FT-NIRS Coupled with PLS Regression as a Complement to HPLC Routine Analysis of Caffeine in Tea Samples.

Najeeb Ur Rehman,Fazal Mabood,Ricard Boqué,Muhammed Al-Broumi,Javid Hussain,Ahmed Al-Harrasi,Saif Alameri

doi:10.3390/foods9060827

Najeeb Ur Rehman, Fazal Mabood + Show 5 more

Open Access

https://doi.org/10.3390/foods9060827

Copy DOI

Abstract

Daily consumption of caffeine in coffee, tea, chocolate, cocoa, and soft drinks has gained wide and plentiful public and scientific attention over the past few decades. The concentration of caffeine in vivo is a crucial indicator of some disorders—for example, kidney malfunction, heart disease, increase of blood pressure and alertness—and can cause some severe diseases including type 2 diabetes mellitus (DM), stroke risk, liver disease, and some cancers. In the present study, near-infrared spectroscopy (NIRS) coupled with partial least-squares regression (PLSR) was proposed as an alternative method for the quantification of caffeine in 25 commercially available tea samples consumed in Oman. This method is a fast, complementary technique to wet chemistry procedures as well as to high-performance liquid chromatography (HPLC) methods for the quantitative analysis of caffeine in tea samples because it is reagent-less and needs little or no pre-treatment of samples. In the current study, the partial least-squares (PLS) algorithm was built by using the near-infrared NIR spectra of caffeine standards prepared in tea samples scanned by a Frontier NIR spectrophotometer (L1280034) by PerkinElmer. Spectra were collected in the absorption mode in the wavenumber range of 10,000–4000 cm−1, using a 0.2 mm path length and CaF2 sealed cells with a resolution of 2 cm−1. The NIR results for the contents of caffeine in tea samples were also compared with results obtained by HPLC analysis. Both techniques provided good results for predicting the caffeine contents in commercially available tea samples. The results of the proposed study show that the suggested FT-NIRS coupled with PLS regression algorithun has a high potential to be routinely used for the quick and reproducible analysis of caffeine contents in tea samples. For the NIR method, the limit of quantification (LOQ) was estimated as 10 times the error of calibration (root mean square error of calibration (RMSECV)) of the model; thus, RMSEC was calculated as 0.03 ppm and the LOQ as 0.3 ppm.

Highlights

The applications and advances of near-infrared spectroscopy (NIRS) for the rapid and simultaneous determination of several analytes—e.g., in the detection of adulterated samples, quantification of active ingredients in food samples and medicinal plants, as well as the discrimination of food samples—areFoods 2020, 9, 827; doi:10.3390/foods9060827 www.mdpi.com/journal/foodsFoods 2020, 9, 827 well documented [1,2,3,4,5,6,7,8]
A calibrated NIRS method and high-performance liquid chromatography (HPLC) have been used as complementary techniques for the quantification of caffeine in 25 commercially available tea samples consumed in Oman
Each extract of the tea samples was prepared in triplicates and was used for both NIRS and HPLC analysis

Summary

Introduction

Foods 2020, 9, 827 well documented [1,2,3,4,5,6,7,8]. NIRS with partial least-squares (PLS) regression has been considered as a complementary procedure for quantitative analysis in food and agriculture products [10,11,12,13]. The popularity of tea is increasing constantly due to its beneficial and fundamental medicinal properties [18,19,20,21,22]. Some previous studies have demonstrated that tea has numerous beneficial effects for consumer health, including the reduction of cholesterol, antimicrobial and antioxidant properties, decrease in hypertension, and protection against cancer and cardiovascular disease [24,25].

Methods

Results

Conclusion