A Comparative Study Between Partial Least Squares and Principal Component Regression for Nondestructive Quantification of Piperine Contents in Black Pepper by Raman Spectroscopy

被引：1

作者：

Sing, Dilip ^{[1
]}

Dastidar, Sudarshana Ghosh ^{[1
]}

Akram, Wasim ^{[1
]}

Guchhait, Sourav ^{[1
]}

Jana, Shibu Narayan ^{[2
]}

Banerjee, Subhadip ^{[2
]}

Mukherjee, Pulok Kumar ^{[2
,3
]}

Bandyopadhyay, Rajib ^{[1
]}

机构：

[1] Jadavpur Univ, Dept Instrumentat & Elect Engn, Salt Lake Campus, Kolkata 700106, India

[2] Jadavpur Univ, Sch Nat Prod Studies, Kolkata 700032, India

[3] Govt India, Dept Biotechnol, Inst Bioresources & Sustainable Dev, Imphal 795004, Manipur, India

来源：

SMART SENSORS MEASUREMENT AND INSTRUMENTATION, CISCON 2021 | 2023年 / 957卷

关键词：

Raman spectroscopy; Partial least squares (PLS); Principal component regression (PCR); Medicinal plant; Black pepper; Piperine; PREDICTION; TOOL; PCR; PLS;

D O I：

10.1007/978-981-19-6913-3_33

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The aim of this work was to compare principal component regression (PCR) and partial least squares (PLS) regression methods while estimating the piperine content in black pepper using Raman spectroscopy. The calibration and prediction models of the regression analysis on Raman spectra were developed using PCR and PLS algorithm. The efficiency of the developed models was evaluated by means of root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), and correlation coefficient (R2). For PCR algorithm, these parameters were obtained as 0.1, 0.1, and 0.97, respectively; and for PLS regression, the parameters were found as 0.05, 0.08, and 0.99, respectively. The results revealed that Raman spectroscopy with PCR and PLS algorithm could be used for determining the concentration of piperine in black pepper with an accuracy of 92.35% and 94.74% respectively.

引用

页码：483 / 489

页数：7

共 50 条

[11] A Hybrid Least Squares and Principal Component Analysis Algorithm for Raman Spectroscopy
Van de Sompel, Dominique
Garai, Ellis
Zavaleta, Cristina
Gambhir, Sanjiv Sam
PLOS ONE, 2012, 7 (06):
[12] Comparison of partial least squares regression and principal component regression for pelvic shape prediction
Schumann, Steffen
Nolte, Lutz-P.
Zheng, Guoyan
JOURNAL OF BIOMECHANICS, 2013, 46 (01) : 197 - 199
[13] Development of Nondestructive Detection Method for Adulterated Powder Products Using Raman Spectroscopy and Partial Least Squares Regression
Lee, Sangdae
Lohumi, Santosh
Cho, Byoung-Kwan
Kim, Moon S.
Lee, Soo-Hee
JOURNAL OF THE KOREAN SOCIETY FOR NONDESTRUCTIVE TESTING, 2014, 34 (04) : 283 - 289
[14] Approach to the quantification of milk mixtures by partial least-squares, principal component and multiple linear regression techniques
Rodriguez-Nogales, JM
FOOD CHEMISTRY, 2006, 98 (04) : 782 - 789
[15] Quantification of brain lipids by FTIR spectroscopy and partial least squares regression
Dreissig, Isabell
Machill, Susanne
Salzer, Reiner
Krafft, Christoph
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 2009, 71 (05) : 2069 - 2075
[16] An improved partial least-squares regression method for Raman spectroscopy
Monfared, Ali Momenpour Tehran
Anis, Hanan
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 2017, 185 : 98 - 103
[17] Partial least squares improvement and research principal component regression extraction methods
Xiong, Wangping
Du, Jianqiang
Nie, Wang
2014 IEEE 11TH INTL CONF ON UBIQUITOUS INTELLIGENCE AND COMPUTING AND 2014 IEEE 11TH INTL CONF ON AUTONOMIC AND TRUSTED COMPUTING AND 2014 IEEE 14TH INTL CONF ON SCALABLE COMPUTING AND COMMUNICATIONS AND ITS ASSOCIATED WORKSHOPS, 2014, : 583 - 585
[18] The Comparison of Robust Partial Least Squares Regression with Robust Principal Component Regression on a Real Data
Polat, Esra
Gunay, Suleyman
11TH INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2013, PTS 1 AND 2 (ICNAAM 2013), 2013, 1558 : 1458 - 1461
[19] Determination of Antioxidant Properties of Fruit Juice by Partial Least Squares and Principal Component Regression
Sahin, Saliha
Demir, Cevdet
INTERNATIONAL JOURNAL OF FOOD PROPERTIES, 2016, 19 (07) : 1455 - 1464
[20] The equivalence of partial least squares and principal component regression in the sufficient dimension reduction framework
Lin, You-Wu
Deng, Bai-Chuan
Xu, Qing-Song
Yun, Yong-Huan
Liang, Yi-Zeng
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2016, 150 : 58 - 64

← 1 2 3 4 5 →