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

• Published in: Agriculture (Basel) Vol. 14; no. 9; p. 1653
• Main Authors: Xue, Xiaoyu, Tian, Haiqing, Zhao, Kai, Yu, Yang, Xiao, Ziqing, Zhuo, Chunxiang, Sun, Jianying
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Adaptive algorithms; Adaptive sampling; Algorithms; Amino acids; Boxes; Chromatography; Color sensitivity; colorimetric sensor array; Colorimetry; Corn; Corn silage; Coronaviruses; Data acquisition; Developing countries; Dyes; Feeds; Fermentation; Herd immunity; hyperspectral images; Hyperspectral imaging; Lactic acid; lactic acid content; Least squares method; Livestock; Livestock farms; maize silage; Methods; Methyl salicylate; Microorganisms; non-destructive detection; Optimization; Parameter sensitivity; Polyethylene; Prediction models; Quantitative analysis; Redundancy; Regression models; Sensor arrays; Sensors; Silage; Support vector machines; VOCs; Volatile organic compounds; Wavelengths; China
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture14091653

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 (RP2), 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.