Identification and quantification of adulteration in frozen-thawed meat of different breeds by NIR spectroscopy and chemometrics

IF 4.6 2区 农林科学 Q2 CHEMISTRY, APPLIED
Jing Bai , Mingwu Zang, Yuxuan Shi , Chenchen Xu , Jingyi Hao , Jiapeng Li , Shouwei Wang , Yan Zhao
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Abstract

This work aims to compare the characteristic near-infrared absorption bands of different breeds of meats and to develop a qualitative and semi-quantitative method for the detection of adulteration in various types of frozen meat based on near-infrared spectroscopy and chemometrics methods. The adulterated samples were in various combinations of pork, beef and mutton adulterated with different proportions of pork, beef, lamb, duck and chicken. The results showed that the accuracy of the PLS-DA model that identified all adulteration types together was the lowest (AUCP= 0.8265), while the accuracy of the PLS-DA model of lamb-chicken and beef-chicken was the highest (AUCP= 1). AUCP of other adulteration types was higher than 0.92. And the PLSR model constructed by all the adulteration types together had the worst result (Rp2= 0.5348, RMSECV=0.1895, RPD=1.48). The results of PLSR prediction models constructed by the adulteration types separately all reached the expectation (Rp2= 0.8707–0.9703,RMSECV= 0.0637–0.1089,RPD= 2.78–5.80). The results of the PLS-DA models and PLSR models using characteristic band spectral information are basically the same or slightly improved compared with those results of the models using full-band spectral information. Therefore, NIR spectroscopy and chemometrics methods can directly qualitatively identified all adulteration types, but it is more accurate to semi-quantitatively predict the proportion of adulteration according to different adulteration types. The near-infrared absorption of moisture and fat contributed more to the detection of meat adulteration.

利用近红外光谱和化学计量学鉴定和量化不同品种冻融肉中的掺假物质
这项工作旨在比较不同品种肉类的特征近红外吸收带,并基于近红外光谱和化学计量学方法,开发一种定性和半定量方法,用于检测各类冷冻肉类中的掺假情况。掺假样品是在猪肉、牛肉和羊肉的不同组合中掺入不同比例的猪肉、牛肉、羊肉、鸭肉和鸡肉。结果表明,同时识别所有掺假类型的 PLS-DA 模型的准确度最低(AUCP= 0.8265),而羊肉-鸡肉和牛肉-鸡肉的 PLS-DA 模型的准确度最高(AUCP= 1)。其他掺假类型的 AUCP 均高于 0.92。而所有掺假类型共同构建的 PLSR 模型结果最差(Rp2= 0.5348,RMSECV=0.1895,RPD=1.48)。按掺假类型分别建立的 PLSR 预测模型的结果均达到了预期(Rp2= 0.8707-0.9703,RMSECV= 0.0637-0.1089,RPD= 2.78-5.80)。使用特征波段光谱信息的 PLS-DA 模型和 PLSR 模型与使用全波段光谱信息的模型相比,结果基本相同或略有提高。因此,近红外光谱和化学计量学方法可以直接定性识别所有掺假类型,但根据不同掺假类型半定量预测掺假比例更为准确。水分和脂肪的近红外吸收对肉类掺假的检测贡献更大。
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来源期刊
Journal of Food Composition and Analysis
Journal of Food Composition and Analysis 工程技术-食品科技
CiteScore
6.20
自引率
11.60%
发文量
601
审稿时长
53 days
期刊介绍: The Journal of Food Composition and Analysis publishes manuscripts on scientific aspects of data on the chemical composition of human foods, with particular emphasis on actual data on composition of foods; analytical methods; studies on the manipulation, storage, distribution and use of food composition data; and studies on the statistics, use and distribution of such data and data systems. The Journal''s basis is nutrient composition, with increasing emphasis on bioactive non-nutrient and anti-nutrient components. Papers must provide sufficient description of the food samples, analytical methods, quality control procedures and statistical treatments of the data to permit the end users of the food composition data to evaluate the appropriateness of such data in their projects. The Journal does not publish papers on: microbiological compounds; sensory quality; aromatics/volatiles in food and wine; essential oils; organoleptic characteristics of food; physical properties; or clinical papers and pharmacology-related papers.
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