Hyperspectral Imaging Technology Combined with the Extreme Gradient Boosting Algorithm (XGBoost) for the Rapid Analysis of the Moisture and Acidity Contents in Fermented Grains

Abstract

AbstractThe moisture content (MC) and acidity content (AC) of the fermented grains used in liquor production directly affect the liquor quality and yield; as such, they are important indicators used to evaluate the quality of fermented grains. In this study, extreme gradient enhancement algorithm (XGBoost), partial least square regression (PLSR), and extreme learning machine (ELM) models were developed based on spectral data collected by near-infrared (NIR) hyperspectral imaging (HSI) technology. First, PLSR models were established after SNV and MSC algorithms preprocessed the HSI data, and the best preprocessing method was determined (MC: SNV; AC: MSC). Then, the competitive adaptive reweighting sampling (CARS) algorithm and principal component analysis (PCA), both combined with the successive projection algorithm (SPA), were used to extract the characteristic wavelengths from the full-band spectral data. Ultimately, the XGBoost model developed using the characteristic wavelengths extracted by CARS-SPA most accurately predicted the MC (RPD = 6.4167, RP2 = 0.9757, RMSEP = 0.0442 g·100 g−1) and AC (RPD = 13.0308, RP2 = 0.9941, RMSEP = 0.0216 mmol·10 g−1). The results showed that the XGBoost model could more accurately predict the MC and AC of the fermented grains from hyperspectral images of the grains, providing an effective method for the rapid analysis of raw materials used in the fermentation of liquor.Keywords: Characteristic wavelengthshyperspectral imaging technologyliquor fermented grainsmoisture and acidityvisualizationXGBoost AcknowledgmentsMoreover, thanks to Jianping Tian and Xinjun Hu for providing theoretical and financial support. Thanks to Xinna Jiang for providing valuable advice and guidance.Author contributionsLipeng Han: writing—original draft, writing—review and editing. Xinna Jiang: resources. Shuyu Zhou: resources. Jianping Tian: supervision. Xinjun Hu: supervision. Dan Huang and Huibo Luo: resources.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available from the corresponding author upon reasonable request.Additional informationFundingThe authors are grateful to the Luzhou Laojiao Innovation Fund of Sichuan University of Science and Engineering (LJCX2022-9) for its support. This research was also supported by Sichuan Science and Technology Program (2022YFS0552) and the Liquor Making Biological Technology and Application of Key Laboratory of Sichuan Province (NJ2022-04).