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.

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.

The increasing portability and accessibility of miniaturized NIR spectrometers are promoting the spread of in-field and online applications. Alongside the successful outcomes, there are also several problems related to the acquisition strategies for each instrument and to experimental factors that can influence the collected signals. An insightful investigation of such factors is necessary and could lead to advancements in experimental set-up and data modelling. This work aimed to identify variation sources when using miniaturized NIR sensors and to propose a methodology to investigate such sources based on a multivariate method (ANOVA—Simultaneous Component Analysis) that considers the effects and interactions between them. Five different spectrometers were chosen for their different spectroscopic range and technical characteristics, and samples of worldwide interest were chosen as the case study. Comparing various portable sensors is interesting since results could significantly vary in the same application, justifying the idea that this kind of spectrometer is not to be treated as a general class of instruments.

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.

The accurate sorting and recycling of textiles are crucial for reducing waste and promoting sustainability, but the inherent complexity of textile materials presents significant challenges. This study explores the potential of Near-Infrared (NIR) hyperspectral imaging (HSI) in textile sorting, focusing on its capacity to address two key challenges: differentiating textiles with diverse chemical compositions and detecting minor components such as elastane in cotton fibers. We investigate the spectral characterization of natural, synthetic, and blended fibers, demonstrating how NIR-HSI, combined with complementary spectroscopic approaches, can provide both chemical composition and spatial distribution insights. The integration of portable spectroscopic techniques improved spectral data interpretation and models reliability. The study also highlights the challenges of detecting low concentrations of elastane, which complicate recycling processes. Through the application of multivariate chemometric techniques, such as Principal Component Analysis (PCA) and Multivariate Curve Resolution (MCR), we analyze the spectral data and identify patterns that may facilitate more effective textile classification. The results indicate that while NIR-HSI shows promise in textile sorting and recycling, the variability in spectral features due to textile treatments, environmental factors, and compositional differences remains a significant challenge. The study highlights the importance of the development of standardized spectral databases to combine with interpretation. Ultimately, the findings offer valuable insights into the feasibility of near-infrared (NIR) hyperspectral imaging as a tool for sustainable textile sorting and recycling.

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.