On the Importance of Investigating Data Structure in Miniaturized NIR Spectroscopy Measurements of Food: The Case Study of Sugar

Abstract

Alongside the increasing proofs of efficacy of miniaturized NIR instruments in food-related scenarios, it is progressively growing the number of end-users, even incentivized by the low-cost of the sensors. While attention is paid to the analytical protocol–from sampling to data collection, up to the data processing, the importance of error investigation in raw data is generally underestimated. Understanding the sources and the structure of uncertainty related to the raw data improves the quality of measurements and suggests the correct planning of the experiments, as well as helps in chemometric model development. The goal of chemometric modeling is to separate information from noise; therefore, a description of the nature of measurement error structure is necessary. Among the different approaches, we present the study of the Error Covariance Matrices (ECMs) and their decomposition in a bilinear structure as a powerful method to study the main sources of variability when using miniaturized NIR sensors in the actual way of use. Granulated and lump sugar samples were chosen as the case study and analyzed with two miniaturized spectrometers working in the NIR regions around 1350–2550 nm and 900–1750 nm, respectively, in dispersive reflectance mode. Results show that having some insights on multivariate measurement errors associated with spectra could be interesting in paving the way for several applications.

Effect of Sample Presentation on the Classification of Black Soldier Fly Larvae Using Near-Infrared Spectroscopy

“…Neither the aluminum back cover nor the number of BSFL samples scanned have a large effect on the NIR spectra, and consequently, they did not affect the classification results obtained by the LDA models, as the accuracy was around 100%. Similar results were reported by other authors when different variables were tested on the accurate classification of foods [9]. However, these authors indicated that the influence of sample characteristics and the type of sensors used will affect the classification results [9].…”

“…Similar results were reported by other authors when different variables were tested on the accurate classification of foods [9]. However, these authors indicated that the influence of sample characteristics and the type of sensors used will affect the classification results [9]. The results are of importance for the industry as they indicate that a portable instrument can be used to assess or monitor the traceability, as well as to evaluate the composition of the BSFL reared with different types of organic waste streams.…”

“…NIR spectroscopy is a fast method compared to other routine methods used in conventional and commercial analytical laboratories [1][2][3]. This technique has been widely used as an analytical tool due to its ability to measure different types of samples or materials including powders, liquids, solids, and slurries with little or no sample preparation [4][5][6][7][8][9][10]. In recent years, novel applications of NIR spectroscopy have been possible due to the availability of inexpensive and portable instrumentation [4][5][6][7][8][9][10].…”

“…This technique has been widely used as an analytical tool due to its ability to measure different types of samples or materials including powders, liquids, solids, and slurries with little or no sample preparation [4][5][6][7][8][9][10]. In recent years, novel applications of NIR spectroscopy have been possible due to the availability of inexpensive and portable instrumentation [4][5][6][7][8][9][10]. Advantages of these instruments such as portability allow researchers and the food manufacturing industry to move away from the laboratory into the field, and to analyze samples during the processing, transport, and storage of agricultural products and foods [6,8,[11][12][13][14].…”

Biosensors and Smart Analytical Systems in Food Quality and Safety: Status and Perspectives

“…While considerable attention is given to the analytical protocol, including sampling, data collection, and data processing, the significance of investigating errors in raw data is often overlooked. Giulia Gorla and co-authors [ 8 ] proposed a chemometric method based on the study of error covariance matrices (ECMs), useful for studying uncertainty not only in data produced by miniaturised instruments but in spectroscopic instruments in general, using sugar in different packaging forms as the case study. The results showed that the impact of different sources of variability on measurement errors was contingent upon the characteristics of the sample and sensor.…”