1
|
Aït-Kaddour A, Loudiyi M, Boukria O, Safarov J, Sultanova S, Andueza D, Listrat A, Cahyana Y. Beef muscle discrimination based on two-trace two-dimensional correlation spectroscopy (2T2D COS) combined with snapshot visible-near infrared multispectral imaging. Meat Sci 2024; 214:109533. [PMID: 38735067 DOI: 10.1016/j.meatsci.2024.109533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 04/29/2024] [Accepted: 05/05/2024] [Indexed: 05/14/2024]
Abstract
The purpose of this work was to assess the potential of 2T2D COS PLS-DA (two-trace two-dimensional correlation spectroscopy and partial least squares discriminant analysis) in conjunction with Visible Near infrared multispectral imaging (MSI) as a quick, non-destructive, and precise technique for classifying three beef muscles -Longissimus thoracis, Semimembranosus, and Biceps femoris- obtained from three breeds - the Blonde d'Aquitaine, Limousine, and Aberdeen Angus. The experiment was performed on 240 muscle samples. Before performing PLS-DA, spectra were extracted from MSI images and processed by SNV (Standard Normal Variate), MSC (Multivariate Scattering Correction) or AREA (area under curve equal 1) and converted in synchronous and asynchronous 2T2D COS maps. The results of the study highlighted that combining synchronous and asynchronous 2T2D COS maps before performing PLS-DA was the best strategy to discriminate between the three muscles (100% of classification accuracy and 0% of error).
Collapse
Affiliation(s)
- Abderrahmane Aït-Kaddour
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMRF, Lempdes F-63370, France; Laboratory of Food Chemistry, Department of Food Technology, Universitas Padjadjaran, Bandung, Indonesia.
| | - Mohammed Loudiyi
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMRF, Lempdes F-63370, France
| | - Oumayma Boukria
- Applied Organic Chemistry Laboratory, Sciences and Techniques Faculty, Sidi Mohamed Ben Abedallah University, BP 2202 route d'Immouzer, Fès, Morocco
| | - Jasur Safarov
- Department of Food Engineering, Faculty of Mechanical Building, Tashkent State Technical University named after Islam Karimov, University Str. 2, Tashkent 100095, Uzbekistan
| | - Shaxnoza Sultanova
- Joint Belarusian-Uzbek Intersectoral Institute of Applied Technical Qualifications in Tashkent, 111200, Tashkent region, Kibray district, Koramurt street, 1, Uzbekistan
| | - Donato Andueza
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, Saint-Genès-Champanelle F-63122, France
| | - Anne Listrat
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, Saint-Genès-Champanelle F-63122, France
| | - Yana Cahyana
- Laboratory of Food Chemistry, Department of Food Technology, Universitas Padjadjaran, Bandung, Indonesia
| |
Collapse
|
2
|
Dung CD, Trueman SJ, Wallace HM, Farrar MB, Gama T, Tahmasbian I, Bai SH. Hyperspectral imaging for estimating leaf, flower, and fruit macronutrient concentrations and predicting strawberry yields. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:114166-114182. [PMID: 37858016 PMCID: PMC10663281 DOI: 10.1007/s11356-023-30344-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 10/04/2023] [Indexed: 10/21/2023]
Abstract
Managing the nutritional status of strawberry plants is critical for optimizing yield. This study evaluated the potential of hyperspectral imaging (400-1,000 nm) to estimate nitrogen (N), phosphorus (P), potassium (K), and calcium (Ca) concentrations in strawberry leaves, flowers, unripe fruit, and ripe fruit and to predict plant yield. Partial least squares regression (PLSR) models were developed to estimate nutrient concentrations. The determination coefficient of prediction (R2P) and ratio of performance to deviation (RPD) were used to evaluate prediction accuracy, which often proved to be greater for leaves, flowers, and unripe fruit than for ripe fruit. The prediction accuracies for N concentration were R2P = 0.64, 0.60, 0.81, and 0.30, and RPD = 1.64, 1.59, 2.64, and 1.31, for leaves, flowers, unripe fruit, and ripe fruit, respectively. Prediction accuracies for Ca concentrations were R2P = 0.70, 0.62, 0.61, and 0.03, and RPD = 1.77, 1.63, 1.60, and 1.15, for the same respective plant parts. Yield and fruit mass only had significant linear relationships with the Difference Vegetation Index (R2 = 0.256 and 0.266, respectively) among the eleven vegetation indices tested. Hyperspectral imaging showed potential for estimating nutrient status in strawberry crops. This technology will assist growers to make rapid nutrient-management decisions, allowing for optimal yield and quality.
Collapse
Affiliation(s)
- Cao Dinh Dung
- Centre for Bioinnovation, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- School of Science, Technology and Engineering, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- Potato, Vegetable and Flower Research Center - Institute of Agricultural Science for Southern Vietnam, Thai Phien Village, Ward 12, Da Lat, Lam Dong, Vietnam
| | - Stephen J Trueman
- Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD, 4111, Australia
| | - Helen M Wallace
- Centre for Bioinnovation, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- School of Science, Technology and Engineering, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD, 4111, Australia
| | - Michael B Farrar
- Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD, 4111, Australia
| | - Tsvakai Gama
- Centre for Bioinnovation, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
- School of Science, Technology and Engineering, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia
| | - Iman Tahmasbian
- Department of Agriculture and Fisheries, Queensland Government, Toowoomba, QLD, 4350, Australia
| | - Shahla Hosseini Bai
- Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD, 4111, Australia.
| |
Collapse
|
3
|
Zuo J, Peng Y, Li Y, Zou W, Chen Y, Huo D, Chao K. Nondestructive detection of nutritional parameters of pork based on NIR hyperspectral imaging technique. Meat Sci 2023; 202:109204. [PMID: 37146500 DOI: 10.1016/j.meatsci.2023.109204] [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: 11/29/2022] [Revised: 03/22/2023] [Accepted: 04/24/2023] [Indexed: 05/07/2023]
Abstract
Nondestructive detection of the nutritional parameters of pork is of great importance. This study aimed to investigate the feasibility of applying hyperspectral image technology to detect the nutrient content and distribution of pork nondestructively. Hyperspectral cubes of 100 pork samples were collected using a line-scan hyperspectral system, the effects of different preprocessing methods on the modeling effects were compared and analyzed, the feature wavelengths of fat and protein were extracted, and the full-wavelength model was optimized using the regressor chains (RC) algorithm. Finally, pork's fat, protein, and energy value distributions were visualized using the best prediction model. The results showed that standard normal variate was more effective than other preprocessing methods, the feature wavelengths extracted by the competitive adaptive reweighted sampling algorithm had better prediction performance, and the protein model prediction performance was optimized after using the RC algorithm. The best prediction models were developed, with the correlation coefficient of prediction (RP) = 0.929, the root mean square error in prediction (RMSEP) = 0.699% and residual prediction deviation (RPD) = 2.669 for fat, and RP = 0.934, RMSEP = 0.603% and RPD = 2.586 for protein. The pseudo-color maps were helpful for the analysis of nutrient distribution in pork. Hyperspectral image technology can be a fast, nondestructive, and accurate tool for quantifying the composition and assessing the distribution of nutrients in pork.
Collapse
Affiliation(s)
- Jiewen Zuo
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Yankun Peng
- College of Engineering, China Agricultural University, Beijing 100083, China.
| | - Yongyu Li
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Wenlong Zou
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Yahui Chen
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Daoyu Huo
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Kuanglin Chao
- Environmental Microbial and Food Safety Laboratory, USDA-ARS, Beltsville, MD 20705, United States
| |
Collapse
|
4
|
Long C, Zhang N, Tao X, Tao Y, Ye C. Resolution Enhanced Array ECT Probe for Small Defects Inspection. SENSORS (BASEL, SWITZERLAND) 2023; 23:2070. [PMID: 36850668 PMCID: PMC9964679 DOI: 10.3390/s23042070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 02/03/2023] [Accepted: 02/09/2023] [Indexed: 06/18/2023]
Abstract
It is a continual and challenging problem to detect small defects in metallic structures for array eddy current testing (ECT) probes, which require the probe to have ultra-high resolution and sensitivity. However, the spatial resolution of an ECT array probe is limited by the size of the induction coils. Even if it is possible to increase the spatial resolution by using smaller coils, the sensitivity of the sensor also decreases. To obtain finer spatial resolution without sacrificing sensitivity, this paper proposes a resolution enhanced ECT array probe with four rows of coils attached to a flexible printed circuit board (FPCB). The distance between each two adjacent coils in a row is 2 mm and the position of each row is offset by 0.5 mm along the horizontal direction related to its prior row. The outputs of the four rows are aligned and interpolated in a line, and in this way the image resolution of the probe is increased to 0.5 mm. The probe is configured to operate with the differential setting, namely two differential coils operate simultaneously at each time. The currents in the two coils can be controlled to have the same flowing direction or opposite flowing direction, resulting in different distributions of the induced eddy current and two sets of output images. A patch-image model and an image fusion method based on discrete wavelet transforms are employed to suppress the noise and highlight the defects' indications. Experimental results show that small defects with dimensions as small as length × width × depth = 1 mm × 0.1 mm × 0.3 mm on a 304 stainless-steel sample can be detected from the fused image, demonstrating that the probe has super sensitivity for small defects inspection.
