Rapid Lactic Acid Content Detection in Secondary Fermentation of Maize Silage Using Colorimetric Sensor Array Combined with Hyperspectral Imaging

Xiaoyu Xue, Haiqing Tian (), Kai Zhao, Yang Yu, Ziqing Xiao, Chunxiang Zhuo and Jianying Sun
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Xiaoyu Xue: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010010, China
Haiqing Tian: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010010, China
Kai Zhao: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010010, China
Yang Yu: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010010, China
Ziqing Xiao: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010010, China
Chunxiang Zhuo: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010010, China
Jianying Sun: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010010, China

Agriculture, 2024, vol. 14, issue 9, 1-19

Abstract: Lactic acid content is a crucial indicator for evaluating maize silage quality, and its accurate detection is essential for ensuring product quality. In this study, a quantitative prediction model for the change of lactic acid content during the secondary fermentation of maize silage was constructed based on a colorimetric sensor array (CSA) combined with hyperspectral imaging. Volatile odor information from maize silage samples with different days of aerobic exposure was obtained using CSA and recorded by a hyperspectral imaging (HSI) system. Subsequently, the acquired spectral data were subjected to preprocessing through five distinct methods before being modeled using partial least squares regression (PLSR). The coronavirus herd immunity optimizer (CHIO) algorithm was introduced to screen three color-sensitive dyes that are more sensitive to changes in lactic acid content of maize silage. To minimize model redundancy, three algorithms, such as competitive adaptive reweighted sampling (CARS), were used to extract the characteristic wavelengths of the three dyes, and the combination of the characteristic wavelengths obtained by each algorithm was used as an input variable to build an analytical model for quantitative prediction of the lactic acid content by support vector regression (SVR). Moreover, two optimization algorithms, namely grid search (GS) and crested porcupine optimizer (CPO), were compared to determine their effectiveness in optimizing the parameters of the SVR model. The results showed that the prediction accuracy of the model can be significantly improved by choosing appropriate pretreatment methods for different color-sensitive dyes. The CARS-CPO-SVR model had better prediction, with a prediction set determination coefficient ( R P 2 ), root mean square error of prediction ( RMSEP ), and a ratio of performance to deviation ( RPD ) of 0.9617, 2.0057, and 5.1997, respectively. These comprehensive findings confirm the viability of integrating CSA with hyperspectral imaging to accurately quantify the lactic acid content in silage, providing a scientific and novel method for maize silage quality testing.

Keywords: lactic acid content; maize silage; colorimetric sensor array; hyperspectral images; non-destructive detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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