Nondestructive evaluation of biogenic amines in crayfish (Prokaryophyllus clarkii) by near infrared spectroscopy

IF 1.6 4区 化学 Q3 CHEMISTRY, APPLIED
Yan Liu, Chao Wang, Zhenzhen Xia, Jiwang Chen
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引用次数: 1

Abstract

Biogenic amines are a group of nitrogen substances and widely adopted to assess the food safety, especially for the aquatic products. In China, crayfish (Prokaryophyllus clarkii) have become one of the most famous aquatic products and form a complete industrial value chain. To ensure the safety of the crayfish industrial chain, a rapid and nondestructive method for determining the biogenic amines of crayfish by near infrared spectroscopy coupled with chemometrics was proposed in this study. The quantitative models of histamine, tyramine, cadaverine, and putrescine were built by using the partial least squares (PLS) regression. The spectral preprocessing and the wavelength selection methods were adopted to optimize the models. For histamine, cadaverine, and putrescine in peeled or whole tails, reasonable quantitative results can be obtained by using the optimized models; the coefficient of determination (r2) are 0.88 and 0.90, 0.88 and 0.91, 0.89, and 0.84, respectively. As for tyramine in peeled or whole tails, the results are acceptable and the coefficient of determination (r2) is 0.83 and 0.74, respectively.
近红外光谱法无损评价克氏原核螯虾中生物胺
生物胺是一类含氮物质,被广泛用于食品安全评价,特别是水产品安全评价。在中国,克氏原核虾(Prokaryophyllus clarkii)已成为最著名的水产品之一,形成了完整的产业价值链。为了保证小龙虾产业链的安全,本研究提出了一种近红外光谱结合化学计量学快速无损检测小龙虾生物胺的方法。采用偏最小二乘法(PLS)建立组胺、酪胺、尸胺和腐胺的定量模型。采用光谱预处理和波长选择方法对模型进行优化。对去皮尾和整尾中的组胺、尸胺和腐胺,利用优化后的模型可以得到合理的定量结果;决定系数(r2)分别为0.88与0.90、0.88与0.91、0.89与0.84。去皮尾酪胺和整尾酪胺的测定结果可接受,决定系数r2分别为0.83和0.74。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.30
自引率
5.60%
发文量
35
审稿时长
6 months
期刊介绍: JNIRS — Journal of Near Infrared Spectroscopy is a peer reviewed journal, publishing original research papers, short communications, review articles and letters concerned with near infrared spectroscopy and technology, its application, new instrumentation and the use of chemometric and data handling techniques within NIR.
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