Ricerc@Sapienza

Near infrared (NIR) spectroscopy is widely used for non-destructive prediction of fruit traits. Common traits such as dry matter (DM) and soluble solids contents (SSC) can be predicted with reliable accuracy. However, the main problem with NIR spectroscopy is that a model developed on one batch may not perform very well when tested on other batches. Reasons for that are the physical, chemical and environmental differences between the experiments performed in different batches.

Data generated by analytical instruments, such as spectrometers, may contain unwanted variation due to measurement mode, sample state and other external physical, chemical and environmental factors. Preprocessing is required so that the property of interest can be predicted correctly. Different correction methods may remove specific types of artefacts while still leaving some effects behind. Using multiple preprocessing in a complementary way can remove the artefacts that would be left behind by using only one technique.

Background: The investigation of human bones unearthed from necropolises is a useful tool to enhance our knowledge about ancient cultures. In the present study, the possibility of using the activation energy (EA) values of thermogravimetric degradation processes coupled with exploratory analysis methods in order to investigate human remains, has been tested.

In recent years, due to advances in sensor technology, multi-modal measurement of process and products properties has become easier. However, multi-modal measurements are only of use if the data from adding new sensors is worthwhile, especially in the case of industrial applications where financial justification is needed for new sensor purchase and integration, and if the multi-modal data generated can be properly utilised.

The concept of orthogonalized partial least squares regression or, better, as it was originally named, orthogonalized projection to latent structures (O-PLS) was first introduced in 2001 by Johann Trygg and Svante Wold, as a way to deal with the large amount of variation in predictor matrices for multivariate calibration (and classification), not correlated to the responses. In this context, O-PLS operates by partitioning the systematic variance in the X block into a Y relevant and an orthogonal data sets, both having a bilinear structure.

Urban air contains benzene and polycyclic aromatic hydrocarbons (PAHs) which have carcinogenic properties. The objective of this paper is to study the correlation of exposure biomarkers with biomarkers of nucleic acid oxidation also considering smoking. In 322 subjects, seven urinary dose biomarkers were analyzed for benzene, pyrene, nitropyrene, benzo[a]pyrene, and naphthalene exposure, and four effect biomarkers for nucleic acid and protein oxidative stress.

Thermogravimetry coupled with chemometrics has proved to be a rapid and cost-effective diagnostic tool for β-thalassemia screening. This model, consisting of Partial Least Square-Discriminant Analysis (PLS-DA), permitted the discrimination of thalassemic patients and healthy individuals, using thermogravimetric curves of blood samples. In this study, the impact of aspirin on the capability of the TGA/chemometric validated model to screen for thalassemia was investigated.

Background: Δ9-Tetrahydrocannabinol (THC) is already considered one of the most addictive substances since an increasing number of consumers/abusers of THC and THC based products are observed worldwide. In this work, the capabilities of a novel miniaturized and portable MicroNIR spectrometer were investigated in order to propose a practical and intelligible test allowing the rapid and easy screening of Δ9-Tetrahydrocannabinol (THC) oral fluids without any pretreatment.

A novel, entirely automated MicroNIR-chemometric platform was developed for the "lab-on-click" detection of illicit drugs in nonpretreated oral fluids, and a novel tool for the first-level test is proposed. Calibration of the method was achieved by collecting oral-fluid specimens from volunteers, and chemometric analysis was considered for the development of models for prediction for cocaine, amphetamine, and Δ9-tetrahydrocannabinol.

In this work, the capabilities of a novel miniaturized and portable microNIR spectrometer were investigated in order to propose a practical and intelligible test allowing the rapid and easy screening of cannabinoids in veterinary feeds. In order to develop a predictive model that could identify and simultaneously quantify the residual amounts of cannabinoids, specimens from popular veterinary feeds were considered and spiked with increasing amounts of cannabidiol (CBD), Δ9-tetrahydrocannabinol (THC), and cannabigerol (CBG).