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Rapid non-destructive detection of spoilage of intact chicken breast muscle using near-infrared and Fourier transform mid-infrared spectroscopy and multivariate statistics.

机译:使用近红外和傅里叶变换中红外光谱法和多元统计数据,快速无损检测完整的鸡胸肉变质。

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摘要

Near-infrared (NIR) transflectance and Fourier transform-infrared (FT-IR) attenuated total reflectance spectra of intact chicken breast muscle packed under aerobic conditions and stored at 4 degrees for 14 days were collected and investigated for their potential use in rapid non-destructive detection of spoilage. Multiplicative scatter correction-transformed NIR and standard normal variate-transformed FT-IR spectra were analysed using principal component analysis (PCA), partial least-squares discriminant analysis (PLS2-DA) and outer product analysis (OPA). PCA and PLS2-DA regression failed to completely discriminate between days 0 and 4 samples (total viable count (TVC) days 0 and 4=5.23 and 6.75 log10 cfu g-1) which had bacterial loads smaller than the accepted levels (8 log10 cfu g-1) of sensory spoilage detection but classified correctly days 8 and 14 samples (TVC days 8 and 14=9.61 and 10.37 log10 cfu g-1). OPA performed on both NIR and FT-IR datasets revealed several correlations that highlight the effect of proteolysis in influencing the spectra. These correlations indicate that increase in free amino acids and peptides could be the main factor in the discrimination of intact chicken breast muscle. This investigation suggests that NIR and FT-IR spectroscopy can become useful, rapid, non-destructive tools for spoilage detection.
机译:收集在有氧条件下包装并在4度下保存14天的完整鸡胸肌的近红外(NIR)透射反射和傅里叶变换红外(FT-IR)衰减的全反射光谱,并研究其在快速非快速反射中的潜在用途破坏性破坏性检测。使用主成分分析(PCA),偏最小二乘判别分析(PLS2-DA)和外部乘积分析(OPA)分析了乘法散射校正变换的NIR和标准正态变量变换的FT-IR光谱。 PCA和PLS2-DA回归未能完全区分第0天和第4天样本(第0天和第4天总生存计数(TVC)= 5.23和6.75 log 10 cfu g -1 )的细菌载量小于感官腐败检测的可接受水平(8 log 10 cfu g -1 ),但正确分类了第8天和第14天样品(TVC第8天)和14 = 9.61和10.37 log 10 cfu g -1 )。在NIR和FT-IR数据集上执行的OPA揭示了一些相关性,这些相关性突出了蛋白水解在影响光谱中的作用。这些相关性表明游离氨基酸和肽的增加可能是完整鸡胸肉鉴别的主要因素。这项研究表明,NIR和FT-IR光谱学可以成为有用的,快速的,无损检测损坏的工具。

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