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Matenda RT, Rip D, Marais J, Williams PJ. Exploring the potential of hyperspectral imaging for microbial assessment of meat: A review. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 315:124261. [PMID: 38608560 DOI: 10.1016/j.saa.2024.124261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 04/04/2024] [Accepted: 04/06/2024] [Indexed: 04/14/2024]
Abstract
Food safety is always of paramount importance globally due to the devasting social and economic effects of foodborne disease outbreaks. There is a high consumption rate of meat worldwide, making it an essential protein source in the human diet, hence its microbial safety is of great importance. The food industry stakeholders are always in search of methods that ensure safe food whilst maintaining food quality and excellent sensory attributes. Currently, there are several methods used in microbial food analysis, however, these methods are often time-consuming and do not allow real-time analysis. Considering the recent technological breakthroughs in artificial intelligence and machine learning, it raises the question of whether these advancements could be leveraged within the meat industry to improve turnaround time for microbial assessments. Hyperspectral imaging (HSI) is a highly prospective technology worth exploring for microbial analysis. The rapid, non-destructive method has the potential to be integrated into food production systems and allows foodborne pathogen detection in food samples, thus saving time. Although there has been a substantial increase in research on the utilisation of HSI in food applications over the past years, its use in the microbial assessment of meat is not yet optimal. This review aims to provide a basic understanding of the visible-near infrared HSI system, recent applications in the microbial assessment of meat products, challenges, and possible future applications.
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Affiliation(s)
- Rumbidzai T Matenda
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Diane Rip
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Jeannine Marais
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Paul J Williams
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa.
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2
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Zhang Z, Li Y, Zhao S, Qie M, Bai L, Gao Z, Liang K, Zhao Y. Rapid analysis technologies with chemometrics for food authenticity field: A review. Curr Res Food Sci 2024; 8:100676. [PMID: 38303999 PMCID: PMC10830540 DOI: 10.1016/j.crfs.2024.100676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 12/15/2023] [Accepted: 01/07/2024] [Indexed: 02/03/2024] Open
Abstract
In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.
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Affiliation(s)
- Zixuan Zhang
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yalan Li
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Shanshan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Mengjie Qie
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lu Bai
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Zhiwei Gao
- Hangzhou Nutritome Biotech Co., Ltd., Hangzhou, China
| | - Kehong Liang
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
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Sijilmassi O, López Alonso JM, Del Río Sevilla A, Barrio Asensio MDC. Multispectral Imaging Method for Rapid Identification and Analysis of Paraffin-Embedded Pathological Tissues. J Digit Imaging 2023; 36:1663-1674. [PMID: 37072579 PMCID: PMC10406798 DOI: 10.1007/s10278-023-00826-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 03/28/2023] [Accepted: 03/30/2023] [Indexed: 04/20/2023] Open
Abstract
The study of the interaction between light and biological tissue is of great help in the identification of diseases as well as structural alterations in tissues. In the present study, we have developed a tissue diagnostic technique by using multispectral imaging in the visible spectrum combined with principal component analysis (PCA). We used information from the propagation of light through paraffin-embedded tissues to assess differences in the eye tissues of control mouse embryos compared to mouse embryos whose mothers were deprived of folic acid (FA), a crucial vitamin necessary for the growth and development of the fetus. After acquiring the endmembers from the multispectral images, spectral unmixing was used to identify the abundances of those endmembers in each pixel. For each acquired image, the final analysis was performed by performing a pixel-by-pixel and wavelength-by-wavelength absorbance calculation. Non-negative least squares (NNLS) were used in this research. The abundance maps obtained for the first endmember revealed vascular alterations (vitreous and choroid) in the embryos with maternal FA deficiency. However, the abundance maps obtained for the third endmember showed alterations in the texture of some tissues such as the lens and retina. Results indicated that multispectral imaging applied to paraffin-embedded tissues enhanced tissue visualization. Using this method, first, it can be seen tissue damage location and then decide what kind of biological techniques to apply.
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Affiliation(s)
- Ouafa Sijilmassi
- Faculty of Optics and Optometry, Anatomy and Embryology Department, Universidad Complutense de Madrid, Madrid, Spain.
- Optics Department, Faculty of Optics and Optometry, Universidad Complutense De Madrid, Madrid, Spain.
| | - José-Manuel López Alonso
- Optics Department, Faculty of Optics and Optometry, Universidad Complutense De Madrid, Madrid, Spain
| | - Aurora Del Río Sevilla
- Faculty of Optics and Optometry, Anatomy and Embryology Department, Universidad Complutense de Madrid, Madrid, Spain
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Pu H, Yu J, Sun DW, Wei Q, Shen X, Wang Z. Distinguishing Fresh and Frozen-thawed Beef Using Hyperspectral Imaging Technology Combined with Convolutional Neural Networks. Microchem J 2023. [DOI: 10.1016/j.microc.2023.108559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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5
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Hyperspectral Imaging Coupled with Multivariate Analyses for Efficient Prediction of Chemical, Biological and Physical Properties of Seafood Products. FOOD ENGINEERING REVIEWS 2023. [DOI: 10.1007/s12393-022-09327-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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6
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Zou W, Peng Y, Yang D, Zuo J, Li Y, Guo Q. An Intelligent Detector for Sensing Pork Freshness In Situ Based on a Multispectral Technique. BIOSENSORS 2022; 12:998. [PMID: 36354507 PMCID: PMC9688451 DOI: 10.3390/bios12110998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/07/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
Fresh pork is prone to spoilage during storage, transportation, and sale, resulting in reduced freshness. The total viable count (TVC) and total volatile basic nitrogen (TVB-N) content are key indicators for evaluating the freshness of fresh pork, and when they reach unacceptable limits, this seriously threatens dietary safety. To realize the on-site, low-cost, rapid, and non-destructive testing and evaluation of fresh pork freshness, a miniaturized detector was developed based on a cost-effective multi-channel spectral sensor. The partial least squares discriminant analysis (PLS-DA) model was used to distinguish fresh meat from deteriorated meat. The detector consists of microcontroller, light source, multi-channel spectral sensor, heat-dissipation modules, display system, and battery. In this study, the multispectral data of pork samples with different freshness levels were collected by the developed detector, and its ability to distinguish pork freshness was based on different spectral shape features (SSF) (spectral ratio (SR), spectral difference (SD), and normalized spectral intensity difference (NSID)) were compared. The experimental results show that compared with the original multispectral modeling, the performance of the model based on spectral shape features is significantly improved. The model established by optimizing the spectral shape feature variables has the best performance, and the discrimination accuracy of its prediction set is 91.67%. In addition, the validation accuracy of the optimal model was 86.67%, and its sensitivity and variability were 87.50% and 85.71%, respectively. The results show that the detector developed in this study is cost-effective, compact in its structure, stable in its performance, and suitable for the on-site digital rapid non-destructive testing of freshness during the storage, transportation, and sale of fresh pork.
