Design and implementation of on-line nondestructive rapid testing system for wheat flour quality

Authors

SUN Xiao-rong, School of Artificial Intelligence Beijing Technology and Business University, Beijing 100048 , China ；Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing 100048 , China
ZHENG Dong-yu, School of Artificial Intelligence Beijing Technology and Business University, Beijing 100048 , China ；Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing 100048 , China
LIU Cui-ling, School of Artificial Intelligence Beijing Technology and Business University, Beijing 100048 , China ；Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing 100048 , China
WU Jing-zhu, School of Artificial Intelligence Beijing Technology and Business University, Beijing 100048 , China ；Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing 100048 , China
JIN Jia-rui, School of Artificial Intelligence Beijing Technology and Business University, Beijing 100048 , China ；Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing 100048 , China

Abstract

Objective: This study aimed to detect the quality of wheat flour quickly and non-destructively. Methods: The online detection system for wheat flour quality was built in the experiment, which mainly includes the flowing four parts: micro near-infrared spectrometer integrated device, production line adjustable speed simulation device, spectrum online acquisition and control software and online modeling and analysis software. Different modeling algorithms were used to establish quantitative analysis models for moisture, ash and gluten, and the robustness of the online quantitative analysis model was optimized. The influence of different experimental conditions on the modeling results was analyzed. Results: Partial least squares regression (PLSR) algorithm is superior to multiple linear regression (MLR) and principal component regression (PCR) algorithm for quantitative analysis of moisture, ash and gluten. The best modeling effect was obtained by using quartz glass to collect wheat flour samples online, and the best ratio of 15 speed/integration time was 6 000 ms. Conclusion: The wheat flour quality online detection system based on near-infrared spectrum analysis technology realize the nondestructive and rapid online detection of wheat flour quality.

Publication Date

12-28-2022

First Page

87

Last Page

91

DOI

10.13652/j.spjx.1003.5788.2022.80152

Recommended Citation

Xiao-rong, SUN; Dong-yu, ZHENG; Cui-ling, LIU; Jing-zhu, WU; and Jia-rui, JIN (2022) "Design and implementation of on-line nondestructive rapid testing system for wheat flour quality," Food and Machinery: Vol. 38: Iss. 12, Article 15.
DOI: 10.13652/j.spjx.1003.5788.2022.80152
Available at: https://www.ifoodmm.cn/journal/vol38/iss12/15

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