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Hyperspectral Imaging and Chemometrics for Nondestructive Quantification of Total Volatile Basic Nitrogen in Pacific Oysters (Crassostrea gigas)

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Abstract

Total volatile basic nitrogen (TVB-N) content is used to evaluate Pacific oyster (Crassostrea gigas) freshness. In this work, hyperspectral imaging (HSI; 400–1000 nm) was used to measure the TVB-N content in Pacific oysters. Accordingly, Pacific oyster samples stored in 15 °C water were assessed at intervals after 1, 3, 5, 7, or 9 days. Minimum noise separation processing of the hyperspectral images was performed before determining the region of interest for data dimension reduction. The effects of multiplicative scatter correction (MSC) on the obtained data were then investigated. To simplify the calibration model, 12 characteristic wavelengths were selected from the Pacific oyster hypercube using the correlation coefficient method. Finally, multiple linear regression (MLR) and back-propagation artificial neural network (BP-ANN) models were built from the selected wavelengths. The experimental results showed that the correlation coefficients between the corrected, predicted, and cross-validated datasets were lower in the MLR model than in the BP-ANN. However, the MLR model outperformed the BP-ANN in terms of the root-mean-square errors of correction, prediction, and interaction verification. Overall, both the MLR and BP-ANN models demonstrated that the combination of HSI with chemometric methods can be used to detect and accurately predict Pacific Oyster freshness during storage.

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References

  • Aaraas R, Hernar IJ et al (2010) Sensory, histological, and bacteriological changes in flat oysters, Ostrea edulis L., during different storage conditions. J Food Sci 69(6):S205–S210

    Article  Google Scholar 

  • Arana I, Jaren C, Arazuri S (2005) Maturity, variety and origin determination in white grapes (Vitis Villifera L.) using near infrared reflectance technology. J Near Infrared Spectrosc 13:349–357

    Article  CAS  Google Scholar 

  • Cai J, Chen Q, Wan X, Zhao J (2011) Determination of total volatile basic nitrogen (TVB-N) content and Warner-Bratzler shear force (WBSF) in pork using Fourier transform near infrared (FT-NIR) spectroscopy. Food Chem 126:1354–1360

    Article  CAS  Google Scholar 

  • Cao R, Xue CH, Liu Q, Xue Y (2009) Microbiological, chemical, and sensory assessment of Pacific oysters (Crassostrea gigas) stored at different temperatures. Czech J Food Sci 27(2):102–108

    Article  CAS  Google Scholar 

  • Cen H, He Y (2007) Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends Food Sci Technol 18:72–83

    Article  CAS  Google Scholar 

  • Chen Q, Hui Z, Zhao J, Ouyang Q (2014) Evaluation of chicken freshness using a low-cost colorimetric sensor array with AdaBoost-OLDA classification algorithm. LWT Food Sci Technol 57:502–507

    Article  CAS  Google Scholar 

  • Chen Q, Zhang Y, Zhao J, Hui Z (2013) Nondestructive measurement of total volatile basic nitrogen (TVB-N) content in salted pork in jelly using a hyperspectral imaging technique combined with efficient hypercube processing algorithms. Anal Methods 5:6382–6388

    Article  CAS  Google Scholar 

  • Cheng JH, Sun DW (2014) Hyperspectral imaging as an effective tool for quality analysis and control of fish and other seafoods: current research and potential applications. Trends Food Sci Technol 37(2):78–91

    Article  CAS  Google Scholar 

  • Cheng JH, Sun DW, Pu HB, Wang QJ, Chen YN (2015) Suitability of hyperspectral imaging for rapid evaluation of thiobarbituric acid (TBA) value in grass carp (Ctenopharyngodon idella) fillet. Food Chem 171(4):258–265

    Article  CAS  PubMed  Google Scholar 

  • Cheng J, Sun D, Pu H, Zeng X (2014) Comparison of visible and long-wave near-infrared hyperspectral imaging for colour measurement of grass carp (Ctenopharyngodon idella). Food Bioprocess Technol 7:3109–3120

