Spyrelli Evgenia D, Papachristou Christina K, Nychas George-John E, Panagou Efstathios Z
Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.
Foods. 2021 Nov 7;10(11):2723. doi: 10.3390/foods10112723.
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 ( = 402) were subjected to spoilage experiments at eight isothermal and two dynamic temperature profiles. Samples were analyzed microbiologically (total viable counts (TVCs) and 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/cm. Partial least squares regression (PLS-R) models were employed in the assessment of TVCs and 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 spp. counts satisfactorily, with root mean squared error (RMSE) values of 0.987 and 1.215 log CFU/cm, 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.
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能力。对402个鸡大腿肉片在8种等温条件和2种动态温度曲线下进行了腐败实验。对样品进行了微生物分析（总活菌数（TVC）和特定菌数），同时采集了MSI和FT-IR光谱。感官小组还对样品的感官质量进行了评估，确定TVC腐败阈值为6.99 log CFU/cm。采用偏最小二乘回归（PLS-R）模型评估鸡表面的TVC和特定菌数。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））来区分两个质量等级的样品（新鲜与腐败）。基于MSI数据开发的PLS-R模型对TVC和特定菌数的预测效果良好，均方根误差（RMSE）值分别为0.987和1.215 log CFU/cm。与MSI数据结合的SVM模型表现出最高的性能，总体准确率为94.4%，而对于FT-IR，QDA模型的分类效果有所改善（总体准确率71.4%）。这些结果证实了MSI和FT-IR作为评估禽肉产品质量的快速方法的有效性。
