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In this work, multivariate detection limits (MDL) estimator was obtained based on the micro-electro-mechanical systems–near infrared (MEMS–NIR) technology coupled with two sampling accessories to assess the detection capability of four quality parameters (glycyrrhizic acid, liquiritin, liquiritigenin and isoliquiritin) in licorice from different geographical regions. 112 licorice samples were divided into two parts (calibration set and prediction set) using Kennard–Stone (KS) method. Four quality parameters were measured using high-performance liquid chromatography (HPLC) method according to Chinese pharmacopoeia and previous studies. The MEMS–NIR spectra were acquired from fiber optic probe (FOP) and integrating sphere, then the partial least squares (PLS) model was obtained using the optimum processing method. Chemometrics indicators have been utilized to assess the PLS model performance. Model assessment using chemometrics indicators is based on relative mean prediction error of all concentration levels, which indicated relatively low sensitivity for low-content analytes (below 1000 parts per million (ppm)). Therefore, MDL estimator was introduced with alpha error and beta error based on good prediction characteristic of low concentration levels. The result suggested that MEMS–NIR technology coupled with fiber optic probe (FOP) and integrating sphere was able to detect minor analytes. The result further demonstrated that integrating sphere mode (i.e., MDL0.05,0.05, 0.22%) was more robust than FOP mode (i.e., MDL0.05,0.05, 0.48%). In conclusion, this research proposed that MDL method was helpful to determine the detection capabilities of low-content analytes using MEMS–NIR technology and successful to compare two sampling accessories

Estimation of multivariate detection limits of four quality parameters in licorice using MEMS–NIR spectrometry coupled with two sampling accessories