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Lytou A, Fengou LC, Koukourikos A, Karampiperis P, Zervas P, Schultz Carstensen A, Del Genio A, Michael Carstensen J, Schultz N, Chorianopoulos N, Nychas GJ. Seabream quality monitoring throughout the supply chain using a portable multispectral imaging device. J Food Prot 2024:100274. [PMID: 38583716 DOI: 10.1016/j.jfp.2024.100274] [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: 01/27/2024] [Revised: 03/29/2024] [Accepted: 04/02/2024] [Indexed: 04/09/2024]
Abstract
Monitoring food quality throughout the supply chain in a rapid and cost-effective way allows on-time decision making, reducing food waste and increasing sustainability. In that framework, a portable multispectral imaging sensor was used, while the acquired data in combination with neural networks were evaluated for the prediction of fish fillets quality. Images of fish fillets were acquired using samples from both aquaculture and retail stores of different packaging and fish parts. The obtained products (air or vacuum packaged) were further stored at different temperature conditions. In parallel to image acquisition, microbial quality was estimated as well. The data were used for the training of predictive neural models that aimed to estimate total aerobic counts (TAC). The models were developed and validated using data from aquaculture and were externally validated with samples purchased from the retail stores. The set up allowed the evaluation of models for the different parts of the fish and conditions. The performance for the validation set was similar for flesh (RMSE: 0.402-0.547) and skin side (RMSE: 0.500-0.533) of the fish fillets. The performance for the different packaging conditions was also similar, however, in the external validation, the vacuum-packaged samples showed better performance in terms of RMSE compared to the air-packaged ones. Models irrespective of packaging condition are very important for cases where the products' history is unknown although the prediction capability was not as high as in the models per packaging condition individually. The models tested with unknown samples (i.e., from retail stores) showed poorer performance (RMSE: 1.061-1.414) compared to the models validated with data partitioning (RMSE: 0.402-0.547). Multispectral imaging sensor appeared to be efficient for the rapid assessment of the microbiological quality of fish fillets for all the different cases evaluated.
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Affiliation(s)
- Anastasia Lytou
- 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
| | - 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
| | - Antonis Koukourikos
- SCiO P.C., Technology Park Lefkippos, P. Grigoriou & Neapoleos Str., Agia Paraskevi, Greece, GR-15310
| | - Pythagoras Karampiperis
- SCiO P.C., Technology Park Lefkippos, P. Grigoriou & Neapoleos Str., Agia Paraskevi, Greece, GR-15310
| | - Panagiotis Zervas
- SCiO P.C., Technology Park Lefkippos, P. Grigoriou & Neapoleos Str., Agia Paraskevi, Greece, GR-15310
| | | | | | | | - Nette Schultz
- Videometer A/S, Hørkær 12B 3., DK-2730 Herlev, Denmark
| | - Nikos Chorianopoulos
- 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 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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Eftekhariyazdi M, Zenoozian MS, Milani E, Elhami Rad AH, Armin M. Optimization of the extrusion parameters for the production of lentil-quinoa extrudates enriched with pumpkin. FOOD SCI TECHNOL INT 2024:10820132241243240. [PMID: 38556930 DOI: 10.1177/10820132241243240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
This study aimed to develop a protein-fiber-rich extruded product based on yellow lentil, quinoa, and pumpkin flours. The final product quality is affected by formulation and extrusion parameters. Therefore, the effect of the pumpkin-flour ratio (A: 25-75%) and feed moisture content (C: 14-22%) besides barrel screw speed (B: 120-180 rpm) on the physical attributes of extrudates was investigated. Box-Behnken experimental design and stepwise-response surface method were used to analyze the effects of various process variables and ingredients on extrudates. The pumpkin-flour ratio had a significant positive correlation with bulk density (BD), water solubility index (WSI), and oil absorption index. Whereas the correlation between pumpkin-flour ratio with hardness, porosity, expansion ratio (ER), and water absorption index (WAI) was negative (P < 0.05). The feed moisture content positively affected the water activity (aw) and WAI and negatively affected the harness of samples (P < 0.05). The screw speed had a positive effect on ER, porosity, and WSI, whereas it negatively influenced the hardness, BD, and aw. By increasing the pumpkin-flour ratio, air cell size and wall thickness of samples had been decreased. The results showed that 44.2% pumpkin flour, 22% feed moisture, and 172.1 rpm screw speed gave an optimized product. There was no significant difference between predicted and experimental values (except for ER). The optimized snack was a good source of fiber (around 15%), protein (17.3%), and antioxidants (TPC = 15.28 mg GAE.g-1 and antiradical scavenging activity (DPPH) = 33.66%). The caloric value of the optimized snack was 362.6 cal.100g-1. The current formulation can be considered as the base of snack food or plant-based meat alternatives.
