Monitoring quality loss of pasteurized skim milk using visible and short wavelength near-infrared spectroscopy and multivariate analysis
Monitoring quality loss of pasteurized skim milk using visible and short wavelength near-infrared spectroscopy and multivariate analysis
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DOI：
10.3168/jds.2007-0618
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发表时间：
2008-03-01
影响因子：
3.5
通讯作者：
Rasco, B. A.
中科院分区：
文献类型：
--
作者：
Al-Qadiri, H. M.;Lin, M.;Rasco, B. A.
评估了可见光和短波长近红外漫反射光谱（600 至 1,100 nm）作为检测和监测巴氏灭菌脱脂牛奶在 3 种储存温度（6、21 和 37°C）3 至 30 小时（对照，t = 0 小时；n = 3）腐败情况的技术。获得光谱、需氧菌总数和 pH 值，每个样品总共获得 60 个光谱。开发了多变量统计程序，包括主成分分析、类别类比的软独立建模和偏最小二乘校准模型，用于预测牛奶腐败程度。主成分分析显示，在不同时间间隔储存的牛奶样品存在明显的聚类和分离。在不同温度下储存 30 小时或更短时间的牛奶样品与对照明显分离并且明显聚集。类别类比分析的软独立建模可以正确分类 30 小时时对照孵化样品的 88% 至 93% 的光谱。具有 5 个潜在变量的偏最小二乘模型将光谱特征与细菌计数和 pH 值相关联，分别产生相关系数（R = 0.99 和 0.99）和预测标准误差（0.34 log(10) cfu/mL 和 0.031 pH 单位）。通过将光谱特征的变化与细菌增殖和牛奶腐败水平相关联，使用短波长近红外光谱来快速、无创地检测和监测牛奶腐败可能是可行的。
Visible and short wavelength near-infrared diffuse reflectance spectroscopy (600 to 1,100 nm) was evaluated as a technique for detecting and monitoring spoilage of pasteurized skim milk at 3 storage temperatures (6, 21, and 37 C) over 3 to 30 h (control, t = 0 h; n = 3). Spectra, total aerobic plate count, and pH were obtained, with a total of 60 spectra acquired per sample. Multivariate statistical procedures, including principal component analysis, soft independent modeling of class analogy, and partial least squares calibration models were developed for predicting the degree of milk spoilage. Principal component analysis showed apparent clustering and segregation of milk samples that were stored at different time intervals. Milk samples that were stored for 30 h or less at different temperatures were noticeably separated from control and distinctly clustered. Soft independent modeling of class analogy analysis could correctly classify 88 to 93% of spectra of incubated samples from control at 30 h. A partial least squares model with 5 latent variables correlating spectral features with bacterial counts and pH yielded a correlation coefficient (R = 0.99 and 0.99) and a standard error of prediction (0.34 log(10) cfu/mL and 0.031 pH unit), respectively. It may be feasible to use short wavelength near-infrared spectroscopy to detect and monitor milk spoilage rapidly and noninvasively by correlating changes in spectral features with the level of bacterial proliferation and milk spoilage.