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Hashemi-Nasab FS, Talebian S, Parastar H. Multiple adulterants detection in turmeric powder using VIS-SWNIR hyperspectral imaging followed by multivariate curve resolution and classification techniques. Microchem J 2022. [DOI: 10.1016/j.microc.2022.108203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Zhang Y, Kim Y, Puolanne E, Ertbjerg P. Role of freezing-induced myofibrillar protein denaturation in the generation of thaw loss: A review. Meat Sci 2022; 190:108841. [DOI: 10.1016/j.meatsci.2022.108841] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 03/13/2022] [Accepted: 05/01/2022] [Indexed: 01/08/2023]
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Zhuang Q, Peng Y, Yang D, Wang Y, Zhao R, Chao K, Guo Q. Detection of frozen pork freshness by fluorescence hyperspectral image. J FOOD ENG 2022. [DOI: 10.1016/j.jfoodeng.2021.110840] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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10
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Currò S, Fasolato L, Serva L, Boffo L, Ferlito JC, Novelli E, Balzan S. Use of a portable near-infrared tool for rapid on-site inspection of freezing and hydrogen peroxide treatment of cuttlefish (Sepia officinalis). Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108524] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Swanson A, Gowen A. Detection of previously frozen poultry through plastic lidding film using portable visible spectral imaging (443-726 NM). Poult Sci 2021; 101:101578. [PMID: 34894425 PMCID: PMC8665413 DOI: 10.1016/j.psj.2021.101578] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 10/15/2021] [Accepted: 11/02/2021] [Indexed: 11/16/2022] Open
Abstract
The objective of this study is to use a portable visible spectral imaging system (443–726 nm) to detect poultry thawed from frozen at the pixel level using multivariate analysis methods commonly used in machine learning (decision tree, logistic regression, linear discriminant analysis [LDA], k-nearest neighbors [KNN], support vector machines [SVM]). The selection of the most suitable method is based on the amount of data required to build an accurate model, computational speed, and the robustness of the model. The training set consists of pixel spectra from packages of chicken thighs without plastic lidding to evaluate the robustness of the models when implemented on the test set with and without plastic lidding. Data subsets were created by randomly selecting 1, 5, 10, 20, and 50% of the pixel spectra of each sample for both the training and test data sets. The subsets of pixel spectra and the full training set were used to train the machine learning algorithms to evaluate how the amount of data influences computational time. Logistic regression was found to be the best algorithm for detecting poultry thawed from frozen with and without plastic lidding film. Although logistic regression and SVM both performed with the same high accuracy and sensitivity for all training subset sizes, the computational time needed to implement SVM makes it the less suitable algorithm for detecting poultry thawed from frozen with and without plastic lidding film.
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Affiliation(s)
- Anastasia Swanson
- UCD School of Biosystems and Food Engineering, University College Dublin, Dublin 4, Ireland.
| | - Aoife Gowen
- UCD School of Biosystems and Food Engineering, University College Dublin, Dublin 4, Ireland
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Siddiqui MA, Khir MHM, Witjaksono G, Ghumman ASM, Junaid M, Magsi SA, Saboor A. Multivariate Analysis Coupled with M-SVM Classification for Lard Adulteration Detection in Meat Mixtures of Beef, Lamb, and Chicken Using FTIR Spectroscopy. Foods 2021; 10:foods10102405. [PMID: 34681455 PMCID: PMC8535455 DOI: 10.3390/foods10102405] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 09/20/2021] [Accepted: 09/21/2021] [Indexed: 11/16/2022] Open
Abstract
Adulteration of meat products is a delicate issue for people around the globe. The mixing of lard in meat causes a significant problem for end users who are sensitive to halal meat consumption. Due to the highly similar lipid profiles of meat species, the identification of adulteration becomes more difficult. Therefore, a comprehensive spectral detailing of meat species is required, which can boost the adulteration detection process. The experiment was conducted by distributing samples labeled as "Pure (80 samples)" and "Adulterated (90 samples)". Lard was mixed with the ratio of 10-50% v/v with beef, lamb, and chicken samples to obtain adulterated samples. Functional groups were discovered for pure pork, and two regions of difference (RoD) at wavenumbers 1700-1800 cm-1 and 2800-3000 cm-1 were identified using absorbance values from the FTIR spectrum for all samples. The principal component analysis (PCA) described the studied adulteration using three principal components with an explained variance of 97.31%. The multiclass support vector machine (M-SVM) was trained to identify the sample class values as pure and adulterated clusters. The acquired overall classification accuracy for a cluster of pure samples was 81.25%, whereas when the adulteration ratio was above 10%, 71.21% overall accuracy was achieved for a group of adulterated samples. Beef and lamb samples for both adulterated and pure classes had the highest classification accuracy value of 85%, whereas chicken had the lowest value of 78% for each category. This paper introduces a comprehensive spectrum analysis for pure and adulterated samples of beef, chicken, lamb, and lard. Moreover, we present a rapid M-SVM model for an accurate classification of lard adulteration in different samples despite its low-level presence.
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Affiliation(s)
- Muhammad Aadil Siddiqui
- Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Seri Iskandar 32610, Malaysia; (M.H.M.K.); (S.A.M.)
