Nondestructive prediction of lime acidity with a single scan using two types of near infrared spectrometers and ensemble learning strategy
文献类型: 外文期刊
作者: Li, Pao 1 ; Dong, Yiqing 1 ; Jiang, Liwen 1 ; Du, Guorong 1 ; Shan, Yang 1 ;
作者机构: 1.Hunan Agr Univ, Coll Food Sci & Technol, Changsha 410128, Peoples R China
2.Hunan Acad Agr Sci, Hunan Agr Prod Proc Inst, Changsha 410125, Peoples R China
3.Shaoguan Univ, Guangdong Prov Key Lab Utilizat & Conservat Food &, Shaoguan 512005, Peoples R China
4.Shanghai Tobacco Grp Co Ltd, Beijing Work Stn, Technol Ctr, Beijing 101121, Peoples R China
关键词: Nondestructive detection; Near infrared spectroscopy; Lime; Available acidity; Total acidity; Ensemble learning
期刊名称:JOURNAL OF FOOD ENGINEERING ( 影响因子:5.5; 五年影响因子:5.7 )
ISSN: 0260-8774
年卷期: 2024 年 368 卷
页码:
收录情况: SCI
摘要: An accurate and nondestructive prediction method of lime acidity was established based on near infrared (NIR) spectroscopy and ensemble learning strategy. Dual-band spectra were obtained nondestructively with a single scan using a desk-top Fourier transform spectrometer and a grating portable spectrometer. Spectral preprocessing methods were used to eliminate the interferences in the spectra. The quantification models of available acidity (pH and 10-pH) and total acidity (TA) were developed with the ensemble learning strategy compared with partial least squares (PLS) and variable selection methods. The results indicated that due to the high-energy light source, the models of the grating portable spectrometer were much better than those of the Fourier transform spectrometer. Short-wave NIR (SWNIR) was more suitable for quantitative analysis of available acidity, while longwave NIR (LWNIR) was more effective for quantitative analysis of TA. Besides, the models of available acidity were ahead of those of TA. Compared with PLS and variables selection methods, the ensemble learning strategy can produce models with higher prediction accuracy and better robustness. In the optimized models, the correlation coefficients of pH, 10-pH and TA for the prediction set were 0.84, 0.82 and 0.66, respectively. The experiment results show that accurate and nondestructive prediction of lime acidity can be achieved with the grating portable NIR spectrometer and ensemble learning strategy.
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