NIR news

First of all, I want to transmit my most humble thanks to all people who believe that I deserve the “2019 Thomas Hirschfeld” award (kindly supported by FOSS) for my work on near-infrared spectroscopy and, especially, applied on hyperspectral images. I must confess that this award caught me by surprise and that I felt a bit overwhelmed when I received it. It is an honour full of respect and responsibility. I have been given the opportunity of writing this article, and I will profit it to express different personal thoughts about general but relevant aspects of near infrared applied to hyperspectral imaging. Also, since I am more a practitioner in chemometrics (or machine learning or data mining, or …) than a developer, I will also include some insights about the beautiful combination of near-infrared hyperspectral image with chemometrics. This article is just a glimpse of constructive criticism with personal thoughts that comes from my little experience in this field. Therefore, and of course, all opinions here are open for constructive discussion with the only purpose of learning (like the machines do nowadays).

The monitoring and quantification of the illegal harvest of protected animal products is very vital for the conservation and protection of endangered species. Most of the methods and techniques used in the trade of these products are recognised to be incredibly time consuming and labour intensive requiring significant analyst expertise. In this study, we have demonstrated the potential of near-infrared spectroscopy combined with either principal component analysis or partial least square discriminant analysis regression as a rapid and non-invasive tool to classify horn and ivory samples stored in the Australian Museum, Sydney. This study has also demonstrated the attractiveness of the near-infrared technique as a screening tool that could revolutionise the tracking and identification of contraband materials produced from horn and ivory biomaterials.

Water plays an important role in chemical and biological processes. For understanding the role of water in the aggregation of proteins and polymers, the variation of water structures in the process of aggregation was studied by near-infrared spectroscopy. The near-infrared spectra of the aqueous R2/wt and poly(N,N-dimethylaminoethyl methacrylate) solutions of different concentrations were measured at different temperatures. The spectral changes of the solutes and water with temperature were analyzed with the help of chemometric methods. In the aggregation of R2/wt, the water species with one hydrogen bond around the NH groups dissociate to initiate the change of the hydrogen bonding network of the hydration water, and then, the water molecules with two hydrogen bonds (S₂) near the hydrophobic side chains release from the R2/wt, resulting in the formation of the ordered amyloid fibers. In the aggregation process of low concentration poly(N,N-dimethylaminoethyl methacrylate) solutions, the chains of the polymer tend to form a loose hydrophobic structure below 36°C and then aggregate into a micelle at a lower critical solution temperature of around 39°C. S₂ acts as a bridge to connect the polymer chains in the loose hydrophobic structure, and the dissociation of the S₂ bridge at high temperature is the reason for the formation of the micelle. For high concentration solution, however, the spectral information of S₂ was not found in the aggregation, suggesting a direct formation of the micelle from the dehydrated chains.

The present study is focused on the identification of geographical origin (Zhejiang, Yunnan and Anhui, China) of Dendrobium officinale’s dried stem called Tiepi fengdou by mean of the handheld near-infrared spectrometer. Raw data were preprocessed to reduce unwanted spectral variations by the first-order derivative followed by standard normal variate transformation, and partial least squares discriminant analysis model was developed for calibration. The results showed that more than 90% of the origins were identified. Therefore, it is possible to classify the geographical origin of Tiepi fengdou by the use of the handheld near-infrared spectrometer for effective quality control.

This paper has the purpose of updating the NIR scientific community about the activities that the Group of Analytical Chemistry and Chemometrics of the University of Genova (Italy) is carrying out in recent years. In more detail, the research lines are presented together with the laboratory equipment available. Moreover, an in-depth focus of the teaching activities is given, taking the occasion for presenting the last news regarding software development. The role of the Group in the organisation of schools and conferences in the context of the Italian Society of NIR Spectroscopy (SISNIR) is also detailed.

With the latest release of Unscrambler, Camo Analytics introduced support for Python scripting, giving users the best of two worlds. This Python extension allows users to tap into the vast ecosystem of Data Science tools that are continually being produced in the Python community, while still leveraging the familiar data handling, validation and visualization features of Unscrambler – all contained within a fully compliant framework. This paper discusses the value propositions that the Python extension can provide to Unscrambler users, and follows this up with some specific examples of common workflows that are enabled by this extension: Data Importing, Spectral Preprocessing and Innovative Modeling methods.