Development of new NIR-spectroscopy method combined with multivariate analysis for detection of adulteration in camel milk with goat milk

New robust sensitive fluorescence spectroscopy coupled with PLSR for estimation of quercetin in Ziziphus mucronata and Ziziphus sativa

Flavonoids are natural antioxidants derived from plants and commonly found in a variety of foods to sequester free radicals. Quercetin, belonging to flavonol subclass of flavonoids, has received considerable attention because of its wide uses as a nutritional supplement as well as a phytochemical remedy for a number of diseases. In the current study, quantification of quercetin was carried out in two medicinally important flavonoid rich plant Ziziphus mucronata and Ziziphus sativa. Emission spectroscopy was utilized as a new method coupled with Partial Least Squares Regression (PLSR) and the cross validation was done by UV-Visible spectroscopy. The results indicated the higher quercetin content in Z. mucronata (1.50±0.034%) than Z. sativa (1.21±0.052%), and were further verified through Folin-Ciocalteu Colorimetric method (Z. mucronata; 1.41±0.26% and Z. sativa; 1.13±0.136%). In this study the sensitivity was explained in term of slope i.e. Slope=0.9973.

Non-destructive geographical traceability of sea cucumber (Apostichopus japonicus) using near infrared spectroscopy combined with chemometric methods

Sea cucumber is the major tonic seafood worldwide, and geographical origin traceability is an important part of its quality and safety control. In this work, a non-destructive method for origin traceability of sea cucumber (Apostichopus japonicus) from northern China Sea and East China Sea using near infrared spectroscopy (NIRS) and multivariate analysis methods was proposed. Total fat contents of 189 fresh sea cucumber samples were determined and partial least-squares (PLS) regression was used to establish the quantitative NIRS model. The ordered predictor selection algorithm was performed to select feasible wavelength regions for the construction of PLS and identification models. The identification model was developed by principal component analysis combined with Mahalanobis distance and scaling to the first range algorithms. In the test set of the optimum PLS models, the root mean square error of prediction was 0.45, and correlation coefficient was 0.90. The correct classification rates of 100% were obtained in both identification calibration model and test model. The overall results indicated that NIRS method combined with chemometric analysis was a suitable tool for origin traceability and identification of fresh sea cucumber samples from nine origins in China.

Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps

This review highlights and discusses critically various knowledge gaps in classification modelling using PLS-DA for high dimensional data.

Application of NIR Spectroscopy Coupled with PLS Regression for Quantification of Total Polyphenol Contents from the Fruit and Aerial Parts of Citrullus colocynthis

INTRODUCTION

Citrullus colocynthis (L.) Schrad is extensively used to treat diabetes, obesity, fever, cancer, amenorrhea, jaundice, leukemia, rheumatism, and respiratory diseases. Chemical studies have indicated the presence of several cucurbitacins, flavones, and other polyphenols in this plant. These phytochemical constituents are responsible for the interesting antioxidant and other biological activities of C. colocynthis.

OBJECTIVE

In the present study, for the first time, near infrared (NIR) spectroscopy coupled with partial least square (PLS) regression analysis was used to quantify the polyphenolic phytochemicals of C. colocynthis.

METHODOLOGY

The fruit and aerial parts of the C. colocynthis were extracted individually in methanol followed by fractionation in n-hexane, chloroform, ethyl acetate, n-butanol, and water. Near infrared (NIR) spectra were obtained in absorption mode in the wavelength range 700-2500 nm. The PLS regression model was then built from the obtained spectral data to quantify the total polyphenol contents in the selected plant samples.

RESULTS

The PLS regression model obtained had a R² value of 99% with a 0.98 correlationship value and a good prediction with a root mean square error of prediction (RMSEP) value of 1.89% and correlation of 0.98. These results were further confirmed through UV-vis spectroscopy and it is found that the ethyl acetate fraction has the maximum value for polyphenol contents (101.7 mg/100 g; NIR, 100.4 mg/100 g; UV-vis).

CONCLUSIONS

The polyphenolic phytochemicals of the fruit and aerial parts of C. colocynthis have been quantified successfully by using multivariate analysis in a non-destructive, economical, precise, and highly sensitive method, which uses very simple sample preparation. Copyright © 2017 John Wiley & Sons, Ltd.