Abstract：Multivariate detection limits (MDLs) of different types of near-infrared instruments were investigated to guide the selection of device type for TCM NIR analysis. In this paper, near-infrared spectroscopy of Qingkailing injection was performed in transmission mode on four near-infrared spectrometers named a, b, c and d, respectively. High performance liquid chromatography (HPLC) was used as the reference method to determine the content of baicalin in Qingkailing injection. Partial least squares (PLS) and interval partial least squares (iPLS) quantitative models of baicalin in Qingkailing injection were established and MDLs of quantitative models based on different types of instruments were calculated. The determination coefficient of prediction(R2pre)and the root mean square errors of prediction (SEP) of PLS models of four different near-infrared spectrometers are 0.976 2 and 230.4 μg·mL-1 (a), 0.956 1 and 246.4 μg·mL-1 (b), 0.966 2 and 264.4 μg·mL-1 (c), 0.998 5 and 71.5 μg·mL-1 (d). And the model of instrument d shows a better prediction performance than the other three types. There are no remarkable superiorities in predictability in iPLS models of instruments a and b after variable selection, since the R2pre and SEP values for instruments a and b are 0.977 1 and 218.4 μg·mL-1, and 0.975 4 and 219.4 μg·mL-1, respectively. Models c and d show no results of variable selection. MDLs (Δ0.05, 0.05) of different instruments are all less than 250 μg·mL-1, and the MDLs of instruments c and d reach to 58 and 2.9 μg·mL-1 respectively. The results reveal that the predictability of models and corresponding MDLs are different for different detection equipments. This paper innovatively used the theory of MDL to investigate the detection performance of different types of NIR instruments. The results demonstrated the feasibility of the approach. And it is expected that in actual applications, choosing the right type of instrument should be based on the characteristics of the study carrier to ensure quantitative accuracy.
