, Kang Z. Determination of Tibetan Tea Quality by Hyperspectral Imaging Technology and Multivariate Analysis. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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Pu H, Wei Q, Sun DW. Recent advances in muscle food safety evaluation: Hyperspectral imaging analyses and applications. Crit Rev Food Sci Nutr 2022; 63:1297-1313. [PMID: 36123794 DOI: 10.1080/10408398.2022.2121805] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
As there is growing interest in process control for quality and safety in the meat industry, by integrating spectroscopy and imaging technologies into one system, hyperspectral imaging, or chemical or spectroscopic imaging has become an alternative analytical technique that can provide the spatial distribution of spectrum for fast and nondestructive detection of meat safety. This review addresses the configuration of the hyperspectral imaging system and safety indicators of muscle foods involving biological, chemical, and physical attributes and other associated hazards or poisons, which could cause safety problems. The emphasis focuses on applications of hyperspectral imaging techniques in the safety evaluation of muscle foods, including pork, beef, lamb, chicken, fish and other meat products. Although HSI can provide the spatial distribution of spectrum, characterized by overtones and combinations of the C-H, N-H, and O-H groups using different combinations of a light source, imaging spectrograph and camera, there still needs improvement to overcome the disadvantages of HSI technology for further applications at the industrial level.
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Affiliation(s)
- Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China.,Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China
| | - Qingyi Wei
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China.,Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China.,Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China.,Food Refrigeration and Computerized Food Technology, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Ireland
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Kong D, Shi Y, Sun D, Zhou L, Zhang W, Qiu R, He Y. Hyperspectral imaging coupled with CNN: A powerful approach for quantitative identification of feather meal and fish by-product meal adulterated in marine fishmeal. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107517] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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