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Asadi M, Ghasemnezhad M, Bakhshipour A, Olfati JA, Mirjalili MH. Predicting the quality attributes related to geographical growing regions in red-fleshed kiwifruit by data fusion of electronic nose and computer vision systems. BMC Plant Biol 2024; 24:13. [PMID: 38163882 PMCID: PMC10759769 DOI: 10.1186/s12870-023-04661-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 12/04/2023] [Indexed: 01/03/2024]
Abstract
The ability of a data fusion system composed of a computer vision system (CVS) and an electronic nose (e-nose) was evaluated to predict key physiochemical attributes and distinguish red-fleshed kiwifruit produced in three distinct regions in northern Iran. Color and morphological features from whole and middle-cut kiwifruits, along with the maximum responses of the 13 metal oxide semiconductor (MOS) sensors of an e-nose system, were used as inputs to the data fusion system. Principal component analysis (PCA) revealed that the first two principal components (PCs) extracted from the e-nose features could effectively differentiate kiwifruit samples from different regions. The PCA-SVM algorithm achieved a 93.33% classification rate for kiwifruits from three regions based on data from individual e-nose and CVS. Data fusion increased the classification rate of the SVM model to 100% and improved the performance of Support Vector Regression (SVR) for predicting physiochemical indices of kiwifruits compared to individual systems. The data fusion-based PCA-SVR models achieved validation R2 values ranging from 90.17% for the Brix-Acid Ratio (BAR) to 98.57% for pH prediction. These results demonstrate the high potential of fusing artificial visual and olfactory systems for quality monitoring and identifying the geographical growing regions of kiwifruits.
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Affiliation(s)
- Mojdeh Asadi
- Department of Horticultural Sciences, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
| | - Mahmood Ghasemnezhad
- Department of Horticultural Sciences, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.
| | - Adel Bakhshipour
- Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.
| | - Jamal-Ali Olfati
- Department of Horticultural Sciences, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
| | - Mohammad Hossein Mirjalili
- Department of Agriculture, Medicinal Plants and Drugs Research Institute, Shahid Beheshti University, Tehran, Iran
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Fan KJ, Su WH. Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review. Biosensors (Basel) 2022; 12:bios12020076. [PMID: 35200337 PMCID: PMC8869398 DOI: 10.3390/bios12020076] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/21/2022] [Accepted: 01/26/2022] [Indexed: 05/12/2023]
Abstract
Fluorescence spectroscopy, color imaging and multispectral imaging (MSI) have emerged as effective analytical methods for the non-destructive detection of quality attributes of various white meat products such as fish, shrimp, chicken, duck and goose. Based on machine learning and convolutional neural network, these techniques can not only be used to determine the freshness and category of white meat through imaging and analysis, but can also be used to detect various harmful substances in meat products to prevent stale and spoiled meat from entering the market and causing harm to consumer health and even the ecosystem. The development of quality inspection systems based on such techniques to measure and classify white meat quality parameters will help improve the productivity and economic efficiency of the meat industry, as well as the health of consumers. Herein, a comprehensive review and discussion of the literature on fluorescence spectroscopy, color imaging and MSI is presented. The principles of these three techniques, the quality analysis models selected and the research results of non-destructive determinations of white meat quality over the last decade or so are analyzed and summarized. The review is conducted in this highly practical research field in order to provide information for future research directions. The conclusions detail how these efficient and convenient imaging and analytical techniques can be used for non-destructive quality evaluation of white meat in the laboratory and in industry.
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Martinho VJPD, Bartkiene E, Djekic I, Tarcea M, Barić IC, Černelič-Bizjak M, Szűcs V, Sarcona A, El-Kenawy A, Ferreira V, Klava D, Korzeniowska M, Vittadini E, Leal M, Bolhuis D, Papageorgiou M, Guiné RPF. Determinants of economic motivations for food choice: insights for the understanding of consumer behaviour. Int J Food Sci Nutr 2021; 73:127-139. [PMID: 34148490 DOI: 10.1080/09637486.2021.1939659] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Food consumption involves several dimensions, being some of them directly associated with the consumers' characteristics. The interrelationships between these domains impact consumer behaviour for food choice and the consequent decisions for food consumption. In these frameworks, economic motivations are determinant. On the other hand, the scientific literature highlights that the economic-based stimuli to choose food is still underexplored. In this perspective, the objective of this study was to assess the main sociodemographic and anthropometric determinants of the economic motivations for food choice. For that, a questionnaire survey was carried out involving 11,919 respondents from 16 countries. A validated questionnaire was used, translated into the native languages in all participating countries, using a back-translation process. First, the information obtained was assessed through factor analysis to reduce the number of variables associated with the economic motivations and to identify indexes. After, and considering the indexes obtained as dependent variables, a classification and regression tree analysis was performed. As main insights, it is highlighted that the main determinants of the economic motivations are country of residence, age, gender, civil state, professional activity, educational level, living environment, responsibility for buying food, weight, height, body mass index, healthy diets and physical exercise practices. Additionally, the results also reveal that economic motivations may be associated with two indexes, one related to convenience attitudes and the other to quality concerns. Finally, the younger persons and the women are the social groups more concerned with healthy diets and food quality. In conclusion, this work confirmed that food choice is to a high extent influenced by several sociodemographic and behavioural factors.