Collapse
|
5
|
Gab-Allah MA, Choi K, Kim B. Type B Trichothecenes in Cereal Grains and Their Products: Recent Advances on Occurrence, Toxicology, Analysis and Post-Harvest Decontamination Strategies. Toxins (Basel) 2023; 15:85. [PMID: 36828399 PMCID: PMC9963506 DOI: 10.3390/toxins15020085] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
Type B trichothecenes (deoxynivalenol, nivalenol, 3-acetyldeoxynivalenol, 15-acetyldeoxynivalenol) and deoxynivalenol-3-glucoside (DON-3G) are secondary toxic metabolites produced mainly by mycotoxigenic Fusarium fungi and have been recognized as natural contaminants in cereals and cereal-based foods. The latest studies have proven the various negative effects of type B trichothecenes on human health. Due to the widespread occurrence of Fusarium species, contamination by these mycotoxins has become an important aspect for public health and agro-food systems worldwide. Hence, their monitoring and surveillance in various foods have received a significant deal of attention in recent years. In this review, an up-to-date overview of the occurrence profile of major type B trichothecenes and DON-3G in cereal grains and their toxicological implications are outlined. Furthermore, current trends in analytical methodologies for their determination are overviewed. This review also covers the factors affecting the production of these mycotoxins, as well as the management strategies currently employed to mitigate their contamination in foods. Information presented in this review provides good insight into the progress that has been achieved in the last years for monitoring type B trichothecenes and DON-3G, and also would help the researchers in their further investigations on metabolic pathway analysis and toxicological studies of these Fusarium mycotoxins.
Collapse
Affiliation(s)
- Mohamed A. Gab-Allah
- Organic Metrology Group, Division of Chemical and Biological Metrology, Korea Research Institute of Standards and Science, Daejeon 34113, Republic of Korea
- Department of Bio-Analytical Science, University of Science and Technology, Daejeon 34113, Republic of Korea
- Reference Materials Lab, National Institute of Standards, P.O. Box 136, Giza 12211, Egypt
| | - Kihwan Choi
- Organic Metrology Group, Division of Chemical and Biological Metrology, Korea Research Institute of Standards and Science, Daejeon 34113, Republic of Korea
- Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Byungjoo Kim
- Organic Metrology Group, Division of Chemical and Biological Metrology, Korea Research Institute of Standards and Science, Daejeon 34113, Republic of Korea
- Department of Bio-Analytical Science, University of Science and Technology, Daejeon 34113, Republic of Korea
| |
Collapse
|
6
|
Edwards K, Hoffman LC, Manley M, Williams PJ. Raw Beef Patty Analysis Using Near-Infrared Hyperspectral Imaging: Identification of Four Patty Categories. SENSORS (BASEL, SWITZERLAND) 2023; 23:697. [PMID: 36679493 PMCID: PMC9867321 DOI: 10.3390/s23020697] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/18/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
South African legislation regulates the classification/labelling and compositional specifications of raw beef patties, to combat processed meat fraud and to protect the consumer. A near-infrared hyperspectral imaging (NIR-HSI) system was investigated as an alternative authentication technique to the current destructive, time-consuming, labour-intensive and expensive methods. Eight hundred beef patties (ca. 100 g) were made and analysed to assess the potential of NIR-HSI to distinguish between the four patty categories (200 patties per category): premium 'ground patty'; regular 'burger patty'; 'value-burger/patty' and the 'econo-burger'/'budget'. Hyperspectral images were acquired with a HySpex SWIR-384 (short-wave infrared) imaging system using the Breeze® acquisition software, in the wavelength range of 952-2517 nm, after which the data was analysed using image analysis, multivariate techniques and machine learning algorithms. It was possible to distinguish between the four patty categories with accuracies ≥97%, indicating that NIR-HSI offers an accurate and reliable solution for the rapid identification and authentication of processed beef patties. Furthermore, this study has the potential of providing an alternative to the current authentication methods, thus contributing to the authenticity and fair-trade of processed meat products locally and internationally.
Collapse
Affiliation(s)
- Kiah Edwards
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Louwrens C. Hoffman
- Department of Animal Sciences, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Health and Food Sciences Precinct, 39 Kessels Rd, Coopers Plains 4108, Australia
| | - Marena Manley
- 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
| |
Collapse
|
7
|
Shi Y, Wang Y, Hu X, Li Z, Huang X, Liang J, Zhang X, Zhang D, Zou X, Shi J. Quantitative characterization of the diffusion behavior of sucrose in marinated beef by HSI and FEA. Meat Sci 2023; 195:109002. [DOI: 10.1016/j.meatsci.2022.109002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 10/06/2022] [Accepted: 10/07/2022] [Indexed: 11/09/2022]
|
8
|
Kim J, Semyalo D, Rho TG, Bae H, Cho BK. Non-Destructive Detection of Abnormal Chicken Eggs by Using an Optimized Spectral Analysis System. SENSORS (BASEL, SWITZERLAND) 2022; 22:9826. [PMID: 36560195 PMCID: PMC9786918 DOI: 10.3390/s22249826] [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: 10/27/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 06/17/2023]
Abstract
Environmental pressures, such as temperature and light intensity, food, and genetic factors, can cause chicken eggs to develop abnormalities. The common types of internal egg abnormalities include bloody and damaged egg yolk. Spectrometers have been recently used in real-time abnormal egg detection research. However, there are very few studies on the optimization of measurement systems. This study aimed to establish optimum parameters for detecting of internal egg abnormalities (bloody and damaged-yolk eggs) using visible and near-infrared (Vis/NIR) spectrometry (192-1110 nm range) and multivariate data analysis. The detection performance using various system parameters, such as the types of light sources, the configuration of the light, and sensor positions, was investigated. With the help of collected data, a partial least-squares discriminant analysis (PLS-DA) model was developed to classify normal and abnormal eggs. The highest classification accuracy for the various system parameters was 98.7%. Three band selection methods, such as weighted regression coefficient (WRC), sequential feature selection (SFS), and successive projection algorithm (SPA) were used for further model optimization, to reduce the spectral bands from 1028 to less than 7. In conclusion the results indicate that the types of light sources and the configuration design of the sensor and illumination affect the detection accuracy for abnormal eggs.
Collapse
Affiliation(s)
- Juntae Kim
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseoung-gu, Daejeon 34134, Republic of Korea
| | - Dennis Semyalo
- Department of Smart Agricultural Systems, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseoung-gu, Daejeon 34134, Republic of Korea
| | - Tae-Gyun Rho
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseoung-gu, Daejeon 34134, Republic of Korea
| | - Hyungjin Bae
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseoung-gu, Daejeon 34134, Republic of Korea
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseoung-gu, Daejeon 34134, Republic of Korea
- Department of Smart Agricultural Systems, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseoung-gu, Daejeon 34134, Republic of Korea
| |
Collapse
|
9
|
Wu X, Liang X, Wang Y, Wu B, Sun J. Non-Destructive Techniques for the Analysis and Evaluation of Meat Quality and Safety: A Review. Foods 2022; 11:foods11223713. [PMID: 36429304 PMCID: PMC9689883 DOI: 10.3390/foods11223713] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/04/2022] [Accepted: 11/15/2022] [Indexed: 11/22/2022] Open
Abstract
With the continuous development of economy and the change in consumption concept, the demand for meat, a nutritious food, has been dramatically increasing. Meat quality is tightly related to human life and health, and it is commonly measured by sensory attribute, chemical composition, physical and chemical property, nutritional value, and safety quality. This paper surveys four types of emerging non-destructive detection techniques for meat quality estimation, including spectroscopic technique, imaging technique, machine vision, and electronic nose. The theoretical basis and applications of each technique are summarized, and their characteristics and specific application scope are compared horizontally, and the possible development direction is discussed. This review clearly shows that non-destructive detection has the advantages of fast, accurate, and non-invasive, and it is the current research hotspot on meat quality evaluation. In the future, how to integrate a variety of non-destructive detection techniques to achieve comprehensive analysis and assessment of meat quality and safety will be a mainstream trend.
