NIR; spectroscopy; IR spectroscopy; flour milling; flour; food quality control

Chapter 11

The detectives bake up an NIR plan for quality control of raw material in flour milling

📁 Case overview: The detectives feed their appetite for a new case by investigating how to analyse raw material quality in flour milling. Nancy Beef takes on the case and suggests using the NIR method on-line to achieve reliable real-time results. Will her idea work or will the detectives fail in satisfying customer requirements?

Shallot Holmes walks into the office and lets out three hefty sneezes. Nancy Beef immediately notices a few white specks on his coat and the case file in his hands. The detective calls his colleagues into the meeting room and they gather around the table, eager to hear all about their new case.

This time, their client is interested in monitoring the quality of raw material at the start of his flour milling process. Nancy Beef chuckles satisfied she has found an explanation for the white spots on Shallot Holmes’ clothing and for his sneezing. He had just met with the client and was still reacting to the flour particles. Shallot Holmes notices how content she is, winks at her and goes on to explain their new challenge.

Like most of their clients, the new customer is interested in obtaining quick and reliable data. Nancy Beef volunteers to oversee this case and the others readily agree.

The youngest detective chalks up a bit of background information on incoming goods in the flour milling process just to bring the rest of her team up to speed.

Typically, the quality of raw material such as:

  • Wheat
  • Durum
  • Rye
  • Corn

Needs to be monitored prior to the milling process in order to produce the correct type of flour and satisfy customer requirements.

To achieve proper quality control, key parameters including moisture, protein and grain appearance need to be measured.

This analysis is instrumental for making informed decisions on:
– whether to reject the incoming goods based on presence of foreign grains, contamination, or ergot
– how to segregate the raw material according to protein content and other parameters
– what a fair payment to the suppliers would constitute
– how to optimize the mixing step to achieve highest final quality and profit from the flour milling process by reducing energy costs and unnecessary rework .

Nancy Beef thoughtfully bites on her marker and scratches her elbow. After a few minutes of silence, she announces that the best method for the analysis of the raw material for flour milling would be NIR On-line. One reason for her decision, she explains, is because with an NIR system directly at the grain intake, you can monitor the external appearance of grains with a high-resolution camera. This real-time view adds more transparency to the delivery process.

Miss Mapple raises her hand and begins to argue that they should use classical reference methods instead. Nancy Beef hears her out, but points out significant drawbacks of traditional laboratory methods, including the substantial amount of time and quantities of solvents these techniques require. These lab methods also rely on representative sampling, so quality control of the entire load is not possible. In fact, wrong average values of important parameters, such as moisture or protein, could lead to inaccurate judgement of the incoming goods.
Nancy Beef insists that with NIR technology, analysis of grain composition and quality can be achieved quickly, easily and reliably.

Shallot Holmes agrees with her. The team decides to support the client by testing out the efficiency of an online NIR process analyzer at the raw material intake in the characterization of grain composition.

They set-up their NIR system as follows:

  • Wavelength range: 900-1,00 nm (NIR), 350 – 900 nm (VIS)
  • Sensor equipped with VIS & NIR Spectrometer and a high resolution CCD camera
  • Measurement principle: Reflection
  • Interface to process: Flange

And within milliseconds, they manage to measure several parameters continuously and simultaneously. They take a look at data accuracy:

ParameterRange (%)Standard error of calibration (absolute)
Protein7.0 - 18.80.16
Moisture7.0 - 20.50.17
Ash0.80 - 2.60.05

And are very pleased with the performance as indicated by low standard errors.

Shallot Holmes is more than satisfied with Nancy Beef’s solution. He is certain that thanks to their work, their client will be able to obtain online measurements and real-time analysis of critical quality attributes. This would, in turn, enable him to make immediate decisions on the loading operation and optimization of further production steps to ensure maximal efficiency and profitability.

He lets out one final sneeze and promises Nancy Beef to buy her some cake, since this case was such a piece of cake for her. Nancy Beef accepts the offer happily and the five head out for a sweet afternoon to celebrate another successful challenge solved 🍰.