Determine moisture content in breadcrumbs with AOTF NIR technology
Precise measurements of moisture content in foodstuff has great economic relevance for food manufacturers. Moisture influences not only taste and texture of food, but weight, appearance and shelf life of food. Measuring and controlling moisture content in food with high accuracy ensures optimal balance between product quality, safety and profitability, in addition to respecting national and international standard for commercial food products. Here we describe how to determine moisture content in breadcrumbs with AOTF NIR technology.
There are many available methods out there to measure water in food. A fairly comprehensive list can be found in the book Food Analysis edited by S. Nielsen. Near-infrared (NIR) analysis is one of these methods, that has the advantage of being non-destructive and non-contact, with proven applications in the laboratory, at-line, and on-line situations.
For water, NIR bands 1400–1450 nm and 1920–1950 nm can be used to determine the moisture content of a food after proper calibration work. NIR is extremely useful to determine moisture content in bulk food, such as grains, wheat, baby formula, and breadcrumbs. The reason is that near-infrared light interaction range is within a 2-3 mm depth in solids, and therefore foodstuff that is already in powder or granular form is very well suited for NIR.
Determination of moisture content in breadcrumbs was recently done by Brimrose for one of their customers using an AOTF-NIR Luminar 5030 spectrometer. They provided 60 samples of breadcrumbs (from three different types of bread), from which they chose eight validation samples. Here’s the NIR spectra from these samples (note the prominent water peaks at 1420 nm and 1950 nm.
The data was used to build a calibration model using Regression Analysis. Once the model was built, the validation samples were tested, and the moisture values predicted by NIR analysis were compared to the known value to assess the accuracy of the technique.
Reference moisture value
Predicted moisture value
Relative error (%)
On the eight validation samples, the prediciton error was well within the acceptable accuracy range for the food manufacturer.
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Physicist and an entrepreneur. Founder and Managing Director at Instruments & Data Tools, specialising in optical design and analytical instrumentation. Founder at Rubens Technologies, the intelligence system for the fresh fruit export industry.