Collapse
Affiliation(s)
- Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
- Correspondence:
| | - Xinyue Liang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Yixuan Wang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
| | - Jun Sun
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| |
Collapse
|
10
|
Roscioli JD, Desanker M, Patankar KA, Grzesiak A, Chen X. Simultaneous High-Throughput Monitoring of Urethane Reactions Using Near-Infrared Hyperspectral Imaging. APPLIED SPECTROSCOPY 2022; 76:1329-1334. [PMID: 35712891 DOI: 10.1177/00037028221110914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
High-throughput (HTP) research is becoming more widely utilized due to its advantages in rapid screening of large parameter space. When HTP is used for reaction screening, often only the end products are analyzed by off-line techniques, leaving behind valuable process information. Information-rich spectroscopy tools have remained under-utilized in HTP workflows. In this study, near-infrared (NIR) hyperspectral imaging (HSI) is demonstrated to be a versatile and accurate tool that can simultaneously monitor multiple reactions, opening up future opportunities to maximize information extraction from such HTP reaction screening experiments. Model urethane reactions are used here to demonstrate the concept, and the general approach can be widely applied to any reactions involving NIR-active functional groups. The fast speed and accurate chemical information made possible by NIR HSI are expected be another important addition to the toolkit of HTP research.
Collapse
|
11
|
A decision fusion method based on hyperspectral imaging and electronic nose techniques for moisture content prediction in frozen-thawed pork. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113778] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
12
|
Modzelewska-Kapituła M, Jun S. The application of computer vision systems in meat science and industry - A review. Meat Sci 2022; 192:108904. [PMID: 35841854 DOI: 10.1016/j.meatsci.2022.108904] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 07/05/2022] [Accepted: 07/05/2022] [Indexed: 11/19/2022]
Abstract
Computer vision systems (CVS) are applied to macro- and microscopic digital photographs captured using digital cameras, ultrasound scanners, computer tomography, and wide-angle imaging cameras. Diverse image acquisition devices make it technically feasible to obtain information about both the external features and internal structures of targeted objects. Attributes measured in CVS can be used to evaluate meat quality. CVS are also used in research related to assessing the composition of animal carcasses, which might help determine the impact of cross-breeding or rearing systems on the quality of meat. The results obtained by the CVS technique also contribute to assessing the impact of technological treatments on the quality of raw and cooked meat. CVS have many positive attributes including objectivity, non-invasiveness, speed, and low cost of analysis and systems are under constant development an improvement. The present review covers computer vision system techniques, stages of measurements, and possibilities for using these to assess carcass and meat quality.
Collapse
Affiliation(s)
- Monika Modzelewska-Kapituła
- Department of Meat Technology and Chemistry, Faculty of Food Sciences, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-719 Olsztyn, Poland.
| | - Soojin Jun
- Department of Human Nutrition, Food and Animal Sciences, University of Hawaii, Honolulu, HI 96822, USA
| |
Collapse
|
13
|
Jia W, van Ruth S, Scollan N, Koidis A. Hyperspectral imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends. Curr Res Food Sci 2022; 5:1017-1027. [PMID: 35755306 PMCID: PMC9218168 DOI: 10.1016/j.crfs.2022.05.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/25/2022] [Accepted: 05/29/2022] [Indexed: 12/01/2022] Open
Abstract
Meat products are particularly plagued by safety problems because of their complicated structure, various production processes and complex supply chains. Rapid and non-invasive analytical methods to evaluate meat quality have become a priority for the industry over the conventional chemical methods. To achieve rapid analysis of safety and quality parameters of meat products, hyperspectral imaging (HSI) is now widely applied in research studies for detecting the various components of different meat products, but its application in meat production and supply chain integrity as a quality control (QC) solution is still ambiguous. This review presents the fresh look at the current states of HSI research as both the scope and the applicability of the HSI in the meat quality evaluation expanded. The future application scenarios of HSI in the supply chain and the future development of HSI hardware and software are also discussed, by which HSI technology has the potential to enable large scale meat product testing. With a fully adapted for factory setting HSI, the inspection coverage can reliably identify the chemical properties of meat products. With the introduction of Food Industry 4.0, HSI advances can change the meat industry to become from reactive to predictive when facing meat safety issues. HSI has shown promising early signs in the non-destructive analysis of meat quality and safety. Hyperspectral imaging (HSI) is now widely applied in research studies for different meat products with the help of machine learning methods. With a fully adapted factory setting and robust machine learning of HSI, the inspection coverage can reach 100% of the target meat. HSI can change the meat industry to become from reactive to predictive when facing issues, this will be translated into fewer recalls, less meat fraud, and less waste.
Collapse
Affiliation(s)
- Wenyang Jia
- Institute for Global Food Security, School of Biological Sciences, Queen's University, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland, UK
| | - Saskia van Ruth
- Food Quality and Design Group, Wageningen University and Research, P.O. Box 17, 6700 AA, Wageningen, the Netherlands
| | - Nigel Scollan
- Institute for Global Food Security, School of Biological Sciences, Queen's University, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland, UK
| | - Anastasios Koidis
- Institute for Global Food Security, School of Biological Sciences, Queen's University, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland, UK
| |
Collapse
|
14
|
Coombs CEO, Allman BE, Morton EJ, Gimeno M, Horadagoda N, Tarr G, González LA. Differentiation of Livestock Internal Organs Using Visible and Short-Wave Infrared Hyperspectral Imaging Sensors. SENSORS (BASEL, SWITZERLAND) 2022; 22:3347. [PMID: 35591036 PMCID: PMC9102734 DOI: 10.3390/s22093347] [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: 03/03/2022] [Revised: 04/20/2022] [Accepted: 04/22/2022] [Indexed: 06/15/2023]
Abstract
Automatic identification and sorting of livestock organs in the meat processing industry could reduce costs and improve efficiency. Two hyperspectral sensors encompassing the visible (400-900 nm) and short-wave infrared (900-1700 nm) spectra were used to identify the organs by type. A total of 104 parenchymatous organs of cattle and sheep (heart, kidney, liver, and lung) were scanned in a multi-sensory system that encompassed both sensors along a conveyor belt. Spectral data were obtained and averaged following manual markup of three to eight regions of interest of each organ. Two methods were evaluated to classify organs: partial least squares discriminant analysis (PLS-DA) and random forest (RF). In addition, classification models were obtained with the smoothed reflectance and absorbance and the first and second derivatives of the spectra to assess if one was superior to the rest. The in-sample accuracy for the visible, short-wave infrared, and combination of both sensors was higher for PLS-DA compared to RF. The accuracy of the classification models was not significantly different between data pre-processing methods or between visible and short-wave infrared sensors. Hyperspectral sensors, particularly those in the visible spectrum, seem promising to identify organs from slaughtered animals which could be useful for the automation of quality and process control in the food supply chain, such as in abattoirs.
