Prediction of some quality properties of rice and its flour by near‐infrared spectroscopy (NIRS) analysis

Burestan, Nasrollah Fazeli; Sayyah, A H Afkari; Taghinezhad, Ebrahim

doi:10.1002/fsn3.2086

Cited by 25 publications

(6 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Literature suggests that ground homogenous samples provide consistent spectra with which better prediction models could be fitted compared with rough unground samples (Bao et al, 2007; Fazeli Burestan et al, 2021). Although this was not considered in this study, as it would potentially increase the turn‐around time and make it less feasible for high‐throughput applications.…”

Section: Discussionmentioning

confidence: 99%

Estimation of grain quality parameters in rice for high‐throughput screening with near‐infrared spectroscopy and deep learning

Ravichandran

Viswanathan²,

Ravichandran³

et al. 2022

Cereal Chem

View full text Add to dashboard Cite

Background and Objectives Grain quality is a complex trait in rice, compared with other staple crops as it is predominantly consumed as a whole grain. Although considered secondary to yield, to align with consumer preferences, breeders are increasingly interested in quality. At the early stages of a breeding program, grain quality‐related traits are often ignored as they are arduous and time‐consuming. Near‐infrared spectroscopy (NIRS) could be a suitable high‐throughput alternative to conventional wet chemistry and image processing‐related methods to be adopted for early screening. This study aims to quantify traits essential for rice breeders such as amylose, chalkiness, length, width, and the length/width ratio in rice samples with NIRS. We used conventional algorithms such as principal component analysis (PCA), partial least square regression (PLSR), multilayer perceptron (MLP), support vector classification (SVC), and linear discriminant analysis (LDA) to compare with the proposed convolutional neural network (CNN) for regression and classification. Findings Our results showed that the proposed CNN outperformed the conventional models in estimating all traits. Unlike conventional models, CNN models could be developed with raw spectra with minimal to no preprocessing, and along with the transfer‐learning capabilities, the time required for model development could be significantly reduced. Conclusion We recommend NIRS for quantitative estimation of amylose and chalkiness in rice and rather use classification/categorized estimation for other physical dimension‐related traits such as length and length/width ratio. Significance and Novelty We found NIRS to be an appropriate alternative to wet chemistry and image‐based methods for screening lines at the early stages of the breeding program. Estimation of physical parameters such as length and length/width ratio with NIRS is novel and appears reasonable for high‐throughput applications.

show abstract

Section: Discussionmentioning

confidence: 99%

Estimation of grain quality parameters in rice for high‐throughput screening with near‐infrared spectroscopy and deep learning

Ravichandran

Viswanathan²,

Ravichandran³

et al. 2022

Cereal Chem

View full text Add to dashboard Cite

show abstract

“…In conclusion, the commercial application of microwave dielectric spectroscopy still needs further research [ 53 ]. On the other hand, visible and near-infrared (VIS/NIR) spectroscopy was reported as one of the most rapid and appropriate techniques for the prediction and detection of different parameters such as rice and its flour quality [ 54 ], tomato pesticide residues [ 55 , 56 ], besides the prediction of MC. But the most important advantages of the application of the HSI technology in the food industry rather than other techniques are as follows [ 13 ]:…”

Section: Comparison Of Various Techniques For Prediction Of Moisture ...mentioning

confidence: 99%

The Application of Hyperspectral Imaging Technologies for the Prediction and Measurement of the Moisture Content of Various Agricultural Crops during the Drying Process

2023

Self Cite

View full text Add to dashboard Cite

Drying is one of the common procedures in the food processing steps. The moisture content (MC) is also of crucial significance in the evaluation of the drying technique and quality of the final product. However, conventional MC evaluation methods suffer from several drawbacks, such as long processing time, destruction of the sample and the inability to determine the moisture of single grain samples. In this regard, the technology and knowledge of hyperspectral imaging (HSI) were addressed first. Then, the reports on the use of this technology as a rapid, non-destructive, and precise method were explored for the prediction and detection of the MC of crops during their drying process. After spectrometry, researchers have employed various pre-processing and merging data techniques to decrease and eliminate spectral noise. Then, diverse methods such as linear and multiple regressions and machine learning were used to model and predict the MC. Finally, the best wavelength capable of precise estimation of the MC was reported. Investigation of the previous studies revealed that HSI technology could be employed as a valuable technique to precisely control the drying process. Smart dryers are expected to be commercialised and industrialised soon by the development of portable systems capable of an online MC measurement.

show abstract

“…However, there are always inconsistencies with artificial sensory tests, and large uniform samples are still needed, making these methods unsuitable for objectifying appearance quality [22]. In addition, to improve the prediction accuracy of rice eating quality, some mining techniques based on the measurement of rice components and physicochemical characteristics have been developed using statistical methods, such as principal component analysis (PCA), multiple linear regression analysis (MLRA), and partial least squares regression analysis (PLSRA) [23][24][25]. However, there are no reports that combine these techniques and the components of eating quality to systematically predict eating quality in cooked rice.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Cooked Rice for Eating Quality and Its Components in Geng Rice

Li,

Yao,

Song

et al. 2023

Foods

View full text Add to dashboard Cite

At present, ‘‘eating well” is increasingly desired by people instead of merely ‘‘being full”. Rice provides the majority of daily caloric needs for half of the global human population. However, eating quality is difficult to objectively evaluate in rice breeding programs. This study was carried out to objectively quantify and predict eating quality in Geng rice. First, eating quality and its components were identified by trained panels. Analysis of variance and broad-sense heritability showed that variation among varieties was significant for all traits except hardness. Among them, viscosity, taste, and appearance were significantly correlated with eating quality. We established an image acquisition and processing system to quantify cooked rice appearance and optimized the process of measuring cooked rice viscosity with a texture analyzer. The results show that yellow areas of the images were significantly correlated with appearance, and adhesiveness was significantly correlated with viscosity. Based on these results, multiple regression analysis was used to predict eating quality: eating quality = 0.37 × adhesiveness − 0.71 × yellow area + 0.89 × taste − 0.34, R2 = 0.85. The correlation coefficient between the predicted and actual values was 0.86. We anticipate that this predictive model will be useful in future breeding programs for high-eating-quality rice.

show abstract

Prediction of some quality properties of rice and its flour by near‐infrared spectroscopy (NIRS) analysis

Cited by 25 publications

References 29 publications

Estimation of grain quality parameters in rice for high‐throughput screening with near‐infrared spectroscopy and deep learning

Estimation of grain quality parameters in rice for high‐throughput screening with near‐infrared spectroscopy and deep learning

The Application of Hyperspectral Imaging Technologies for the Prediction and Measurement of the Moisture Content of Various Agricultural Crops during the Drying Process

Evaluation of Cooked Rice for Eating Quality and Its Components in Geng Rice

Contact Info

Product

Resources

About