Abstract Escherichia coli is a main cause of microbial contamination in lettuce. Detection of microbial contamination is the essential key to ensure the consumption of fresh lettuce. In this investigation, the potential of Vis/NIR spectroscopy system with chemometrics analysis was probed for detecting different microbial loads (0.1, 0.2 and 0.3 ml concentration of E. coli solution) on the lettuce samples in the wavelength range of 350–1100 nm. Five different chemometrics analyses consist of soft independent modeling of class analogies (SIMCA), support vector machine (SVM), Partial least Squares Discriminant Analysis (PLS-DA) (supervised techniques), principal component analysis (PCA) and hierarchical cluster analysis (HCA) (unsupervised techniques) were used. The results proved that HCA were correctly clustered unsafe samples with 0.2 ml and 0.3 ml microbial contamination. However, some overlapping was observed between safe and unsafe samples with 0.1 ml microbial contamination. Vis/NIR spectral data with pattern recognition methods (SIMCA and SVM) can be obtained acceptable degree of classification (87.1% and 89.39% accuracy, respectively) between safe and unsafe samples. Compare with 5 different methods, the best model was recommended by PLS-DA using standard normal variate (SNV) + second derivate (D2) pre-processing methods in 6 optimal selected wavelengths (520, 670, 700, 750, 900, 970 nm) with the minimum standard error of cross validation (SECV = 0.176). Besides, a good correlation (rc = 0.989) between Vis/NIR spectral data and E. coli contamination proved the possibility of using Vis/NIR spectroscopy system to detection and evaluation of microbial contamination in lettuce.

中文翻译：

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基于可见/近红外光谱分析的不同监督和非监督分类方法，用于区分微生物污染的生菜样品：大肠杆菌 ATCC 案例研究

摘要 大肠杆菌是生菜中微生物污染的主要原因。检测微生物污染是确保新鲜生菜消费的关键。在这项研究中，探索了具有化学计量学分析的 Vis/NIR 光谱系统在 350-1100 nm 波长范围内检测生菜样品上不同微生物负荷（0.1、0.2 和 0.3 ml 浓度的大肠杆菌溶液）的潜力。五种不同的化学计量学分析包括类类比的软独立建模 (SIMCA)、支持向量机 (SVM)、偏最小二乘判别分析 (PLS-DA)（监督技术）、主成分分析 (PCA) 和层次聚类分析 (HCA) )（无监督技术）被使用。结果证明，HCA 正确聚类了 0.2 ml 和 0 的不安全样本。3毫升微生物污染。然而，在 0.1 毫升微生物污染的安全和不安全样品之间观察到一些重叠。使用模式识别方法（SIMCA 和 SVM）的 Vis/NIR 光谱数据可以获得安全和不安全样品之间可接受的分类程度（分别为 87.1% 和 89.39% 准确度）。与5种不同的方法相比，PLS-DA使用标准正态变量（SNV）+二阶导数（D2）预处理方法在6个最佳选择波长（520、670、700、750、900、970 nm）推荐了最佳模型) 与交叉验证的最小标准误差 (SECV = 0.176)。此外，Vis/NIR 光谱数据与大肠杆菌污染之间的良好相关性 (rc = 0.989) 证明了使用 Vis/NIR 光谱系统检测和评估生菜中微生物污染的可能性。