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利用傅里叶变换红外光谱法和主成分分析法分析甜菜根中的水分含量。

Analysis of Moisture Content in Beetroot using Fourier Transform Infrared Spectroscopy and by Principal Component Analysis.

机构信息

Electrodics and Electrocatalysis Division, CSIR-Central Electrochemical Research Institute, Karaikudi, 630003, Tamil Nadu, India.

School of Computing, SASTRA Deemed University, Thanjavur, 613401, Tamil Nadu, India.

出版信息

Sci Rep. 2018 May 22;8(1):7996. doi: 10.1038/s41598-018-26243-5.

Abstract

The moisture content of beetroot varies during long-term cold storage. In this work, we propose a strategy to identify the moisture content and age of beetroot using principal component analysis coupled Fourier transform infrared spectroscopy (FTIR). Frequent FTIR measurements were recorded directly from the beetroot sample surface over a period of 34 days for analysing its moisture content employing attenuated total reflectance in the spectral ranges of 2614-4000 and 1465-1853 cm with a spectral resolution of 8 cm. In order to estimate the transmittance peak height (T ) and area under the transmittance curve [Formula: see text] over the spectral ranges of 2614-4000 and 1465-1853 cm, Gaussian curve fitting algorithm was performed on FTIR data. Principal component and nonlinear regression analyses were utilized for FTIR data analysis. Score plot over the ranges of 2614-4000 and 1465-1853 cm allowed beetroot quality discrimination. Beetroot quality predictive models were developed by employing biphasic dose response function. Validation experiment results confirmed that the accuracy of the beetroot quality predictive model reached 97.5%. This research work proves that FTIR spectroscopy in combination with principal component analysis and beetroot quality predictive models could serve as an effective tool for discriminating moisture content in fresh, half and completely spoiled stages of beetroot samples and for providing status alerts.

摘要

甜菜根在长期冷藏过程中的水分含量会发生变化。在这项工作中,我们提出了一种使用主成分分析结合傅里叶变换红外光谱(FTIR)来识别甜菜根水分含量和年龄的策略。在 34 天的时间内,我们从甜菜根样本表面直接记录了频繁的 FTIR 测量,以使用衰减全反射在 2614-4000 和 1465-1853 cm 的光谱范围内分析其水分含量,光谱分辨率为 8 cm。为了估计透射峰高度(T)和透射曲线下面积[公式:见文本]在 2614-4000 和 1465-1853 cm 的光谱范围内,对 FTIR 数据进行了高斯曲线拟合算法。主成分和非线性回归分析用于 FTIR 数据分析。在 2614-4000 和 1465-1853 cm 的范围内进行得分图分析,可以区分甜菜根的质量。通过采用双相剂量响应函数,建立了甜菜根质量预测模型。验证实验结果证实,甜菜根质量预测模型的准确性达到 97.5%。这项研究工作证明,FTIR 光谱结合主成分分析和甜菜根质量预测模型可以作为一种有效的工具,用于区分新鲜、半变质和完全变质的甜菜根样本的水分含量,并提供状态警报。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d18/5964165/065e09183c1d/41598_2018_26243_Fig1_HTML.jpg

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