The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
Partial Least Squares Regression (PLSR) Applied to NIR and HSI Spectral Data Modeling to Predict Chemical Properties of Fish Muscle
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Citations
Cites
Review of the most common pre-processing techniques for near-infrared spectra
Rinnan, Å., Berg, F. V. D., & Engelsen, S. B. (2009) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2009.07.007 · Cited by 3,057
Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review
Nicolaï, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I., & Lammertyn, J. (2007) · Postharvest Biology and Technology · doi:10.1016/j.postharvbio.2007.06.024 · Cited by 2,252
A review of variable selection methods in Partial Least Squares Regression
Mehmood, T., Liland, K. H., Snipen, L., & Sæbø, S. (2012) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2012.07.010 · Cited by 1,591
Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation
Wong, T.-T. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.03.009 · Cited by 1,562
A user-friendly guide to Multivariate Calibration and Classification
Næs, T., Isaksson, T., Fearn, T., & Davies, T. (2017) · doi:10.1255/978-1-906715-25-0 · Cited by 1,401
Practical NIR spectroscopy with applications in food and beverage analysis.
Osborne, B. G., Fearn, T., & Hindle, P. H. (1993) · UCL Discovery (University College London) · Cited by 1,080
Theory and application of near infrared reflectance spectroscopy in determination of food quality
Cen, H., & He, Y. (2007) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2006.09.003 · Cited by 1,030
A User-Friendly Guide to Multivariate Calibration and Classification
Ziegel, E. R. (2004) · Technometrics · doi:10.1198/004017004000000167 · Cited by 916
NIR spectroscopy: a rapid-response analytical tool
Blanco, M., & Villarroya, I. (2002) · TrAC Trends in Analytical Chemistry · doi:10.1016/s0165-9936(02)00404-1 · Cited by 874
Data fusion methodologies for food and beverage authentication and quality assessment – A review
Borràs, E., Ferré, J., Boqué, R., Mestres, M., Aceña, L., & Busto, O. (2015) · Analytica Chimica Acta · doi:10.1016/j.aca.2015.04.042 · Cited by 743
Cited by
Li, X., Chen, K., & He, Y. (2020) · Algal Research · doi:10.1016/j.algal.2019.101680 · Cited by 26
Metadata
- Authors
- Cheng, J.-H., & Sun, D.-W.
- Venue
- Food Engineering Reviews
- Published
- 2016-07-12
- Volume
- 9
- Issue
- 1
- Pages
- 36-49
- ISSN
- 1866-7910 (ISSN-L)
- 1866-7929
- OpenAlex
- W2468213734
- Cited by
- 256
- References
- 68
- FWCI
- 15.60
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Partial Least Squares Regression (PLSR) Applied to NIR and HSI Spectral Data Modeling to Predict Chemical Properties of Fish Muscle. https://app.cochat.ai/papers/10.1007/s12393-016-9147-1
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- Cheng, J.-H., & Sun, D.-W. (2017). Partial Least Squares Regression (PLSR) Applied to NIR and HSI Spectral Data Modeling to Predict Chemical Properties of Fish Muscle. Food Engineering Reviews. https://doi.org/10.1007/s12393-016-9147-1
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