    Article  Google Scholar 

  • De MM, Penasa M et al (2011) Feasibility of the direct application of near-infrared reflectance spectroscopy on intact chicken breasts to predict meat color and physical traits. Poult Sci 90(7):1594–1599

    Article  Google Scholar 

  • Du CJ, Sun DW (2005) Pizza sauce spread classification using colour vision and support vector machines. J Food Eng 66:137–145

    Article  Google Scholar 

  • FAO (2016) https://www.fao.org/fishery/statistics/global-production/en

  • Fassnacht FE, Neumann C, Foerster M, Buddenbaum H, Ghosh A, Clasen A et al (2014) Comparison of feature reduction algorithms for classifying tree species with hyperspectral data on three central European test sites. IEEE J-STARS 7:2547–2561

    Google Scholar 

  • Fathi M, Mohebbi M, Razavi SMA (2011) Application of image analysis and artificial neural network to predict mass transfer kinetics and color changes of osmotically dehydrated kiwifruit. Food Bioprocess Technol 4:1357–1366

    Article  Google Scholar 

  • Feng Y, Sun D (2013) Determination of total viable count (TVC) in chicken breast fillets by near-infrared hyperspectral imaging and spectroscopic transforms. Talanta 105:244–249

    Article  CAS  Google Scholar 

  • Gao L, Zhang B, Sun X, Li S, Du Q et al (2013) Optimized maximum noise fraction for dimensionality reduction of Chinese HJ-1A hyperspectral data. EURASIP J ADV SIG PR 2013:65

    Article  Google Scholar 

  • Garini Y, Young IT, McNamara G (2006) Spectral imaging: principles and applications. Cytometry Part A 69A:735–747

    Article  Google Scholar 

  • Gaston E, Frias JM, Cullen PJ, O'Donnell CP, Gowenb AA (2010) Visible-near infrared hyperspectral imaging for the identification and discrimination of brown blotch disease on mushroom (Agaricus bisporus) caps. J Near Infrared Spectrosc 18:341–353

    Article  CAS  Google Scholar 

  • Huang L, Zhao J, Chen Q, Zhang Y (2013) Rapid detection of total viable count (TVC) in pork meat by hyperspectral imaging. Food Res Int 54:821–828

    Article  CAS  Google Scholar 

  • Huang L, Zhao J, Chen Q, Zhang Y (2014) Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques. Food Chem 145:228–236

    Article  CAS  PubMed  Google Scholar 

  • Iqbal A, Sun D, Allen P (2013) Prediction of moisture, color and pH in cooked, pre-sliced turkey hams by NIR hyperspectral imaging system. J Food Eng 117:42–51

    Article  CAS  Google Scholar 

  • Jackman P, Sun D, Du C, Allen P, Downey G (2008) Prediction of beef eating quality from colour, marbling and wavelet texture features. Meat Sci 80:1273–1281

    Article  PubMed  Google Scholar 

  • Jin JW, Chen ZP, Li LM, Steponavicius R, Thennadil SN, Yang J, Yu RQ (2012) Quantitative spectroscopic analysis of heterogeneous mixtures: the correction of multiplicative effects caused by variations in physical properties of samples. Anal Chem 84(1):320–326

    Article  CAS  PubMed  Google Scholar 

  • Kamruzzaman M, Barbin D, ElMasry G, Sun D, Allen P (2012a) Potential of hyperspectral imaging and pattern recognition for categorization and authentication of red meat. Innovative Food Sci Emerg Technol 16:316–325

    Article  CAS  Google Scholar 

  • Kamruzzaman M, ElMasry G, Sun D, Allen P (2012b) Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression. Innovative Food Sci Emerg Technol 16:218–226

    Article  CAS  Google Scholar 

  • Khulal U, Zhao J et al (2016) Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms. Food Chem 197(Pt B):1191–1199

    Article  CAS  PubMed  Google Scholar 

  • Leng X, Wang J et al (2017) Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses. Chemosphere 180:513–522

