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Hyperspectral scattering profiles for prediction of the microbial spoilage of beef

机译：高光谱散射曲线可预测牛肉的微生物变质

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摘要

Spoilage in beef is the result of decomposition and the formation of metabolites caused by the growth and enzymatic activity of microorganisms. There is still no technology for the rapid, accurate and non-destructive detection of bacterially spoiled or contaminated beef. In this study, hyperspectral imaging technique was exploited to measure biochemical changes within the fresh beef. Fresh beef rump steaks were purchased from a commercial plant, and left to spoil in refrigerator at 8℃. Every 12 hours, hyperspectral scattering profiles over the spectral region between 400 nm and 1100 nm were collected directly from the sample surface in reflection pattern in order to develop an optimal model for prediction of the beef spoilage, in parallel the total viable count (TVC) per gram of beef were obtained by classical microbiological plating methods. The spectral scattering profiles at individual wavelengths were fitted accurately by a two-parameter Lorentzian distribution function. TVC prediction models were developed, using multi-linear regression, on relating individual Lorentzian parameters and their combinations at different wavelengths to log_(10)(TVC) value. The best predictions were obtained with r~2= 0.96 and SEP = 0.23 for log_(10)(TVC). The research demonstrated that hyperspectral imaging technique is a valid tool for real-time and non-destructive detection of bacterial spoilage in beef.
机译：牛肉变质是微生物的生长和酶促活性引起的分解和代谢产物形成的结果。尚无用于快速，准确和无损检测细菌变质或污染牛肉的技术。在这项研究中，利用高光谱成像技术来测量鲜牛肉中的生化变化。新鲜的牛肉臀部牛排购自一家商业工厂，并置于8℃的冰箱中变质。每隔12小时，以反射模式直接从样品表面收集400 nm至1100 nm光谱范围内的高光谱散射曲线，以便开发出预测牛肉变质的最佳模型，并与总可行计数（TVC）平行每克牛肉通过经典的微生物平板方法获得。通过两参数洛伦兹分布函数精确拟合了各个波长处的光谱散射曲线。使用多线性回归开发了TVC预测模型，该模型将各个Lorentzian参数及其在不同波长的组合与log_（10）（TVC）值相关。 log_（10）（TVC）的r〜2 = 0.96和SEP = 0.23获得了最佳预测。研究表明，高光谱成像技术是实时，无损检测牛肉中细菌变质的有效工具。

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