- Correspondence:
| | - Mohd Haris Md Khir
- Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Seri Iskandar 32610, Malaysia; (M.H.M.K.); (S.A.M.)
| | - Gunawan Witjaksono
- BRI Research Institute, Jl. Harsono RM No. 2, Ragunan, Passsar Minggu, Jakarta 12550, Indonesia;
| | | | - Muhammad Junaid
- Department of Electronics Engineering, FICT, BUITEMS, Quetta 87300, Pakistan;
| | - Saeed Ahmed Magsi
- Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Seri Iskandar 32610, Malaysia; (M.H.M.K.); (S.A.M.)
| | - Abdul Saboor
- High Performance Cloud Computing Centre (HPC), Universiti Teknologi Petronas, Seri Iskandar 32610, Malaysia;
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Rapid determination of TBARS content by hyperspectral imaging for evaluating lipid oxidation in mutton. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.104110] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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14
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Hyperspectral Dimensionality Reduction Based on Inter-Band Redundancy Analysis and Greedy Spectral Selection. REMOTE SENSING 2021. [DOI: 10.3390/rs13183649] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Hyperspectral imaging systems are becoming widely used due to their increasing accessibility and their ability to provide detailed spectral responses based on hundreds of spectral bands. However, the resulting hyperspectral images (HSIs) come at the cost of increased storage requirements, increased computational time to process, and highly redundant data. Thus, dimensionality reduction techniques are necessary to decrease the number of spectral bands while retaining the most useful information. Our contribution is two-fold: First, we propose a filter-based method called interband redundancy analysis (IBRA) based on a collinearity analysis between a band and its neighbors. This analysis helps to remove redundant bands and dramatically reduces the search space. Second, we apply a wrapper-based approach called greedy spectral selection (GSS) to the results of IBRA to select bands based on their information entropy values and train a compact convolutional neural network to evaluate the performance of the current selection. We also propose a feature extraction framework that consists of two main steps: first, it reduces the total number of bands using IBRA; then, it can use any feature extraction method to obtain the desired number of feature channels. We present classification results obtained from our methods and compare them to other dimensionality reduction methods on three hyperspectral image datasets. Additionally, we used the original hyperspectral data cube to simulate the process of using actual filters in a multispectral imager.
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Tippala T, Koomkrong N, Kayan A. Influence of freeze-thawed cycles on pork quality. Anim Biosci 2021; 34:1375-1381. [PMID: 33171034 PMCID: PMC8255891 DOI: 10.5713/ajas.20.0416] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 09/17/2020] [Accepted: 10/27/2020] [Indexed: 12/04/2022] Open
Abstract
OBJECTIVE This study was conducted to determine the effect of freeze-thawed cycles (Fresh meat, F-T 1 cycle and F-T 2 cycles) on the quality characteristics of porcine longissimus dorsi muscle. METHODS A total of 20 three-crossbred pigs (Duroc×[Large White×Landrace]) were randomly obtained from a commercial slaughterhouse in Thailand. Muscle samples were immediately taken from 10 to 11th of the longissimus dorsi for histochemical analysis. The muscles were cut into 2.54 cm-thick chops. A minimum of 20 chops were used for each treatment (fresh meat, freeze-thawed 1 and 2 cycles). Individually chops were packaged in polyethylene bags and frozen at -20°C for 6 months followed by thawing in refrigerator at 4°C for 24 h (the 1st freeze-thawed cycle). The freeze-thawed procedure was repeated for two cycles (the 2nd freeze-thawed cycle). Thawing loss, shear force value, citrate synthase activity and muscle fiber characteristics were determined on the muscles. RESULTS Results showed that increasing of freeze-thawed cycle increased the thawing loss (p<0.01) and citrate synthase activity (p<0.001). Shear force value of fresh meat was higher than freeze-thawed 1 and 2 cycles (F-T 1 cycle and F-T 2 cycles). Freeze-thawed cycles affected muscle characteristics. Muscle fiber area and muscle fiber diameter decreased with an increasing number of freeze-thawed cycles (p<0.001), while the thickness of endomysium and perimysium were increased (p<0.001). CONCLUSION Repeated freeze-thawed cycles degraded muscle fiber structure and deteriorated pork quality.
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Affiliation(s)
- Tiprawee Tippala
- Department of Animal Science, Faculty of Agriculture, Kasetsart University, Bangkok 10900,
Thailand
| | - Nunyarat Koomkrong
- Department of Animal Science, Faculty of Science and Technology, Suratthani Rajabhat University, Suratthani 84100,
Thailand
| | - Autchara Kayan
- Department of Animal Science, Faculty of Agriculture, Kasetsart University, Bangkok 10900,
Thailand
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16
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Shortwave infrared hyperspectral imaging system coupled with multivariable method for TVB-N measurement in pork. Food Control 2021. [DOI: 10.1016/j.foodcont.2020.107854] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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17
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Cheng LJ, Liu GS, He JG, Wan GL, Ban JJ, Yuan RR, Fan NY. Development of a novel quantitative function between spectral value and metmyoglobin content in Tan mutton. Food Chem 2020; 342:128351. [PMID: 33172751 DOI: 10.1016/j.foodchem.2020.128351] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 09/16/2020] [Accepted: 10/07/2020] [Indexed: 12/29/2022]
Abstract
This study was aimed to establish a quantitative function between spectral reflectance values and metmyoglobin (MetMb) content in Tan mutton during refrigeration. Near-infrared hyperspectral data combined with generalized two-dimensional correlation spectroscopy (G2D-COS) method to identify characteristic bands and investigate the sequence of chemical waveband changes. Characteristic wavebands identified by G2D-COS analysis had the best performance in predicting the content of MetMb, with a high R2p of 0.849, a low RMSEP of 2.695 and a high RPD of 2.786. The results showed that the G2D-COS may be a powerful tool for describing intensity changes of MetMb band. The partial least square regression method was used to develop the relationships between the spectral values and MetMb content in Tan mutton meat for predicting MetMb content. This study has provided a convenient and rapid non-destructive quantitative method for assessing the color of Tan mutton meat.
