首页> 外文期刊>Talanta: The International Journal of Pure and Applied Analytical Chemistry >Determination of total viable count (TVC) in chicken breast fillets by near-infrared hyperspectral imaging and spectroscopic transforms
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Determination of total viable count (TVC) in chicken breast fillets by near-infrared hyperspectral imaging and spectroscopic transforms

机译:通过近红外高光谱成像和光谱转换来确定鸡胸肉鱼片中的总存活数(TVC)

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摘要

Near infrared (NIR) hyperspectral imaging (HSI) and different spectroscopic transforms were investigated for their potential in detecting total viable counts in raw chicken fillets. A laboratory-based pushbroom hyperspectral imaging system was utilized to acquire images of raw chicken breast fillets and the resulting reflectance images were corrected and transformed into hypercubes in absorbance and Kubelka-Munck (K-M) units. Full wavelength partial least regression models were established to correlate the three spectral profiles with measured bacterial counts, and the best calibration model was based on absorbance spectra, where the correlation coefficients (R) were 0.97 and 0.93, and the root mean squared errors (RMSEs) were 0.37 and 0.57 log10 colony forming units (CFU) per gram for calibration and cross validation, respectively. To simplify the models, several wavelengths were selected by stepwise regression. More robustness was found in the resulting simplified models and the model based on K-M spectra was found to be excellent with an indicative high ratio of performance to deviation (RPD) value of 3.02. The correlation coefficients and RMSEs for this model were 0.96 and 0.40 log10 CFU per gram as well as 0.94 and 0.50 log10 CFU per gram for calibration and cross validation, respectively. Visualization maps produced by applying the developed models to the images could be an alternative to test the adaptability of a calibration model. Moreover, multi-spectral imaging systems were suggested to be developed for online applications.
机译:研究了近红外(NIR)高光谱成像(HSI)和不同的光谱转换在检测生鸡肉鱼片中总可行计数方面的潜力。利用基于实验室的推扫式高光谱成像系统获取未加工的鸡胸肉鱼片的图像,并对所得的反射率图像进行校正,并将其转换为吸收度和Kubelka-Munck(K-M)单位的超立方体。建立了全波长偏最小回归模型,以将三个光谱图与测得的细菌数相关联,并且最佳校准模型基于吸收光谱,其中相关系数(R)为0.97和0.93,均方根误差(RMSEs) )分别为每克0.37和0.57 log10集落形成单位(CFU),用于校准和交叉验证。为了简化模型,通过逐步回归选择了几个波长。在生成的简化模型中发现了更高的鲁棒性,并且发现基于K-M光谱的模型非常出色,具有3.02的指示性高性能偏差(RPD)值。该模型的相关系数和RMSE分别为每克0.96和0.40 log10 CFU,以及用于校准和交叉验证的每克0.94和0.50 log10 CFU。通过将开发的模型应用于图像产生的可视化图可以替代测试校准模型的适应性。此外,建议为在线应用开发多光谱成像系统。

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