The quality and shelf life of biscuits with cryo-ground proso millet and buckwheat by-products

Cereal by-products can negatively affect sensory properties and shelf life of foods if they are not pre-processed in order to alter their physico-chemical properties. Cryo-grinding is an innovative method for processing cereal by-products; therefore, we investigated the effect of adding cryo-ground proso millet bran (10%) and buckwheat hulls (2%) on the quality and shelf life of sugar-free whole-wheat biscuits. Shelf life was investigated by texture and sensory analysis, peroxide value (PV), free fatty acids (FFA), and near-infrared spectroscopy (NIRS). Regardless of particle size, by-products similarly affected the quality of biscuits and increased their nutritional value, while their shelf life was comparable to that of whole-wheat biscuits. NIRS and chemometric analysis of spectra successfully separated biscuits by composition and storage time. While developed partial least squares models showed lower precision in predicting storage, FFA, and PV based on NIR spectra, the artificial neural networks showed high prediction accuracy and low errors. Novelty Impact Statement While cereal by-products account for a high percentage of food production waste, some techniques, such as cryogenic grinding, still remain unexplored in food processing. This research showed that millet bran and buckwheat hulls can be successfully used in biscuits suitable for diabetic patients, while cryogenic comminution additionally contributes to some sensory aspects of biscuits. The research also showed that near-infrared spectroscopy combined with artificial neural networks is more accurate than commonly utilized partial least squares models in predicting storage time, free fatty acid content, and peroxide value of biscuits.

Automated summary

This study demonstrates that cryo-ground proso millet bran and buckwheat hulls can be successfully used in biscuits suitable for diabetic patients, while cryogenic grinding also contributes to certain sensory aspects of biscuits. Near-infrared spectroscopy combined with artificial neural networks is shown to be more accurate than commonly used partial least squares models in predicting storage time, free fatty acid content, and peroxide value of biscuits.