Collapse
Affiliation(s)
- Cassius E. O. Coombs
- Sydney Institute of Agriculture, School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Sydney, NSW 2006, Australia;
| | - Brendan E. Allman
- Rapiscan Systems Pty Ltd., 6-8 Herbert Street, Unit 27, Sydney, NSW 2006, Australia;
| | | | - Marina Gimeno
- University Veterinary Teaching Hospital Camden, Sydney School of Veterinary Science, Faculty of Science, The University of Sydney, Sydney, NSW 2006, Australia; (M.G.); (N.H.)
| | - Neil Horadagoda
- University Veterinary Teaching Hospital Camden, Sydney School of Veterinary Science, Faculty of Science, The University of Sydney, Sydney, NSW 2006, Australia; (M.G.); (N.H.)
| | - Garth Tarr
- School of Mathematics and Statistics, Faculty of Science, The University of Sydney, Sydney, NSW 2006, Australia;
| | - Luciano A. González
- Sydney Institute of Agriculture, School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Sydney, NSW 2006, Australia;
| |
Collapse
|
15
|
Kamruzzaman M, Kalita D, Ahmed MT, ElMasry G, Makino Y. Effect of variable selection algorithms on model performance for predicting moisture content in biological materials using spectral data. Anal Chim Acta 2022; 1202:339390. [DOI: 10.1016/j.aca.2021.339390] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 11/23/2021] [Accepted: 12/20/2021] [Indexed: 11/26/2022]
|
16
|
Tunny SS, Amanah HZ, Faqeerzada MA, Wakholi C, Kim MS, Baek I, Cho BK. Multispectral Wavebands Selection for the Detection of Potential Foreign Materials in Fresh-Cut Vegetables. SENSORS (BASEL, SWITZERLAND) 2022; 22:1775. [PMID: 35270921 PMCID: PMC8914723 DOI: 10.3390/s22051775] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 02/08/2022] [Accepted: 02/22/2022] [Indexed: 06/14/2023]
Abstract
Ensuring the quality of fresh-cut vegetables is the greatest challenge for the food industry and is equally as important to consumers (and their health). Several investigations have proven the necessity of advanced technology for detecting foreign materials (FMs) in fresh-cut vegetables. In this study, the possibility of using near infrared spectral analysis as a potential technique was investigated to identify various types of FMs in seven common fresh-cut vegetables by selecting important wavebands. Various waveband selection methods, such as the weighted regression coefficient (WRC), variable importance in projection (VIP), sequential feature selection (SFS), successive projection algorithm (SPA), and interval PLS (iPLS), were used to investigate the optimal multispectral wavebands to classify the FMs and vegetables. The application of selected wavebands was further tested using NIR imaging, and the results showed good potentiality by identifying 99 out of 107 FMs. The results indicate the high applicability of the multispectral NIR imaging technique to detect FMs in fresh-cut vegetables for industrial application.
Collapse
Affiliation(s)
- Salma Sultana Tunny
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (S.S.T.); (H.Z.A.); (M.A.F.); (C.W.)
| | - Hanim Z. Amanah
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (S.S.T.); (H.Z.A.); (M.A.F.); (C.W.)
- Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Gadjah Mada University, Yogyakarta 55281, Indonesia
| | - Mohammad Akbar Faqeerzada
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (S.S.T.); (H.Z.A.); (M.A.F.); (C.W.)
| | - Collins Wakholi
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (S.S.T.); (H.Z.A.); (M.A.F.); (C.W.)
| | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, Beltsville, MD 20705, USA; (M.S.K.); (I.B.)
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, Beltsville, MD 20705, USA; (M.S.K.); (I.B.)
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (S.S.T.); (H.Z.A.); (M.A.F.); (C.W.)
- Department of Smart Agriculture Systems, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
| |
Collapse
|
17
|
Squeo G, De Angelis D, Summo C, Pasqualone A, Caponio F, Amigo JM. Assessment of macronutrients and alpha-galactosides of texturized vegetable proteins by near infrared hyperspectral imaging. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104459] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
|
18
|
TAN F, ZHAN P, ZHANG Y, YU B, TIAN H, WANG P. Development stage prediction of flat peach by SVR model based on changes in characteristic taste attributes. FOOD SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1590/fst.18022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Affiliation(s)
| | | | - Yuyu ZHANG
- Beijing Technology and Business University, China
| | | | - Honglei TIAN
- Shaanxi Normal University, China; Shaanxi Normal University, China
| | | |
Collapse
|
19
|
Kapoor R, Malvandi A, Feng H, Kamruzzaman M. Real-time moisture monitoring of edible coated apple chips during hot air drying using miniature NIR spectroscopy and chemometrics. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2021.112602] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
|
20
|
Nieto-Ortega S, Melado-Herreros Á, Foti G, Olabarrieta I, Ramilo-Fernández G, Gonzalez Sotelo C, Teixeira B, Velasco A, Mendes R. Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR). Foods 2021; 11:foods11010055. [PMID: 35010181 PMCID: PMC8750308 DOI: 10.3390/foods11010055] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 12/22/2021] [Accepted: 12/23/2021] [Indexed: 11/16/2022] Open
Abstract
The performances of three non-destructive sensors, based on different principles, bioelectrical impedance analysis (BIA), near-infrared spectroscopy (NIR) and time domain reflectometry (TDR), were studied to discriminate between unfrozen and frozen-thawed fish. Bigeye tuna (Thunnus obesus) was selected as a model to evaluate these technologies. The addition of water and additives is usual in the fish industry, thus, in order to have a wide range of possible commercial conditions, some samples were injected with different water solutions (based on different concentrations of salt, polyphosphates and a protein hydrolysate solution). Three different models, based on partial least squares discriminant analysis (PLS-DA), were developed for each technology. This is a linear classification method that combines the properties of partial least squares (PLS) regression with the classification power of a discriminant technique. The results obtained in the evaluation of the test set were satisfactory for all the sensors, giving NIR the best performance (accuracy = 0.91, error rate = 0.10). Nevertheless, the classification accomplished with BIA and TDR data resulted also satisfactory and almost equally as good, with accuracies of 0.88 and 0.86 and error rates of 0.14 and 0.15, respectively. This work opens new possibilities to discriminate between unfrozen and frozen-thawed fish samples with different non-destructive alternatives, regardless of whether or not they have added water.
Collapse
Affiliation(s)
- Sonia Nieto-Ortega
- AZTI, Food Research, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, Astondo Bidea, Edificio 609, 48160 Derio, Spain; (Á.M.-H.); (G.F.); (I.O.)
- Correspondence: ; Tel.: +34-667-174-323
| | - Ángela Melado-Herreros
- AZTI, Food Research, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, Astondo Bidea, Edificio 609, 48160 Derio, Spain; (Á.M.-H.); (G.F.); (I.O.)
| | - Giuseppe Foti
- AZTI, Food Research, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, Astondo Bidea, Edificio 609, 48160 Derio, Spain; (Á.M.-H.); (G.F.); (I.O.)
| | - Idoia Olabarrieta
- AZTI, Food Research, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, Astondo Bidea, Edificio 609, 48160 Derio, Spain; (Á.M.-H.); (G.F.); (I.O.)
| | - Graciela Ramilo-Fernández
- Instituto de Investigaciones Marinas, CSIC, Eduardo Cabello, 6, 36208 Vigo, Spain; (G.R.-F.); (C.G.S.); (A.V.)
| | - Carmen Gonzalez Sotelo
- Instituto de Investigaciones Marinas, CSIC, Eduardo Cabello, 6, 36208 Vigo, Spain; (G.R.-F.); (C.G.S.); (A.V.)
| | - Bárbara Teixeira
- Portuguese Institute for the Sea and Atmosphere, IPMA, R. Alfredo Magalhães Ramalho, 6, 1449-006 Lisbon, Portugal; (B.T.); (R.M.)
- Interdisciplinary Center of Marine and Environmental Research (CIIMAR), University of Porto, Rua das Bragas 289, 4050-123 Porto, Portugal
| | - Amaya Velasco
- Instituto de Investigaciones Marinas, CSIC, Eduardo Cabello, 6, 36208 Vigo, Spain; (G.R.-F.); (C.G.S.); (A.V.)
| | - Rogério Mendes
- Portuguese Institute for the Sea and Atmosphere, IPMA, R. Alfredo Magalhães Ramalho, 6, 1449-006 Lisbon, Portugal; (B.T.); (R.M.)
- Interdisciplinary Center of Marine and Environmental Research (CIIMAR), University of Porto, Rua das Bragas 289, 4050-123 Porto, Portugal
| |
Collapse
|
21
|
Kothakota A, Pandiselvam R, Siliveru K, Pandey JP, Sagarika N, Srinivas CHS, Kumar A, Singh A, Prakash SD. Modeling and Optimization of Process Parameters for Nutritional Enhancement in Enzymatic Milled Rice by Multiple Linear Regression (MLR) and Artificial Neural Network (ANN). Foods 2021; 10:2975. [PMID: 34945526 PMCID: PMC8700668 DOI: 10.3390/foods10122975] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 11/03/2021] [Accepted: 11/05/2021] [Indexed: 11/17/2022] Open
Abstract
This study involves information about the concentrations of nutrients (proteins, phenolic compounds, free amino acids, minerals (Ca, P, and Iron), hardness) in milled rice processed with enzymes; xylanase and cellulase produced by Aspergillus awamori, MTCC 9166 and Trichoderma reese, MTCC164. Brown rice was processed with 60-100% enzyme (40 mL buffer -undiluted) for 30 to 150 min at 30 °C to 50 °C followed by polishing for 20-100 s at a safe moisture level. Multiple linear regression (MLR) and artificial neural network (ANN) models were used for process optimization of enzymes. The MLR correlation coefficient (R2) varied between 0.87-0.90, and the sum of square (SSE) was placed within 0.008-8.25. While the ANN R2 (correlation coefficient) varied between 0.97 and 0.9999(1), MSE changed from 0.005 to 6.13 representing that the ANN method has better execution across MLR. The optimized cellulase process parameters (87.2% concentration, 80.1 min process time, 33.95 °C temperature and 21.8 s milling time) and xylanase process parameters (85.7% enzyme crude, 77.1 min process time, 35 °C temperature and 20 s) facilitated the increase of Ca (70%), P (64%), Iron (17%), free amino acids (34%), phenolic compounds (78%) and protein (84%) and decreased hardness (20%) in milled rice. Scanning electron micrographs showed an increased rupture attributing to enzymes action on milled rice.
