Bio-Raman research using principal component analysis and non-negative matrix factorization on rice grains: detections of ordered and disordered states of starch in the cooking process

被引：2

作者：

Wang, Ziteng ^{[1
]}

He, Mengmeng ^{[1
]}

Sari, Wulan Intan ^{[1
]}

Kishimoto, Naoki ^{[1
]}

Morita, Shin-ichi ^{[1
]}

机构：

[1] Tohoku Univ, Grad Sch Sci, Aoba Ku, 6-3 Aramaki Aza Aoba, Sendai, Miyagi 9808578, Japan

来源：

JAPANESE JOURNAL OF APPLIED PHYSICS | 2021年 / 60卷 / 06期

关键词：

bio; Raman; rice; PCA; NMF; BACKGROUND REMOVAL; PASTE VISCOSITY; AMYLOSE CONTENT; FT-RAMAN; IN-VIVO; SPECTROSCOPY; SPECTRA; CELLS; DIFFERENTIATION; IDENTIFICATION;

D O I：

10.35848/1347-4065/abff39

中图分类号：

O59 [应用物理学];

学科分类号：

摘要：

We measured Raman spectra in a cooking process of rice grains and applied principal component analysis (PCA) to confirm binary states of starch: ordered and disordered states of starch in the cooking process by analytically separating sharper and broader components for the bands around 870 and 940 cm(-1) due to starch. These sharper and broader components were optimized by non-negative matrix factorization (NMF), based on the PCA. The ratio defined using these two components clearly distinguished before/after the cooking of rice grains. The ratio can be an effective indicator to estimate the degree of cooking.

引用

页数：4

共 10 条

[1] Bio-Raman non-negative matrix factorization: its practical methodology
He, Jianhai
Abdel-Galeil, Mohamed M.
Nemoto, Mana
Kishimoto, Naoki
Morita, Shin-ichi
APPLIED PHYSICS EXPRESS, 2023, 16 (02)
[2] Graphical interpretation of non-negative factorization expecting bio-Raman research
Yu, Hua
Wang, Ziteng
Nemoto, Mana
Suzuta, Kazuyuki
Ito, Len
Morita, Shin-ichi
APPLIED PHYSICS EXPRESS, 2021, 14 (08)
[3] Wind Field Reconstruction Using Non-negative Matrix Factorization and Principal Component Analysis with CFD Basis
Qin, Li
Shahzad, Muhammad Ali
Pan, Xin Yuan
Liu, Shi
PROCEEDINGS OF 2017 2ND INTERNATIONAL CONFERENCE ON POWER AND RENEWABLE ENERGY (ICPRE), 2017, : 461 - 465
[4] Identification of of regional activation by factorization of high-density surface EMG signals: A comparison of Principal Component Analysis and Non-negative Matrix factorization
Gallina, Alessio
Garland, S. Jayne
Wakeling, James M.
JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY, 2018, 41 : 116 - 123
[5] Analysis of Network-level Traffic States using Locality Preservative Non-negative Matrix Factorization
Han, Yufei
Moutarde, Fabien
2011 14TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2011, : 501 - 506
[6] The application of principal component analysis and non-negative matrix factorization to analyze time-resolved optical waveguide absorption spectroscopy data
Liu, Ping
Zhou, Xia
Li, Yanling
Li, Minqiang
Yu, Daoyang
Liu, Jinhuai
ANALYTICAL METHODS, 2013, 5 (17) : 4454 - 4459
[7] Revealing the spatial and temporal distribution of different chemical states of lithium by EELS analysis using non-negative matrix factorization
Wang, Zeyu
Yu, Yi
MICRON, 2022, 154
[8] Revealing the spatial and temporal distribution of different chemical states of lithium by EELS analysis using non-negative matrix factorization
Wang, Zeyu
Yu, Yi
Micron, 2022, 154
[9] Fast Grain Mapping with Sub-Nanometer Resolution Using 4D-STEM with Grain Classification by Principal Component Analysis and Non-Negative Matrix Factorization
Allen, Frances, I
Pekin, Thomas C.
Persaud, Arun
Rozeveld, Steven J.
Meyers, Gregory F.
Ciston, Jim
Ophus, Colin
Minor, Andrew M.
MICROSCOPY AND MICROANALYSIS, 2021, 27 (04) : 794 - 803
[10] Estimation of Heart Rate and Respiratory Rate from PPG Signal Using Complementary Ensemble Empirical Mode Decomposition with both Independent Component Analysis and Non-Negative Matrix Factorization
Lei, Ruisheng
Ling, Bingo Wing-Kuen
Feng, Peihua
Chen, Jinrong
SENSORS, 2020, 20 (11) : 1 - 13

← 1 →