来自希腊雅典农业大学的科学家利用VideometerLab多光谱成像系统，近来发表了题为“Spectroscopic Data for the Rapid Assessment of Microbiological Quality of Chicken Burgers”的文章，文章发表于期刊Foods.2022 Aug; 11(16): 2386.

用于快速评估鸡肉汉堡微生物质量的光谱数据

摘要

快速评估高易腐食品的微生物质量非常重要。光谱数据结合机器学习方法近年来得到了广泛的研究，因为它们具有快速、无损、生态友好的特性，并且具有嵌入、在线或旁线使用的潜力。在本研究中，使用傅里叶变换红外光谱（FTIR）和多光谱成像（MSI）结合机器学习算法对鸡肉汉堡的微生物质量进行了评估。从食品行业购买了六个独立批次，并在0、4和8°C下储存。定期（特别是每24小时）对重复样品进行微生物分析、FTIR测量和MSI取样。在数据收集过程中采集的样本（n=274）被分为三个微生物质量组：“满意”：4-7 log CFU/g，“可接受”：7-8 log CFU/g，“不可接受”：＞8 log CFU/g。随后，使用几种机器学习方法训练和测试分类模型（外部验证），即偏最小二乘判别分析（PLSDA）、支持向量机（SVM）、随机森林（RF）、逻辑回归（LR）和顺序逻辑回归（OLR）。外部验证的准确度得分显示FTIR数据值在79.41–89.71%范围内，MSI数据的准确度分数在74.63–85.07%范围内。这些模型的性能在鸡肉汉堡的微生物质量评估方面显示了优势。

关键词：鸡肉汉堡，傅里叶变换红外光谱，多光谱成像，机器学习

Foods.2022 Aug; 11(16): 2386.

22 Aug 9.doi:10.3390/foods11162386

Spectroscopic Data for the Rapid Assessment of Microbiological Quality of Chicken Burgers

Abstract

The rapid assessment of the microbiological quality of highly perishable food commodities is of great importance. Spectroscopic data coupled with machine learning methods have been investigated intensively in recent years, because of their rapid, non-destructive, eco-friendly qualities and their potential to be used on-, in- or at-line. In the present study, the microbiological quality of chicken burgers was evaluated using Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI) in tandem with machine learning algorithms. Six independent batches were purchased from a food industry and stored at 0, 4, and 8 °C. At regular time intervals (specifically every 24 h), duplicate samples were subjected to microbiological analysis, FTIR measurements, and MSI sampling. The samples (n = 274) acquired during the data collection were classified into three microbiological quality groups: “satisfactory”: 4–7 log CFU/g, “acceptable”: 7–8 log CFU/g, and “unacceptable”: >8 logCFU/g. Subsequently, classification models were trained and tested (external validation) with several machine learning approaches, namely partial least squares discriminant analysis (PLSDA), support vector machine (SVM), random forest (RF), logistic regression (LR), and ordinal logistic regression (OLR). Accuracy scores were attained for the external validation, exhibiting FTIR data values in the range of 79.41–89.71%, and, for the MSI data, in the range of 74.63–85.07%. The performance of the models showed merit in terms of the microbiological quality assessment of chicken burgers.

Keywords: chicken burgers, Fourier transform infrared (FTIR) spectroscopy, multispectral imaging (MSI), machine learning