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Affiliation(s)
- Li-Juan Cheng
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
| | - Gui-Shan Liu
- School of Food & Wine, Ningxia University, Yinchuan 750021, China.
| | - Jian-Guo He
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
| | - Guo-Ling Wan
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
| | - Jing-Jing Ban
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
| | - Rui-Rui Yuan
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
| | - Nai-Yun Fan
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
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18
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Hossain MAM, Uddin SMK, Sultana S, Wahab YA, Sagadevan S, Johan MR, Ali ME. Authentication of Halal and Kosher meat and meat products: Analytical approaches, current progresses and future prospects. Crit Rev Food Sci Nutr 2020; 62:285-310. [DOI: 10.1080/10408398.2020.1814691] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- M. A. Motalib Hossain
- Nanotechnology and Catalysis Research Centre (NANOCAT), University of Malaya, Kuala Lumpur, Malaysia
| | - Syed Muhammad Kamal Uddin
- Nanotechnology and Catalysis Research Centre (NANOCAT), University of Malaya, Kuala Lumpur, Malaysia
| | - Sharmin Sultana
- Nanotechnology and Catalysis Research Centre (NANOCAT), University of Malaya, Kuala Lumpur, Malaysia
| | - Yasmin Abdul Wahab
- Nanotechnology and Catalysis Research Centre (NANOCAT), University of Malaya, Kuala Lumpur, Malaysia
| | - Suresh Sagadevan
- Nanotechnology and Catalysis Research Centre (NANOCAT), University of Malaya, Kuala Lumpur, Malaysia
| | - Mohd Rafie Johan
- Nanotechnology and Catalysis Research Centre (NANOCAT), University of Malaya, Kuala Lumpur, Malaysia
| | - Md. Eaqub Ali
- Nanotechnology and Catalysis Research Centre (NANOCAT), University of Malaya, Kuala Lumpur, Malaysia
- Centre for Research in Biotechnology for Agriculture (CEBAR), University of Malaya, Kuala Lumpur, Malaysia
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Differentiation of South African Game Meat Using Near-Infrared (NIR) Spectroscopy and Hierarchical Modelling. Molecules 2020; 25:molecules25081845. [PMID: 32316308 PMCID: PMC7221759 DOI: 10.3390/molecules25081845] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 04/10/2020] [Accepted: 04/13/2020] [Indexed: 11/17/2022] Open
Abstract
Near-infrared (NIR) spectroscopy, combined with multivariate data analysis techniques, was used to rapidly differentiate between South African game species, irrespective of the treatment (fresh or previously frozen) or the muscle type. These individual classes (fresh; previously frozen; muscle type) were also determined per species, using hierarchical modelling. Spectra were collected with a portable handheld spectrophotometer in the 908-1676-nm range. With partial least squares discriminant analysis models, we could differentiate between the species with accuracies ranging from 89.8%-93.2%. It was also possible to distinguish between fresh and previously frozen meat (90%-100% accuracy). In addition, it was possible to distinguish between ostrich muscles (100%), as well as the forequarters and hindquarters of the zebra (90.3%) and springbok (97.9%) muscles. The results confirm NIR spectroscopy's potential as a rapid and non-destructive method for species identification, fresh and previously frozen meat differentiation, and muscle type determination.
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Zhang S, Tan Z, Liu J, Xu Z, Du Z. Determination of the food dye indigotine in cream by near-infrared spectroscopy technology combined with random forest model. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 227:117551. [PMID: 31677907 DOI: 10.1016/j.saa.2019.117551] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Revised: 09/09/2019] [Accepted: 09/17/2019] [Indexed: 06/10/2023]
Abstract
Artificial pigment is a common food additive in cream products. If added in excess, it will do harm to human body. At present, there is no research on the detection of cream pigment by Near Infrared (NIR) spectroscopy. In this paper, a method based on random forest was applied to determine the indigotine in cream. Weighting in the experiments was accomplished using analytical balances with precision as low as 0.0001 g. The NIR spectra data of cream with different concentration of indigotine were recorded. The original spectra was pretreated by SG smoothing, mean centering and second derivative. Random forest was applied to establish a quantitative analysis model for cream pigment content, and multiple evaluation criteria were selected to comprehensively evaluate the model. The R2 was 0.9402, RMSEP was 0.2509 and RPD was 4.0893. Consequently, NIR spectroscopy, combined with data pretreatments and random forest model, was confirmed to be an interesting tool for non-destructive evaluation of pigment content in cream.
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Affiliation(s)
- Supei Zhang
- School of Computer Science & Engineering, Wuhan Institute of Technology, Wuhan, 430205, China
| | - Zhenglin Tan
- Department of Cuisine and Nutrition, Hubei University of Economics, Wuhan, 430205, China.
| | - Jun Liu
- Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, 430205, China; School of Computer Science & Engineering, Wuhan Institute of Technology, Wuhan, 430205, China
| | - Zihan Xu
- School of Computer Science & Engineering, Wuhan Institute of Technology, Wuhan, 430205, China
| | - Zhuang Du
- School of Computer Science & Engineering, Wuhan Institute of Technology, Wuhan, 430205, China
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21
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Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging. Foods 2020; 9:foods9020154. [PMID: 32041126 PMCID: PMC7073906 DOI: 10.3390/foods9020154] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 01/28/2020] [Accepted: 02/04/2020] [Indexed: 01/18/2023] Open
Abstract
Minced pork jowl meat, also called the sticking-piece, is commonly used to be adulterated in minced pork, which influences the overall product quality and safety. In this study, hyperspectral imaging (HSI) methodology was proposed to identify and visualize this kind of meat adulteration. A total of 176 hyperspectral images were acquired from adulterated meat samples in the range of 0%–100% (w/w) at 10% increments using a visible and near-infrared (400–1000 nm) HSI system in reflectance mode. Mean spectra were extracted from the regions of interests (ROIs) and represented each sample accordingly. The performance comparison of established partial least square regression (PLSR) models showed that spectra pretreated by standard normal variate (SNV) performed best with Rp2 = 0.9549 and residual predictive deviation (RPD) = 4.54. Furthermore, functional wavelengths related to adulteration identification were individually selected using methods of principal component (PC) loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC). After that, the multispectral RC-PLSR model exhibited the most satisfactory results in prediction set that Rp2 was 0.9063, RPD was 2.30, and the limit of detection (LOD) was 6.50%. Spatial distribution was visualized based on the preferred model, and adulteration levels were clearly discernible. Lastly, the visualization was further verified that prediction results well matched the known distribution in samples. Overall, HSI was tested to be a promising methodology for detecting and visualizing minced jowl meat in pork.
