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化学计量学与高光谱成像联用检测生熟羊肉卷的真伪

Chemometrics in Tandem with Hyperspectral Imaging for Detecting Authentication of Raw and Cooked Mutton Rolls.

作者信息

Jiang Hongzhe, Yang Yi, Shi Minghong

机构信息

College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.

Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China.

出版信息

Foods. 2021 Sep 9;10(9):2127. doi: 10.3390/foods10092127.

Abstract

Authentication assurance of meat or meat products is critical in the meat industry. Various methods including DNA- or protein-based techniques are accurate for assessing meat authenticity, however, they are destructive, expensive, or laborious. This study explores the feasibility of chemometrics in tandem with hyperspectral imaging (HSI) for identifying raw and cooked mutton rolls substitution by pork and duck rolls. Raw or cooked samples ( = 180) of three meat species were prepared to collect hyperspectral images in range of 400-1000 nm. Spectra were extracted from representative regions of interest (ROIs), and spectral principal component analysis (PCA) revealed that PC and PC were effective for the identification. Different methods including standard normal variable (SNV), first and second derivatives, and normalization were individually employed for spectral preprocessing, and modeling methods of partial least squares-discriminant analysis (PLS-DA) and support vector machines (SVM) were also individually applied to develop classification models for both the raw and the cooked. Results showed that PLS-DA model developed by raw spectra presented the highest 100% correct classification rate (CCR) of success in all sets. After that, effective wavelengths selected by successive projections algorithm (SPA) built optimal simplified models which didn't influence the modeling results compared with full spectra regardless of the meat roll states. Therefore, SPA-PLS-DA models were subsequently used to visualize the raw and cooked meat rolls classification. As a consequence, the general meat species of both raw and cooked meat rolls were readily discernible in pixel-wise manner by generating classification maps. The results showed that HSI combined with chemometrics can be used to identify the authentication of raw and cooked mutton rolls substituted by pork and duck rolls accurately. This promising methodology provides a reference which can be extended to the classification or grading of other meat rolls.

摘要

在肉类行业中,肉类或肉制品的认证保证至关重要。包括基于DNA或蛋白质的技术在内的各种方法在评估肉类真实性方面是准确的,然而,它们具有破坏性、成本高或费力。本研究探讨了化学计量学与高光谱成像(HSI)相结合用于识别生熟羊肉卷被猪肉卷和鸭肉卷替代的可行性。制备了三种肉类的生或熟样本(n = 180),以收集400 - 1000 nm范围内的高光谱图像。从代表性感兴趣区域(ROI)提取光谱,光谱主成分分析(PCA)表明PC1和PC2对识别有效。分别采用标准正态变量变换(SNV)、一阶和二阶导数以及归一化等不同方法进行光谱预处理,还分别应用偏最小二乘判别分析(PLS - DA)和支持向量机(SVM)建模方法来建立生熟样本的分类模型。结果表明,由生肉光谱建立的PLS - DA模型在所有样本集中呈现出最高的100%正确分类率(CCR)。之后,通过连续投影算法(SPA)选择的有效波长构建了最优简化模型,无论肉卷状态如何,与全光谱相比,该模型不影响建模结果。因此,随后使用SPA - PLS - DA模型对生熟肉卷分类进行可视化。结果,通过生成分类图,可以以像素方式轻松辨别生熟肉卷的一般肉类种类。结果表明,HSI与化学计量学相结合可用于准确识别被猪肉卷和鸭肉卷替代的生熟羊肉卷的真伪。这种有前景的方法提供了一个可扩展到其他肉卷分类或分级的参考。

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