LAUSR

Graphical abstract Figure. No Caption available. HighlightsSpectroscopic technique was applied in production of Gardeniae Fructus.Seven analytes of Gardeniae Fructus were studied.Models showed a good predictive ability of the seven analytes.Different spectroscopy were compared in detecting content of the seven analytes. Abstract Gardeniae Fructus is widely used in the pharmaceutical industry, and many studies have confirmed its medical and economic value. In this study, samples collected from different liquid‐liquid extraction batches of Gardeniae Fructus were detected by mid‐infrared (MIR) and near‐infrared (NIR) spectroscopy. Seven analytes, neochlorogenic acid (5‐CQA), cryptochlorogenic acid (4‐CQA), chlorogenic acid (3‐CQA), geniposidic acid (GEA), deacetyl‐asperulosidic acid methyl ester (DAAME), genipin‐gentiobioside (GGB), and gardenoside (GA), were chosen as quality property indexes of Gardeniae Fructus. The two kinds of spectra were each used to build models by single partial least squares (PLS). Additionally, both spectral data were combined and modeled by multiblock PLS. For single spectroscopy modeling results, NIR had a better prediction for high‐concentration analytes (3‐CQA, DAAME, GGB, and GA) whereas MIR performed better for low‐concentration analytes (5‐CQA, 4‐CQA, and GEA). The multiblock methodology was found to be better compared to single spectroscopy models for all seven analytes. Specifically, the coefficients of determination (R2) of the NIR, MIR, and multiblock PLS calibration models of all seven components were higher than 0.95. Relative standard errors of prediction (RSEP) were all less than 7%, except for models of GGB, which were 10.36%, 13.24%, and 8.15% for the NIR‐PLS, MIR‐PLS, and multiblock models, respectively. These results indicate that MIR and NIR spectrographic techniques could provide a new choice for quality control in industrial production of Gardeniae Fructus.