Article Highlights
- A study in Food Chemistry introduces FT-NIR as a method to predict nutritional parameters in cookies, ensuring their safety and quality.
- FT-NIR spectroscopy is noted for its rapid, nondestructive analysis capability across various parameters in food samples.
- The study analyzed 120 cookie samples using FT-NIR spectroscopy to predict nutritional parameters and identify the types of cookies and cereals present.
- While effective for most parameters, the study revealed variations in accuracy, particularly with parameters like salt and fibers.
A new study published in Food Chemistry introduces how Fourier transform near-infrared spectroscopy (FT-NIR) could predict nutritional parameters in cookies, ensuring their safety and quality for consumers (1).
FT-NIR spectroscopy is used for various purposes in the food and beverage industry. Because it is a rapid and nondestructive technique, FT-NIR can analyze food samples quickly and accurately without destroying the sample. Many parameters can be simultaneously analyzed using FT-NIR, including fat, protein, dry matter, and lactose (2).
For flours and cereals, FT-NIR spectroscopy is effective for checking several chemical and physical parameters to make sure flour is appropriate for baking (2). FT-NIR is often used to check the moisture and protein content of flours and cereals because it helps speed up the process and lead to considerable cost savings (2).
The study in Food Chemistry, which was led by Cristina Quintelas and Antonio L. Amaral from the University of Minho, focused on using FT-NIR spectroscopy to analyze cookies. The study, which analyzed 120 commercially acquired cookie samples, aimed to predict various nutritional parameters including lipids, carbohydrates, fibers, proteins, salt, and energy contents, as well as to identify the type of cookies and the main cereals present in the samples (1). By utilizing FT-NIR spectroscopy combined with chemometric techniques, such as ordinary least squares (OLS) and partial least squares (PLS), the researchers achieved a high accuracy, with correlation coefficients exceeding 0.9 for all studied parameters (1).
One of the notable aspects to this study was the partial least squares (PLS)-kNN methodology. This methodology was integral to the study because it not only accurately identified all five main cereals present in the cookies (wheat, integral wheat, oat, corn, and rice), but it also distinguished between 14 different types of cookies based on their nutritional contents (1).
The PLS-kNN methodology also had several benefits. It was reliable, environmentally friendly, fast, and non-destructive, making it a promising alternative to standard analytical methods, the scientists wrote (1).
However, the study also revealed that the method’s effectiveness depended on the parameter under study. The researchers demonstrated that parameters such as salt and fibers were predicted with less accuracy (1). Meanwhile, the PLS methodology was effective in accurately predicting the lipidic content, energy, and carbohydrates present in cookies (1).
The researchers emphasize the need for further comparative studies with current standard analytical techniques to validate the efficacy and cost-effectiveness of this methodology for full implementation in the food industry. They suggest broadening the sample size and scope of the study to address accuracy, precision, and other practical considerations (1).
The researchers demonstrated an approach that could be used in the food industry for quality assurance by providing rapid and reliable analysis of nutritional parameters. As the demand for food safety and quality control (QC) continues to rise, FT-NIR spectroscopy is an analytical technique that can meet these challenges effectively.
References
(1) Quintelas, C.; Rodriguez, C.; Sousa, C.; et al. Cookie Composition Analysis by Fourier Transform Near Infrared Spectroscopy Coupled to Chemometric Analysis. Food Chem. 2024, 435, 137607. DOI: 10.1016/j.foodchem.2023.137607
(2) AZO Materials, Using FT-NIR Spectroscopy for Food Quality. Available at: https://www.azom.com/article.aspx?ArticleID=19299#:~:text=FT%2DNIR%20is%20an%20effective,Composition%20analysis (accessed 2024-04-03).
