【摘要】：利用近紅外光譜分析技術對植物蛋白飲料中脂肪和可溶性固形物含量進行定量分析。采用向后間隔偏最小二乘法(Bi PLS)、組合間隔偏最小二乘法(Si PLS)、遺傳偏最小二乘法(GA-PLS)、競爭性自適應重加權法(CARS)優(yōu)選波段,并結(jié)合偏最小二乘法(PLS)建立植物蛋白飲料中脂肪和可溶性固形物的定量分析模型。結(jié)果表明,4種方法對模型均有優(yōu)化效果,可提高模型的穩(wěn)定性和精準性,其中GA-Bi PLS、GA-Si PLS優(yōu)化效果最為明顯,脂肪、可溶性固形物的決定系數(shù)R2分別達到了0.984、0.97和0.988、0.990,預測標準均方差(RMSEP)分別為0.026、0.030和0.170、0.155,相對分析誤差(RPD)分別為8.077、7.000和9.112、10.000。表明近紅外光譜技術作為一種快速、便捷的檢測手段,適用于植物蛋白飲料品質(zhì)的快速檢測分析。
[Abstract]:The content of fat and soluble solids in vegetable protein beverage was quantitatively analyzed by near infrared spectroscopy (NIR). The backward interval partial least squares (Bi PLS),) combined interval partial least squares (Si PLS),) genetic partial least squares (GA-PLS) method and competitive adaptive reweighting method (CARS) were used to optimize the band selection. The quantitative analysis model of fat and soluble solids in vegetable protein beverage was established by partial least square method (PLS). The results show that all of the four methods can improve the stability and accuracy of the model, and the optimization effect of GA-Bi PLS,GA-Si PLS is the most obvious. The coefficient of determination (R2) of soluble solids was 0.9840.97 and 0.9880.900.The standard mean deviation (RMSEP) of the prediction was 0.026 ~ 0.030 and 0.170 ~ 0.155, respectively. The relative analysis error (RPD) was 8.077 ~ 7.000 and 9.112 ~ 10.000, respectively. Near-infrared spectroscopy is a rapid and convenient method to detect and analyze the quality of vegetable protein beverage.
【作者單位】： 中國食品發(fā)酵工業(yè)研究院;東北農(nóng)業(yè)大學工程學院管理科學工程系;河北養(yǎng)元智匯飲品股份有限公司;紅牛維他命飲料有限公司;
【基金】：國家自然科學基金項目(31671937)
【分類號】：O657.33;TS275.4

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