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首页> 外文期刊>LWT-Food Science & Technology >Visible and NIR hyperspectral imaging and chemometrics for prediction of microbial quality of beef Longissimus dorsi muscle under simulated normal and abuse storage conditions
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Visible and NIR hyperspectral imaging and chemometrics for prediction of microbial quality of beef Longissimus dorsi muscle under simulated normal and abuse storage conditions

机译：可见和NIR高光谱成像和化学计量学，用于预测模拟正常和滥用储存条件下的牛肉镰刀肌的微生物质量

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摘要

There is a need to develop a rapid technique to provide real time information on the microbial load of meat along the supply chain. Hyperspectral imaging (HSI) is a rapid, non-destructive technique well suited to food analysis applications. In this study, HSI in both the visible and near infrared spectral ranges, and chemometrics were studied for prediction of the bacterial growth on beef Longissimus dorsi muscle (LD) under simulated normal (4 degrees C) and abuse (10 degrees C) storage conditions. Total viable count (TVC) prediction models were developed using partial least squares regression (PLS-R), spectral pre-treatments, band selection and data fusion methods. The best TVC prediction models developed for storage at 4 (RMSEp 0.58 log CFU/g, RPDp 4.13, RPp2 0.96), 10 degrees C (RMSEp 0.97 log CFU/g, RPDp 3.28, RPp2 0.94) or at either 4 or 10 degrees C (RMSEp 0.89 log CFU/g, RPDp 2.27, R-p(2) 0.86) were developed using high-level data fusion of both spectral regions. The use of appropriate spectral pretreatments and band selection methods was key for robust model development. This study demonstrated the potential of HSI and chemometrics for real time monitoring to predict microbial growth on LD along the meat supply chain.
机译：需要开发一种快速技术，以提供有关供应链的微生物负荷的实时信息。高光谱成像（HSI）是一种快速，无损性的技术，适合于食品分析应用。在本研究中，研究了在模拟正常（4摄氏度）和滥用（10摄氏度）储存条件下的可见和近红外光谱范围和近红外光谱范围的HSI，用于预测牛力镰刀菌肌（LD）上的细菌生长。 。使用部分最小二乘回归（PLS-R），光谱预处理，频带选择和数据融合方法，开发了总活计数（TVC）预测模型。用于存储的最佳TVC预测模型4（RMSEP 0.58 LOG CFU / G，RPDP 4.13，RPP2 0.96），10摄氏度（RMSEP 0.97 LOG CFU / G，RPDP 3.28，RPP2 0.94）或在4或10摄氏度之前（RMSEP 0.89 LOG CFU / G，RPDP 2.27，RP（2）0.86）使用两个光谱区域的高级数据融合来开发。使用适当的光谱预处理和带选择方法是鲁棒模型开发的关键。本研究证明了HSI和化学计量学的实时监测，以预测沿肉类供应链的LD上的微生物生长。

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