Determination of curcumin, starch and moisture content in turmeric by Fourier transform near infrared spectroscopy (FT-NIR)

Q2 Engineering

Engineering in Agriculture, Environment and Food Pub Date : 2019-04-01 DOI:10.1016/j.eaef.2019.02.003

K. Thangavel , K. Dhivya

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引用次数: 24

Abstract

Fourier transform near infrared spectroscopy (FT-NIR) in diffuse reflectance mode was used for the rapid estimation of curcumin, starch and moisture contents in turmeric samples. Thirty samples each of fingers and bulbs from varieties ‘Erode local’ and ‘Salem local’ (n = 120) were used for the study. Calibration models were developed and evaluated to describe the relationship between the three quality attributes with the NIR spectra of the turmeric powder. NIR reflectance spectra were acquired for each turmeric sample at a resolution of 8 cm⁻¹ over a wave number range of 12,500 to 3600 cm⁻¹. Vector normalization, first derivative and first derivative plus vector normalization were used as spectral pre-processing options. The relationship between the acquired spectra of turmeric samples and the quality attributes was examined through partial least square (PLS) regression algorithm. First derivative plus vector normalization technique predicted curcumin content with best accuracy with lowest root mean square error of cross validation (RMSECV) of 0.178% and maximum correlation coefficient for validation plots (R² = 91.9). Vector normalization technique predicted the starch and moisture content with RMSECV and R² value of 0.076%, 96.8 and 0.032%, 81.1 respectively. The results demonstrated that FT-NIR could be used as a rapid technique for quantification of curcumin, starch and moisture content in turmeric rhizomes for online grading in spice processing.

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傅里叶变换近红外光谱(FT-NIR)测定姜黄中姜黄素、淀粉和水分含量

采用漫反射模式傅里叶变换近红外光谱(FT-NIR)快速测定了姜黄样品中的姜黄素、淀粉和水分含量。研究使用了来自“侵蚀本地”和“塞勒姆本地”品种的手指和球茎各30个样本( = 120)。建立并评价了姜黄粉三种质量属性与近红外光谱的关系的标定模型。获得了每个姜黄样品的近红外反射光谱，分辨率为8 cm−1，波数范围为12,500至3600 cm−1。采用矢量归一化、一阶导数和一阶导数加矢量归一化作为光谱预处理选项。利用偏最小二乘法(PLS)回归分析了姜黄样品的光谱与质量属性之间的关系。一阶导数加向量归一化技术预测姜黄素含量的准确度最高，交叉验证的均方根误差(RMSECV)最低为0.178%，验证图的相关系数最高(R2 = 91.9)。载体归一化技术预测淀粉和水分的RMSECV和R2值分别为0.076%、96.8和0.032%、81.1。结果表明，傅里叶变换近红外光谱可作为一种快速定量测定姜黄根茎中姜黄素、淀粉和水分含量的技术，用于香料加工中的在线分级。

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来源期刊

Engineering in Agriculture, Environment and Food Engineering-Industrial and Manufacturing Engineering

CiteScore

1.00

自引率

0.00%

发文量

期刊介绍： Engineering in Agriculture, Environment and Food (EAEF) is devoted to the advancement and dissemination of scientific and technical knowledge concerning agricultural machinery, tillage, terramechanics, precision farming, agricultural instrumentation, sensors, bio-robotics, systems automation, processing of agricultural products and foods, quality evaluation and food safety, waste treatment and management, environmental control, energy utilization agricultural systems engineering, bio-informatics, computer simulation, computational mechanics, farm work systems and mechanized cropping. It is an international English E-journal published and distributed by the Asian Agricultural and Biological Engineering Association (AABEA). Authors should submit the manuscript file written by MS Word through a web site. The manuscript must be approved by the author''s organization prior to submission if required. Contact the societies which you belong to, if you have any question on manuscript submission or on the Journal EAEF.