Modeling of flaxseed protein, oil content, linoleic acid, and lignan content prediction based on hyperspectral imaging

Protein, oil content, linoleic acid, and lignan are several key indicators for evaluating the quality of flaxseed. In order to optimize the testing methods for flaxseed’s nutritional quality and enhance the efficiency of screening high-quality flax germplasm resources, we selected 30 flaxseed specie...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Frontiers in plant science Ročník 15; s. 1344143
Hlavní autoři: Zhu, Dongyu, Han, Junying, Liu, Chengzhong, Zhang, Jianping, Qi, Yanni
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland Frontiers Media SA 12.02.2024
Frontiers Media S.A
Témata:
Adaptive sampling
Algorithms
Error correction
Flax
flaxseed
Germplasm
Hyperspectral imaging
Least squares method
lignan
Lignans
Linoleic acid
Nondestructive testing
Nutrient content
Nutrients
Nutritive value
oil content
Oils & fats
Optimization
Plant Science
Prediction models
protein
Proteins
Regression
Regression analysis
Root-mean-square errors
Seeds
Software
Spectrum analysis
Support vector machines
China
linoleic acid
flaxseed
lignan
hyperspectral imaging
protein
oil content
ISSN:1664-462X, 1664-462X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Protein, oil content, linoleic acid, and lignan are several key indicators for evaluating the quality of flaxseed. In order to optimize the testing methods for flaxseed’s nutritional quality and enhance the efficiency of screening high-quality flax germplasm resources, we selected 30 flaxseed species widely cultivated in Northwest China as the subjects of our study. Firstly, we gathered hyperspectral information regarding the seeds, along with data on protein, oil content, linoleic acid, and lignan, and utilized the SPXY algorithm to classify the sample set. Subsequently, the spectral data underwent seven distinct preprocessing methods, revealing that the PLSR model exhibited superior performance after being processed with the SG smoothing method. Feature wavelength extraction was carried out using the Successive Projections Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling (CARS). Finally, four quantitative analysis models, namely Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Multiple Linear Regression (MLR), and Principal Component Regression (PCR), were individually established. Experimental results demonstrated that among all the models for predicting protein content, the SG-CARS-MLR model predicted the best, with and of 0.9563 and 0.9336, with the corresponding Root Mean Square Error Correction (RMSEC) and Root Mean Square Error Prediction (RMSEP) of 0.4892 and 0.5616, respectively. In the optimal prediction models for oil content, linoleic acid and lignan, the R p 2 was 0.8565, 0.8028, 0.9343, and the RMSEP was 0.8682, 0.5404, 0.5384, respectively. The study results show that hyperspectral imaging technology has excellent potential for application in the detection of quality characteristics of flaxseed and provides a new option for the future non-destructive testing of the nutritional quality of flaxseed.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
Reviewed by: Liu Zhang, China Agricultural University, China
Hengchang Zang, Shandong University, China
Leiqing Pan, Nanjing Agricultural University, China
Edited by: Xi Tian, Beijing Academy of Agriculture and Forestry Sciences, China
ISSN:1664-462X
1664-462X
DOI:10.3389/fpls.2024.1344143