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Affiliation(s)
- Minoo Eftekhariyazdi
- Department of Food Science and Technology, Sabzevar Branch, Islamic Azad University, Sabzevar, Iran
| | - Masoud Shafafi Zenoozian
- Department of Food Science and Technology, Sabzevar Branch, Islamic Azad University, Sabzevar, Iran
| | - Elnaz Milani
- Department of Food Processing, Iranian Academic Center for Education Culture and Research, Mashhad, Iran
| | - Amir Hossein Elhami Rad
- Department of Food Science and Technology, Sabzevar Branch, Islamic Azad University, Sabzevar, Iran
| | - Mohammad Armin
- Department of Agronomy and Plant Breeding, Sabzevar Branch, Islamic Azad University, Sabzevar, Iran
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Zhang X, Gong Z, Liang X, Sun W, Ma J, Wang H. Line Laser Scanning Combined with Machine Learning for Fish Head Cutting Position Identification. Foods 2023; 12:4518. [PMID: 38137322 PMCID: PMC10742530 DOI: 10.3390/foods12244518] [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: 11/29/2023] [Revised: 12/09/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023] Open
Abstract
Fish head cutting is one of the most important processes during fish pre-processing. At present, the identification of cutting positions mainly depends on manual experience, which cannot meet the requirements of large-scale production lines. In this paper, a fast and contactless identification method of cutting position was carried out by using a constructed line laser data acquisition system. The fish surface data were collected by a linear laser scanning sensor, and Principal Component Analysis (PCA) was used to reduce the dimensions of the dorsal and abdominal boundary data. Based on the dimension data, Least Squares Support Vector Machines (LS-SVMs), Particle Swarm Optimization-Back Propagation (PSO-BP) networks, and Long and Short Term Memory (LSTM) neural networks were applied for fish head cutting position identification model establishment. According to the results, the LSTM model was considered to be the best prediction model with a determination coefficient (R2) value, root mean square error (RMSE), mean absolute error (MAE), and residual predictive deviation (RPD) of 0.9480, 0.2957, 0.1933, and 3.1426, respectively. This study demonstrated the reliability of combining line laser scanning techniques with machine learning using LSTM to identify the fish head cutting position accurately and quickly. It can provide a theoretical reference for the development of intelligent processing and intelligent cutting equipment for fish.
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Affiliation(s)
- Xu Zhang
- School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian 116034, China; (X.Z.); (Z.G.); (W.S.); (J.M.)
| | - Ze Gong
- School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian 116034, China; (X.Z.); (Z.G.); (W.S.); (J.M.)
| | - Xinyu Liang
- School of Food Science & Technology, Dalian Polytechnic University, Dalian 116034, China;
| | - Weichen Sun
- School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian 116034, China; (X.Z.); (Z.G.); (W.S.); (J.M.)
| | - Junxiao Ma
- School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian 116034, China; (X.Z.); (Z.G.); (W.S.); (J.M.)
| | - Huihui Wang
- School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian 116034, China; (X.Z.); (Z.G.); (W.S.); (J.M.)