Collapse
Affiliation(s)
- Anjineyulu Kothakota
- Agro-Processing & Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology, Thiruvananthapuram 695019, Kerala, India
| | - Ravi Pandiselvam
- Physiology, Biochemistry and Post-Harvest Technology Division, ICAR-Central Plantation Crops Research Institute, Chowki 671124, Kerala, India;
| | - Kaliramesh Siliveru
- Department of Grain Science & Industry, Kansas State University, Manhattan, KS 66502, USA;
| | - Jai Prakash Pandey
- Department of Post-Harvest Process and Food Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar 263145, Uttarakhand, India; (J.P.P.); (A.S.)
| | - Nukasani Sagarika
- Department of Food Process Engineering, College of Food Processing Technology & Bio-Energy, Anand Agricultural University, Anand 388110, Gujarat, India;
| | | | - Anil Kumar
- Department of Food Science and Technology, College of Agriculture, G.B. Pant University of Agriculture and Technology, Pantnager 263145, India;
| | - Anupama Singh
- Department of Post-Harvest Process and Food Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar 263145, Uttarakhand, India; (J.P.P.); (A.S.)
| | - Shivaprasad D. Prakash
- Department of Grain Science & Industry, Kansas State University, Manhattan, KS 66502, USA;
| |
Collapse
|
22
|
Maduro Dias C, Nunes H, Melo T, Rosa H, Silva C, Borba A. Application of Near Infrared Reflectance (NIR) spectroscopy to predict the moisture, protein, and fat content of beef for gourmet hamburger preparation. Livest Sci 2021. [DOI: 10.1016/j.livsci.2021.104772] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
23
|
Zhang X, Sun J, Li P, Zeng F, Wang H. Hyperspectral detection of salted sea cucumber adulteration using different spectral preprocessing techniques and SVM method. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2021.112295] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
24
|
Tian XY, Aheto JH, Huang X, Zheng K, Dai C, Wang C, Bai JW. An evaluation of biochemical, structural and volatile changes of dry-cured pork using a combined ion mobility spectrometry, hyperspectral and confocal imaging approach. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:5972-5983. [PMID: 33856705 DOI: 10.1002/jsfa.11251] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 04/04/2021] [Accepted: 04/15/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Food processing induces various modifications that affect the structure, physical and chemical properties of food products and hence the acceptance of the product by the consumer. In this work, the evolution of volatile components, 2-thiobarbituric acid reactive substances (TBARS), moisture content (MC) and microstructural changes of pork was investigated by hyperspectral (HSI) and confocal imaging (CLSM) techniques in synergy with gas chromatography-ion mobility spectrometry (GC-IMS). Models based on partial least squares regression (PLSR) were developed using the full HSI spectrum variables as well as optimum variables selected through a competitive adaptive reweighted sampling algorithm. RESULTS Prediction results for MC and TBARS using multiplicative scatter correction pre-processed spectra models demonstrated greater efficiency and predictability with determination coefficient of prediction of 0.928, 0.930 and root mean square error of prediction of 0.114, 1.002, respectively. Major structural changes were also observed during CLSM imaging, which were greatly pronounced in pork samples oven cooked for 15 and 20 h. These structural changes could be related to the denaturation of the major meat components, which could explain the loss of moisture and the formation of TBARS visualized from the HSI chemical distribution maps. GC-IMS identified 35 volatile components, including hexanal and pentanal, which are also known to have a higher lipid oxidation specificity. CONCLUSION The synergistic application of HSI, CLSM and GC-IMS enhanced data mining and interpretation and provided a convenient way for analyzing the chemical, structural and volatile changes occurring in meat during processing. © 2021 Society of Chemical Industry.
Collapse
Affiliation(s)
- Xiao-Yu Tian
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
| | - Joshua H Aheto
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
| | - Xingyi Huang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
| | - Kaiyi Zheng
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
| | - Chunxia Dai
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, PR China
| | - Chengquan Wang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
| | - Jun-Wen Bai
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
| |
Collapse
|
25
|
Statistical Analysis of Chemical Element Compositions in Food Science: Problems and Possibilities. Molecules 2021; 26:molecules26195752. [PMID: 34641296 PMCID: PMC8510397 DOI: 10.3390/molecules26195752] [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: 08/09/2021] [Revised: 09/16/2021] [Accepted: 09/17/2021] [Indexed: 11/17/2022] Open
Abstract
In recent years, many analyses have been carried out to investigate the chemical components of food data. However, studies rarely consider the compositional pitfalls of such analyses. This is problematic as it may lead to arbitrary results when non-compositional statistical analysis is applied to compositional datasets. In this study, compositional data analysis (CoDa), which is widely used in other research fields, is compared with classical statistical analysis to demonstrate how the results vary depending on the approach and to show the best possible statistical analysis. For example, honey and saffron are highly susceptible to adulteration and imitation, so the determination of their chemical elements requires the best possible statistical analysis. Our study demonstrated how principle component analysis (PCA) and classification results are influenced by the pre-processing steps conducted on the raw data, and the replacement strategies for missing values and non-detects. Furthermore, it demonstrated the differences in results when compositional and non-compositional methods were applied. Our results suggested that the outcome of the log-ratio analysis provided better separation between the pure and adulterated data and allowed for easier interpretability of the results and a higher accuracy of classification. Similarly, it showed that classification with artificial neural networks (ANNs) works poorly if the CoDa pre-processing steps are left out. From these results, we advise the application of CoDa methods for analyses of the chemical elements of food and for the characterization and authentication of food products.
Collapse
|
26
|
Schreuders FK, Schlangen M, Kyriakopoulou K, Boom RM, van der Goot AJ. Texture methods for evaluating meat and meat analogue structures: A review. Food Control 2021. [DOI: 10.1016/j.foodcont.2021.108103] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
|
27
|
Improving Intramuscular Fat Assessment in Pork by Synergy Between Spectral and Spatial Features in Hyperspectral Image. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-021-02113-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
28
|
Park E, Kim YS, Omari MK, Suh HK, Faqeerzada MA, Kim MS, Baek I, Cho BK. High-Throughput Phenotyping Approach for the Evaluation of Heat Stress in Korean Ginseng ( Panax ginseng Meyer) Using a Hyperspectral Reflectance Image. SENSORS 2021; 21:s21165634. [PMID: 34451076 PMCID: PMC8402434 DOI: 10.3390/s21165634] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/15/2021] [Accepted: 08/19/2021] [Indexed: 11/16/2022]
Abstract
Panax ginseng has been used as a traditional medicine to strengthen human health for centuries. Over the last decade, significant agronomical progress has been made in the development of elite ginseng cultivars, increasing their production and quality. However, as one of the significant environmental factors, heat stress remains a challenge and poses a significant threat to ginseng plants’ growth and sustainable production. This study was conducted to investigate the phenotype of ginseng leaves under heat stress using hyperspectral imaging (HSI). A visible/near-infrared (Vis/NIR) and short-wave infrared (SWIR) HSI system were used to acquire hyperspectral images for normal and heat stress-exposed plants, showing their susceptibility (Chunpoong) and resistibility (Sunmyoung and Sunil). The acquired hyperspectral images were analyzed using the partial least squares-discriminant analysis (PLS-DA) technique, combining the variable importance in projection and successive projection algorithm methods. The correlation of each group was verified using linear discriminant analysis. The developed models showed 12 bands over 79.2% accuracy in Vis/NIR and 18 bands with over 98.9% accuracy at SWIR in validation data. The constructed beta-coefficient allowed the observation of the key wavebands and peaks linked to the chlorophyll, nitrogen, fatty acid, sugar and protein content regions, which differentiated normal and stressed plants. This result shows that the HSI with the PLS-DA technique significantly differentiated between the heat-stressed susceptibility and resistibility of ginseng plants with high accuracy.
