Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Application of Near-Infrared Spectroscopy for Rice Characterization Using Machine Learning
https://orcid.org/http://orcid.org/0000-0003-3338-1121
Near-infrared (NIR) spectroscopy was investigated to relate the intrinsic properties of rice to its extrinsic properties, and thereby, to provide a better solution at the consumer’s level for identification of rice characteristics. Spectral data in the wavelength range of 740–1070 nm are collected with the help of a portable NIR sensor and processed with machine learning techniques were used to develop a rapid predictive model for on-site evaluation of rice quality. Rice properties like glycemic index (GI), amylose content (AC) and viscogram, obtained from laboratory measurements, were mapped to the spectral data employing the machine learning techniques like principal component analysis, linear discriminant analysis, random forest classifier and partial least square (PLS). The regression coefficient and root mean squared error of the PLS model for AC estimation are 0.715 and 1.736; however, a lower value for regression coefficient was obtained for the GI model. Similarly, a confusion matrix of 100% true value prediction was obtained at lower AC values, 83% at high AC values; however, at intermediate range of AC confusion matrix yielded 60% true value prediction. A comparison of classification of rice for parboiling, based on the viscogram and NIR spectral data, revealed that the NIR data produce better clusters with Euclidean distance of 5.46 units between the centroid of the closest clusters, viz., open parboiled and pressure parboiled. The developed model was used to develop a smartphone-based applet for the estimation of AC in rice.
Application of Near-Infrared Spectroscopy for Rice Characterization Using Machine Learning
https://orcid.org/http://orcid.org/0000-0003-3338-1121
Near-infrared (NIR) spectroscopy was investigated to relate the intrinsic properties of rice to its extrinsic properties, and thereby, to provide a better solution at the consumer’s level for identification of rice characteristics. Spectral data in the wavelength range of 740–1070 nm are collected with the help of a portable NIR sensor and processed with machine learning techniques were used to develop a rapid predictive model for on-site evaluation of rice quality. Rice properties like glycemic index (GI), amylose content (AC) and viscogram, obtained from laboratory measurements, were mapped to the spectral data employing the machine learning techniques like principal component analysis, linear discriminant analysis, random forest classifier and partial least square (PLS). The regression coefficient and root mean squared error of the PLS model for AC estimation are 0.715 and 1.736; however, a lower value for regression coefficient was obtained for the GI model. Similarly, a confusion matrix of 100% true value prediction was obtained at lower AC values, 83% at high AC values; however, at intermediate range of AC confusion matrix yielded 60% true value prediction. A comparison of classification of rice for parboiling, based on the viscogram and NIR spectral data, revealed that the NIR data produce better clusters with Euclidean distance of 5.46 units between the centroid of the closest clusters, viz., open parboiled and pressure parboiled. The developed model was used to develop a smartphone-based applet for the estimation of AC in rice.
Application of Near-Infrared Spectroscopy for Rice Characterization Using Machine Learning
https://orcid.org/http://orcid.org/0000-0003-3338-1121
Near-infrared (NIR) spectroscopy was investigated to relate the intrinsic properties of rice to its extrinsic properties, and thereby, to provide a better solution at the consumer’s level for identification of rice characteristics. Spectral data in the wavelength range of 740–1070 nm are collected with the help of a portable NIR sensor and processed with machine learning techniques were used to develop a rapid predictive model for on-site evaluation of rice quality. Rice properties like glycemic index (GI), amylose content (AC) and viscogram, obtained from laboratory measurements, were mapped to the spectral data employing the machine learning techniques like principal component analysis, linear discriminant analysis, random forest classifier and partial least square (PLS). The regression coefficient and root mean squared error of the PLS model for AC estimation are 0.715 and 1.736; however, a lower value for regression coefficient was obtained for the GI model. Similarly, a confusion matrix of 100% true value prediction was obtained at lower AC values, 83% at high AC values; however, at intermediate range of AC confusion matrix yielded 60% true value prediction. A comparison of classification of rice for parboiling, based on the viscogram and NIR spectral data, revealed that the NIR data produce better clusters with Euclidean distance of 5.46 units between the centroid of the closest clusters, viz., open parboiled and pressure parboiled. The developed model was used to develop a smartphone-based applet for the estimation of AC in rice.
Application of Near-Infrared Spectroscopy for Rice Characterization Using Machine Learning
J. Inst. Eng. India Ser. A
Rizwana, Shagufta (Autor:in) / Hazarika, Manuj Kumar (Autor:in)
Journal of The Institution of Engineers (India): Series A ; 101 ; 579-587
01.12.2020
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
| DOAJ | 2018
Non Destructive Characterization of Wooden Members Using near Infrared Spectroscopy
| British Library Conference Proceedings | 2013
Application of Near Infrared Spectroscopy (NIR) to light-irradiated wood
| British Library Online Contents | 2003
| British Library Online Contents | 2011
Application of Near Infrared spectroscopy technology to qualitative inspection of textiles
| British Library Online Contents | 2009