1
|
Lintvedt TA, Andersen PV, Afseth NK, Heia K, Lindberg SK, Wold JP. Raman spectroscopy and NIR hyperspectral imaging for in-line estimation of fatty acid features in salmon fillets. Talanta 2023; 254:124113. [PMID: 36473242 DOI: 10.1016/j.talanta.2022.124113] [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: 06/29/2022] [Revised: 11/14/2022] [Accepted: 11/16/2022] [Indexed: 12/03/2022]
Abstract
Raman spectroscopy was compared with near infrared (NIR) hyperspectral imaging for determination of fat composition (%EPA + DHA) in salmon fillets at short exposure times. Fillets were measured in movement for both methods. Salmon were acquired from several different farming locations in Norway with different feeding regimes, representing a realistic variation of salmon in the market. For Raman, we investigated three manual scanning strategies; i) line scan of loin, ii) line scan of belly and iii) sinusoidal scan of belly at exposure times of 2s and 4s. NIR images were acquired while the fillets moved on a conveyor belt at 40 cm/s, which corresponds to an acquisition time of 1s for a 40 cm long fillet. For NIR images, three different regions of interest (ROI) were investigated including the i) whole fillet, ii) belly segment, and iii) loin segment. For both Raman and NIR measurements, we investigated an untrimmed and trimmed version of the fillets, both relevant for industrial in-line evaluation. For the trimmed fillets, a fat rich deposition layer in the belly was removed. The %EPA + DHA models were validated by cross validation (N = 51) and using an independent test set (N = 20) which was acquired in a different season. Both Raman and NIR showed promising results and high performances in the cross validation, with R2CV = 0.96 for Raman at 2s exposure and R2CV = 0.97 for NIR. High performances were obtained also for the test set, but while Raman had low and stable biases for the test set, the biases were high and varied for the NIR measurements. Analysis of variance on the squared test set residuals showed that performance for Raman measurements were significantly higher than NIR at 1% significance level (p = 0.000013) when slope-and-bias errors were not corrected, but not significant when residuals were slope-and-bias corrected (p = 0.28). This indicated that NIR was more sensitive to matrix effects. For Raman, signal-to-noise ratio was the main limitation and there were indications that Raman was close to a critical sample exposure time at the 2s signal accumulation.
Collapse
Affiliation(s)
- Tiril Aurora Lintvedt
- Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, Tromsø, 9291, Norway; Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, 1432, Norway.
| | - Petter Vejle Andersen
- Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, Tromsø, 9291, Norway
| | - Nils Kristian Afseth
- Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, Tromsø, 9291, Norway
| | - Karsten Heia
- Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, Tromsø, 9291, Norway
| | - Stein-Kato Lindberg
- Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, Tromsø, 9291, Norway
| | - Jens Petter Wold
- Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, Tromsø, 9291, Norway
| |
Collapse
|
2
|
The impact of high-quality data on the assessment results of visible/near-infrared hyperspectral imaging and development direction in the food fields: a review. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01822-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
|
3
|
Jayapal PK, Joshi R, Sathasivam R, Van Nguyen B, Faqeerzada MA, Park SU, Sandanam D, Cho BK. Non-destructive measurement of total phenolic compounds in Arabidopsis under various stress conditions. FRONTIERS IN PLANT SCIENCE 2022; 13:982247. [PMID: 36119609 PMCID: PMC9478847 DOI: 10.3389/fpls.2022.982247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
Quantifying the phenolic compounds in plants is essential for maintaining the beneficial effects of plants on human health. Existing measurement methods are destructive and/or time consuming. To overcome these issues, research was conducted to develop a non-destructive and rapid measurement of phenolic compounds using hyperspectral imaging (HSI) and machine learning. In this study, the Arabidopsis was used since it is a model plant. They were grown in controlled and various stress conditions (LED lights and drought). Images were captured using HSI in the range of 400-1,000 nm (VIS/NIR) and 900-2,500 nm (SWIR). Initially, the plant region was segmented, and the spectra were extracted from the segmented region. These spectra were synchronized with plants' total phenolic content reference value, which was obtained from high-performance liquid chromatography (HPLC). The partial least square regression (PLSR) model was applied for total phenolic compound prediction. The best prediction values were achieved with SWIR spectra in comparison with VIS/NIR. Hence, SWIR spectra were further used. Spectral dimensionality reduction was performed based on discrete cosine transform (DCT) coefficients and the prediction was performed. The results were better than that of obtained with original spectra. The proposed model performance yielded R 2-values of 0.97 and 0.96 for calibration and validation, respectively. The lowest standard errors of predictions (SEP) were 0.05 and 0.07 mg/g. The proposed model out-performed different state-of-the-art methods. These demonstrate the efficiency of the model in quantifying the total phenolic compounds that are present in plants and opens a way to develop a rapid measurement system.
