欧洲科学家利用VideometerLab多光谱成像系统，发表了题为Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis的文章，文章发表于Foods 2021,10(11),2723。

https://doi.org/10.3390/foods10112723

光谱传感器和多元数据分析在鸡大腿片微生物质量评价中的应用

摘要

通过回归和分类模型，评估了傅里叶变换红外光谱（FT-IR）和多光谱成像（MSI）对禽肉微生物质量的预测。对鸡大腿肉片（n=402）在八个等温和两个动态温度下进行了腐败试验。对样品进行微生物学分析（总活菌数（TVCs）和假单胞菌属），同时获得MSI和FT-IR光谱。通过感官组评估样品的感官质量，确定TVC变质阈值为6.99 log CFU/cm2。偏最小二乘回归（PLS-R）模型用于评估鸡表面TVCs和假单胞菌数量。此外，还开发了分类模型（线性判别分析（LDA）、二次判别分析（QDA）、支持向量机（SVM）和二次支持向量机（QSVM））来区分两个质量类别（新鲜和变质）中的样本。基于MSI数据开发的PLS-R模型预测TVCs和假单胞菌数量的结果令人满意，均方根误差（RMSE）值分别为0.987和1.215 log CFU/cm2。与MSI数据耦合的SVM模型表现出最佳性能，总体准确率为94.4%，而在FT-IR的情况下，使用QDA模型获得了改进的分类（总体准确率为71.4%）。这些结果证实了MSI和FT-IR作为家禽产品质量快速评估方法的有效性。

关键词：禽肉；腐败；多光谱成像；傅里叶变换红外光谱（FT-IR）；回归模型；分类模型；多元数据分析

Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis

Abstract

Fourier transform infrared spectroscopy (FT-IR) and multispectral imaging (MSI) were evaluated for the prediction of the microbiological quality of poultry meat via regression and classification models. Chicken thigh fillets (n = 402) were subjected to spoilage experiments at eight isothermal and two dynamic temperature profiles. Samples were analyzed microbiologically (total viable counts (TVCs) and Pseudomonas spp.), while simultaneously MSI and FT-IR spectra were acquired. The organoleptic quality of the samples was also evaluated by a sensory panel, establishing a TVC spoilage threshold at 6.99 log CFU/cm2. Partial least squares regression (PLS-R) models were employed in the assessment of TVCs and Pseudomonas spp. counts on chicken’s surface. Furthermore, classification models (linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVMs), and quadratic support vector machines (QSVMs)) were developed to discriminate the samples in two quality classes (fresh vs. spoiled). PLS-R models developed on MSI data predicted TVCs and Pseudomonas spp. counts satisfactorily, with root mean squared error (RMSE) values of 0.987 and 1.215 log CFU/cm2, respectively. SVM model coupled to MSI data exhibited the highest performance with an overall accuracy of 94.4%, while in the case of FT-IR, improved classification was obtained with the QDA model (overall accuracy 71.4%). These results confirm the efficacy of MSI and FT-IR as rapid methods to assess the quality in poultry products.

Keywords:poultry meat;spoilage;multispectral imaging;Fourier-Transform Infrared spectroscopy (FT-IR);regression models;classification models; multivariate data analysis