    Article  CAS  PubMed  Google Scholar 

  • Leroy B, Lambotte S, Dotreppe O, Lecocq H, Istasse L et al (2004) Prediction of technological and organoleptic properties of beef Longissimus thoracis from near-infrared reflectance and transmission spectra. Meat Sci 66:45–54

    Article  CAS  PubMed  Google Scholar 

  • Li H, Chen Q, Zhao J, Wu M (2015) Nondestructive detection of total volatile basic nitrogen (TVB-N) content in pork meat by integrating hyperspectral imaging and colorimetric sensor combined with a nonlinear data fusion. LWT Food Sci Technol 63(1):268–274

    Article  CAS  Google Scholar 

  • Liu D, Sun D, Zeng X (2014) Recent advances in wavelength selection techniques for hyperspectral image processing in the food industry. Food Bioprocess Technol 7:307–323

    Article  Google Scholar 

  • Luo G, Chen G, Tian L, Qin K, Qian S (2016) Minimum noise fraction versus principal component analysis as a preprocessing step for hyperspectral imagery denoising. Can J Remote Sens 42:106–116

    Article  Google Scholar 

  • Mohammadi-Moghaddam T, Razavi SMA et al (2018) Hyperspectral imaging as an effective tool for prediction the moisture content and textural characteristics of roasted pistachio kernels. J FOOD MEAS CHARACT 3–4:1–10

    Google Scholar 

  • Nakariyakul S, Casasent DP (2009) An improvement on floating search algorithms for feature subset selection. Pattern Recogn 42:1932–1940

    Article  Google Scholar 

  • Pontes M, Santos S, Araujo M, Almeida LF, Lima R, Gaiao EN et al (2006) Classification of distilled alcoholic beverages and verification of adulteration by near infrared spectrometry. Food Res Int 39:182–189

    Article  CAS  Google Scholar 

  • Prats-Montalbán JM, Juan AD et al (2011) Multivariate image analysis: a review with applications. Chemom Intell Lab Syst 107(1):1–23

    Article  CAS  Google Scholar 

  • Pu H, Kamruzzaman M, Sun D (2015) Selection of feature wavelengths for developing multispectral imaging systems for quality, safety and authenticity of muscle foods-a review. Trends Food Sci Technol 45:86–104

    Article  CAS  Google Scholar 

  • Rodtong S, Nawong S, Yongsawatdigul J (2005) Histamine accumulation and histamine-forming bacteria in Indian anchovy (Stolephorus indicus). Food Microbiol 22:475–482

    Article  CAS  Google Scholar 

  • Sun, D. W., Brosnan, T. (2003). Pizza quality evaluation using computer vision - Part 2 - Pizza topping analysis. Journal Of Food Engineering, 57(PII S0260–8774(02)00276–51) : 91–95

  • Tao F, Peng Y, Li Y, Chao K, Dhakal S (2012) Simultaneous determination of tenderness and Escherichia coli contamination of pork using hyperspectral scattering technique. Meat Sci 90:851–857

    Article  PubMed  Google Scholar 

  • Valous NA, Mendoza F, Sun D, Allen P (2009) Texture appearance characterization of pre-sliced pork ham images using fractal metrics: Fourier analysis dimension and lacunarity. Food Res Int 42:353–362

    Article  Google Scholar 

  • Wang L, Lee F, Wang XR, He Y (2006) Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR. Food Chem 95:529–536

    Article  CAS  Google Scholar 

  • Xiong Z, Sun D, Dai Q, Han Z, Zeng X et al (2015a) Application of visible hyperspectral imaging for prediction of springiness of fresh chicken meat. Food Anal Methods 8:380–391

    Article  Google Scholar 

  • Xiong Z, Sun D, Pu H, Gao W, Dai Q (2017) Applications of emerging imaging techniques for meat quality and safety detection and evaluation: a review. Crit Rev Food Sci Nutr 57:755–768

