Electric Drive Supervisor for Milling Process 4.0 Automation: A Process Analytical Approach with IIoT NIR Devices for Common Wheat.

Silvia Grassi,Sergio Casalino,Alessandra Marti,Giuseppe Leonardo Cascella,Davide Cascella

doi:10.3390/s20041147

Abstract

The milling industry envisions solutions to become fully compatible with the industry 4.0 technology where sensors interconnect devices, machines and processes. In this contest, the work presents an integrated solution merging a deeper understanding and control of the process due to real-time data collection by MicroNIR sensors (VIAVI, Santa Rosa, CA)—directly from the manufacturing process—and data analysis by Chemometrics. To the aim the sensors were positioned at wheat cleaning and at the flour blends phase and near infrared spectra (951–1608 nm) were collected online. Regression models were developed merging the spectra information with the results obtained by reference analyses, i.e., chemical composition and rheological properties of dough by Farinograph® (Brabender GmbH and Co., Duisburg, Germany), Alveograph® (Chopin, NG Villeneuve-la-Garenne Cedex, France) and Extensograph®.(Brabender GmbH and Co., Duisburg, Germany) The model performance was tested by an external dataset obtaining, for most of the parameters, RPRED higher than 0.80 and Root Mean Squares Errors in prediction lower than two-fold the value of the reference method errors. The real-time implementation resulted in optimal (100% of samples) or really good (99.9%–80% of samples) prediction ability. The proposed work succeeded in the implementation of a process analytical approach with Industrial Internet of Things near infrared (IIoT NIR) devices for the prediction of relevant grain and flour characteristics of common wheat at the industrial level.

Highlights

IntroductionThe needs along the value chain of wheat have changed. For each one of the many wheat uses, specific grain-quality requirements are preferred
In recent years, the needs along the value chain of wheat have changed
The proposed work succeeded in the implementation of a process analytical approach with Industrial Internet of Things near infrared (IIoT Near Infrared Spectroscopy (NIR)) devices for the prediction of relevant grain and flour characteristics of common wheat at the industrial level

Summary

Introduction

The needs along the value chain of wheat have changed. For each one of the many wheat uses, specific grain-quality requirements are preferred. The possibility to obtain reliable and quick information about kernels quality is constantly becoming more important for all the players of the wheat value chain. Quality control is of great importance for productivity maximization and standardization since wheat and flour have to respect specific compositional and functional requirements to satisfy customers’ requests. The physico-chemical properties of the raw materials strongly affect the properties of the doughs during kneading, the consistency of the endproducts and the process efficiency. In this context, along decades, several approaches have been developed and proposed to assess quality along the Sensors 2020, 20, 1147; doi:10.3390/s20041147 www.mdpi.com/journal/sensors

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