小麦粉品质在线无损快速检测系统设计与实现

目的:快速无损检测小麦粉的品质。方法:搭建主要包括微型近红外光谱仪集成装置、生产线可调速模拟装置、光谱在线采集控制软件和在线建模分析软件四大部分小麦粉品质在线检测系统,针对小麦粉中水分、灰分、面筋指标采用不同建模算法建立定量分析模型,并对在线定量分析模型稳健性进行优化,分析不同试验条件对建模结果的影响。结果:偏最小二乘回归(PLSR)算法对水分、灰分、面筋的定量分析模型均优于多元线性回归(MLR)和主成分回归(PCR)算法。用石英玻璃杯在线采集小麦粉样品的建模效果最佳,15档速度/积分时间6 000 ms最佳配比的建模效果最佳。结论:基于近红外光谱分析技术的小麦粉品质在线检测系统,可以实现小麦粉品质在线无损快速检测。

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

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.