利用紫外-近红外光谱快速、无损地测定脱水蘑菇的总酚含量

IF 2.7 3区 农林科学 Q3 ENGINEERING, CHEMICAL
Shoaib Younas, Muhammad Sajid Manzoor, Ukasha Arqam, Farhan Ali, Ayesha Murtaza, Muhammad Abdul Wahab, Muhammad Aamir Manzoor, Muhammad Imran, Xin Wang
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引用次数: 0

摘要

多光谱成像(MSI)是一种新兴技术,其光谱范围为紫外-近红外(405-970 nm),用于快速测定酚含量。目前的研究重点是以快速、无创的方式测定热风脱水扁豆中的总酚含量(TPC)。MSI 的光谱信息与偏最小二乘法 (PLS)、反向传播神经网络 (BPNN) 和最小二乘支持向量机 (LS-SVM) 等多种化学计量学方法相结合,用于酚含量的定量预测。在含水量为 10%的加工样品中,新鲜蘑菇的酚含量分别为 1.49 和 1.73 GAE g kg-1 TPC。比较模型,PLS 获得了显著的 Rp,RMSEP 分别为 0.9980 和 0.1039,被认为是一种出色的算法,其次是 LS-SVM,其决定系数和预测数据集误差分别为 0.9777 和 0.1417。由此得出结论,MSI 光谱法能以快速、无损的方式确定食品的功能特性,并产生非常显著的结果。通过将化学计量学与多光谱成像系统的光谱相结合,利用光谱技术对酚类物质含量进行无损检测,强调了对采后园艺产品的控制处理。预测结果 R2 超过 80%,说明这些化学计量学能够成功地估算出控制加工过程中的总酚含量,从而保证质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rapid and non-destructive determination of total phenolic contents using UV–NIR spectroscopy of dehydrated mushroom (Lentinus edodes)

Multispectral imaging (MSI) is an emerging technique that ranges from light spectrum of UV–NIR (405–970 nm) used for rapid determination of phenolic contents. The current study focuses on the determination of total phenolic content (TPC) in a fast and non-invasive way in hot-air dehydrated Lentinus edodes. The spectral information of MSI has been combined with various chemometrics like partial least squares (PLS), back propagation neural networks (BPNN), and least squares-support vector machines (LS-SVM) for the quantitative prediction of phenolic contents. Fresh mushrooms possessed 1.49 and 1.73 GAE g kg−1 TPC in processed samples at 10% moisture content. Comparing models, PLS acquired significant Rp with a lower RMSEP of 0.9980 and 0.1039 and was considered an outstanding algorithm, followed by LS-SVM of 0.9777 and 0.1417 coefficient of determination and error of prediction data set, respectively. It is concluded that MSI spectroscopy produces highly significant results to determine functional food properties in a rapid and non-destructive way. The present study will provide a strong platform for the fast online determination of phenolic profile of agricultural commodities.

Practical applications

Utilization of spectroscopy for non-destructive detection of phenolic contents is defined through the combination of chemometrics with spectra of multispectral imaging system and emphasizes control processing of postharvest horticulture produces. Prediction results R2 above 80% describe that these chemometrics are successfully capable of estimating total phenolic contents in control processing for quality preservation.

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来源期刊
Journal of Food Process Engineering
Journal of Food Process Engineering 工程技术-工程:化工
CiteScore
5.70
自引率
10.00%
发文量
259
审稿时长
2 months
期刊介绍: This international research journal focuses on the engineering aspects of post-production handling, storage, processing, packaging, and distribution of food. Read by researchers, food and chemical engineers, and industry experts, this is the only international journal specifically devoted to the engineering aspects of food processing. Co-Editors M. Elena Castell-Perez and Rosana Moreira, both of Texas A&M University, welcome papers covering the best original research on applications of engineering principles and concepts to food and food processes.
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