- National Engineering Research Center of Seafood, Dalian 116034, China
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Hyperspectral imaging combined with convolutional neural network for accurately detecting adulteration in Atlantic salmon. Food Control 2023. [DOI: 10.1016/j.foodcont.2022.109573] [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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5
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Jin S, Liu X, Wang J, Pan L, Zhang Y, Zhou G, Tang C. Hyperspectral imaging combined with fluorescence for the prediction of microbial growth in chicken breasts under different packaging conditions. Lebensm Wiss Technol 2023. [DOI: 10.1016/j.lwt.2023.114727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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6
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Prediction of TVB-N content in beef with packaging films using visible-near infrared hyperspectral imaging. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Fusion of electronic nose and hyperspectral imaging for mutton freshness detection using input-modified convolution neural network. Food Chem 2022; 385:132651. [DOI: 10.1016/j.foodchem.2022.132651] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 02/01/2022] [Accepted: 03/04/2022] [Indexed: 01/19/2023]
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Wan G, Fan S, Liu G, He J, Wang W, Li Y, lijuan Cheng, Ma C, Guo M. Fusion of spectra and texture data of hyperspectral imaging for prediction of myoglobin content in nitrite-cured mutton. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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9
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Rapid evaluation of texture parameters of Tan mutton using hyperspectral imaging with optimization algorithms. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.108815] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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10
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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 2022; 12:bios12020076. [PMID: 35200337 PMCID: PMC8869398 DOI: 10.3390/bios12020076] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [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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Moosavi-Nasab M, Khoshnoudi-Nia S. Combining Knowledge- and Data-Driven Fuzzy Approach to Evaluate Shelf-Life of Various Seafood Products. FOOD QUALITY AND SAFETY 2021. [DOI: 10.1093/fqsafe/fyab022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Abstract
Due to the complexity of the deterioration process of seafood products, relying on one indicator is not adequate to determine the quality of such products. Usually, shelf-life was estimated based on various indicators complicating the decision-making process. Decision Support Systems are considered as a good solution. The current study aims to establish a simple and novel fuzzy model based on a combination of knowledge- and data-driven approaches to define a fuzzy quality deterioration index (FQDI) in various seafood products (rainbow trout, threadfin bream, and white shrimp samples) during cold storage. Total volatile basic nitrogen (TVB-N) and psychrotrophic microorganisms counts (PMCs) were determined based on traditional methods. The sensory analysis was performed by a data-driven fuzzy approach. Overall, the shelf-life of the rainbow trout fillet was estimated to be 8 days, based on all the freshness parameters. However, the shelf-life of the Japanese threadfin bream fillet was 5–7 days according to the microbial and chemical parameters, respectively. This time for shrimp samples was 6–8 days using sensory score and TVB-N contents. The results of data-driven fuzzy approach showed all of the quality properties were considered as the ‘Important’–‘Very Important’ (defuzzification score >75). The TVB-N and PMCs were the most and weakest freshness quality properties (defuzzified-values: 84.64 and 78.75, respectively). Based on FQDI, the shelf-life of the rainbow trout, Japanese threadfin bream, and shrimp samples were estimated to be 8, 5, and 7 days, respectively. This method was able to successfully provide a comprehensive deterioration index for evaluating the seafood shelf-life. Such a total index can be considered as a comprehensive output (y variable) to predict seafood freshness by rapid and nondestructive method.
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Affiliation(s)
- Marzieh Moosavi-Nasab
- Department of Food Science and Technology, School of Agriculture, Shiraz University, Shiraz, Iran
- Seafood Processing Research Group, School of Agriculture, Shiraz University, Shiraz, Iran
| | - Sara Khoshnoudi-Nia
- Seafood Processing Research Group, School of Agriculture, Shiraz University, Shiraz, Iran
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12
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Tonezzer M, Thai NX, Gasperi F, Van Duy N, Biasioli F. Quantitative Assessment of Trout Fish Spoilage with a Single Nanowire Gas Sensor in a Thermal Gradient. NANOMATERIALS 2021; 11:nano11061604. [PMID: 34207259 PMCID: PMC8235061 DOI: 10.3390/nano11061604] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 06/11/2021] [Accepted: 06/17/2021] [Indexed: 12/24/2022]
Abstract
The response of a single tin oxide nanowire was collected at different temperatures to create a virtual array of sensors working as a nano-electronic nose. The single nanowire, acting as a chemiresistor, was first tested with pure ammonia and then used to determine the freshness status of trout fish (Oncorhynchus mykiss) in a rapid and non-invasive way. The gas sensor reacts to total volatile basic nitrogen, detecting the freshness status of the fish samples in less than 30 s. The sensor response at different temperatures correlates well with the total viable count (TVC), demonstrating that it is a good (albeit indirect) way of measuring the bacterial population in the sample. The nano-electronic nose is not only able to classify the samples according to their degree of freshness but also to quantitatively estimate the concentration of microorganisms present. The system was tested with samples stored at different temperatures and classified them perfectly (100%), estimating their log(TVC) with an error lower than 5%.