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22
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Sannia M, Serva L, Balzan S, Segato S, Novelli E, Fasolato L. Application of near-infrared spectroscopy for frozen-thawed characterization of cuttlefish ( Sepia officinalis). Journal of Food Science and Technology 2019; 56:4437-4447. [PMID: 31686675 DOI: 10.1007/s13197-019-03957-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 07/11/2019] [Accepted: 07/15/2019] [Indexed: 12/17/2022]
Abstract
Few works to date have reported the application of near-infrared spectroscopy (NIRS) for the characterization and authentication of cephalopods. This study investigated the feasibility of a portable NIRS instrument for the non-destructive freshness evaluation of fresh (F) and frozen-thawed (FT) cuttlefish (Sepia officinalis). Samples were examined by chemical, microbiological and sensorial analyses, during 13 days of conservation at 3 °C. The spectral data were collected on lateral mantle of cuttlefish, and different partial least squares discriminant analyses (PLS-DA) were applied for classification purposes. The interpretation of spectra was also investigated by applying the specific water coordinates, using aquaphotomics. Few significant differences in the wet chemistry and microbiological data were detected between F and FT during storage. The quality index method and microbiological analyses suggested similar behavior between F and FT samples until to near 9 days of shelf life. PLS-DA models with the spectral range 900-1650 nm achieved a classification precision of 0.91 between F and FT, while the performances for the prediction of storage days were less effective. The results of aquaphotomics plotted in aquagrams were suitable for the interpretation of the main physicochemical changes of cuttlefish throughout the shelf life. The water coordinates suggested a different molecular conformation of water species in the FT than F samples, with more free water molecules and a lower amount of bound species and the water solvation shell, respectively.
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Affiliation(s)
- Michele Sannia
- 1Health Science and Technologies Interdepartmental Center for Industrial Research (HST-ICIR), University of Bologna, Via Tolara di Sopra, 41/E, 40064 Ozzano dell'Emilia, BO Italy
| | - Lorenzo Serva
- 2Department of Animal Medicine, Production and Health, University of Padova, Viale dell'Università 16, 35020 Legnaro, PD Italy
| | - Stefania Balzan
- 3Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
| | - Severino Segato
- 2Department of Animal Medicine, Production and Health, University of Padova, Viale dell'Università 16, 35020 Legnaro, PD Italy
| | - Enrico Novelli
- 3Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
| | - Luca Fasolato
- 3Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
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23
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Detectability of the degree of freeze damage in meat depends on analytic-tool selection. Meat Sci 2019; 152:8-19. [DOI: 10.1016/j.meatsci.2019.02.002] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 01/30/2019] [Accepted: 02/04/2019] [Indexed: 11/23/2022]
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24
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Investigation on Texture Changes and Classification between Cold-Fresh and Freeze-Thawed Tan Mutton. J FOOD QUALITY 2019. [DOI: 10.1155/2019/1957486] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
To study the texture, microstructural changes, and classification of cold-fresh (C-F), freeze-thawed once (F-T0), and freeze-thawed twice Tan mutton (F-Tt), the aforementioned three types of Tan mutton were subjected to near-infrared hyperspectrum scanning, scanning electron microscopy, and TPA testing. The original spectrum of Tan mutton was obtained at a wavelength range of 900∼1,700 nm after hyperspectrum scanning; a spectrum fragment ranging from 918 nm to 1,008 nm was intercepted, and the remaining original spectrum was used as a studied spectrum (“full spectrum” hereafter). The full spectrum was pretreated by SNV (standard normal variate), MSC (multiple scattering correction), and SNV + MSC and then extracted feature wavelengths by SPA (successive projections algorithm) and CARS (competitive adaptive reweighted sampling) algorithm, and 25 feature wavelengths were obtained. By combining these feature wavelengths with classified variables, the SNV + MSC−CARS−PLS-DA (partial least squares-discriminate analysis, PLS-DA) and SNV + MSC−SPA−PLS-DA models for classification of C-F and F-T Tan mutton were established. In contrast, SNV + MSC−CARS−PLS-DA yielded the highest classification rate of 98% and 100% for calibration set and validation set, respectively. The results indicated that the texture and surface microstructure of F-T Tan mutton deteriorated, and more worsely with F-T time. SNV+MSC-CARS-PLS-DA could be well used to classify C-F, F-T0, and F-Tt Tan mutton.
