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Introduction
Wheat flour, derived from the finely powdered wheat kernel, is a fundamental ingredient in the production of various bakery and pasta items such as bread, cakes, biscuits, and noodles. Beyond its role in culinary creations, wheat flour is a significant consumable raw material that provides essential nutrients, including carbohydrates, proteins, and minerals, contributing to daily dietary requirements. However, the quality and safety of wheat flour products face persistent challenges due to variations in quality parameters and potential adulteration, posing risks to human health.
Overview
The need for a systematic and efficient analytical approach to monitor the quality and safety of wheat flour becomes increasingly evident. Traditional laboratory methods, while precise, often prove to be laborious and time-consuming. Key quality parameters for wheat flour, including moisture, protein, ash, and wet gluten content, as well as technological parameters such as sedimentation value, falling number, and rheological properties, collectively define its overall quality. The precision of these laboratory analyses notwithstanding, the industry demands a rapid and accurate method that provides real-time information at the point of need.
Near-Infrared (NIR) infrared technology has emerged as a promising alternative to traditional laboratory analysis methods. However, until now, the development of a portable system that seamlessly integrates this technology for on-the-spot analysis has been lacking. In addressing this gap, NeoSpectra, a handheld full spectrometer, presents an innovative solution that can revolutionize the monitoring of wheat flour quality and safety. In this study, we explore the application of NeoSpectra in assessing key quality parameters and discuss its potential implications for the wheat flour industry.
Materials and Methods
To evaluate the performance of NeoSpectra, samples were collected from three different production sites representing diverse suppliers and wheat flour qualities—000, 0000, and whole wheat flour. The analysis involved placing the flour on a rotating cup, with six measurements per sample collected within 1.5 minutes. A comprehensive dataset comprising 438 samples was utilized to develop Partial Least Squares (PLS) regression models, and cross-validation (Venetian blinds) was employed to assess model performance. The evaluation metrics included root mean square error (RMSE) and R2, compared to the distribution of the parameters. The laboratory analysis focused on three crucial parameters: moisture, ash, and wet gluten.
Results and Discussion
The results of this study demonstrate the robust performance of the NeoSpectra models in accurately assessing all three parameters—moisture, ash, and wet gluten. The prediction errors were found to be three times smaller than the standard deviation (RPD), and the ratio of rang to error (RER) exceeded 15, indicating the high reliability of the models.
NeoSpectra showcased its capability to provide rapid and precise results in diverse operational scenarios, from raw material reception to end-of-the-production-line analysis, thereby enhancing the overall traceability of the wheat flour production process.
Moreover, the seamless transferability of models between instruments allows for collaboration with key suppliers, extending the quality control system beyond the facility. The spectral data obtained from the samples serve as distinctive fingerprints, enabling not only the identification of materials but also confirmation of lot conformity.
Conclusions
NeoSpectra's introduction as a handheld full spectrometer marks a significant advancement in the field of wheat flour quality monitoring. Its portability, efficiency, and capacity to collect data from various flour types, including flours, whole grains, and liquids, position it as a viable on-the-spot solution. The study, based on a substantial sample collection, highlights the instrument's performance parity with traditional bench-top systems and identifies avenues for improvement. Recommendations include the implementation of quality-specific models to address the inherent variability between wheat flour qualities, paving the way for enhanced quality control practices in the wheat flour industry.