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Affiliation(s)
- Matteo Tonezzer
- Research and Innovation Centre, Department of Food Quality and Nutrition, Fondazione Edmund Mach, Via E. Mach 1, 38098 San Michele all’Adige, Italy; (F.G.); (F.B.)
- Center Agriculture Food Environment, University of Trento/Fondazione Edmund Mach, Via E. Mach 1, 38010 San Michele all’Adige, Italy
- IMEM-CNR, Sede di Trento—FBK, Via alla Cascata 56/C, Povo, 38123 Trento, Italy
- Correspondence: (M.T.); (N.V.D.)
| | - Nguyen Xuan Thai
- International Training Institute for Materials Science, Hanoi University of Science and Technology, Hanoi 100000, Vietnam;
| | - Flavia Gasperi
- Research and Innovation Centre, Department of Food Quality and Nutrition, Fondazione Edmund Mach, Via E. Mach 1, 38098 San Michele all’Adige, Italy; (F.G.); (F.B.)
- Center Agriculture Food Environment, University of Trento/Fondazione Edmund Mach, Via E. Mach 1, 38010 San Michele all’Adige, Italy
| | - Nguyen Van Duy
- International Training Institute for Materials Science, Hanoi University of Science and Technology, Hanoi 100000, Vietnam;
- Correspondence: (M.T.); (N.V.D.)
| | - Franco Biasioli
- Research and Innovation Centre, Department of Food Quality and Nutrition, Fondazione Edmund Mach, Via E. Mach 1, 38098 San Michele all’Adige, Italy; (F.G.); (F.B.)
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Moosavi-Nasab M, Khoshnoudi-Nia S, Azimifar Z, Kamyab S. Evaluation of the total volatile basic nitrogen (TVB-N) content in fish fillets using hyperspectral imaging coupled with deep learning neural network and meta-analysis. Sci Rep 2021; 11:5094. [PMID: 33658634 PMCID: PMC7930251 DOI: 10.1038/s41598-021-84659-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 01/25/2021] [Indexed: 11/09/2022] Open
Abstract
Recently, hyperspectral-imaging (HSI), as a rapid and non-destructive technique, has generated much interest due to its unique potential to monitor food quality and safety. The specific aim of the study is to investigate the potential of the HSI (430-1010 nm) coupled with Linear Deep Neural Network (LDNN) to predict the TVB-N content of rainbow trout fillet during 12 days storage at 4 ± 2 °C. After the acquisition of hyperspectral images, the TVB-N content of fish fillets was obtained by a conventional method (micro-Kjeldahl distillation). To simplify the calibration models, nine optimal wavelengths were selected by the successive projections algorithm. A seven layers LDNN was designed to estimate the TVB-N content of samples. The LDNN model showed acceptable performance for prediction of TVB-N content of fish fillet (R2p = 0.853; RSMEP = 3.159 and RDP = 3.001). The performance of LDNN model was comparable with the results of previous works. Although, the results of the meta-analysis did not show any significant difference between various chemometric models. However, the least-squares support vector machine algorithm showed better prediction results as compared to the other models (RMSEP: 2.63 and R2p = 0.897). Further studies are required to improve the prediction power of the deep learning model for prediction of rainbow-trout fish quality.
