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Development of classification models to detect Salmonella Enteritidis and Salmonella Typhimurium found in poultry carcass rinses by visible-near infrared hyperspectral imaging

机译：通过可见-近红外高光谱成像技术检测家禽rinse体冲洗物中发现的肠炎沙门氏菌和鼠伤寒沙门氏菌分类模型的开发

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Salmonella is a major cause of foodborne disease outbreaks resulting from the consumption of contaminated food products in the United States. This paper reports the development of a hyperspectral imaging technique for detecting and differentiating two of the most common Salmonella serotypes, Salmonella Enteritidis (SE) and Salmonella Typhimurium (ST), from background microflora that are often found in poultry carcass rinse. Presumptive positive screening of colonies with a traditional direct plating method is a labor intensive and time consuming task. Thus, this paper is concerned with the detection of differences in spectral characteristics among the pure SE, ST, and background microflora grown on brilliant green sulfa (BGS) and xylose lysine tergitol 4 (XLT4) agar media with a spread plating technique. Visible near-infrared hyperspectral imaging, providing the spectral and spatial information unique to each microorganism, was utilized to differentiate SE and ST from the background microflora. A total of 10 classification models, including five machine learning algorithms, each without and with principal component analysis (PCA), were validated and compared to find the best model in classification accuracy. The five machine learning (classification) algorithms used in this study were Mahalanobis distance (MD), k-nearest neighbor (kNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM). The average classification accuracy of all 10 models on a calibration (or training) set of the pure cultures on BGS agar plates was 98% (Kappa coefficient = 0.95) in determining the presence of SE and/or ST although it was difficult to differentiate between SE and ST. The average classification accuracy of all 10 models on a training set for ST detection on XLT4 agar was over 99% (Kappa coefficient = 0.99) although SE colonies on XLT4 agar were difficult to differentiate from background microflora. The average classification accuracy of all 10 models on a validation set of chicken carcass rinses spiked with SE or ST and incubated on BGS agar plates was 94.45% and 83.73%, without and with PCA for classification, respectively. The best-performing classification model on the validation set was QDA without PCA by achieving the classification accuracy of 98.65% (Kappa coefficient=0.98). The overall best performing classification model regardless of using PCA was MD with the classification accuracy of 94.84% (Kappa coefficients=0.88) on the validation set.
机译：沙门氏菌是在美国消费受污染食品引起的食源性疾病暴发的主要原因。本文报道了一种高光谱成像技术的发展，该技术用于从家禽rinse体冲洗物中经常发现的背景微生物区系中检测和区分两种最常见的沙门氏菌血清型肠炎沙门氏菌（SE）和鼠伤寒沙门氏菌（ST）。用传统的直接铺板方法对菌落进行阳性推定筛选是一项费力且费时的工作。因此，本文涉及通过铺板技术检测在亮绿磺胺（BGS）和木糖赖氨酸tergitol 4（XLT4）琼脂培养基上生长的纯SE，ST和背景微生物区系的光谱特征差异。提供每种微生物特有的光谱和空间信息的可见近红外高光谱成像可用于将SE和ST与背景微生物区分开来。总共对10种分类模型进行了验证，包括5种机器学习算法，每种模型都没有主成分分析（PCA），并进行了比较，以找到分类精度最佳的模型。本研究中使用的五种机器学习（分类）算法是马氏距离（MD），k近邻（kNN），线性判别分析（LDA），二次判别分析（QDA）和支持向量机（SVM）。在确定SE和/或ST的存在时，在BGS琼脂平板上的纯培养物的校准（或训练）组上，所有10个模型的平均分类准确度均为98％（Kappa系数= 0.95），尽管很难区分SE和ST。尽管很难将XLT4琼脂上的SE菌落与背景菌群区分开，但是在训练集上对XLT4琼脂进行ST检测的所有10个模型的平均分类准确性均超过99％（卡伯系数= 0.99）。在不加PCA和加PCA进行分类的情况下，在用SE或ST加标并在BGS琼脂板上孵育的验证的鸡car体冲洗液的所有10个模型的平均分类准确性分别为94.45％和83.73％。验证集上表现最佳的分类模型是不使用PCA的QDA，其分类精度达到98.65％（Kappa系数= 0.98）。无论使用PCA还是什么，总体上表现最佳的分类模型都是MD，在验证集上的分类精度为94.84％（Kappa系数= 0.88）。

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