    Article  CAS  PubMed  Google Scholar 

  • Xiong Z, Sun D, Pu H, Xie A, Han Z et al (2015b) Non-destructive prediction of thiobarbituric acid reactive substances (TSARS) value for freshness evaluation of chicken meat using hyperspectral imaging. Food Chem 179:175–181

    Article  CAS  PubMed  Google Scholar 

  • Xiong Z, Sun D, Xie A, Han Z, Wang L (2015c) Potential of hyperspectral imaging for rapid prediction of hydroxyproline content in chicken meat. Food Chem 175:417–422

    Article  CAS  PubMed  Google Scholar 

  • Xiong Z, Sun D, Zeng X, Xie A (2014) Recent developments of hyperspectral imaging systems and their applications in detecting quality attributes of red meats: a review. J Food Eng 132:1–13

    Article  CAS  Google Scholar 

  • Xu B, Zhang G, Xu C, Sun S (2015) Analysis of fingerprints features of infrared spectra of various processed products of Rhizoma Coptidis and their different extracts. J Mol Struct 1096:147–156

    Article  CAS  Google Scholar 

  • Xu J, Chen Y, Li W, Peng PY, Yang Y, Song C et al (2013) Combining BPANN and wavelet analysis to simulate hydro-climatic processes-a case study of the Kaidu River, North-west China. FRONT EARTH SCI-PRC 7:227–237

    Article  Google Scholar 

  • Yoon SC, Park B, Lawrence KC, Windham WR, Heitschmidt GW (2011) Line-scan hyperspectral imaging system for real-time inspection of poultry carcasses with fecal material and ingesta. Comput Electron Agric 79:159–168

    Article  Google Scholar 

  • Yu X, Tang L, Wu X, Lu H (2018) Nondestructive freshness discriminating of shrimp using visible/near-infrared hyperspectral imaging technique and deep learning algorithm. Food Anal Methods 11:768–780

    Article  Google Scholar 

  • Zhang Z, Tong J, Chen D, Lan Y (2008) Electronic nose with an air sensor matrix for detecting beef freshness. J Bionic Eng 5:67–73

    Article  Google Scholar 

  • Zhao Y, Huang Y, Zhang X, Zhang S (2015) A quantitative prediction of the viscosity of ionic liquids using S(σ-profile) molecular descriptors. Phys Chem Chem Phys 17(5):3761–3767

    Article  CAS  PubMed  Google Scholar 

  • Zheng C, Sun D, Zheng L (2006) Recent developments and applications of image features for food quality evaluation and inspection—a review. Trends Food Sci Technol 17:642–655

    Article  CAS  Google Scholar 

  • Zou X, Shi J, Hao L, Zhao J, Mao H, Chen Z, Li Y, Holmes M (2011) In vivo noninvasive detection of chlorophyll distribution in cucumber (Cucumis sativus) leaves by indices based on hyperspectral imaging. Anal Chim Acta 706:105–112

    Article  CAS  PubMed  Google Scholar 

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Funding

This work was financially supported by the earmarked fund for Modern Agro-industry Technology Research System (CARS-49).

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Authors and Affiliations

  1. College of Food Science and Engineering, Ocean University of China, Qingdao, 266003, China

    Lipin Chen, Zhaojie Li, Fanqianhui Yu, Xu Zhang, Yong Xue & Changhu Xue

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  1. Lipin Chen
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  2. Zhaojie Li
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  3. Fanqianhui Yu
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  4. Xu Zhang
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Correspondence to Zhaojie Li.

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Lipin Chen declares that she has no conflict of interest. Zhaojie Li declares that he has no conflict of interest. Fanqianhui Yu declares that she has no conflict of interest. Xu Zhang declares that she has no conflict of interest Yong Xue declares that he has no conflict of interest. Changhu Xue declares that he has no conflict of interest.

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Chen, L., Li, Z., Yu, F. et al. Hyperspectral Imaging and Chemometrics for Nondestructive Quantification of Total Volatile Basic Nitrogen in Pacific Oysters (Crassostrea gigas). Food Anal. Methods 12, 799–810 (2019). https://doi.org/10.1007/s12161-018-1400-1

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