A Multivariate Analysis-Driven Workflow to Tackle Uncertainties in Miniaturized NIR Data.

This study focuses on exploring and understanding measurement errors in analytical procedures involving miniaturized near-infrared instruments. Despite recent spreading in different application fields, there remains a lack of emphasis on the accuracy and reliability of these devices, which is a critical concern for accurate scientific outcomes. The study investigates multivariate measurement errors, revealing their complex nature and the influence that preprocessing techniques can have. The research introduces a possible workflow for practical error analysis in experiments involving diverse samples and instruments. Notably, it investigates how sample characteristics impact errors in the case of solid pills and tablets, typical pharmaceutical samples. ASCA was used for understanding critical instrumental factors and the potential and limitations of the method in the current application were discussed. The joint interpretation of multivariate error matrices and their resume through image histograms and K index are discussed in order to evaluate the impact of common preprocessing methods and to assess their influence on signals.

Process understanding and monitoring: A glimpse into data strategies for miniaturized NIR spectrometers.

BACKGROUND: The implementation of process analytical technologies (PAT) has gained attention since 2004 when its formal introduction through the U.S. Food and Drug Administration was introduced. Manufacturers that need to evaluate the employment of new monitoring systems could face different challenges: identification of suitable sensors, verification of data meaning, evaluation of several statistical strategies to obtain insights about data and achieve process understanding and finally, the actual possibilities for monitoring. Kefir fermentations were chosen as an example because of the chemical and physical transformations that occurred during the process, which could be common to several other fermentation processes. In order to pave the way for monitoring establish the information contained in the data and find the right tools for extracting them is of extreme importance. Strategies to identify different experimental conditions in the spectra acquired with a miniaturized NIR (1350-2550 nm) during process occurrence were addressed. RESULTS: The study aims to offer insights into good practices and steps to pave the way for process monitoring with handheld NIR data. The main aspects of interest for batch processes in preliminary evaluations were investigated and discussed. On the one hand, process understanding and, on the other, the possibilities for process monitoring and endpoint determination were examined. The combination of different statistical tools allowed the extraction of information from the data and the identification of the link between them and the chemical and physical changes during the process. In addition, insights into the spectra characteristics in the studied spectroscopic range for kefir fermentation were reported. SIGNIFICANCE: The capabilities for miniaturized NIR spectra to represent and statistical strategies to characterize different experimental conditions in a real case fermentation occurrence were proved. The strengths and limitations of some of the common approaches to catch changes in fermentation condition were highlighted. For the various statistical approaches, the chances offered in the research and development stages and to set the scene for monitoring and end-point detection were explored.

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

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.

Historical Silk: A Novel Method to Evaluate Degumming with Non-Invasive Infrared Spectroscopy and Spectral Deconvolution.

To correctly manage a collection of historical silks, it is important to detect if the yarn has been originally subjected to degumming. This process is generally applied to eliminate sericin; the obtained fiber is named soft silk, in contrast with hard silk which is unprocessed. The distinction between hard and soft silk gives both historical information and useful indications for informed conservation. With this aim, 32 samples of silk textiles from traditional Japanese samurai armors (15th-20th century) were characterized in a non-invasive way. ATR-FTIR spectroscopy has been previously used to detect hard silk, but data interpretation is challenging. To overcome this difficulty, an innovative analytical protocol based on external reflection FTIR (ER-FTIR) spectroscopy was employed, coupled with spectral deconvolution and multivariate data analysis. The ER-FTIR technique is rapid, portable, and widely employed in the cultural heritage field, but rarely applied to the study of textiles. The ER-FTIR band assignment for silk was discussed for the first time. Then, the evaluation of the OH stretching signals allowed for a reliable distinction between hard and soft silk. Such an innovative point of view, which exploits a "weakness" of FTIR spectroscopy-the strong absorption from water molecules-to indirectly obtain the results, can have industrial applications too.

Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview.

Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.

Unravelling error sources in miniaturized NIR spectroscopic measurements: The case study of forages.