Collapse
Affiliation(s)
- Eunsoo Park
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea; (E.P.); (M.K.O.); (M.A.F.)
| | - Yun-Soo Kim
- R&D Headquarters, Korea Ginseng Corporation, Daejeon 34128, Korea;
| | - Mohammad Kamran Omari
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea; (E.P.); (M.K.O.); (M.A.F.)
| | - Hyun-Kwon Suh
- Department of Life Resources Industry, Dong-A University, Busan 49315, Korea;
| | - Mohammad Akbar Faqeerzada
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea; (E.P.); (M.K.O.); (M.A.F.)
| | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville MD 20705, USA; (M.S.K.); (I.B.)
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville MD 20705, USA; (M.S.K.); (I.B.)
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea; (E.P.); (M.K.O.); (M.A.F.)
- Department of Smart Agriculture System, Chungnam National University, Daejeon 34134, Korea
- Correspondence:
| |
Collapse
|
29
|
Geographical origin discriminant analysis of Chia seeds (Salvia hispanica L.) using hyperspectral imaging. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.103916] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
30
|
Khaled AY, Parrish CA, Adedeji A. Emerging nondestructive approaches for meat quality and safety evaluation-A review. Compr Rev Food Sci Food Saf 2021; 20:3438-3463. [PMID: 34151512 DOI: 10.1111/1541-4337.12781] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 03/29/2021] [Accepted: 05/11/2021] [Indexed: 11/28/2022]
Abstract
Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.
Collapse
Affiliation(s)
- Alfadhl Y Khaled
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Chadwick A Parrish
- Department of Electrical and Computer Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Akinbode Adedeji
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
| |
Collapse
|
31
|
Tian XY, Aheto JH, Dai C, Ren Y, Bai JW. Monitoring microstructural changes and moisture distribution of dry-cured pork: a combined confocal laser scanning microscopy and hyperspectral imaging study. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:2727-2735. [PMID: 33124042 DOI: 10.1002/jsfa.10899] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 10/13/2020] [Accepted: 10/30/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Various spectral profiles, including reflectance, absorbance, and Kubelka-Munk spectra, have been derived from hyperspectral images and used to develop multivariate models to evaluate changes in the quality of meat and meat products as a function of processing. However, none of these has the capacity to produce images of the structural changes often associated with processing. This study explored the feasibility of combining hyperspectral imaging (HSI) with confocal laser scanning microscopy (CLSM) to examine the impact of processing on microstructural changes and the evolution of moisture. Reflectance spectra features were obtained and transformed into absorbance and Kubelka-Munk spectra and their ability to predict moisture content using models established on partial least-squares regression were evaluated. RESULTS The partial least-squares regression model (full-band wavelength) dubbed Rs-MSC yielded the best result, with R c 2 = 0.967 , RMSEC = 0.127, R cv 2 = 0.949 , RMSECV = 0.418, R p 2 = 0.937 , RMSEP = 0.824. Next, a total of 16 optimum wavelengths were selected using the competitive adaptive reweighted sampling algorithm. These wavelengths also yielded good results for Rs-MSC, with R c 2 = 0.958 , RMSEC = 0.840, R cv 2 = 0.931 , RMSECV = 0.118, R p 2 = 0.926 , RMSEP = 0.121. Regarding moisture distribution and microstructure analysis, HSI and CLSM were able to reveal moisture content distribution and conformational differences in microstructure in the test samples. CONCLUSION Using HSI in synergy with CLSM may offer a reliable means for assessing both the chemical and structural changes that occur in other congener food products during processing. © 2020 Society of Chemical Industry.
Collapse
Affiliation(s)
- Xiao-Yu Tian
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
| | - Joshua H Aheto
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
| | - Chunxia Dai
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, P. R. China
| | - Yi Ren
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
- School of Smart Agriculture, Suzhou Polytechnic Institute of Agriculture, Suzhou, P. R. China
| | - Jun-Wen Bai
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
| |
Collapse
|
32
|
Antequera T, Caballero D, Grassi S, Uttaro B, Perez-Palacios T. Evaluation of fresh meat quality by Hyperspectral Imaging (HSI), Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI): A review. Meat Sci 2021; 172:108340. [DOI: 10.1016/j.meatsci.2020.108340] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 09/23/2020] [Accepted: 10/09/2020] [Indexed: 12/31/2022]
|
33
|
Quest of Intelligent Research Tools for Rapid Evaluation of Fish Quality: FTIR Spectroscopy and Multispectral Imaging Versus Microbiological Analysis. Foods 2021; 10:foods10020264. [PMID: 33525540 PMCID: PMC7912049 DOI: 10.3390/foods10020264] [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: 12/10/2020] [Revised: 01/22/2021] [Accepted: 01/24/2021] [Indexed: 11/26/2022] Open
Abstract
The aim of the present study was to assess the microbiological quality of farmed sea bass (Dicentrarchus labrax) fillets stored under aerobic conditions and modified atmosphere packaging (MAP) (31% CO2, 23% O2, 46% Ν2,) at 0, 4, 8, and 12 °C using Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI) in tandem with data analytics, taking into account the results of conventional microbiological analysis. Fish samples were subjected to microbiological analysis (total viable counts (TVC), Pseudomonas spp., H2S producing bacteria, Brochothrix thermosphacta, lactic acid bacteria (LAB), Enterobacteriaceae, and yeasts) and sensory evaluation, together with FTIR and MSI spectral data acquisition. Pseudomonas spp. and H2S-producing bacteria were enumerated at higher population levels compared to other microorganisms, regardless of storage temperature and packaging condition. The developed partial least squares regression (PLS-R) models based on the FTIR spectra of fish stored aerobically and under MAP exhibited satisfactory performance in the estimation of TVC, with coefficients of determination (R2) at 0.78 and 0.99, respectively. In contrast, the performances of PLS-R models based on MSI spectral data were less accurate, with R2 values of 0.44 and 0.62 for fish samples stored aerobically and under MAP, respectively. FTIR spectroscopy is a promising tool to assess the microbiological quality of sea bass fillets stored in air and under MAP that could be effectively employed in the future as an alternative method to conventional microbiological analysis.
Collapse
|
34
|
Hyperspectral Imaging (HSI) Technology for the Non-Destructive Freshness Assessment of Pearl Gentian Grouper under Different Storage Conditions. SENSORS 2021; 21:s21020583. [PMID: 33467476 PMCID: PMC7830432 DOI: 10.3390/s21020583] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 01/12/2021] [Accepted: 01/13/2021] [Indexed: 11/17/2022]
Abstract
This study used visible/near-infrared hyperspectral imaging (HSI) technology combined with chemometric methods to assess the freshness of pearl gentian grouper. The partial least square discrimination analysis (PLS-DA) and competitive adaptive reweighted sampling-PLS-DA (CARS-PLS-DA) models were used to classify fresh, refrigerated, and frozen–thawed fish. The PLS-DA model achieved better classification of fresh, refrigerated, and frozen–thawed fish with the accuracy of 100%, 96.43%, and 96.43%, respectively. Further, the PLS regression (PLSR) and CARS-PLS regression (CARS-PLSR) models were used to predict the storage time of fish under different storage conditions, and the prediction accuracy was assessed using the prediction correlation coefficients (Rp2), root mean squared error of prediction (RMSEP), and residual predictive deviation (RPD). For the prediction of storage time, the CARS-PLS model presented the better result of room temperature (Rp2 = 0.948, RMSEP = 0.255, RPD = 4.380) and refrigeration (Rp2 = 0.9319, RMSEP = 1.188, RPD = 3.857), while the better prediction of freeze was by obtained by the PLSR model (Rp2 = 0.9250, RMSEP = 2.910, RPD = 3.469). Finally, the visualization of storage time based on the PLSR model under different storage conditions were realized. This study confirmed the potential of HSI as a rapid and non-invasive technique to identify fish freshness.
Collapse
|
35
|
Coombs CEO, Fajardo M, González LA. Comparison of smartphone and lab-grade NIR spectrometers to measure chemical composition of lamb and beef. ANIMAL PRODUCTION SCIENCE 2021. [DOI: 10.1071/an21069] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Context
Near-infrared reflectance spectroscopy (NIRS) has been extensively investigated for non-destructive and rapid determination of pH and chemical composition of meat including water, crude protein, intramuscular fat (IMF) and stable isotopes. Smaller, cheaper NIRS sensors that connect to a smartphone could enhance the accessibility and uptake of this technology by consumers. However, the limited wavelength range of these sensors could restrict the accuracy of predictions compared with benchtop laboratory NIRS models.