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25
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Kutsanedzie FYH, Guo Z, Chen Q. Advances in Nondestructive Methods for Meat Quality and Safety Monitoring. FOOD REVIEWS INTERNATIONAL 2019. [DOI: 10.1080/87559129.2019.1584814] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Affiliation(s)
| | - Zhiming Guo
- School of Food & Biological Engineering, Jiangsu University, Zhenjiang, P.R. China
| | - Quansheng Chen
- School of Food & Biological Engineering, Jiangsu University, Zhenjiang, P.R. China
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26
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Feasibility of using spectral profiles for modeling water activity in five varieties of white quinoa grains. J FOOD ENG 2018. [DOI: 10.1016/j.jfoodeng.2018.06.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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27
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Predicting pork freshness using multi-index statistical information fusion method based on near infrared spectroscopy. Meat Sci 2018; 146:59-67. [DOI: 10.1016/j.meatsci.2018.07.023] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2018] [Revised: 07/17/2018] [Accepted: 07/17/2018] [Indexed: 01/24/2023]
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28
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Wang Z, He Z, Li H. The effect of repeated freeze-thaw cycles on the meat quality of rabbit. WORLD RABBIT SCIENCE 2018. [DOI: 10.4995/wrs.2018.8616] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
<p>We investigated the effect of repeated freeze-thaw cycles on the quality of rabbit meat. Twenty-five Hyla rabbits were slaughtered using standard commercial procedures. A freeze-thaw procedure—i.e., seven days frozen at –18°C followed by thawing at 4°C for 12h— was repeated 5 times, and 9 <em>Longissimus thoracis et lumborum</em> muscles were randomly selected at pre-set cycles (0, 1, 2, 3, and 5). The <em>Longissimus lumborum</em> muscles were used to determine meat quality parameters, while the <em>Longissimus thoracis</em> muscles were used for chemical analysis. During the repeated freeze-thaw process, muscle pH, redness, hardness, and water holding capacity gradually decreased, whereas meat lightness and yellowness gradually increased. The amount of total volatile basic nitrogen significantly increased (<em>P</em><0.05) and exceeded the threshold value for frozen meat after 5 repeated freeze-thaw cycles. The metmyoglobin proportion, thiobarbituric acid-reactive substances (TBARS) and protein carbonyl content in rabbit meat samples increased with a higher number of freeze-thaw cycles (<em>P</em><0.05), and the proportions of these compounds were positively correlated. During the repeated freeze-thaw process, extractable haeme iron levels significantly decreased (<em>P</em><0.05), and non-haeme iron levels markedly increased (<em>P</em><0.05). An sodium dodecyl sulfate polyacrylamide gel electrophoresis analysis indicated that the degradation of both water- and salt-soluble proteins was more prevalent in samples subjected to higher numbers of freeze-thaw cycles. Additionally, a principal component analysis identified good correlations between physicochemical properties (TBARS, protein carbonyl levels and metmyoglobin content) and quality parameters (thawing loss, redness, lightness and hardness). Taken together, we conclude that the repeated freeze-thaw process can strongly affect rabbit meat quality as well as its physicochemical properties.</p>
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29
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What are the scientific challenges in moving from targeted to non-targeted methods for food fraud testing and how can they be addressed? – Spectroscopy case study. Trends Food Sci Technol 2018. [DOI: 10.1016/j.tifs.2018.04.001] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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30
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Spectral Detection Techniques for Non-Destructively Monitoring the Quality, Safety, and Classification of Fresh Red Meat. FOOD ANAL METHOD 2018. [DOI: 10.1007/s12161-018-1256-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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31
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Wang WL, Chen WH, Tian HY, Liu Y. Detection of Frozen-Thawed Cycles for Frozen Tilapia (Oreochromis) Fillets Using Near Infrared Spectroscopy. JOURNAL OF AQUATIC FOOD PRODUCT TECHNOLOGY 2018. [DOI: 10.1080/10498850.2018.1461156] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Wen-Li Wang
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Technology & Business University (BTBU), Beijng, PR China
- College of Food Science and Technology, Shanghai Ocean University, Shanghai, PR China
| | - Wei-Hua Chen
- College of Food Science and Technology, Shanghai Ocean University, Shanghai, PR China
| | - Hong-Yu Tian
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Technology & Business University (BTBU), Beijng, PR China
| | - Yuan Liu
- Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
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32
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Visible and Near-Infrared Hyperspectral Imaging for Cooking Loss Classification of Fresh Broiler Breast Fillets. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8020256] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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33
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Barreto A, Cruz-Tirado J, Siche R, Quevedo R. Determination of starch content in adulterated fresh cheese using hyperspectral imaging. FOOD BIOSCI 2018. [DOI: 10.1016/j.fbio.2017.10.009] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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34
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Comparison between artificial neural network and partial least squares regression models for hardness modeling during the ripening process of Swiss-type cheese using spectral profiles. J FOOD ENG 2018. [DOI: 10.1016/j.jfoodeng.2017.09.008] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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35
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Xu JC, Zhang M, Mujumdar AS, Adhikari B. Recent developments in smart freezing technology applied to fresh foods. Crit Rev Food Sci Nutr 2018; 57:2835-2843. [PMID: 26463561 DOI: 10.1080/10408398.2015.1074158] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Due to the increased awareness of consumers in sensorial and nutritional quality of frozen foods, the freezing technology has to seek new and innovative technologies for better retaining the fresh like quality of foods. In this article, we review the recent developments in smart freezing technology applied to fresh foods. The application of these intelligent technologies and the associated underpinning concepts have greatly improved the quality of frozen foods and the freezing efficiency. These technologies are able to automatically collect the information in-line during freezing and help control the freezing process better. Smart freezing technology includes new and intelligent technologies and concepts applied to the pretreatment of the frozen product, freezing processes, cold chain logistics as well as warehouse management. These technologies enable real-time monitoring of quality during the freezing process and help improve product quality and freezing efficiency. We also provide a brief overview of several sensing technologies used to achieve automatic control of individual steps of freezing process. These sensing technologies include computer vision, electronic nose, electronic tongue, digital simulation, confocal laser, near infrared spectroscopy, nuclear magnetic resonance technology and ultrasound. Understanding of the mechanism of these new technologies will be helpful for applying them to improve the quality of frozen foods.
