A Non-destructive method to assess freshness of raw bovine milk using FT-NIR spectroscopy

A Non-destructive method to assess freshness of raw bovine milk using FT-NIR spectroscopy
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一种使用 FT-NIR 光谱法评估生牛乳新鲜度的无损方法

DOI:
10.1007/s13197-014-1574-5
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发表时间:
2015-08
影响因子:
3.1
通讯作者:
Jurick, Wayne M., II
Jurick, Wayne M., II
中科院分区:
农林科学3区
文献类型:
--
作者:
Kou, Liping;Li, Liang;Wang, Chen;Jurick, Wayne M., II

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利用傅立叶变换近红外光谱仪和光纤探头,建立了一种非破坏性的原料奶新鲜度检测方法。采集了西北农林科技大学畜牧养殖场的原料奶样品,在833 ~ 2,500 nm的光谱范围内获得了漫透射光谱。每次光谱采集后,通过传统检测方法测量酸度、pH值和乳糖含量等质量参数。对于所有的牛奶样品,PLS(偏最小二乘回归),MLR(多元线性回归),和ANN(人工神经网络)进行分析,以开发模型来预测参数,指示新鲜度。预测模型显示酸度、pH值和乳糖含量的R2值分别高达0.9647、0.9876和0.8772(验证集验证)。采用聚类分析方法对原料奶在贮藏过程中的新鲜度进行了相似性分析和分类。在8天的储存期内,第1天和第2天之间的异质性最高。
A non-destructive method to analyze the freshness of raw milk was developed using a FT-NIR spectrometer and a fiber optic probe. Diffuse transmittance spectra were acquired in the spectral range 833 ~ 2,500 nm from raw milk samples collected from Northwest A&F University Animal Husbandry Station. After each spectral acquisition, quality parameters such as acidity, pH, and lactose content were measured by traditional detection methods. For all milk samples, PLS (partial least square regression), MLR (multiple linear regression), and ANN (artificial neural networks) analyses were carried out in order to develop models to predict parameters that were indicative of freshness. Predictive models showed R2values up to 0.9647, 0.9876 and 0.8772 for acidity, pH, and lactose content, respectively (validation set validations). The similarity analysis and classification between raw milk freshness during storage was also conducted by means of hierarchical cluster analysis. Over an 8 day storage period, the highest heterogeneity was evident between days 1 and 2.
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