Aims
To compare the precision and accuracy metrics of predicting pH, water, crude protein and IMF of three sample presentations and two sensors.
Methods
Fresh intact (FI) store-bought beef and lamb steak samples (n = 43) were ground and freeze-dried (FD), and then oven-dried to create freeze-dried oven-dried (FDOD) samples. All three forms of sample presentation (FI, FD, FDOD) were scanned using the smartphone and benchtop NIRS sensors.
Key results
The IMF was the best predicted trait in FD and FDOD forms by the smartphone NIRS (R2 >0.75; RPD >1.40) with limited differences between the two sensors. However, predictions on FI meat were poorer for all traits regardless of the NIRS scanner used (R2 ≤ 0.67; RPD ≤ 1.58) and not suitable for use in research or industry.
Conclusion
The smartphone NIRS sensor showed accuracy and precision comparable to benchtop NIRS to predict meat composition. However, these preliminary results found that neither of the two sensors reliably predicted quality attributes for industry or consumer applications.
Implications
Miniaturised NIRS sensors connected to smartphones could provide a practical solution to measure some meat quality attributes such as IMF, but the accuracy depends on sample presentation.
Collapse
|
36
|
Chen X, Patankar KA, Larive M. Monitoring Polyurethane Foaming Reactions Using Near-Infrared Hyperspectral Imaging. APPLIED SPECTROSCOPY 2021; 75:46-56. [PMID: 32584146 DOI: 10.1177/0003702820941877] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Polyurethane (PU) foams are finding increasingly wider applications ranging from memory foams and mattresses to cushions and insulation materials. They are prepared by reactions between multifunctional isocyanates and polyols as the two main building blocks, along with other additives, including the blowing agents. A non-contact near-infrared (NIR) hyperspectral imaging (HSI) camera was used in this study to monitor PU foaming reactions between a polymeric methylene diphenyl diisocyanate, polyol, and water. Five foams were prepared with three process variables: water content, mixing time, and catalyst levels. Spectral changes characteristic of the PU reactions were observed and clear difference in kinetics could be effectively extracted from such NIR HSI results. The NIR HSI technology offers two substantial advantages over the conventional Fourier transform- (FT-) NIR systems: (i) faster spectral acquisition time and (ii) higher spatial resolution of line images rather than the point measurement. Examples are provided to illustrate these two advantages. The potential to acquire chemical images of PU foams is also demonstrated.
Collapse
|
37
|
Wieja K, Kiełczyński P, Szymański P, Szalewski M, Balcerzak A, Ptasznik S. Identification and investigation of mechanically separated meat (MSM) with an innovative ultrasonic method. Food Chem 2020; 348:128907. [PMID: 33513528 DOI: 10.1016/j.foodchem.2020.128907] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 12/07/2020] [Accepted: 12/10/2020] [Indexed: 11/17/2022]
Abstract
An innovative analytical ultrasonic method for identification and investigation of Mechanically Separated Meat (MSM) samples is presented. To this end, the ultrasonic wave velocity (f=5MHz) in the investigated meat samples was measured. The measured ultrasonic velocity ranged from 1553.4 to 1589.9 m/s. The investigations were performed for: 1) minced hand deboned chicken fillets, 2) low pressure MSM from chicken carcasses, 3) low pressure MSM from chicken collarbones, 4) high pressure MSM from chicken carcasses and 5) high pressure MSM from chicken collarbones. Statistically significant (p<0.001) differences in the ultrasonic velocity were observed for each of investigated kinds of meat. High significant correlations were found between the ultrasonic velocity and the content of protein, fat, sodium and density of the investigated meat. The applicability of the developed ultrasonic method for identifying various kinds of meat and to determine the content of protein, fat, sodium and density was demonstrated.
Collapse
Affiliation(s)
- K Wieja
- Institute of Fundamental Technological Research, Polish Academy of Sciences, ul. Pawińskiego 5B, 02-106 Warsaw, Poland
| | - P Kiełczyński
- Institute of Fundamental Technological Research, Polish Academy of Sciences, ul. Pawińskiego 5B, 02-106 Warsaw, Poland.
| | - P Szymański
- Department of Meat and Fat Technology, Prof. Wacław Dąbrowski Institute of Agricultural and Food Biotechnology, 36 Rakowiecka St., 02-532 Warsaw, Poland
| | - M Szalewski
- Institute of Fundamental Technological Research, Polish Academy of Sciences, ul. Pawińskiego 5B, 02-106 Warsaw, Poland
| | - A Balcerzak
- Institute of Fundamental Technological Research, Polish Academy of Sciences, ul. Pawińskiego 5B, 02-106 Warsaw, Poland
| | - S Ptasznik
- Department of Meat and Fat Technology, Prof. Wacław Dąbrowski Institute of Agricultural and Food Biotechnology, 36 Rakowiecka St., 02-532 Warsaw, Poland
| |
Collapse
|
38
|
Tian X, Aheto JH, Bai J, Dai C, Ren Y, Chang X. Quantitative analysis and visualization of moisture and anthocyanins content in purple sweet potato by Vis–NIR hyperspectral imaging. J FOOD PROCESS PRES 2020. [DOI: 10.1111/jfpp.15128] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Xiao‐Yu Tian
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
| | - Joshua H. Aheto
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
| | - Jun‐Wen Bai
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
| | - Chunxia Dai
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
- School of Electrical and Information Engineering Jiangsu University Zhenjiang P.R. China
| | - Yi Ren
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
- School of Smart Agriculture Suzhou Polytechnic Institute of Agriculture Suzhou P.R. China
| | - Xianhui Chang
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
| |
Collapse
|
39
|
Srinivas Y, Mathew SM, Kothakota A, Sagarika N, Pandiselvam R. Microwave assisted fluidized bed drying of nutmeg mace for essential oil enriched extracts: An assessment of drying kinetics, process optimization and quality. INNOV FOOD SCI EMERG 2020. [DOI: 10.1016/j.ifset.2020.102541] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
|
40
|
Silva S, Guedes C, Rodrigues S, Teixeira A. Non-Destructive Imaging and Spectroscopic Techniques for Assessment of Carcass and Meat Quality in Sheep and Goats: A Review. Foods 2020; 9:E1074. [PMID: 32784641 PMCID: PMC7466308 DOI: 10.3390/foods9081074] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 07/25/2020] [Accepted: 07/27/2020] [Indexed: 02/06/2023] Open
Abstract
In the last decade, there has been a significant development in rapid, non-destructive and non-invasive techniques to evaluate carcass composition and meat quality of meat species. This article aims to review the recent technological advances of non-destructive and non-invasive techniques to provide objective data to evaluate carcass composition and quality traits of sheep and goat meat. We highlight imaging and spectroscopy techniques and practical aspects, such as accuracy, reliability, cost, portability, speed and ease of use. For the imaging techniques, recent improvements in the use of dual-energy X-ray absorptiometry, computed tomography and magnetic resonance imaging to assess sheep and goat carcass and meat quality will be addressed. Optical technologies are gaining importance for monitoring and evaluating the quality and safety of carcasses and meat and, among them, those that deserve more attention are visible and infrared reflectance spectroscopy, hyperspectral imagery and Raman spectroscopy. In this work, advances in research involving these techniques in their application to sheep and goats are presented and discussed. In recent years, there has been substantial investment and research in fast, non-destructive and easy-to-use technology to raise the standards of quality and food safety in all stages of sheep and goat meat production.
Collapse
Affiliation(s)
- Severiano Silva
- Veterinary and Animal Research Centre (CECAV) Universidade Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal;
| | - Cristina Guedes
- Veterinary and Animal Research Centre (CECAV) Universidade Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal;
| | - Sandra Rodrigues
- Mountain Research Centre (CIMO), Escola Superior Agrária/Instituto Politécnico de Bragança, Campus Sta Apolónia Apt 1172, 5301-855 Bragança, Portugal; (S.R.); (A.T.)
| | - Alfredo Teixeira
- Mountain Research Centre (CIMO), Escola Superior Agrária/Instituto Politécnico de Bragança, Campus Sta Apolónia Apt 1172, 5301-855 Bragança, Portugal; (S.R.); (A.T.)
| |
Collapse
|
41
|
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.