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Affiliation(s)
- Marzieh Moosavi-Nasab
- Seafood Processing Research Group, Department of Food Science and Technology, School of Agriculture, Shiraz University, P.O. Box 71441-65186, Shiraz, Iran.
| | - Sara Khoshnoudi-Nia
- Seafood Processing Research Group, School of Agriculture, Shiraz University, P.O. Box 71441-65186, Shiraz, Iran
| | - Zohreh Azimifar
- Department of Computer Science and Engineering, Shiraz University, P.O. Box 71936-16548, Shiraz, Iran
| | - Shima Kamyab
- Department of Computer Science and Engineering, Shiraz University, P.O. Box 71936-16548, Shiraz, Iran
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New method for rapid identification and quantification of fungal biomass using ergosterol autofluorescence. Talanta 2020; 219:121238. [PMID: 32887129 DOI: 10.1016/j.talanta.2020.121238] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 05/28/2020] [Accepted: 05/29/2020] [Indexed: 01/06/2023]
Abstract
This research reports on the development of a method to identify and quantify fungal biomass based on ergosterol autofluorescence using excitation-emission matrix (EEM) measurements. In the first stage of this work, several ergosterol extraction methods were evaluated by APCI-MS, where the ultrasound-assisted procedure showed the best results. Following an experimental design, various quantities of the dried mycelium of the fungus Schizophyllum commune were mixed with the starchy solid residue (BBR) from the babassu (Orbignya sp.) oil industry, and these samples were subjected to several ergosterol extraction methods. The EEM spectral data of the samples were subjected to Principal Component Analysis (PCA), which showed the possibility to qualitatively evaluate the presence of ergosterol in the samples by ergosterol autofluorescence without the addition of any reagent. In order to assess the feasibility of quantifying fungal biomass using ergosterol autofluorescence, the EEM spectral data and known amounts of fungal biomass were modeled using partial least squares (PLS) regression and a procedure of backward selection of predictors (AutoPLS) was applied to select the Excitation-Emission wavelength pairs that provide the lowest prediction error. The results revealed that the amount of fungal biomass in samples containing interfering substances (BBR) can be accurately predicted with R2CV = 0.939, R2P = 0.936, RPDcv = 4.07, RPDp = 4.06, RMSECV = 0.0731 and RMSEP = 0.0797. In order to obtain an easy-to-understand equation that expresses the relationship between fungal biomass and fluorescence intensity, multiple linear regression (MLR) was applied to the VIP variables selected by the AutoPLS method. The MLR model selected only 2 variables and showed a very good performance, with R2CV = 0.862, R2P = 0.809, RPDcv = 2.18, RPDp = 2.35, RMSECV = 0.137 and RMSEP = 0.138. This study demonstrated that ergosterol autofluorescence can be successfully used to quantify fungal biomass even when mixed with agroindustrial residues, in this case BBR.
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Liu Q, Zhou D, Tu S, Xiao H, Zhang B, Sun Y, Pan L, Tu K. Quantitative Visualization of Fungal Contamination in Peach Fruit Using Hyperspectral Imaging. FOOD ANAL METHOD 2020. [DOI: 10.1007/s12161-020-01747-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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16
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Khoshnoudi-Nia S, Moosavi-Nasab M. Prediction of various freshness indicators in fish fillets by one multispectral imaging system. Sci Rep 2019; 9:14704. [PMID: 31605023 PMCID: PMC6789145 DOI: 10.1038/s41598-019-51264-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 09/29/2019] [Indexed: 01/16/2023] Open
Abstract
In current study, a simple multispectral imaging (430–1010 nm) system along with linear and non-linear regressions were used to assess the various fish spoilage indicators during 12 days storage at 4 ± 2 °C. The indicators included Total-Volatile Basic Nitrogen (TVB-N) and Psychrotrophic Plate Count (PPC) and sensory score in fish fillets. immediately, after hyperspectral imaging, the reference values (TVB-N, PPC and sensory score) of samples were obtained by traditional method. To simplify the calibration models, nine optimal wavelengths were selected by genetic algorithm. The prediction performance of various chemometric models including partial least-squares regression (PLSR), multiple-linear regression (MLR), least-squares support vector machine (LS-SVM) and back-propagation artificial neural network (BP-ANN) were compared. All models showed acceptable performance for simultaneous predicting of PPC, TVB-N and sensory score (R2P ≥ 0.853 and RPD ≥ 2.603). Non-linear models were considered better quantitative model to predict all of three freshness indicators in fish fillets. Among the three spoilage indices, the best predictive power was obtained for PPC value and the weakest one was acquired for TVB-N content prediction. The best model for prediction TVB-N (R2p = 0.862; RMSEP = 3.542 and RPD = 2.678) and sensory score (R2p = 0.912; RMSEP = 1.802 and RPD = 3.33) belonged to GA-LS-SVM and for prediction of PPC value was BP-ANN (R2p = 0.921; RMSEP = 0.504 and RPD = 3.64). Therefore, developing multispectral imaging system based on LS-SVM model seems to be suitable for simultaneous prediction of all three indicators (R2P > 0.862 and RPD > 2.678). Further studies needed to improve the accuracy and applicability of HSI system for predicting freshness of rainbow-trout fish.