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Affiliation(s)
- Ji-Cheng Xu
- a State Key Laboratory of Food Science and Technology , Jiangnan University , Wuxi , Jiangsu , China
| | - Min Zhang
- a State Key Laboratory of Food Science and Technology , Jiangnan University , Wuxi , Jiangsu , China
| | - Arun S Mujumdar
- b Department of Bioresource Engineering , McGill University , Quebec , Canada
| | - Benu Adhikari
- c School of Applied Sciences, RMIT University, Melbourne City Campus , Victoria , Australia
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36
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Liu Y, Pu H, Sun DW. Hyperspectral imaging technique for evaluating food quality and safety during various processes: A review of recent applications. Trends Food Sci Technol 2017. [DOI: 10.1016/j.tifs.2017.08.013] [Citation(s) in RCA: 185] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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37
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Nondestructive Freshness Discriminating of Shrimp Using Visible/Near-Infrared Hyperspectral Imaging Technique and Deep Learning Algorithm. FOOD ANAL METHOD 2017. [DOI: 10.1007/s12161-017-1050-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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38
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Rapid detection of frozen-then-thawed minced beef using multispectral imaging and Fourier transform infrared spectroscopy. Meat Sci 2017; 135:142-147. [PMID: 29032278 DOI: 10.1016/j.meatsci.2017.09.016] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2017] [Revised: 09/08/2017] [Accepted: 09/27/2017] [Indexed: 11/22/2022]
Abstract
In recent years, fraud detection has become a major priority for food authorities, as fraudulent practices can have various economic and safety consequences. This work explores ways of identifying frozen-then-thawed minced beef labeled as fresh in a rapid, large-scale and cost-effective way. For this reason, freshly-ground beef was purchased from seven separate shops at different times, divided in fifteen portions and placed in Petri dishes. Multi-spectral images and FTIR spectra of the first five were immediately acquired while the remaining were frozen (-20°C) and stored for 7 and 32days (5 samples for each time interval). Samples were thawed and subsequently subjected to similar data acquisition. In total, 105 multispectral images and FTIR spectra were collected which were further analyzed using partial least-squares discriminant analysis and support vector machines. Two meat batches (30 samples) were reserved for independent validation and the remaining five batches were divided in training and test set (75 samples). Results showed 100% overall correct classification for test and external validation MSI data, while FTIR data yielded 93.3 and 96.7% overall correct classification for FTIR test set and external validation set respectively.
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39
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Camin F, Perini M, Bontempo L, Galeotti M, Tibaldi E, Piasentier E. Stable isotope ratios of H, C, O, N and S for the geographical traceability of Italian rainbow trout (Oncorhynchus mykiss). Food Chem 2017; 267:288-295. [PMID: 29934170 DOI: 10.1016/j.foodchem.2017.06.017] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 05/22/2017] [Accepted: 06/05/2017] [Indexed: 11/28/2022]
Abstract
Multielement isotope ratio was assessed in fat and defatted (protein) fillet of 130 rainbow trout, reared with feed incorporating a high or low fish content in 20 Italian farms, focusing on two northern Italian regions (Friuli Venezia Giulia and Trentino). The aim was to investigate the ability of isotopic analysis to trace the geographical origin of trout, also according to the type of feed. The carbon, nitrogen and sulphur isotope ratios of feed and fillet were highly positively correlated both within each matrix (feed or fillet) and between the two matrices and negatively correlated with the δ2H and δ18O of feed and the δ2Hfat. The δ2Hprotein and δ18Oprotein were positively interrelated with the δ18O of tank water. By applying the tested Partial Least Squares - Discriminant Analysis multiclass model (85 fillets) to the validation dataset (45 fillets), 91% accuracy was obtained for the two Italian regions.
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Affiliation(s)
- Federica Camin
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all'Adige, Italy; University of Udine, Department of Agriculture, Food, Environmental and Animal Sciences, via delle Scienze 206, 33100 Udine, Italy
| | - Matteo Perini
- Experiment and Technological Services Department, Technology Transfer Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all'Adige, Italy.
| | - Luana Bontempo
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all'Adige, Italy
| | - Marco Galeotti
- University of Udine, Department of Agriculture, Food, Environmental and Animal Sciences, via delle Scienze 206, 33100 Udine, Italy
| | - Emilio Tibaldi
- University of Udine, Department of Agriculture, Food, Environmental and Animal Sciences, via delle Scienze 206, 33100 Udine, Italy
| | - Edi Piasentier
- University of Udine, Department of Agriculture, Food, Environmental and Animal Sciences, via delle Scienze 206, 33100 Udine, Italy
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40
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Górska-Horczyczak E, Horczyczak M, Guzek D, Wojtasik-Kalinowska I, Wierzbicka A. Chromatographic fingerprints supported by artificial neural network for differentiation of fresh and frozen pork. Food Control 2017. [DOI: 10.1016/j.foodcont.2016.08.010] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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41
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Xiong Z, Sun DW, Pu H, Gao W, Dai Q. Applications of emerging imaging techniques for meat quality and safety detection and evaluation: A review. Crit Rev Food Sci Nutr 2017; 57:755-768. [PMID: 25975703 DOI: 10.1080/10408398.2014.954282] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
With improvement in people's living standards, many people nowadays pay more attention to quality and safety of meat. However, traditional methods for meat quality and safety detection and evaluation, such as manual inspection, mechanical methods, and chemical methods, are tedious, time-consuming, and destructive, which cannot meet the requirements of modern meat industry. Therefore, seeking out rapid, non-destructive, and accurate inspection techniques is important for the meat industry. In recent years, a number of novel and noninvasive imaging techniques, such as optical imaging, ultrasound imaging, tomographic imaging, thermal imaging, and odor imaging, have emerged and shown great potential in quality and safety assessment. In this paper, a detailed overview of advanced applications of these emerging imaging techniques for quality and safety assessment of different types of meat (pork, beef, lamb, chicken, and fish) is presented. In addition, advantages and disadvantages of each imaging technique are also summarized. Finally, future trends for these emerging imaging techniques are discussed, including integration of multiple imaging techniques, cost reduction, and developing powerful image-processing algorithms.
