Rapid Classification of Turmeric Based on DNA Fingerprint by Near-Infrared Spectroscopy Combined with Moving Window Partial Least Squares-Discrimination Analysis

Abstract

In this research, near-infrared (NIR) spectroscopy in combination with moving window partial least squares-discrimination analysis (MWPLS-DA) was utilized to discriminate the variety of turmeric based on DNA markers, which correlated to the quantity of curcuminoid. Curcuminoid was used as a marker compound in variety identification due to the most pharmacological properties of turmeric possessed from it. MWPLS-DA optimized informative NIR spectral regions for the fitting and prediction to {-1/1}-coded turmeric varieties, indicating variables in the development of latent variables in discrimination analysis. Consequently, MWPLS-DA benefited in the selection of combined informative NIR spectral regions of 1100 -1260, 1300 -1500 and 1880 -2500 nm for classification modeling of turmeric variety with 148 calibration samples, and yielded the results better than that obtained from a partial least squares-discrimination analysis (PLS-DA) model built by using the whole NIR spectral region. An effective and rapid strategy of using NIR in combination with MWPLS-DA provided the best variety identification results of 100% in both specificity and total accuracy for 48 test samples.

Pseudo-univariate calibration as an analytical tool to determine antioxidant activity: An alternative to DPPH method applied to the evaluation from extracts of turmeric powder

“…Kasemsumran et al [25,26], in works from turmeric powder, observed absorptions in the NIR range from 1100 to 1620 nm, mainly due to the R-OH bonds and CH absorptions, similar to that identified in this work. Jovanovic et al [27], reported that the mechanism of antioxidant activity from curcumin (present in higher concentrations among curcuminoids) is through the donation of hydrogen from the methylene CH subunit at acid pH, which makes these bonds weakened by facilitating the mechanism of donation of H, in addition to the predicted antioxidant activity related to phenolic hydroxyls [28], suggesting that the pH used in this work to extract the curcuminoids helped to obtain the antioxidant activity from them.…”

Chemometrics and Related Fields in Python

“…In the field of analytical chemistry, discriminant analysis based on not only conventional LDA but also the advanced algorithms have been reported in many kinds of samples, e.g., DNA, 26 phospholipids, 118 cancer cells, 119 etc.…”

“…Many open source projects in Python have been adequately developed and distributed via GitHub, 8,9 e.g., a scikit-learn (sklearn) machine learning (ML) library for Python. [10][11][12] This library fortunately contains many typical tools for multivariate analysis 13,14 and chemometrics, [15][16][17][18] e.g., principal component analysis (PCA), [19][20][21][22][23] partial least squares (PLS), [24][25][26][27][28][29] etc. Other chemometrics tools that are not included in the ML library, e.g., pyMCR 30, 31 for multivariate curve resolution (MCR), [32][33][34][35][36][37] are also independently found in GitHub.…”

An ensemble variable selection method for vibrational spectroscopic data analysis

“…10 A number of algorithms for variable selection in the PLS-DA model have been proposed. 5,[11][12][13][14][15][16][17][18][19][20][21][22] In general, these methods can be classied into three categories and include lter, wrapper and embedded techniques. 23 Wrapper is the most commonly used technique, because this technique is easy to implement and the interaction between the feature subset search and the classier is considered.…”