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Affiliation(s)
- Sara Khoshnoudi-Nia
- Seafood Processing Research Group, School of Agriculture, Shiraz University, PO Box: 71441-65186, Shiraz, Iran.
| | - Marzieh Moosavi-Nasab
- Seafood Processing Research Group & Department of Food Science and Technology, School of Agriculture, Shiraz University, PO Box: 71441-65186, Shiraz, Iran.
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Khoshnoudi‐Nia S, Moosavi‐Nasab M. Comparison of various chemometric analysis for rapid prediction of thiobarbituric acid reactive substances in rainbow trout fillets by hyperspectral imaging technique. Food Sci Nutr 2019; 7:1875-1883. [PMID: 31139402 PMCID: PMC6526668 DOI: 10.1002/fsn3.1043] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 03/13/2019] [Accepted: 03/27/2019] [Indexed: 12/31/2022] Open
Abstract
This study explores the potential application of hyperspectral imaging (HSI; 430-1,010 nm) coupled with different linear and nonlinear models for rapid nondestructive evaluation of thiobarbituric acid-reactive substances (TBARS) value in rainbow trout (Oncorhynchus mykiss) fillets during 12 days of cold storage (4 ± 2°C). HSI data and TBARS value of fillets were obtained in the laboratory. The primary prediction models were established based on linear partial least squares regression (PLSR) and least squares support vector machine (LS-SVM). In full spectral range, the prediction capability of LS-SVM ( R P 2 = 0.829; RMSEP = 0.128 mg malondialdehyde [MDA]/kg) was better than PLSR ( R P 2 = 0.748; RMSEP = 0.155 mg MDA/kg) model and LS-SVM model exhibited satisfactory prediction performance ( R P 2 > 0.82). To simplify the calibration models, a combination of uninformative variable elimination and backward regression (UB) was used as variable selection. Nine wavelengths were selected. Various chemometric analysis methods including linear PLSR and multiple linear regression and nonlinear LS-SVM and back-propagation artificial neural network (BP-ANN) were compared. The simplified models showed better capability than those were built based on the whole dataset in prediction of TBARS values. Moreover, the nonlinear models were preferred over linear models. Among the four chemometric algorithms, the best and weakest models were LS-SVM and PLSR model, respectively. UB-LS-SVM model was the optimal models for predicting TBARS value in rainbow trout fillets ( R P 2 = 0.831; RMSEP = 0.125 mg MDA/kg). The establishing of lipid-oxidation prediction model in rainbow trout fish was complicated, due to the fluctuations of TBARS values during storage. Therefore, further researches are needed to improve the prediction results and applicability of HIS technique for prediction of TBARS value in rainbow trout fish.
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Affiliation(s)
- Sara Khoshnoudi‐Nia
- Seafood Processing Research Group & Department of Food Science and Technology, School of AgricultureShiraz UniversityShirazIran
| | - Marzieh Moosavi‐Nasab
- Seafood Processing Research Group & Department of Food Science and Technology, School of AgricultureShiraz UniversityShirazIran
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