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Affiliation(s)
- Vítor J P D Martinho
- Agricultural School and CERNAS-IPV Research Centre, Polytechnic Institute of Viseu, Viseu, Portugal
| | - Elena Bartkiene
- Department of Food Safety and Quality, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Ilija Djekic
- Faculty of Agriculture, University of Belgrade, Belgrade, Serbia
| | - Monica Tarcea
- Department of Community Nutrition and Food Safety, University of Medicine, Pharmacy, Science and Technology, Targu-Mures, Romania
| | - Irena Colić Barić
- Faculty of Food Technology and Biotechnology, University of Zagreb, Zagreb, Croatia
| | | | - Viktória Szűcs
- Directorate of Food Industry, Hungarian Chamber of Agriculture, Budapest, Hungary
| | - Alessandra Sarcona
- Department of Nutrition, West Chester University of Pennsylvania, West Chester, PA, USA
| | - Ayman El-Kenawy
- Molecular Biology Department, Genetic Engineering and Biotechnology Institute, University of Sadat City, Sadat, Egypt
| | - Vanessa Ferreira
- Department of Nutrition, Faculty of Biological and Health Sciences, UFVJM University, Diamantina, Brazil
| | - Dace Klava
- Faculty of Food Technology, Latvian University of Agriculture, Jelgava, Latvia
| | - Małgorzata Korzeniowska
- Faculty of Biotechnology and Food Science, Wrocław University of Environmental and Life Sciences, Wroclaw, Poland
| | - Elena Vittadini
- School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy
| | - Marcela Leal
- Red IESVIDAS (Investigación en Estilos de Vida Saludable)/CONINUT (Consorcio de Investigadores en Nutriología), Buenos Aires, Argentina
| | - Dieuwerke Bolhuis
- Food Quality and Design Group, Wageningen University & Research, Wageningen, The Netherlands
| | - Maria Papageorgiou
- Department of Food Science and Technology, International Hellenic University, Thessaloniki, Greece
| | - Raquel P F Guiné
- Agricultural School and CERNAS-IPV Research Centre, Polytechnic Institute of Viseu, Viseu, Portugal
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Kunjulakshmi S, Harikrishnan S, Murali S, D'Silva JM, Binsi P, Murugadas V, Alfiya P, Delfiya DA, Samuel MP. Development of portable, non-destructive freshness indicative sensor for Indian Mackerel (Rastrelliger kanagurta) stored under ice. J FOOD ENG 2020. [DOI: 10.1016/j.jfoodeng.2020.110132] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Ye R, Chen Y, Guo Y, Duan Q, Li D, Liu C. NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Identify Shrimp Freshness. Applied Sciences 2020; 10:5498. [DOI: 10.3390/app10165498] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In this study, a hyperspectral imaging system of 866.4–1701.0 nm, combined with a variety of spectral processing methods were adopted to identify shrimp freshness. To gain the optimal model combination, three preprocessing methods (Savitzky-Golay first derivative (SG1), multivariate scatter correction (MSC), and standard normal variate (SNV)), three characteristic wavelength extraction algorithms (random frog algorithm (RFA), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS)), and four discriminant models (partial least squares discrimination analysis (PLS-DA), least squares support vector machine (LSSVM), random forest (RF), and extreme learning machine (ELM)) were employed for experimental study. First of all, due to the full wavelength modeling analysis, three preprocessing methods were utilized to preprocess the original spectral data. The analysis showed that the spectral data processed by the SNV method had the best performance among the four discriminant models. Secondly, due to the characteristic wavelength modeling analysis, three characteristic wavelength extraction algorithms were utilized to extract the characteristic wavelength of the SNV-processed spectral data. It was found that the CARS algorithm achieved the best performance among the three characteristic wavelength extraction algorithms, and the combining adoption of the ELM model and different characteristic wavelength extraction algorithms obtained the best results. Therefore, the model based on SNV-CARS-ELM obtained the best performance and was elected as the optimal model. Lastly, for accurately and explicitly displaying the refrigeration days of shrimps, the original hyperspectral images of shrimps were substituted into the SNV-CARS-ELM model, thus obtaining the general classification accuracy of 97.92%, and the object-wise method was used to visualize the classification results. As a result, the method proposed in this study can effectively detect the freshness of shrimps.
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Sengar N, Dutta MK, Sarkar B. Computer vision based technique for identification of fish quality after pesticide exposure. International Journal of Food Properties 2017. [DOI: 10.1080/10942912.2017.1368553] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Namita Sengar
- Department of Electronics & Communication Engineering, Amity University, Noida, India
| | - Malay Kishore Dutta
- Department of Electronics & Communication Engineering, Amity University, Noida, India
| | - Biplab Sarkar
- ICAR - Indian Institute of Agricultural Biotechnology, Ranchi, Jharkhand, India
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