The use of miniaturized NIR spectrometers is spreading over the scientific literature with a particular focus on developing methods as rapid and easy-to-use as possible and following the philosophy of green analytical chemistry. Several applications and studies are typically presented by comparing results obtained with benchtop instrumentation even when the analytical strategies are substantially different. Indeed, analytical applications that include the use of miniaturized instrumentation are subject to several sources of variability that need to be known at the time of method development. In this study, different statistical strategies were employed to understand the features and limitations of handheld NIR instruments. Because of the high interest in real applications, a common type of hygroscopic powder sample was selected: forages. A step-by-step methodology is presented to statistically address the different issues to consider in order to obtain realistic models when using miniaturized NIR spectrometers. We demonstrate how a careful evaluation of the sources of variability related to an experiment can help in the understanding of the system under study in order to obtain a more reliable development of the method and consciously choose the analytical parameters and strategies of analysis. The results were also compared with those achieved on the same dataset from a benchtop system in order to provide references analogous with those in the literature.

Comparison of the characteristics, morbidity and mortality of COVID-19 between first and second/third wave in a hospital setting in Lombardy: a retrospective cohort study.

Coronavirus disease 2019 (COVID-19) represents a major health problem in terms of deaths and long-term sequelae. We conducted a retrospective cohort study at Montichiari Hospital (Brescia, Italy) to better understand the determinants of outcome in two different COVID-19 outbreaks. A total of 634 unvaccinated patients admitted from local emergency room to the Internal Medicine ward with a confirmed diagnosis of SARS-CoV-2 infection and a moderate-to-severe COVID-19 were included in the study. A group of 260 consecutive patients during SARS-CoV-2 first wave (from February to May 2020) and 374 consecutive patients during SARS-CoV-2 2nd/3rd wave (from October 2020 to May 2021) were considered. Demographic data were not significantly different between waves, except a lower prevalence of female sex during first wave. Mortality was significantly higher during the 1st wave than in the following periods (24.2% vs. 11%; p < 0.001). Time from symptoms onset to hospital admission was longer during first wave (8 ± 6 vs. 6 ± 4 days; p < 0.001), while in-hospital staying was significantly shorter (10 ± 14 vs. 15 ± 11 days; p < 0.001). Other significant differences were a larger use of corticosteroids and low-molecular weight heparin as well less antibiotic prescription during the second wave. Respiratory, bio-humoral and X-ray scores were significantly poorer at the time of admission in first-wave patients. After a multivariate regression analysis, C-reactive protein and procalcitonin values, % fraction of inspired oxygen on admission to the Internal Medicine ward and length of hospital stay and duration of symptoms were the strongest predictors of outcome. Concomitant anti-hypertensive treatment (including ACE-inhibitors and angiotensin-receptor blockers) did not affect the outcome. In conclusion, our data suggest that earlier diagnosis, timely hospital admission and rational use of the therapeutic options reduced the systemic inflammatory response and were associated to a better outcome during the 2nd/3rd wave.

Rapid Analysis of Milk Using Low-Cost Pocket-Size NIR Spectrometers and Multivariate Analysis.

The miniaturisation of analytical devices, reduction of analytical data acquisition time, or the reduction of waste generation throughout the analytical process are important requirements of modern analytical chemistry, and in particular of green analytical chemistry. Green analytical chemistry has fostered the development of a new generation of miniaturized near-infrared spectroscopy (NIR) spectrometric systems. However, one of the drawbacks of these systems is the need for a compromise between the performance parameters (accuracy and sensitivity) and the aforementioned requirements of green analytical chemistry. In this paper, we evaluated the capabilities of two recently developed portable NIR instruments (SCiO and NeoSpectra) to achieve a rapid, simple and low-cost quantitative determination of commercial milk macronutrients. Commercial milk samples from Italy, Switzerland and Spain were chosen, covering the maximum range of variability in protein, carbohydrate and fat content, and multivariate calibration was used to correlate the recorded spectra with the macronutrient content of milk. Both SCiO and NeoSpectra can provide a fast and reliable analysis of fats in commercial milk, and they are able to correctly classify milk according to fat level. SCiO can also provide predictions of protein content and classification according to presence or absence of lactose.