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Affiliation(s)
- Zhenjie Xiong
- a School of Food Science and Engineering , South China University of Technology , Guangzhou , China.,b Academy of Contemporary Food Engineering, South China University of Technology , Guangzhou Higher Education Mega Center , Guangzhou , China
| | - Da-Wen Sun
- a School of Food Science and Engineering , South China University of Technology , Guangzhou , China.,b Academy of Contemporary Food Engineering, South China University of Technology , Guangzhou Higher Education Mega Center , Guangzhou , China.,c Food Refrigeration and Computerised Food Technology , Agriculture and Food Science Centre, University College Dublin, National University of Ireland , Belfield , Dublin , Ireland
| | - Hongbin Pu
- a School of Food Science and Engineering , South China University of Technology , Guangzhou , China.,b Academy of Contemporary Food Engineering, South China University of Technology , Guangzhou Higher Education Mega Center , Guangzhou , China
| | - Wenhong Gao
- a School of Food Science and Engineering , South China University of Technology , Guangzhou , China.,b Academy of Contemporary Food Engineering, South China University of Technology , Guangzhou Higher Education Mega Center , Guangzhou , China
| | - Qiong Dai
- a School of Food Science and Engineering , South China University of Technology , Guangzhou , China.,b Academy of Contemporary Food Engineering, South China University of Technology , Guangzhou Higher Education Mega Center , Guangzhou , China
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42
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Cheng JH, Nicolai B, Sun DW. Hyperspectral imaging with multivariate analysis for technological parameters prediction and classification of muscle foods: A review. Meat Sci 2016; 123:182-191. [PMID: 27750085 DOI: 10.1016/j.meatsci.2016.09.017] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Revised: 09/26/2016] [Accepted: 09/29/2016] [Indexed: 12/20/2022]
Abstract
Muscle foods are very important for a well-balanced daily diet. Due to their perishability and vulnerability, there is a need for quality and safety evaluation of such foods. Hyperspectral imaging (HSI) coupled with multivariate analysis is becoming increasingly popular for the non-destructive, non-invasive, and rapid determination of important quality attributes and the classification of muscle foods. This paper reviews recent advances of application of HSI for predicting some significant muscle foods parameters, including color, tenderness, firmness, springiness, water-holding capacity, drip loss and pH. In addition, algorithms for the rapid classification of muscle foods are also reported and discussed. It will be shown that this technology has great potential to replace traditional analytical methods for predicting various quality parameters and classifying muscle foods.
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Affiliation(s)
- Jun-Hu Cheng
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering (ACFE), South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; MeBioS, Department of Biosystems, KU Leuven, Willem de Croylaan 42, 3001 Heverlee, Belgium
| | - Bart Nicolai
- MeBioS, Department of Biosystems, KU Leuven, Willem de Croylaan 42, 3001 Heverlee, Belgium
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering (ACFE), South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
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43
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Mekki I, Camin F, Perini M, Smeti S, Hajji H, Mahouachi M, Piasentier E, Atti N. Differentiating the geographical origin of Tunisian indigenous lamb using stable isotope ratio and fatty acid content. J Food Compost Anal 2016. [DOI: 10.1016/j.jfca.2016.09.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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44
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Ma J, Sun DW, Pu H. Spectral absorption index in hyperspectral image analysis for predicting moisture contents in pork longissimus dorsi muscles. Food Chem 2016; 197:848-54. [DOI: 10.1016/j.foodchem.2015.11.023] [Citation(s) in RCA: 74] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Revised: 10/31/2015] [Accepted: 11/04/2015] [Indexed: 12/13/2022]
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Siche R, Vejarano R, Aredo V, Velasquez L, Saldaña E, Quevedo R. Evaluation of Food Quality and Safety with Hyperspectral Imaging (HSI). FOOD ENGINEERING REVIEWS 2015. [DOI: 10.1007/s12393-015-9137-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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Cheng JH, Sun DW, Pu HB, Chen X, Liu Y, Zhang H, Li JL. Integration of classifiers analysis and hyperspectral imaging for rapid discrimination of fresh from cold-stored and frozen-thawed fish fillets. J FOOD ENG 2015. [DOI: 10.1016/j.jfoodeng.2015.03.011] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Qu JH, Cheng JH, Sun DW, Pu H, Wang QJ, Ma J. Discrimination of shelled shrimp (Metapenaeus ensis) among fresh, frozen-thawed and cold-stored by hyperspectral imaging technique. Lebensm Wiss Technol 2015. [DOI: 10.1016/j.lwt.2015.01.018] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Rapid detection of frozen pork quality without thawing by Vis-NIR hyperspectral imaging technique. Talanta 2015; 139:208-15. [PMID: 25882428 DOI: 10.1016/j.talanta.2015.02.027] [Citation(s) in RCA: 117] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2014] [Revised: 02/06/2015] [Accepted: 02/17/2015] [Indexed: 11/23/2022]
Abstract
Quality determination of frozen food is a time-consuming and laborious work as it normally takes a long time to thaw the frozen samples before measurements can be carried out. In this research, a rapid and non-destructive determination technique for frozen pork quality was tested with a hyperspectral imaging (HSI) system. In this study, 120 pieces of pork meat were frozen by four kinds of methods with various freezing temperatures from -20 to -120°C. The hyperspectral images of the samples were acquired at the frozen state. Quality indicators including drip loss, pH value, color, cooking loss and Warner-Bratzler shear force (WBSF) of the samples were measured after thawing. The spectral characteristics of the frozen meat samples were studied and it was revealed that the reflectance at 1100nm had a close relationship with the freezing temperature (R=-0.832, p<0.01). Partial least squares regression (PLSR) was applied to establish the spectral models, and the models were then optimized. Results showed that the improved region of interest (ROI) method could be used to extract effective spectral information to withstand the interference of freezing, and choosing appropriate spectral bands and spectral pretreatment techniques were crucial to develop robust mathematical model. The performances of the models established were diverse based on different quality indicators. The coefficients of determination for prediction (Rp(2)) for L*, cooking loss, b*, drip loss and a* were 0.907, 0.845, 0.814, 0.762, and 0.716, respectively. However there were low correlations (Rp(2)) for pH and WBSF measurements. The current study indicated that HSI had the potential for non-destructive determination of frozen meat quality without thawing.
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Kamruzzaman M, Makino Y, Oshita S. Non-invasive analytical technology for the detection of contamination, adulteration, and authenticity of meat, poultry, and fish: A review. Anal Chim Acta 2015; 853:19-29. [DOI: 10.1016/j.aca.2014.08.043] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Revised: 07/22/2014] [Accepted: 08/20/2014] [Indexed: 10/24/2022]
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