Development of simplified models for nondestructive testing of rice (with husk) protein content using hyperspectral imaging technology

Ren, Zhishang; Zhang, Zhehao; Du, Juan; Jin, Chengqian; Yin, Xiang

doi:10.1016/j.vibspec.2021.103230

Cited by 29 publications

(18 citation statements)

References 35 publications

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“…For the SGPC model, the peaks at 1465, 2127, 2290, and 2302 nm were protein-related absorption peaks, and the peak at 1940 nm was a water-related absorption peak, as proteins in the endosperm strongly interact with water [ 24 , 25 , 26 ]. The SGPC model’s results were similar to those based on the population rice grain spectrum using hyperspectral imaging [ 27 ], and slightly worse than the diffuse transmission SGPC model built by Xu et al [ 11 ] because of the difference in the spectral acquisition methods, e.g., the diffuse reflectance spectra that were used in the paper.…”

Section: Discussionmentioning

confidence: 57%

Effects of Variations in the Chemical Composition of Individual Rice Grains on the Eating Quality of Hybrid Indica Rice Based on Near-Infrared Spectroscopy

Cheng

Fan

et al. 2022

Foods

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The chemical composition of individual hybrid rice (F2) varieties varies owing to genetic differences between parental lines, and the effects of these differences on eating quality are unclear. In this study, based on a self-developed near-infrared spectroscopy platform, we explored these effects among a set of 143 hybrid indica rice varieties with different eating qualities. The single-grain amylose content (SGAC) and single-grain protein content (SGPC) models were established with coefficients of determination (R2) of 0.9064 and 0.8847, respectively, and the dispersion indicators (standard deviation, variance, extreme deviation, quartile deviation, and coefficient of variation) were proposed to analyze the variations in the SGAC and SGPC based on the predicted results. Our correlation analysis found that the higher the variation in the SGAC and SGPC, the lower the eating quality of the hybrid indica rice. Moreover, the addition of the dispersion indicators of the SGAC and SGPC improved the R2 of the eating quality model constructed using the composition-related physicochemical indicators (amylose content, protein content, alkali-spreading value, and gel consistency) from 0.657 to 0.850. Therefore, this new method proved to be useful for identifying high-eating-quality hybrid indica rice based on single near-infrared spectroscopy prior to processing and cooking.

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Section: Discussionmentioning

confidence: 57%

Effects of Variations in the Chemical Composition of Individual Rice Grains on the Eating Quality of Hybrid Indica Rice Based on Near-Infrared Spectroscopy

Cheng

Fan

et al. 2022

Foods

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“…The partial least squares regression (PLSR) model combines principal components analysis (PCA) with maximum correlation analysis to fit the distribution of random variables into linear equations. It is widely used in mathematics, statistics, and finance ( Guo et al., 2021 ; Ma et al., 2021 ). Least square support vector machine (LSSVM) replaces the complex secondary optimization problem in the traditional SVM by solving primary linear equations, simplifying the model and improving its operation speed ( Feng et al., 2018 ; Zhang et al., 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging

Chen

Wang

et al. 2023

Front. Plant Sci.

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The soluble solid content (SSC) is one of the important parameters depicting the quality, maturity and taste of fruits. This study explored hyperspectral imaging (HSI) and fluorescence spectral imaging (FSI) techniques, as well as suitable chemometric techniques to predict the SSC in kiwifruit. 90 kiwifruit samples were divided into 70 calibration sets and 20 prediction sets. The hyperspectral images of samples in the spectral range of 387 nm~1034 nm and the fluorescence spectral images in the spectral range of 400 nm~1000 nm were collected, and their regions of interest were extracted. Six spectral pre-processing techniques were used to pre-process the two spectral data, and the best pre-processing method was selected after comparing it with the predicted results. Then, five primary and three secondary feature extraction algorithms were used to extract feature variables from the pre-processed spectral data. Subsequently, three regression prediction models, i.e., the extreme learning machines (ELM), the partial least squares regression (PLSR) and the particle swarm optimization - least square support vector machine (PSO-LSSVM), were established. The prediction results were analyzed and compared further. MASS-Boss-ELM, based on fluorescence spectral imaging technique, exhibited the best prediction performance for the kiwifruit SSC, with the Rp2, Rc2 and RPD of 0.8894, 0.9429 and 2.88, respectively. MASS-Boss-PLSR based on the hyperspectral imaging technique showed a slightly lower prediction performance, with the Rp2, Rc2, and RPD of 0.8717, 0.8747, and 2.89, respectively. The outcome presents that the two spectral imaging techniques are suitable for the non-destructive prediction of fruit quality. Among them, the FSI technology illustrates better prediction, providing technical support for the non-destructive detection of intrinsic fruit quality.

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“…These varieties had great differences in growth environment, growth time, disease resistance, insect resistance, taste, and so on. The appearance of sample was shown in Figure 1, and the basic physical and chemical properties of sample were listed in Table 1 (The protein content of the sample was determined by Kjeldahl method) (Ma et al, 2021). Before imaging experiment, a total of 1,620 rice seeds (324 of each variety) with full grain and clean surface were selected and stored in a dry and low‐temperature place for 2 days.…”

Section: Methodsmentioning

confidence: 99%

A method of information fusion for identification of rice seed varieties based on hyperspectral imaging technology

Sun

Zhang

Zhou

et al. 2021

J Food Process Engineering

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Accurate, rapid, and nondestructive identification of rice seed varieties has great significance for agriculture and food security, a method based on information fusion and artificial fish swarm algorithm (AFSA) combined with the hyperspectral imaging (HSI) of five kinds of rice seeds was proposed in this work. First, the spectral and image data were obtained from HSI, and the spectral data were preprocessed by detrending. Then, bootstrapping soft shrinkage (BOSS), variable iterative space shrinkage approach, successive projections algorithm, and principal component analysis were adopted to select feature variables from the spectral and image data. Next, the support vector machine (SVM) model was constructed based on the spectral and image feature variables. In order to further improve the classification accuracy of single feature model, the model based on fused feature was developed and it was finally optimized by AFSA. The research showed that the feature variables (114 spectral variables and 11 image variables) selected by BOSS were representative, and the model accuracy based on BOSS spectral and image features reached 91.48% and 70%, respectively. The performance of the SVM model based on fused feature was improved significantly, and the model accuracy reached 97.22%. After AFSA optimization, the model accuracy finally reached 99.44%. The above result confirmed that using AFSA to optimize the model based on fused feature could be a promising method to identify rice seed varieties. Practical application Rapid and accurate identification of rice seed varieties contributes to establishment of online rice seed identification system. HSI combined with information fusion and AFSA can overcome the disadvantage of low accuracy of traditional nondestructive testing methods. The method proposed in this article can be recommended to be widely popularized in farms, grain markets, and market regulators.

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Development of simplified models for nondestructive testing of rice (with husk) protein content using hyperspectral imaging technology

Cited by 29 publications

References 35 publications

Effects of Variations in the Chemical Composition of Individual Rice Grains on the Eating Quality of Hybrid Indica Rice Based on Near-Infrared Spectroscopy

Effects of Variations in the Chemical Composition of Individual Rice Grains on the Eating Quality of Hybrid Indica Rice Based on Near-Infrared Spectroscopy

Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging

A method of information fusion for identification of rice seed varieties based on hyperspectral imaging technology

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