Collapse
Affiliation(s)
- Praveen Kumar Jayapal
- Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University, Daejeon, South Korea
- Disruptive and Sustainable Technologies for Agricultural Precision (DiSTAP), Singapore-MIT Alliance for Research and Technology (SMART), Singapore, Singapore
| | - Rahul Joshi
- Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University, Daejeon, South Korea
| | - Ramaraj Sathasivam
- Department of Crop Science, College of Agriculture and Life Science, Chungnam National University, Daejeon, South Korea
| | - Bao Van Nguyen
- Department of Crop Science, College of Agriculture and Life Science, Chungnam National University, Daejeon, South Korea
| | - Mohammad Akbar Faqeerzada
- Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University, Daejeon, South Korea
| | - Sang Un Park
- Department of Crop Science, College of Agriculture and Life Science, Chungnam National University, Daejeon, South Korea
- Department of Smart Agriculture Systems, Chungnam National University, Daejeon, South Korea
| | - Domnic Sandanam
- Department of Computer Applications, National Institute of Technology, Tiruchirappalli, India
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University, Daejeon, South Korea
- Department of Smart Agriculture Systems, Chungnam National University, Daejeon, South Korea
| |
Collapse
|
4
|
Zeng Q, Wang L, Wu S, Fang G, Zhao M, Li Z, Li W. Research progress on the application of spectral imaging technology in pharmaceutical tablet analysis. Int J Pharm 2022; 625:122100. [PMID: 35961418 DOI: 10.1016/j.ijpharm.2022.122100] [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: 05/23/2022] [Revised: 07/23/2022] [Accepted: 08/05/2022] [Indexed: 11/30/2022]
Abstract
Tablet as a traditional dosage form in pharmacy has the advantages of accurate dosage, ideal dissolution and bioavailability, convenient to carry and transport. The most concerned tablet quality attributes include active pharmaceutical ingredient (API) contents and polymorphic forms, components distribution, hardness, density, coating state, dissolution behavior, etc., which greatly affect the bioavailability and consistency of tablet final products. In the pharmaceutical industry, there are usually industry standard methods to analyze the tablet quality attributes. However, these methods are generally time-consuming and laborious, and lack a comprehensive understanding of the properties of tablets, such as spatial information. In recent years, spectral imaging technology makes up for the shortcomings of traditional tablet analysis methods because it provides non-contact and rich information in time and space. As a promising technology to replace the traditional tablet analysis methods, it has attracted more and more attention. The present paper briefly describes a series of spectral imaging techniques and their applications in tablet analysis. Finally, the possible application prospect of this technology and the deficiencies that need to be improved were also prospected.
Collapse
Affiliation(s)
- Qi Zeng
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Long Wang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Sijun Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Guangpu Fang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Mingwei Zhao
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Wenlong Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China.
| |
Collapse
|
5
|
Lintvedt TA, Andersen PV, Afseth NK, Marquardt B, Gidskehaug L, Wold JP. Feasibility of In-Line Raman Spectroscopy for Quality Assessment in Food Industry: How Fast Can We Go? APPLIED SPECTROSCOPY 2022; 76:559-568. [PMID: 35216528 PMCID: PMC9082979 DOI: 10.1177/00037028211056931] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Raman spectroscopy is a viable tool within process analytical technologies due to recent technological advances. In this article, we evaluate the feasibility of Raman spectroscopy for in-line applications in the food industry by estimating the concentration of the fatty acids EPA + DHA in ground salmon samples (n = 63) and residual bone concentration in samples of mechanically recovered ground chicken (n = 66). The samples were measured under industry like conditions: They moved on a conveyor belt through a dark cabinet where they were scanned with a wide area illumination standoff Raman probe. Such a setup should be able to handle relevant industrial conveyor belt speeds, and it was studied how different speeds (i.e., exposure times) influenced the signal-to-noise ratio (SNR) of the Raman spectra as well as the corresponding model performance. For all samples we applied speeds that resulted in 1 s, 2 s, 4 s, and 10 s exposure times. Samples were scanned in both heterogenous and homogenous state. The slowest speed (10 s exposure) yielded prediction errors (RMSECV) of 0.41%EPA + DHA and 0.59% ash for the salmon and chicken data sets, respectively. The more in-line relevant exposure time of 1 s resulted in increased RMSECV values, 0.84% EPA + DHA and 0.84% ash, respectively. The increase in prediction error correlated closely with the decrease in SNR. Further improvements of model performance were possible through different noise reduction strategies. Model performance for homogenous and heterogenous samples was similar, suggesting that the presented Raman scanning approach has the potential to work well also on intact heterogenous foods. The estimation errors obtained at these high speeds are likely acceptable for industrial use, but successful strategies to increase SNR will be key for widespread in-line use in the food industry.
Collapse
Affiliation(s)
- Tiril Aurora Lintvedt
- Nofima AS, Troms∅, Norway
- Tiril Aurora Lintvedt, Faculty of Science and Technology, NMBU, Nofima—Norwegian Institute for Food, Fisheries, and Aquaculture Research, Muninbakken 9-13, Breivika, Tromsø 9291, Norway.
| | | | | | | | | | | |
Collapse
|
6
|
Chapman J, Elbourne A, Truong VK, Cozzolino D. Shining light into meat – a review on the recent advances in
in vivo
and carcass applications of near infrared spectroscopy. Int J Food Sci Technol 2019. [DOI: 10.1111/ijfs.14367] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- James Chapman
- School of Science RMIT University GPO Box 2476 Melbourne Victoria 3001 Australia
| | - Aaron Elbourne
- School of Science RMIT University GPO Box 2476 Melbourne Victoria 3001 Australia
| | - Vi Khanh Truong
- School of Science RMIT University GPO Box 2476 Melbourne Victoria 3001 Australia
| | - Daniel Cozzolino
- School of Science RMIT University GPO Box 2476 Melbourne Victoria 3001 Australia
| |
Collapse
|
7
|
A Clustering-Based Partial Least Squares Method for Improving the Freshness Prediction Model of Crucian Carps Fillets by Hyperspectral Image Technology. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-019-01541-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
|
8
|
Line-Scan Hyperspectral Imaging Techniques for Food Safety and Quality Applications. APPLIED SCIENCES-BASEL 2017. [DOI: 10.3390/app7020125] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|