Collapse
|
42
|
Investigation of a Medical Plant for Hepatic Diseases with Secoiridoids Using HPLC and FT-IR Spectroscopy for a Case of Gentiana rigescens. Molecules 2020; 25:molecules25051219. [PMID: 32182739 PMCID: PMC7179471 DOI: 10.3390/molecules25051219] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 03/02/2020] [Accepted: 03/06/2020] [Indexed: 12/15/2022] Open
Abstract
Secoiridoids could be used as a potential new drug for the treatment of hepatic disease. The content of secoiridoids of G. rigescens varied in different geographical origins and parts. In this study, a total of 783 samples collected from different parts of G. rigescens in Yunnan, Sichuan, and Guizhou Provinces. The content of secoiridoids including gentiopicroside, swertiamarin, and sweroside were determined by using HPLC and analyzed by one-way analysis of variance. Two selected variables including direct selected and variable importance in projection combined with partial least squares regression have been used to establish a method for the determination of secoiridoids using FT-IR spectroscopy. In addition, different pretreatments including multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative and second derivative (SD), and orthogonal signal correction (OSC) were compared. The results indicated that the sample (root, stem, and leaf) with total secoiridoids, gentiopicroside, swertiamarin, and sweroside from west Yunnan had higher content than samples from the other regions. The sample from Baoshan had more total secoiridoids than other samples for the whole medicinal plant. The best performance using FT-IR for the total secoiridoid was with the direct selected variable method involving pretreatment of MSC+OSC+SD in the root and stem, while in leaf, of the best method involved using original data with MSC+OSC+SD. This method could be used to determine the bioactive compounds quickly for herbal medicines.
Collapse
|
43
|
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.
Collapse
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
| |
Collapse
|
44
|
Sahar A, Allen P, Sweeney T, Cafferky J, Downey G, Cromie A, Hamill RM. Online Prediction of Physico-Chemical Quality Attributes of Beef Using Visible-Near-Infrared Spectroscopy and Chemometrics. Foods 2019; 8:foods8110525. [PMID: 31652829 PMCID: PMC6915407 DOI: 10.3390/foods8110525] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 10/15/2019] [Accepted: 10/16/2019] [Indexed: 12/04/2022] Open
Abstract
The potential of visible–near-infrared (Vis–NIR) spectroscopy to predict physico-chemical quality traits in 368 samples of bovine musculus longissimus thoracis et lumborum (LTL) was evaluated. A fibre-optic probe was applied on the exposed surface of the bovine carcass for the collection of spectra, including the neck and rump (1 h and 2 h post-mortem and after quartering, i.e., 24 h and 25 h post-mortem) and the boned-out LTL muscle (48 h and 49 h post-mortem). In parallel, reference analysis for physico-chemical parameters of beef quality including ultimate pH, colour (L, a*, b*), cook loss and drip loss was conducted using standard laboratory methods. Partial least-squares (PLS) regression models were used to correlate the spectral information with reference quality parameters of beef muscle. Different mathematical pre-treatments and their combinations were applied to improve the model accuracy, which was evaluated on the basis of the coefficient of determination of calibration (R2C) and cross-validation (R2CV) and root-mean-square error of calibration (RMSEC) and cross-validation (RMSECV). Reliable cross-validation models were achieved for ultimate pH (R2CV: 0.91 (quartering, 24 h) and R2CV: 0.96 (LTL muscle, 48 h)) and drip loss (R2CV: 0.82 (quartering, 24 h) and R2CV: 0.99 (LTL muscle, 48 h)) with lower RMSECV values. The results show the potential of Vis–NIR spectroscopy for online prediction of certain quality parameters of beef over different time periods.
Collapse
Affiliation(s)
- Amna Sahar
- Teagasc Food Research Centre Ashtown, Dublin D15 KN3K, Ireland.
| | - Paul Allen
- Teagasc Food Research Centre Ashtown, Dublin D15 KN3K, Ireland.
| | - Torres Sweeney
- UCD School of Veterinary Medicine, University College Dublin, Belfield, Dublin D04 W6F6, Ireland.
| | - Jamie Cafferky
- Teagasc Food Research Centre Ashtown, Dublin D15 KN3K, Ireland.
| | - Gerard Downey
- Teagasc Food Research Centre Ashtown, Dublin D15 KN3K, Ireland.
| | - Andrew Cromie
- Irish Cattle Breeders Federation, Highfield House, Shinagh, Bandon, Co. Cork P72 X050, Ireland.
| | - Ruth M Hamill
- Teagasc Food Research Centre Ashtown, Dublin D15 KN3K, Ireland.
| |
Collapse
|
45
|
NIRs calibration models for chemical composition and fatty acid families of raw and freeze-dried beef: A comparison. J Food Compost Anal 2019. [DOI: 10.1016/j.jfca.2019.103257] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
46
|
Peiris KHS, Bean SR, Chiluwal A, Perumal R, Jagadish SVK. Moisture effects on robustness of sorghum grain protein near‐infrared spectroscopy calibration. Cereal Chem 2019. [DOI: 10.1002/cche.10164] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | - Scott R. Bean
- USDA‐ARS Center for Grain and Animal Health Research Manhattan Kansas
| | - Anuj Chiluwal
- Department of Agronomy Kansas State University Manhattan Kansas
| | - Ramasamy Perumal
- Kansas State University Agricultural Research Center Hays Kansas
| | | |
Collapse
|
47
|
Akbarzadeh N, Mireei SA, Askari G, Mahdavi AH. Microwave spectroscopy based on the waveguide technique for the nondestructive freshness evaluation of egg. Food Chem 2019; 277:558-565. [PMID: 30502185 DOI: 10.1016/j.foodchem.2018.10.143] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 10/17/2018] [Accepted: 10/31/2018] [Indexed: 10/27/2022]
Abstract
A rectangular waveguide equipped with a network analyzer was used to assess the quality indices of shell egg. The scattering parameters of the eggs were acquired in the range of 0.9-1.7 GHz and they were then used to calculate microwave spectra of the samples. PLS and ANN regression methods were implemented to predict the egg quality indices and SIMCA and ANN classification methods were applied to classify the eggs based on their storage time. The best predictive models, however, obtained from ANN analysis where the yolk coefficient, air cell height, thick albumen height, Haugh unit, and albumen pH could be predicted with the residual predictive deviation (RPD) values of 3.500, 3.000, 2.411, 2.033, and 1.829, respectively. To classify the eggs according to their storage time, both SIMCA and ANN analyses resulted in the total accuracy of 100% when return loss spectra were used as the input.
Collapse
Affiliation(s)
- Niloufar Akbarzadeh
- Department of Biosystems Engineering, College of Agriculture, Isfahan University of Technology, Isfahan 84156-83111, Iran
| | - Seyed Ahmad Mireei
- Department of Biosystems Engineering, College of Agriculture, Isfahan University of Technology, Isfahan 84156-83111, Iran.
| | - Gholamreza Askari
- Information and Communication Technology Institute, Isfahan University of Technology, Isfahan 84156-83111, Iran
| | - Amir Hossein Mahdavi
- Department of Animal Sciences, College of Agriculture, Isfahan University of Technology, Isfahan 84156-83111, Iran
| |
Collapse
|
48
|
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
| |
Collapse
|
49
|
Protein content evaluation of processed pork meats based on a novel single shot (snapshot) hyperspectral imaging sensor. J FOOD ENG 2019. [DOI: 10.1016/j.jfoodeng.2018.07.032] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
|
50
|
Compressive Sensing Hyperspectral Imaging by Spectral Multiplexing with Liquid Crystal. J Imaging 2018; 5:jimaging5010003. [PMID: 34470182 PMCID: PMC8320867 DOI: 10.3390/jimaging5010003] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2018] [Revised: 11/25/2018] [Accepted: 12/18/2018] [Indexed: 11/16/2022] Open
Abstract
Hyperspectral (HS) imaging involves the sensing of a scene’s spectral properties, which are often redundant in nature. The redundancy of the information motivates our quest to implement Compressive Sensing (CS) theory for HS imaging. This article provides a review of the Compressive Sensing Miniature Ultra-Spectral Imaging (CS-MUSI) camera, its evolution, and its different applications. The CS-MUSI camera was designed within the CS framework and uses a liquid crystal (LC) phase retarder in order to modulate the spectral domain. The outstanding advantage of the CS-MUSI camera is that the entire HS image is captured from an order of magnitude fewer measurements of the sensor array, compared to conventional HS imaging methods.
Collapse
|