The cross-sensitivity of near infrared (NIR) reflectance to the particle size of powders or ground materials has long been documented but not fully exploited for particle size estimation. Diffuse reflectance of a powder sample is dependent on light scattering within its layers, and a powder's absorption and scattering coefficient are related to its particle size. This is the basis of NIR reflectance-particle size calibrations. The availability of fibre optic probes and the speed of NIR spectrometers make them suitable for remote and online sensing of particle size, in addition to providing chemical information of a powder sample. The basics of NIR reflectance spectroscopy relevant to particle size determination and its relation to sample preparations, methods of presentation, reference methods, calibration development and validation are reviewed in this paper.

In flour milling, a granulation sensor for ground wheat is needed for automatic control of a roller mill's roll gap. A near‐infrared (NIR) reflectance spectrometer was evaluated as a potential granulation sensor of first‐break ground wheat using offline methods. Sixty wheat samples, ground independently, representing six classes and five roller mill gaps, were each used for calibration and validation sets. Partial least squares regression was used to develop the models with cumulative mass of size fraction as the reference value. Combinations of four data pretreatments (log (1/R), baseline correction, unit area normalization, and derivatives) and three wavelength regions (700–1,500, 800–1,600, and 600–1,700 nm) were evaluated. Unit area normalization combined with baseline correction or second derivative yielded models that predicted well each size fraction of first‐break ground wheat. Standard errors of performance of 4.07, 1.75, 1.03, and 1.40 and r2 of 0.93, 0.90, 0.88, and 0.38 for the >1,041‐, >375‐, >240‐, and >136‐μm size ranges, respectively, were obtained for the best model. Results indicate that the granulation sensing technique based on NIR reflectance is ready for online evaluation.

Physical properties of ground materials from roller mills are affected by the characteristics of wheat and the operational parameters of the roller mill. Backpropagation neural networks were designed, trained, and tested for the prediction of three physical properties of ground wheat: geometric mean diameter (GMD), specific surface area increase (SSAI), and break release (BR). Eight independent variables were used as input data. Compared to conventional statistical models, the accuracy of prediction was improved substantially, as reflected by the significant reduction in root mean squared error (RMS), relative error (RE), and the increase in coefficient of determination R2 (>0.98). The neural network models are, therefore, capable of predicting the physical properties of the ground wheat.

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